{"id":1509,"date":"2026-04-01T00:13:06","date_gmt":"2026-04-01T06:13:06","guid":{"rendered":"https:\/\/urtec.org\/2026\/?page_id=1509"},"modified":"2026-04-24T07:30:46","modified_gmt":"2026-04-24T13:30:46","slug":"technical-program","status":"publish","type":"page","link":"https:\/\/urtec.org\/2026\/technical-program\/","title":{"rendered":"Technical Program"},"content":{"rendered":"\n<section class=\"technical-program py-5\">\n  <div class=\"container\">\n            <div class=\"main-title mb-2 mt-10\">\n            Technical Program        <\/div>\n      <\/div>\n<\/section>\n\n\n\n<div class=\"container py-8 mb-5\">\n\t<!-- Search and Filter Section -->\n\t<div class=\"scholarone-search-section mb-4\">\n\t\t\t<ul class=\"nav nav-tabs border\" id=\"myTab\" role=\"tablist\">\n\t\t\t\t<li class=\"nav-item flex-1\" role=\"presentation\">\n\t\t\t\t\t<button class=\"nav-link w-full border-0 mb-0 rounded-0 active\" id=\"search-tab\" data-bs-toggle=\"tab\" data-bs-target=\"#search\" type=\"button\" role=\"tab\" aria-controls=\"search\" aria-selected=\"true\">Search<\/button>\n\t\t\t\t<\/li>\n\t\t\t\t<li class=\"nav-item flex-1\" role=\"presentation\">\n\t\t\t\t\t<button class=\"nav-link w-full border-0 mb-0 rounded-0\" id=\"filter-tab\" data-bs-toggle=\"tab\" data-bs-target=\"#filters\" type=\"button\" role=\"tab\" aria-controls=\"filters\" aria-selected=\"false\">Filters<\/button>\n\t\t\t\t<\/li>\n\t\t\t<\/ul>\n\t\t\t<div class=\"tab-content border-l border-b border-r border-gray-200 px-3 py-4\" id=\"myTabContent\">\n\t\t\t\t<div class=\"tab-pane fade show active p-0\" id=\"search\" role=\"tabpanel\" aria-labelledby=\"search-tab\">\n\t\t\t\t\t<div class=\"scholarone-search-box row g-2\">\n\t\t\t\t\t\t<div class=\"col-12 col-md-6\">\n\t\t\t\t\t\t\t<input type=\"text\" id=\"scholarone-search-input\" class=\"form-control\" placeholder=\"Search...\" \/>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"col-auto ms-auto ms-md-0\">\n\t\t\t\t\t\t\t<button type=\"button\" id=\"scholarone-search-button\" class=\"btn btn-sm btn-primary w-100 w-md-auto\">\n\t\t\t\t\t\t\t\tSearch\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<div class=\"col-auto\">\n\t\t\t\t\t\t\t<button type=\"button\" id=\"scholarone-clear-search\" class=\"btn btn-sm btn-secondary w-100 w-md-auto\">\n\t\t\t\t\t\t\t\tClear\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"tab-pane fade\" id=\"filters\" role=\"tabpanel\" aria-labelledby=\"filter-tab\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-filter-box\">\n\t\t\t\t\t\t\t<strong class=\"d-block mb-2\">Filter by Type:<\/strong>\n\t\t\t\t\t\t\t<div class=\"scholarone-filter-options d-flex flex-wrap gap-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Alternate\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tAlternate\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #f093fb;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"New Technology Showcase\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tNew Technology Showcase\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #4facfe;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Opening Plenary Session\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tOpening Plenary Session\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Oral\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tOral\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #fa709a;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Panel\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tPanel\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Special Session\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tSpecial Session\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #ff6348;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Student Poster\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tStudent Poster\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<label class=\"scholarone-filter-label rounded fs-8 py-2 px-4\" style=\"border: 1px solid #ddd; border-top: 3px solid #01a3a4;\">\n\t\t\t\t\t\t\t\t\t\t<input type=\"checkbox\" name=\"session_type[]\" value=\"Topicals\" class=\"form-check-input me-1\" checked>\n\t\t\t\t\t\t\t\t\t\tTopicals\t\t\t\t\t\t\t\t\t<\/label>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t<\/div>\n\n\t<!-- Tabs for Days (Desktop) -->\n\t<ul class=\"scholarone-tabs scholarone-tabs-desktop nav nav-tabs d-none d-md-flex\" role=\"tablist\">\n\t\t\t\t\t\t\t\t\t\t<li class=\"nav-item\" role=\"presentation\">\n\t\t\t\t<button class=\"scholarone-tab nav-link active\" data-tab=\"day-0\" type=\"button\" role=\"tab\">\n\t\t\t\t\tMonday, 22 June\t\t\t\t<\/button>\n\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nav-item\" role=\"presentation\">\n\t\t\t\t<button class=\"scholarone-tab nav-link\" data-tab=\"day-1\" type=\"button\" role=\"tab\">\n\t\t\t\t\tTuesday, 23 June\t\t\t\t<\/button>\n\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nav-item\" role=\"presentation\">\n\t\t\t\t<button class=\"scholarone-tab nav-link\" data-tab=\"day-2\" type=\"button\" role=\"tab\">\n\t\t\t\t\tWednesday, 24 June\t\t\t\t<\/button>\n\t\t\t<\/li>\n\t\t\t\t\t\t\t\t\t\t\t<li class=\"nav-item\" role=\"presentation\">\n\t\t\t\t<button class=\"scholarone-tab nav-link\" data-tab=\"day-3\" type=\"button\" role=\"tab\">\n\t\t\t\t\tAlternates\t\t\t\t<\/button>\n\t\t\t<\/li>\n\t\t\t\t\t\n\t\t\t<\/ul>\n\n\t<!-- Dropdown for Days (Mobile) -->\n\t\t<div class=\"scholarone-tabs-mobile d-md-none mb-3\">\n\t\t<select class=\"form-select scholarone-day-selector\" aria-label=\"Select day\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<option value=\"day-0\"  selected='selected'>\n\t\t\t\t\tMonday, 22 June\t\t\t\t<\/option>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<option value=\"day-1\" >\n\t\t\t\t\tTuesday, 23 June\t\t\t\t<\/option>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<option value=\"day-2\" >\n\t\t\t\t\tWednesday, 24 June\t\t\t\t<\/option>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<option value=\"day-3\" >\n\t\t\t\t\tAlternates\t\t\t\t<\/option>\n\t\t\t\t\t\t\t\t\t<\/select>\n\t<\/div>\n\t\n\t<!-- Tab Content -->\n\t<div class=\"scholarone-tab-content-container p-3 border\">\n\t\t\t\t\t\t\t<div class=\"scholarone-tab-content active\" id=\"day-0\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t8:15 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Opening Plenary Session\" style=\"border-top: 4px solid #4facfe;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Opening Plenary Session: Shale at the Crossroads: Technology, Strategy, and the Next Chapter for Unconventionals<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t372\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:15 AM &#8211; 10:00 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Ali Sloan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Opening Plenary Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Fryklund*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Upstream Energy Group at S&amp;P Global Commodity Insights)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Opening Plenary Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Brackett*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Bernstein (AB\/AllianceBernstein))<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Opening Plenary Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Al Kindi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC &#8211; Upstream)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Opening Plenary Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Bourgeois*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron U.S.A., Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Student Poster\" style=\"border-top: 4px solid #ff6348;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Student Posters- Fracture Mechanics, Modeling and Propagation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Booth 121\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:00 AM &#8211; 11:00 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tKaterina Yared, Hosein Kalaei, David Hume\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Efficient Simulation of Heterogeneous Hydraulic Fracture Closure Using a Fast Multipole Accelerated Iterative Scheme<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Yuan*<sup>1<\/sup>, M. Chen<sup>1<\/sup>, T. Guo<sup>1<\/sup>, D. Weng<sup>2<\/sup> and Y. Liu<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Petroleum; 2. Research Institute of Petroleum Exploration &amp; Development)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Hydraulic fracture closure governs conductivity, flowback efficiency, and long-term production performance. This mechanism is critical for unconventional reservoirs and subsurface energy storage, where non-uniform surface contact poses a key geomechanical challenge. However, traditional Displacement Discontinuity Method (DDM) models require solving dense matrices, causing computational complexity and memory to scale quadratically with mesh size. This bottleneck severely limits DDM application in large-scale fracture networks. To address this, we propose an efficient simulation framework utilizing the Kernel-Independent Fast Multipole Method (KI-FMM).Methods\/Procedures\/Process: The full 3D DDM fracture closure model introduces two key innovations:(1)FMM Acceleration and Proxy Optimization: The KI-FMM algorithm hierarchically compresses the dense coefficient matrix. A multi-radius concentric spherical sampling and mesh alignment strategy mitigate singularities and enhance far-field approximation.(2) Dual-Loop Iterative Strategy: This two-tier architecture resolves closure nonlinearity. The outer loop identifies element contact status (open\/closed) based on physical criteria (negative displacement\/stress deficit). The inner loop uses Preconditioned Conjugate Gradient (PCG) with a constraint projection operator to solve the linear system, handling displacement constraints without explicit matrix reconstruction.Results\/Observations\/Conclusions: Validation against direct DDM benchmarks confirms that the proposed method maintains high accuracy in fracture width calculations while reducing computational complexity to O(N \\log N). In a case study involving approximately 18,000 elements, calculation speed increased by over 45 times, with memory consumption reduced from gigabytes to megabytes. The model successfully simulated single-fracture closure under heterogeneous fluid pressure distributions and random pressure perturbations. Furthermore, ongoing research is extending this algorithm to large-scale fracture networks to elucidate the evolution of non-uniform contact in complex systems.Applications\/Significance\/Novelty: The developed algorithm overcomes the computational barriers in large-scale fracture closure analysis, enabling high-precision, rapid solutions for models exceeding ten thousand elements. This provides a robust computational tool for evaluating post-fracturing conductivity in unconventional reservoirs and establishes a foundational geomechanical model for analyzing fractured reservoirs in subsurface energy storage applications.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">An Efficient Nonlinear Inversion Method for Hydraulic Fracture Geometric Parameters Based on an Analytical Strain Model: Theory and Field Applications<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. Hu*<sup>1<\/sup>, M. Chen<sup>1<\/sup>, T. Guo<sup>1<\/sup>, Z. Hu<sup>1<\/sup>, D. Weng<sup>2<\/sup> and Y. Liu<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Petroleum; 2. Research Institute of Petroleum Exploration &amp; Development)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: With the advancement of unconventional oil and gas resource development, distributed fiber strain monitoring technology has become a key technique for diagnosing fracture propagation patterns during hydraulic fracturing. However, the simultaneous inversion of key hydraulic fracture geometric parameters (fracture height and width) and characterization of asymmetric propagation behavior using offset-well fiber optic strain data remains a significant challenge in field applications. To address this, this paper proposes an efficient nonlinear inversion method for hydraulic fracture geometric parameters based on an analytical strain model. The accuracy of this method is systematically demonstrated and applied in a field fracturing LF-DAS monitoring case study.Methods\/Procedures\/Process: Based on the DDM model of fracture-induced fiber strain, model resolution matrix theory demonstrated fracture parameter interpretability post-contact, confirming inversion feasibility via the Sneddon solution. A nonlinear inversion model was then established using this solution, solved with the Levenberg-Marquardt method. Effective constraints and a multistart method ensured globally optimized results. Synthetic data simulating static\/dynamic strain (with measurement errors) validated accurate simultaneous inversion of fracture height, height asymmetry offset, width, and net pressure. Impacts of error and data volume on accuracy were analyzed. Finally, HFTS-2 field data was applied in a practical case study.Results\/Observations\/Conclusions: The study concludes that: (1) Strain shows high sensitivity to parameters at the intersection but limited resolution elsewhere, due to the spatial distribution of fracture-induced strain. (2) The analytical-based nonlinear inversion model rapidly and simultaneously resolves fracture height, height asymmetry offset, width, and net pressure, with errors typically below 5% across data qualities. (3) It accurately determines the fracture-center vertical offset under asymmetric height growth, overcoming previous neglect of asymmetry. (4) Applied to HFTS-2 data, the inversion yielded height, asymmetry offset, width, and net pressure, with horizontal-well LF-DAS results aligning with vertical-well monitoring, providing real-time diagnostic support for field operations.Applications\/Significance\/Novelty: This study achieves the first simultaneous inversion of fracture height, fracture height asymmetry offset, fracture width and net pressure. The fracture height asymmetry offset reflects the degree of asymmetry in vertical crack propagation, addressing the limitations of previous LF-DAS fracture parameter inversions that were non-simultaneous and did not adequately account for fracture height asymmetry. The inversion method is based on analytical strain theory, significantly reducing computational cost while ensuring good physical interpretability and systematically accounting for measurement errors. This approach facilitates real-time diagnosis of LF-DAS field data and provides important guidance for LF-DAS fracture diagnosis.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mechanisms of Hydraulic Fracture Growth in Highly Laminated Shale Under Multi-layer Development<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Pan* and S. Wang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In shale oil reservoirs, high-density, closely spaced laminations exert a dominant control on hydraulic fracture propagation, particularly in multi-well three-dimensional development scenarios. However, the combined effects of laminations on fracture height, complexity, and inter-well connectivity remain poorly quantified. This study employs a fully coupled three-dimensional finite-discrete element model (FDEM) hydro-mechanical model to simulate fracture initiation, turning, branching, and arrest within highly laminated shale. The work further aims to identify favorable ranges of lamination density, dip, and well patterns that maximize effective stimulated rock volume while mitigating inter-well interference.Methods\/Procedures\/Process: We apply a previously developed three-dimensional finite\u2013discrete element (FDEM-flow3D) hydro-mechanical model to investigate hydraulic fracture propagation in highly laminated shale. High-density bedding planes are explicitly represented as joint elements with prescribed stiffness, strength, and permeability, while the intact rock matrix is discretized using tetrahedral elements. Matrix leakoff and fracture flow are coupled through a unified cubic-law formulation. Systematic simulations of multi-stage stimulation in aligned and staggered multi-well patterns vary lamination density (3\u20137 beds\/cm) and bedding dip (0\u201360\u00b0) to quantify impacts on fracture geometry, failure mode, and near- and cross-well stress and pressure communication.Results\/Observations\/Conclusions: Numerical simulations indicate that increasing lamination density causes fractures to propagate mainly along bedding planes, enhancing intra-layer connectivity and suppressing cross-layer breakthrough. When density increases from 3 to 7 beds\/cm, peak bedding pressure drops from 42.6 to 26.1 MPa and fracture volume from 0.208 to 0.149 cm3, reflecting stronger energy dissipation along weak interfaces. For dipping strata, moderate dips (30\u00b0\u201345\u00b0) give stronger bedding-pressure responses (28.2 MPa for 45\u00b0) and larger fracture volumes (0.164 cm3 for 30\u00b0), whereas steep dips (\u226560\u00b0) lead to layer-parallel slip (20.8 MPa and 0.149 cm3 for 60\u00b0) and more isolated, ribbon-like fracture clusters with limited fluid exchange.Applications\/Significance\/Novelty: This work presents a physics-based, quantitative framework for optimizing multi-well stimulation in shale reservoirs with high-density laminations and identifies operational windows that maximize stimulated rock volume while controlling inter-well interference. The proposed methodology provides practical guidance on well spacing, stage placement, and treatment sizing in multi-layer developments characterized by dense bedding. In particular, the study delineates conditions under which vertical stacking is effective and scenarios in which strong intra-layer interference becomes dominant, thereby offering a systematic basis for mitigating interference risks and enhancing recovery in complex laminated reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Comparative Experimental Study of Fracture Propagation in Coal and Tight Sandstone Reservoirs \u2014 Implications for Hydraulic Fracturing Design<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Li*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Fracture propagation behavior in unconventional reservoirs varies with lithology and stress conditions. This study aims to experimentally investigate fracture geometry evolution in coal and tight sandstone formations under different injection rates, fluid viscosities, and perforation orientations, providing insights for fracturing parameter optimization.Methods\/Procedures\/Process: A true triaxial fracturing experiment was conducted using 10 cm cubic cores under controlled stress conditions simulating different perforation angles. Injection rate and viscosity were designed based on field-scale similarity principles. CT scanning and 3D reconstruction were applied before and after fracturing to quantify fracture morphology using fractal dimension analysis. The experiments compared medium-deep coal (2000\u20132500 m), deep coal (&gt;4500 m), and medium-deep tight sandstone (2500\u20133500 m) samples.Results\/Observations\/Conclusions: Medium-deep coal cores exhibited the most complex fracture networks, requiring high-viscosity fluids and moderate injection rates. Deep coal showed planar fracture propagation due to high stress anisotropy, necessitating higher injection rates. Tight sandstones produced simpler but more uniform fractures with moderate viscosity and rate combinations. Fractal analysis confirmed that fracture complexity decreases with stress anisotropy and increases with fluid viscosity.Applications\/Significance\/Novelty: This study provides a comprehensive comparative framework for understanding lithology-dependent fracture propagation. The integration of CT-based reconstruction with fractal analysis establishes quantitative correlations between stress regime, injection design, and fracture complexity. These results support data-driven optimization of fracturing fluid systems and operational parameters across coal and tight sandstone reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Modeling of Self-Supported Fracture Permeability Enhancement and Dominant Factors in Shale Gas Hydraulic Fracturing<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tW. Rui*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unpropped fractures play an important yet often underestimated role in shale gas production. This study aims to characterize the permeability evolution of self-supported fractures in both vertical and horizontal directions under effective stress variations and to establish a permeability enhancement model that quantifies their contribution to overall reservoir conductivity.Methods\/Procedures\/Process: Core samples from the Fuling shale gas field were tested using hydraulic\u2013electric analogy experiments to simulate flow through vertical and horizontal self-supported fractures under varying confining stress. Permeability variations were analyzed, and equivalent flow models were developed. The vertical system considers laminations, matrix, and horizontal fractures in series with vertical fractures in parallel; the horizontal model combines laminations, matrix, and horizontal fractures in parallel with vertical fractures in series. A sensitivity analysis was conducted to determine the dominant parameters influencing permeability enhancement in both directions.Results\/Observations\/Conclusions: Results show that both vertical and horizontal fractures exhibit nonlinear permeability decay with increasing effective stress, with horizontal fractures maintaining higher permeability. Horizontal enhancement is primarily controlled by horizontal fracture permeability, while vertical enhancement depends on vertical fracture permeability, width, and density. These results confirm the existence of self-supporting flow channels that maintain conductivity even under closure.Applications\/Significance\/Novelty: This work provides a quantitative framework for assessing unpropped fracture contributions in shale gas systems. The findings suggest that fracture stimulation should prioritize vertical fracture creation while improving horizontal fracture permeability to establish a coupled flow network. The proposed hydraulic\u2013electric analogy model offers new insight into permeability retention and stress-dependent fracture behavior, aiding optimization of fracture geometry design and post-fracture production strategies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mechanisms of Advection-Dominated Tracer Transport in Coupled Hydraulic and Natural Fracture Networks<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tQ. Liu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study investigates the governing physics of tracer transport in multiscale fracture networks of unconventional reservoirs, with a focus on isolating advection-dominated transport within coupled hydraulic fracture (HF) and natural fracture (NF) systems from matrix-driven diffusive mechanisms. A fully coupled geomechanical\u2013fluid transport framework is developed to quantify how stress-dependent fracture topology and connectivity control evolving velocity fields. The central objective is to establish a deterministic relationship between Discrete Fracture Network (DFN) complexity and tracer breakthrough dynamics in ultra-low permeability formations.Methods\/Procedures\/Process: A numerical simulator is constructed by coupling the Displacement Discontinuity Method (DDM) for fracture geomechanics with the Finite Volume Method (FVM) for transient fluid transport. The model solves the full advection\u2013dispersion equation on dynamically evolving DFNs, with fracture apertures and velocity fields updated synchronously in response to stress shadowing and NF activation during both injection and flowback. Mass conservation is rigorously enforced at complex HF\u2013NF intersections to resolve localized velocity perturbations that govern hydrodynamic dispersion. A flux-limited discretization scheme is employed to suppress numerical diffusion under strongly advective conditions.Results\/Observations\/Conclusions: Simulation results demonstrate that tracer transport in fractured unconventional media is governed by a high\u2013P\u00e9clet-number, advection-dominated regime. Activated natural fractures emerge as high-conductance preferential pathways, resulting in markedly earlier tracer breakthrough compared to planar fracture geometries. The characteristic long-tail behavior of residence time distributions is shown to be primarily controlled by hydrodynamic dispersion induced by network heterogeneity and tortuosity, rather than matrix diffusion alone. Moreover, stress-dependent aperture closure during flowback non-uniformly restricts advective corridors, intensifying flow channeling and dynamically altering effective network connectivity.Applications\/Significance\/Novelty: This work provides a rigorous physical basis for interpreting tracer responses in Utah FORGE. The key novelty lies in decoupling fracture-network-driven advective transport from matrix interaction effects, demonstrating that advective signatures serve as direct indicators of the Effective Stimulated Reservoir Volume (ESRV). By characterizing hydrodynamic dispersion arising from HF\u2013NF interactions, the framework enables quantitative discrimination of connected fracture intensity from tracer return profiles. These findings improve reservoir characterization fidelity and support the design of injection strategies that interrogate the true hydrodynamic limits of stimulated fracture systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Development of a 1D Mechanical Earth Model and Fracture Analysis at the HFTS-1 Site, Midland Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Sasmaz*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Houston)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The primary objective of this study is to improve the characterization of minimum horizontal stress in unconventional shale reservoirs by evaluating the influence of time-dependent mechanical behavior. The research focuses on the Wolfcamp Formation at the Hydraulic Fracture Test Site 1 and examines the depth-dependent variation of minimum horizontal stress using two Mechanical Earth Models: a poroelastic model based on classical elasticity theory and a viscoelastic model that incorporates stress relaxation. Both models are calibrated using an integrated dataset of well logs, DFIT measurements, and laboratory core data, while also evaluating elastic anisotropy and comparing predictions with field observations. The workflow supports future extension to 3D stress modeling.Methods\/Procedures\/Process: Two Mechanical Earth Models were developed to evaluate in-situ stress conditions in the Wolfcamp Formation. Vertical stress was calculated by integrating the density profile with depth, while pore pressure was constrained using DFIT-derived pressures corrected for hydrostatic gradients. Biot\u2019s coefficient was estimated using empirical correlations based on mineral composition and porosity. A poroelastic model assuming an instantaneous elastic response was implemented using classical poroelastic theory, whereas a viscoelastic model incorporating time-dependent deformation and stress relaxation was developed to represent delayed mechanical behavior in ductile shale intervals. Elastic anisotropy was included using cross-dipole sonic logs, with Thomsen anisotropy parameters integrated.Results\/Observations\/Conclusions: The poroelastic and viscoelastic MEMs produce significantly different minimum horizontal stress profiles, particularly within clay-rich and organic-rich intervals.The viscoelastic model predicts higher stress values in compliant layers due to time-dependent stress relaxation, whereas the poroelastic model tends to underestimate stress in these zones. Stress contrasts between lithological units evolve with depth and are more realistically captured by the viscoelastic framework. Incorporation of elastic anisotropy improves stress prediction across laminated intervals and facies transitions, reducing mismatches with DFIT closure pressures.Applications\/Significance\/Novelty: This study provides a direct comparison between poroelastic and viscoelastic stress modeling approaches under same geological and mechanical conditions in the Wolfcamp Formation. By incorporating time-dependent stress relaxation, the viscoelastic MEM addresses key limitations of conventional elastic models, particularly in ductile and mineralogically heterogeneous shale intervals. The inclusion of elastic anisotropy further improves the reliability of horizontal stress estimates and enhances predictions of fracture propagation and containment. The modeling workflow developed in this study supports more accurate hydraulic fracture design and wellbore stability assessment and offers a transferable framework for stress characterization in other unconventional shale reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Physics-Informed Cascade Machine Learning for Geomechanical Property Prediction Using Mineralogy Data: A Case Study in Mississippian Formations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Xu*<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Oklahoma; 2. Impac Exploration Services)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate geomechanical characterization, specifically Young\u2019s Modulus (YME) and Poisson\u2019s Ratio (PR), is vital for hydraulic fracture modeling. However, dipole sonic and density logs are frequently unavailable in horizontal wells due to high costs. While mineralogy data is accessible, direct statistical mapping often fails to capture nonlinear rock physics. This study proposes a novel &quot;Cascade&quot; Machine Learning (ML) workflow to reconstruct geomechanical logs solely from mineralogical composition. Focusing on 111 core-calibrated log samples from the Mississippian Springer, Caney, and Sycamore formations, we aim to bridge the data gap in legacy wells, enabling cost-effective completion optimization without running expensive logging strings.Methods\/Procedures\/Process: We developed a two-stage Random Forest (RF) regression framework, chosen for its robustness on small datasets. Unlike direct mapping, our &quot;Cascade&quot; approach embeds rock physics constraints. Stage 1 predicts intermediate physical logs\u2014specifically Compressional (DTC) and Shear (DTS) slowness, porosity, and density\u2014from input mineralogy. Stage 2 uses these predicted intermediates alongside mineralogy to predict final moduli. Validation employed a rigorous random stratified splitting (90% train \/ 10% test) to ensure lithological representativeness in the test set while minimizing overfitting. This creates a &quot;gray-box&quot; system where intermediate outputs are physically interpretable.Results\/Observations\/Conclusions: The Cascade scheme significantly outperformed direct prediction. Testing R2 improved from 0.85 (direct) to 0.90 (cascade). Feature importance analysis confirmed that including predicted Porosity and Shear Slowness (DTS) as intermediates was the primary driver for accuracy. In the held-out test set, predicted Poisson\u2019s Ratio achieved a near-perfect correlation (R2 &gt; 0.98, MAE &lt; 0.01), while YME maintained strong consistency (MAE &lt; 0.6 MMpsi). The model successfully captured the implicit Vp\/Vs relationship solely from composition. The derived Brittleness Index accurately identified high-fracability intervals within the mixed carbonate-siliciclastic sequence.Applications\/Significance\/Novelty: This workflow proves geomechanical properties can be reliably estimated without acoustic logging by leveraging mineralogy. Operators can maximize the value of cuttings or geochemical logs in lateral sections where data is scarce. This enables continuous, high-resolution brittleness profiling for optimized perforation placement. The novelty lies in the &quot;Gray-Box&quot; Cascade architecture: unlike &quot;Black-Box&quot; models, it exposes intermediate physical layers (e.g., synthetic DTS) for petrophysical QC. This transparency builds engineering trust and demonstrates superior performance over direct correlations in complex lithologies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Panel\" style=\"border-top: 4px solid #fa709a;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: Physics-Informed Machine Learning (PIML) \u2014 Bridging Physics, Data, and Digital Adoption in Unconventional Reservoirs<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:45 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Utkarsh Sinha\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Chen*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Ben*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy Applied AI Center of Excellence)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Hu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ConocoPhillips)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Paranji*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Xecta Digital Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Beyond Traditional Surfactants &#8211; Novel Chemical EOR Concepts<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tNabila Lazreg, Han Young Park\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Effects and Importance of Chlorine Dioxide (ClO2) Concentration and Diversion on ClO2 EOR and Restimulation Treatments<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Dalamarinis*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(DG Petro Oil and Gas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The first Engineered Chlorine Dioxide (ClO2) work by Dalamarinis et al. (2023, 2025) focused on developing a versatile, economic and easy to execute on the field treatment, resulting in significant production and EUR uplift. A challenge that still needed to be addressed, and this work will focus on, is what improvements would not only allow to increase production, but also to mitigate decline due to re-occurring plugging mechanisms inside the fracture system.Methods\/Procedures\/Process: A typical decline between 30-50% in oil and gas was recorded in the first generation of ClO2 treatments. Utilization of Nano-surfactants and Tailored Metal Oxide Nanoparticles (TMO) compatible with Chlorine Dioxide (ClO2), resulted in better production profile characteristics for the first six months (Dalamarinis et al., 2023), followed by a similar decline as to wells at which normal surfactant had been used (production phase past six months). To mitigate this behavior and to improve the production characteristics past six months, increased concentrations of ClO2 were pumped (&gt;4,000 ppm) and a combination of mechanical &amp; chemical diversion was developed specifically for these treatments.Results\/Observations\/Conclusions: Well A-10 was the first at which an increased concentration of ClO2 was pumped (&gt;4,000 ppm). Concentrations in previous applications ranged from 2,000 \u2013 3,000 ppm depending on the type of the well and scale tendencies. In 06\/2023 a Chlorine Dioxide (ClO2) treatment &gt;4,000 ppm was pumped on Well A-10, without any Nano-surfactants to evaluate if more potent ClO2 treatments can delay the re-development of scale\/biofilm in the fracture system and therefore improve production decline. In previous treatment at the same well with concentration &lt;3,000ppm, the well experienced a 12-month oil and gas decline of 44% &amp; 28%. With ClO2 &gt;4,000 ppm 12-month oil and gas decline of 29% &amp; 16% was recorded respectively, ~40% better decline. The same test was performed on Well A-8 with similar results.Applications\/Significance\/Novelty: Re-stimulation\/EOR with Chlorine Dioxide (ClO2) in its 4th generation, has been chemically and mechanically improved compared to the methodology presented to the industry by Dalamarinis et el. (2025). Improvements in pumping equipment and compatible diversion techniques with ClO2, allowed to pump treatments with concentrations &gt;4,000 ppm. This development resulted in improved production decline by delaying the re-development of skin damage and better placement of ClO2 in the fracture system.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Aqueous Solution of 2-Butanone for Enhanced Oil Recovery in Liquid-Rich Shale Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Ko* and R. Okuno\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: A recent study demonstrated that 3-pentanone effectively shifts rock wettability to a strongly water-wet state in shale without reducing water\/oil interfacial tension. Its non-emulsifying properties and aqueous stability under high-temperature, high-salinity (HTHS) conditions (e.g., above 212\u00b0F and 200,000 ppm) underscore its potential for enhanced oil recovery (EOR) aimed at long-term enhanced oil mobility. This research explored a cost-effective ketone, 2-butanone, which is one-quarter the cost of 3-pentanone, for huff-n-puff applications in liquid-rich shale reservoirs. The primary objective of this study is to evaluate the potential of 2-butanone for EOR in shale reservoirs and to understand how the ketone&#039;s K-value and injection concentration affect oil recovery kinetics under static-soaking conditions.Methods\/Procedures\/Process: First, phase-behavior experiments were conducted to measure the K-value of 2-butanone at 2,500 psig and 183\u00b0F in ternary oil\/2-butanone\/brine systems. Then, spontaneous imbibition experiments were conducted to compare 2-butanone and 3-pentanone under static-soaking conditions. Lastly, huff-n-puff experiments were conducted on reservoir shale cores to confirm that 2-butanone enhances water imbibition into the shale matrix at 2,500 psig and 183\u00b0F. The injected fluids were reservoir brine (RB: 97,553 ppm), followed by 2.0, 4.0, and 6.0 wt% 2-butanone solutions in RB, each with a 3-day soaking period.Results\/Observations\/Conclusions: PVT experiments showed that 2-butanone had a greater affinity for the aqueous phase than 3-pentanone, resulting in a K-value threefold lower than that of 3-pentanone. Consistent with PVT results, 3-pentanone showed faster and greater oil recovery than 2-butanone at the same concentration; however, at higher concentrations, 2-butanone offset its lower K-value and achieved improved performance. Huff-n-puff experiments demonstrated that 2-butanone altered core-scale wettability in the shale matrix. The oil recovery from the matrix was consistent with the ketone imbibed fraction in the previous cycle, and switching to a higher concentration increased the imbibed fraction after its decline.Applications\/Significance\/Novelty: 2-Butanone offers substantial economic advantages for oil field applications. It is readily produced from petrochemical feedstocks, and its existing production by operators further enhances its cost-effectiveness. When properly applied, 2-butanone penetrates both fractures and deep shale matrices and enhances oil mobility in unconventional reservoirs for extended periods.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Chelation-Driven Silicate Disruption as a New EOR Mechanism for Tight Shale Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Herrera*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Maverick X)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work evaluates a new EOR mechanism based on ultra-strong chelation that disrupts silicate and mixed-metal matrices trapping hydrocarbons in tight shale. The study examines how selective chelation breaks apart Fe- and Al-bearing mineral bridges, reduces clay swelling, increases pore accessibility, and enhances hydrocarbon release. The goal is to quantify these effects relative to acid treatments and establish parameters suitable for reservoir-scale simulation.Methods\/Procedures\/Process: Laboratory studies on shale core plugs used ultra-high-affinity chelators with binding strengths twenty-seven orders of magnitude above EDTA. Porosity and pore throat evolution were measured through helium and mercury injection at multiple pressures, supported by third-party laboratory validation. Additional tests quantified plug strength, air and Klinkenberg permeability, swelling behavior, mass changes, and micro-CT\u2013based pore structure shifts. Hydrocarbon liberation was measured under ambient conditions. Comparative acid tests assessed differences in pore geometry and swelling response.Results\/Observations\/Conclusions: Chelation selectively dismantled silicate frameworks and mixed-metal bridges, producing substantial reductions in swelling and generating new micro-connected pore pathways not observed with acid. Core plugs showed increased porosity, significant permeability gains, measurable mass loss from silicate dissolution, and consistent hydrocarbon liberation without thermal or pressure stimulation. Acids did not meaningfully modify tight-rock pore networks and in some cases increased swelling, reinforcing chelation as a distinct EOR mechanism.Applications\/Significance\/Novelty: This study introduces a non-acidic, non-volatile, non-corrosive chemical pathway for improving recovery in unconventional reservoirs. By breaking the mineral structures that physically confine hydrocarbons, chelation reduces formation damage from swelling clays and enhances flow pathways inaccessible to conventional chemicals. The findings provide laboratory-derived parameters for integrating chelation kinetics into reservoir models and offer a novel, infrastructure-compatible EOR strategy for tight shale.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 10: Gas Injection EOR<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMichael Cronin, George Herman\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Advancing Gas EOR in Shale: Lessons from a Chevron-Led Permian Basin Pilot<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Kumar*, J. Alvarez, P. Takulhoon, K. Krezinski, A. Rey, M. Srivastava and T. Malik\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Over the past decade, unconventional shale and tight reservoirs have contributed significantly to U.S. oil production. However, recovery factors in these reservoirs typically remain in the upper single digits, highlighting the need for enhanced oil recovery (EOR) methods such as cyclic gas injection. This paper presents key learnings and recommended practices from a Chevron-led hydrocarbon gas injection pilot in the Permian Basin, which has been operational for over five years. The study also examines observations from water-alternating-gas (WAG) injection cycles conducted within the same pilot.Methods\/Procedures\/Process: Compressor reliability was closely monitored throughout the project. Sustained casing pressure tracking ensured safe operations. Pre-injection baseline for oil and gas production rates were established. Injection, bottomhole pressure was monitored. These steps led to clear uplift estimation. Due to strong inter-well connectivity in the pilot area, recommended practices were developed for operating wells adjacent to the injector. Tracer studies were also conducted. Key performance indicators included the gas utility factor and the fraction of injected gas recovered. Over five years, average of four to five gas injection cycles per well were done. In later project stages, water\/surfactant-alternating-gas cycles were done to improve injection pressure buildup and gas retention.Results\/Observations\/Conclusions: Early in the project, an interconnected well group\u2014termed the \u201ctank\u201d\u2014was identified using gas-oil ratio (GOR), tracer, and oil uplift analyses. A part of the main learning was the importance of tracking uplift at the tank level (both injector and neighbor wells). The project achieved a meaningful cumulative oil uplift and gross gas utility factors. Later cycles showed increasing gas utility factors signaling reservoir depletion and diminishing incremental EOR benefits. Most of the injected gas was also recovered. Pilot results were used to blind-test a simulation method for forecasting gas EOR performance. Scaling pilot uplift to periods of high compressor utilization demonstrated economic recovery and supports the potential for broader application of gas EOR in unconventional reservoirs.Applications\/Significance\/Novelty: This pilot represents one of the few well-documented, large-scale gas injection projects in unconventional reservoirs. It demonstrates the economic viability of gas injection for black oil fluids in the Permian Basin and provides a framework for integrating geology, fluid behavior, tracer analysis, economic evaluation, and advanced simulation in field-scale EOR projects.Interdisciplinarity (Team Presentation\u2019s only): The project integrated geology, fluid characterization, tracer studies, economic analysis, and simulation\/modeling to design, execute, and evaluate the success of gas injection at field scale.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Unlocking Tight Oil Potential: Numerical Validation of Magnesium-Doped Fly Ash Nanoparticle-Enhanced in Supercritical CO2 (scCO2) Foam for Huff-n-Puff EOR in the Middle Bakken<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Noel-Berje*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of North Dakota)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study numerically validates a novel Enhanced Oil Recovery (EOR) technique for the ultra-tight Middle Bakken formation using Magnesium-doped Fly Ash Nanoparticles (Mg-FANP) to stabilize supercritical CO2 (scCO2) foam. Conventional scCO2 Huff-n-Puff (HnP) operations in fractured tight reservoirs often suffer from rapid gas breakthrough and poor volumetric sweep. The primary objective is to demonstrate that Mg-FANP integration creates a rigid foam structure that blocks high-permeability fracture channels, alters wettability from oil-wet to water-wet, and significantly reduces Interfacial Tension (IFT). The scope includes a sector-scale simulation WITH CMG STARS to quantify oil recovery uplift, gas-oil ratio (GOR) suppression, and pressure maintenance compared to conventional methodsMethods\/Procedures\/Process: A compositional reservoir model was constructed using a commercial thermal-compositional simulator representing a Middle Bakken sector (0.05 mD permeability). The fluid model integrates Mg-FANP physics via adsorption isotherms and non-Newtonian foam rheology. Key parameters derived from analytical modeling included a mobility reduction factor of 3.33, IFT reduction from 25 to 1.5 dynes\/cm, and wettability alteration indices. The simulation schedule executed three Huff-n-Puff cycles over 119 days: high-pressure injection (5500 psi) followed by soaking and production periods. The model accounts for the solving of core plugging via the interaction of Mg-doped nanoparticles with carbonate formation fluids to mitigate salt precipitation.Results\/Observations\/Conclusions: The simulation achieved a normal termination with negligible material balance error (&lt;0.33%), validating the physical stability of the Mg-FANP foam model. Crucially, the Gas-Oil Ratio (GOR) remained near zero across all cycles, confirming complete gas containment and mobility control. Oil production rates demonstrated sustained spikes in later cycles (Cycle 3 peaked at ~80% of Cycle 1), contrasting sharply with the steep decline typical of conventional HnP. Grid visualization revealed a distinct &quot;sweep efficiency&quot; with 20-30% oil saturation reduction near fractures, driven by the adsorption-induced wettability shift. Reservoir pressure profiles exhibited stable &quot;sawtooth&quot; behavior, confirming hydraulic integrity and efficient energy maintenance by the foam.Applications\/Significance\/Novelty: This work presents the first numerical proof-of-concept for Mg-FANP-enhanced foam EOR, bridging analytical theory with simulation. The novelty lies in the dual mechanism of nanoparticle-stabilized foam for mobility control and chemical wettability alteration for matrix imbibition. Economically, this method repurposes Class C fly ash (a coal byproduct), offering a sustainable, low-cost additive for tight oil operators. The successful suppression of gas breakthrough and mobilization of trapped oil suggests a viable pathway to unlock billions of barrels in stranded Bakken reserves, potentially increasing recovery factors from &lt;10% to projected 22-37% lifecycles\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 8: Performance Prediction Methods:\u00a0Physics-Based Models and Data-Driven Forecasting Approaches I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAlvaro Betancourt, Johan Antonio Daal, Susan Howes\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Linking PD-SRV to Production: Using Pump-Down Analysis to Forecast Well Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Caruso Carter*, D. Le, M. Han, H. Sufi Karimi, R. Tang and M. Khodabakhshi\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Oil and Gas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: A study presented at URTeC 2025 introduced the pumpdown analysis for stimulated rock volume estimate (PD-SRV) methodology. PD-SRV uses pumpdown pressure data to estimate the stimulated rock volume (SRV) for each completed stage. Validated by both physics-based models and direct measurements such as production logging and fiber-optic sensing, this cost-free, risk-free, real-time approach offers valuable insight into production allocation along the lateral, improving understanding of completion efficiency and reservoir heterogeneity. This work extends PD-SRV by linking it directly to well production performance and applying it as a forecasting tool.Methods\/Procedures\/Process: Originally, the PD-SRV was computed manually for each stage in a well and used as a proxy for production allocation. The sum of the SRV from all stages provides the total stimulated rock volume for that well. Under consistent geological and reservoir conditions, differences in total SRV results in differences in production. To validate this methodology, a fully automated algorithm was developed to calculate PD-SRV across an extensive well dataset. Total SRV values are then compared to one-year cumulative production for wells within the same field and landing zone.Results\/Observations\/Conclusions: PD-SRV calculations were performed for wells in both the Delaware and Midland basins of the Permian. Analysis shows a strong positive correlation between total SRV and one-year cumulative liquid production. Correlations were generated for each area and bench using historical well data. These correlations can be applied to forecast performance of future wells. Once a well is completed, the PD-SRV is calculated automatically, and the corresponding production forecast is generated based on established correlations.Applications\/Significance\/Novelty: Traditional production forecasting methods such as rate transient analysis and decline curve analysis require several months of production history. PD-SRV predicts performance immediately after completion-before a well is brought online-providing a rapid evaluation of completion and development strategies. This capability can significantly accelerate field development planning and optimization by using existing surface pressure data, with no additional cost, no downhole equipment requirements, and no operational risk.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Applications of Numerical Modeling to Develop Insights into RTA Interpretation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Lamidi*<sup>1<\/sup>, M. Almasoodi<sup>2<\/sup>, M. Babazadeh<sup>3<\/sup>, A. Baldwin<sup>2<\/sup>, Y. Barzin<sup>4<\/sup>, M. Dunseith<sup>5<\/sup>, C. Cipolla<sup>1<\/sup>, M. McKimmy<sup>6<\/sup>, A. Tucker<sup>7<\/sup>, M. Paryani<sup>7<\/sup>, M. Shahri<sup>4<\/sup> and M. McClure<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ResFrac Corporation; 2. Devon Energy; 3. ConocoPhilips Company; 4. ExxonMobil Upstream Oil and Gas; 5. Continental Resources; 6. Chevron U.S.A. Inc.; 7. APA Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In this study, we integrated numerical modeling and field data to investigate several topics related to rate-transient analysis (RTA) in unconventional wells.Methods\/Procedures\/Process: First, we tested the accuracy of conventional RTA techniques against synthetic data generated by an integrated hydraulic fracturing and reservoir simulator. As expected, pseudopressure adjustments were able to account for sources of nonlinearity such as multiphase flow and pressure dependent permeability, and they yielded accurate estimates for the linear flow parameter (LFP), fluid in place, and permeability. Two different methods of estimating permeability were assessed, and one proved much more robust than the other. Second, we evaluated whether to perform the analysis with respect to total fluids or with respect to the primary hydrocarbon phase.Results\/Observations\/Conclusions: Results suggest that in some datasets, performing the analysis with respect to total fluids will better capture the character of the RTA transient. The \u2018total fluids\u2019 approach is valuable if linear flow at early time is distorted by changing water cut during early production. Third, we investigated the effect of finite fracture conductivity on RTA behavior. In theory, RTA can be used to identify finite fracture conductivity from the y-intercept on a reciprocal productivity index plot. However, in practice, a clearly discernible y-intercept will only be evident if the conductivity is exceptionally low. Otherwise, the y-intercept can be obscured by the ambiguity created by pressure charging from the hydraulic fracturing treatment. With realistic fracture conductivity values, the fracture is finite conductivity, and yet, in many cases, a y-intercept is not clearly discernable. Fourth, we investigated how to diagnose reservoir behaviors from RTA and GOR plots. Drawing on results from simulations, we made schematic diagrams showing RTA and GOR trends under different conditions.Applications\/Significance\/Novelty: These guidelines can be used for engineering diagnosis of subsurface processes and to guide history matching procedures.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Rapid Multi-Resolution Simulation of Coalbed Methane (CBM) and Enhanced CBM (ECBM) Recovery Processes Using Fast Marching Method<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Nakano*, C. Chan and A. Datta-Gupta\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Numerical models of coalbed methane (CBM) reservoirs are typically characterized by a dual-porosity (DP) system, in which the coal matrix supplies desorbed gas and the fracture network provides the primary flow paths. Capturing this multi-continuum behavior requires fine spatial resolution, which results in enormous computational demands for field-scale history matching and optimization. To accelerate CBM reservoir simulation, this paper proposes a novel Fast Marching Method-based rapid simulation (FMM-SIM) by incorporating a DP formulation. An equivalent multi-resolution model is constructed using diffusive time of flight (DTOF) as spatial coordinate, achieving orders of magnitude speedup in computational time. The proposed approach enables efficient field-scale CBM and ECBM applications.Methods\/Procedures\/Process: The multi-resolution model is constructed based on DTOF, which represents the propagation time of pressure front and is calculated rapidly by Fast Marching Method. Since DTOF captures the reservoir heterogeneity and flow field, its contours are used as the spatial coordinate of the reduced-order model. FMM-SIM converts the grids into a series of DTOF-based 1D grids while preserving the original 3D resolution of the near-wellbore region. The proposed model significantly reduces the number of active cells and accelerates the simulation by orders of magnitude. For CBM applications, the multi-resolution model is first constructed only in fracture system. The corresponding multi-resolution matrix model is then constructed to represent gas desorption source terms in the CBM reservoir simulation.Results\/Observations\/Conclusions: The accuracy and efficiency of the proposed FMM-SIM framework are demonstrated using synthetic low-permeability CBM models. The dual-porosity FMM-SIM is first applied to heterogeneous CBM reservoirs for both vertical and horizontal well cases, demonstrating close agreement with fine-scale simulation results while achieving a 10-20x computational acceleration. Next, the rapid simulation approach is utilized to study the effects of CO\u2082 and N\u2082 injection scenarios for ECBM recovery. The proposed method also achieved ~20 times speedup with less than 2% relative error in ECBM applications. The simulation results demonstrate that CO\u2082 and N\u2082 injection effectively displace adsorbed methane gas and replenish reservoir energy, leading to enhanced methane recovery.Applications\/Significance\/Novelty: Efficient CBM field management requires rapid simulation capable of evaluating complex operating and injection strategies. We propose a fast, multi-resolution, dual-porosity simulation framework based on a finite-volume FMM formulation for CBM reservoirs. The proposed multi-resolution simulation accelerates reservoir simulation by orders of magnitude and enables systematic assessment of a wide range of scenarios, including the effects of injection gases and efficient production optimization, providing a versatile tool for practical application in CBM and ECBM projects.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Subsurface Chemistry of Critical Minerals<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tHadi Nasrabadi, Autumn Shannon\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Quantitative Method to Determine Lithium Concentration in Geological Brines Using Nuclear Magnetic Resonance Spectroscopy<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Vuong*<sup>1<\/sup>, S. AlTheyab<sup>1<\/sup>,<sup>2<\/sup>, N. Truong<sup>1<\/sup>, T. Vo<sup>1<\/sup>, C. Rai<sup>1<\/sup> and S. T. Dang<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Mewbourne School of Petroleum and Geological Engineering, The University of Oklahoma; 2. Upstream Advanced Research Center, Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Lithium (Li) is a critical element for applications in the energy industry. Along with ores, geological brines such as continental, geothermal, and oil-field brines are also Li resources with Li concentrations reaching up to 1700 mg L-1 in the US. Consequently, the need for a rapid analysis method has arisen to screen Li concentrations in brines and build up the database for exploring Li reserve targets. While conventional gold standard methods and electrochemical sensors can detect Li with high sensitivity, they are cost-ineffective and time-consuming. This study aims to develop and validate an affordable and rapid NMR-based method for quantitative Li analysis in brines.Methods\/Procedures\/Process: The 100 MHz benchtop NMR spectroscope with free induction decay (FID) mode was used to collect 7Li spectra. 7Li NMR signal was optimized with the following parameters: receiver gain, scan delay, number of scans, and acquisition time. The spectra of the calibrants were phase corrected before collecting peak areas. Repeatability of the instrument was assessed both intra-day and inter-day. After confirming the repeatability of the instrument, calibration curves were constructed by plotting resultant peak areas versus Li concentration to cover the Li concentration range of interest in geological brines. As geological brines exhibit high salinity relative to Li levels, the effects of ionic strength (salinity) and paramagnetism (Fe2+, Fe3+, Mn2+, etc.) from co-existing ions were evaluated.Results\/Observations\/Conclusions: An NMR-based method to rapidly quantify Li in brines has been developed. Two calibration curves with good linearity were constructed using two different parameter sets, covering the range of Li concentrations from 30 to 5000 mg L-1. Compared to conventional methods, this method allows the measurement for each sample requires only 2 to 30 mins, demonstrating strong potential for rapidly mapping Li in reservoir targets. Although high ionic strength and paramagnetism cause peak shifting and broadening, they do not significantly affect peak areas under the optimized operating conditions. Therefore, intensive sample handling and preparation are not required. High instrument repeatability reduces the need for frequent recalibration, allowing for an increased number of samples to be analyzed.Applications\/Significance\/Novelty: The method requires small amounts of liquid sample (1 mL), allowing the measurement of extracted brine from individual rock cores, instead of the mixed fluid samples from surface facility. Our research developed an analytical approach that minimizes the impact of impurities on the final results while offering a time- and cost-effective solution. This is essential to create a database for mapping Li resources across different mature petroleum fields with high water production, and measurements can be dynamically performed throughout the lifetime of oil fields. The application is not limited only to oil and gas operations, but extends to mining operations, geothermal, and continental brine resources.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Critical Minerals as Drivers of Subsurface Energy Conversion<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Hascakir*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Harold Vance Department of Petroleum Engineering, Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Critical minerals such as Lithium (Li), Nickel (Ni), and rare earth elements (REEs) are widely recognized as key enablers of clean energy technologies. In subsurface systems, however, abundant minerals such as Magnesium (Mg) and Calcium (Ca) become functionally critical due to their dominant role in carbon dioxide (CO2) mineralization and thermochemical processes. Despite their importance, the role of these minerals in subsurface environments remains fragmented across geochemistry, mining, and energy-related literature. This study provides a subsurface-centric evaluation of mineral systems and establishes their integrated function in enabling CO2 mineralization, hydrogen generation, hydrocarbon transformation, and brine-based resource recovery.Methods\/Procedures\/Process: A multidisciplinary synthesis was conducted by integrating thermodynamic analysis, geochemical reaction mechanisms, and laboratory-scale experimental observations (TGA\/DSC, FTIR, SEM-EDS). Reaction pathways were evaluated with emphasis on mineral\u2013fluid\u2013thermal interactions. Scaling considerations were incorporated to bridge laboratory findings with reservoir-scale behavior.Results\/Observations\/Conclusions: Results demonstrate that Mg- and Ca-rich minerals provide favorable pathways for permanent CO2 sequestration through carbonate formation, while Ni- and Fe-bearing minerals enable catalytic hydrogen generation and hydrocarbon upgrading under elevated temperatures. Brine systems contribute both to reaction kinetics and to the recovery potential of critical elements such as lithium. The study shows that these processes are strongly coupled, forming dynamic thermo-chemo-reactive systems in which mineralogy actively governs energy conversion and storage.Applications\/Significance\/Novelty: This work introduces a mineral-driven framework that redefines unconventional reservoirs as subsurface energy reactors rather than passive hydrocarbon storage systems. The framework provides a structured approach for integrating carbon management, hydrogen production, and resource recovery within a single system. The findings identify key engineering opportunities and research gaps, offering a pathway toward scalable, low-carbon subsurface energy solutions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Comparing Geochemistry and Microbial Ecology of Samples Collected from Separators vs. Paired Well-heads in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. W. Flett*<sup>1<\/sup>,<sup>2<\/sup>, K. Tinker<sup>1<\/sup>,<sup>2<\/sup> and D. Gulliver<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. National Energy Technology Laboratory; 2. LEIDOS)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Permian Basin is a vital reservoir for oil and gas production in the United States, producing around 60% of onshore oil and 25% of gas as of May 2024 (EIA 2025). Little is known about the microbiology here, highlighting knowledge gaps on problems with produced water management, including hydrogen sulfide production and scaling from microorganisms. In this study we aim to examine potential differences in samples collected from well-heads vs oilfield separators. Samples are often collected from both wellheads and oilfield separators with little distinction between the two. Microbes within the wellhead can impact potential operational strategies within the wellbore and the reservoir, while microbes within the separator often indicate operation strategies for downstream infrastructure.Methods\/Procedures\/Process: Produced water samples were collected from both the wellhead and the separator at four actively producing hydraulically fractured oil and gas wells located in the Permian Basin in Texas between 2018 and 2019. Major cations and anions were detected in triplicate using ion chromatography. Samples were filtered through a 0.2um polyethersulfone membrane filter to collect microbial biomass, and filters were extracted using the DNeasy Powersoil kit. Recovered DNA was amplified using universal primers targeting the V4 region of the 16S rRNA gene. Subsequent 16S rRNA gene libraries were prepared and sequenced on the Illumina MiSeq.Results\/Observations\/Conclusions: Geochemical measurements revealed a lower alkalinity in all samples collected from the well-head except one. Our 16S sequencing revealed a microbial community characterized by sulfate-reducers, halophiles, and anaerobes in both wellheads and separators. Further analysis shows the well-head and separators have unique dominant microbial taxa, but share many of the same minor microbial taxa. At least one sample showed an increased level of diversity when collected at the wellhead. This study demonstrates similar functional potential between the wellbore and the separator, but a slight variation in the microbial community. Understanding the potential impacts different sampling methodologies have on the microbial community is imperative to ensure tailored operational strategies.Applications\/Significance\/Novelty: The Permian Basin is the largest oil and gas producer in the United States; however, there is almost zero publicly available microbiological data from this region. There remains a wide knowledge gap in how the microbial community may differ when sampled at the wellbore versus the separator. Understanding this knowledge gap will allow for more informed management practices, reducing the risk of biocidal resistance or increased hydrogen sulfide production.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: OXY Special Session: Data\u2011Driven Development Strategies to Maximize Value in the Delaware Basin<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tNuny D. Rincones, Han Jake Li\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">An Integrated Development Optimization Study in the Delaware Basin for Thin Sand Reservoirs with Interbedded Carbonates<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Yin*, T. Boulis, H. Li, F. Adekoya, C. Polgar, J. P. Keevan, S. Noonan, S. Esmaili, O. Raba, X. Xie and S. Liu\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Well spacing decisions in unconventional reservoirs must weigh maximizing hydrocarbon drainage against the risk of excessive fracture overlap. If wells are too sparse, significant reserves remain undrained; if too dense, capital efficiency drops due to interference. The challenge is compounded in thin, high-quality sand reservoirs interbedded with carbonates, especially in multi-landing programs. This study presents a workflow that leverages geologic characterization, geochemical and tracer diagnostics, and production history matching to identify optimal spacing and completion strategies. Integrating these datasets into calibrated models significantly improves predictive accuracy for future drilling spacing unit (DSU) development scenarios, supporting informed and efficient asset planning.Methods\/Procedures\/Process: A detailed 3D geologic model was constructed to capture sand\/carbonate facies variations from logs and core data. Fracture models based on geomechanical inputs were calibrated to geochemistry and tracer data. Full-field reservoir simulations were history matched to pressure and rates, with EURs benchmarked against decline curve analysis for validation. Spacing sensitivities generated EUR vs. wells per section (WPS) relationships, which were screened by economic analysis to guide density changes. Completion optimization involved testing large completion-wide spacing and small completion-tight spacing configurations, plus completion designs tailored for sand-carbonate dual landings. Parent-child interactions within the same bench were modeled to evaluate depletion timing impacts.Results\/Observations\/Conclusions: Thin, high-quality sand targets showed strong productivity with minimal interference at wider spacing, while increasing density from less to more WPS improved total recovery but introduced modest EUR losses per well. Increasing proppant loading and optimizing cluster spacing reduced degradation in lower-quality landings. Tracer diagnostics confirmed mild fracture connectivity in select zones, validating the feasibility of targeted denser spacing. Additional fiber diagnostic data from two different development areas supported this observation. The recommended spacing\/completion configuration from history matched DSU modeling was implemented in a new DSU, with observed production closely matching forecasts, confirming model robustness and calibration fidelity.Applications\/Significance\/Novelty: This workflow delivers a high-confidence, rapid-turn capability for Delaware Basin development optimization. By integrating geochemical insights, tracer data, and calibrated fracture\/reservoir simulations, operators can quickly assess spacing, completion intensity, and parent-child degradation risks. It enables DSU designs that maximize recovery from thin sand and interbedded carbonate sequences while controlling interference, refining landing strategies, and meeting CapEx constraints. This approach preserves predictive accuracy while providing timely, data-driven guidance for unconventional resource development decisions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Sequencing with Consequences: Measuring Inter-Bench Parent-Child Interference in the Delaware Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Li*, Y. Ben, S. Brazell, S. Esmaili and E. A. Kinzler\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In the Delaware Basin, multi\u2011bench development is predominantly implemented through cube or sequenced development. While sequenced development provides operational flexibility, reduces the upfront capex and mitigates surface facility constraints relative to cube or co-development, it introduces risks of reservoir depletion and inter-well interference in subsequent benches. These risks are particularly pronounced under a \u201cBest Bench First\u201d strategy, where early production from high-quality benches can induce significant pressure drawdown, diminishing stimulation efficiency and well performance. This study investigates parent-child well interactions across vertical benches, quantifying how production from parent wells in earlier-developed benches affects child well performance in later-developed benches.Methods\/Procedures\/Process: A dataset was assembled of Delaware Basin wells where the parent wells are completed in Bench A and child wells in the underlying Bench B, with the upper interval of Bench B functioning as a geomechanical fracture barrier. A data-driven model was developed to predict one-year cumulative oil production for child wells. The model incorporates a wide range of inputs, including parent well production when child wells are online, parent-child spacing, geological properties of both benches and the barrier, and completion designs. To enhance the predictability of the model, a novel \u201cMulti-Parent Interference Index\u201d (MPII) is introduced that integrates parent production, overlap factors, distance, and lateral length, enabling better quantification of inter\u2011bench parent-child depletion effects.Results\/Observations\/Conclusions: A robust, high\u2011accuracy model for predicting child well performance was built after testing multiple machine learning algorithms and removing outliers. Shapley analysis revealed two key drivers: (1) a higher proportion of Facies 6, defined as a dense, low permeability facies with a relatively higher Young\u2019s Modulus within the barrier zone correlates with improved child well performance, suggesting effective geomechanical isolation from parent-induced depletion; (2) a higher multi-parent interference index consistently reduces child performance, providing a quantitative measure of inter-bench depletion impact. Using the multi-parent interference index, a degradation factor was derived for one\u2011year cumulative oil by comparing the performance of well in depleted bench with a zero\u2011depletion scenario (Codeveloped scenario). A business case is presented in which the actual child well performance aligns well with the model prediction.Applications\/Significance\/Novelty: To the best of the authors\u2019 knowledge, limited prior study has addressed inter-bench parent-child depletion effects in the Delaware Basin. Leveraging actual production data from the Delaware Basin, this work introduces a novel multi-parent interference index that accounts for multi\u2011parent scenarios, enabling more accurate child well performance predictions. The workflow has been successfully implemented for development sequencing and reserve booking, demonstrating direct operational and economic value.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Delaware Case Studies to Understand the Impact of Stage Spacing on Fracture Geometry and Well Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tQ. Ji*, J. Han, Y. Askabe, J. Dubuisson, V. Muralidharan, A. Haines, J. Ortiz, M. Rios Lopez, J. Ye, B. Stockholm, C. M. Sirois, D. Sutton, M. Copley and R. Vaidya\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study investigates how stage spacing influences fracture geometry and production performance in Delaware Basin wells. The objective is to quantify the relationship between completion design parameters and reservoir response, providing actionable insights for optimizing hydraulic fracturing strategies in unconventional plays.Methods\/Procedures\/Process: We analyzed multiple Delaware Basin case studies using integrated datasets, including microseismic monitoring, wireline fibers, pressure diagnostics, chemical tracers, and production history. Fracture geometry was evaluated through modeling and diagnostic fracture injection tests (DFITs), while well performance was assessed using rate-transient analysis and normalized production metrics. Comparative analysis was performed across wells with varying stage spacing to isolate its impact, controlling for cluster spacing, fluid volumes, proppant loading and other completion parameters.Results\/Observations\/Conclusions: Completion diagnostic results from multiple Delaware Basin case studies show that for certain benches, with shorter stage spacing, fracture geometry, especially fracture height was better controlled. Most of the cases, the Microseismic results also align with other diagnostic tools such as fibers and tracers. Results indicate that tighter stage spacing generally generates more controlled fracture geometry, enhances fracture complexity and reservoir contact, leading to improved early-time production. However, diminishing returns were observed beyond a critical threshold, where excessive overlap increased completion costs without proportional gains in recovery.Applications\/Significance\/Novelty: Optimal spacing balances stimulation efficiency and economic performance, with findings suggesting a spacing range that maximizes EUR while minimizing operational inefficiencies. These insights underscore the importance of tailoring stage spacing to local geomechanical conditions rather than applying uniform designs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Advances in Core Analysis and Rock-Fluid Interaction I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tVincent Zhang, Parag Bandyopadhyay\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Workflow for the Integrated Approach to Rapid Clay Mineral Speciation from the Combined Bulk XRD and CEC Measurements: Matching Large-Scale and Fine-Scale Data<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Derkowski*<sup>1<\/sup>,<sup>2<\/sup>, M. Santiago<sup>2<\/sup> and D. McCarty<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Institute of Geological Sciences Polish Academy of Sciences; 2. CoreSpec Alliance LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Distinguishing and accurately quantifying clay mineral species is a vital task in the exploration of unconventional hydrocarbons Clay minerals are responsible for numerous rock properties, controlling permeability, ineffective porosity, oil\/water wettability, various wireline logging responses, and wellbore behavior. Kaolinite, chlorite, illite, smectite, and their interstratified species (illite\u2013smectite; I\u2013S) each affects rock properties and wireline signals differently and respond differently to fracturing and EOR procedures. Here, we present the protocol and limitations of determining clay mineral structures and contents from bulk rock analyses (including cuttings) and from clay mineral extracts, cross-validating both procedures and clarifying arbitrary definitions.Methods\/Procedures\/Process: All samples were gently crushed and passed through a &lt;0.4 mm sieve, then homogenized and split into representative portions. One portion was ground in a micronizing mill and spray-dried to obtain fully randomized crystallites. The interpretation of its X-ray diffraction (XRD) patterns was performed using the QMIN software designed specifically for clay-bearing rocks. Another portion was analyzed for cation exchange capacity (CEC) using the hexamminecobalt(III) cation and spectrophotometric analysis. The &lt;2 \u00b5m fraction, ideally devoid of non-clay minerals, was separated by chemically removing cements. The oriented mounts of Ca2+ -exchanged clay fractions were recorded by XRD in air-dry and glycolated states. Structural clay mineral interpretation was performed using the Sybilla program.Results\/Observations\/Conclusions: In bulk-rock XRD result, coarse and fine illite (+mica) are quantified together, except in samples with high muscovite content. Kaolinite and chlorite are quantified regardless of their grain size. The &lt;2 \u00b5m fractions, in turn, represent the true clay mineral component. The results determined from bulk samples and &lt;2 \u00b5m fractions were compared for various geological formations. Because total illite + smectite + I\u2013S (I+S) controls bulk-rock CEC, combining CEC with bulk-rock XRD allowed estimation of the smectite layer content in I-S phase (%S) within the I+S group. These results correlate well with %S determined in the clay fraction, which additionally enables distinguishing discrete illite, smectite, and I-S populations, and kaolinite and chlorite populations within the clay size range.Applications\/Significance\/Novelty: We present a unique workflow integrating bulk-rock analysis, CEC data, and detailed clay-fraction characterization for effective formation evaluation, already tested in unconventional reservoirs. Rapid clay mineral speciation from combined XRD bulk-rock analysis and CEC is supported by targeted detailed clay analyses, showing that only a few detailed clay-fraction measurements is generally adequate in formations that received relatively steady deposition where clay reactive transitions responding to thermal gradient are over much greater stratigraphic distances. The verified bulk-rock data serves as a custom calibration set for geosteering and on-site XRF or FTIR analyses. We also demonstrate how CEC and %S can be used to calculate the ineffective porosity.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluating Capillary Displacement Effects in Gas Geostorage and EOR: A Reservoir-Scale Sensitivity Analysis Benchmarked with Experiments<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Truong*, C. Rai, D. Devegowda and S. T. Dang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Oklahoma)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work integrates experimental and simulation methods to develop a high-pressure, high-temperature captive bubble workflow for measuring contact angles on brine saturated reservoir rocks, yielding wettability data that are more representative of in-situ gas\u2013brine\u2013rock systems than conventional drop-shape methods. The study quantifies how cos \u03b8 varies with gas type (N2, CH4, scCO2, H2), pressure, and saturation state, and uses these wettability variations to perturb capillary pressure (Pc) functions. A key objective is to evaluate how such Pc perturbations shift irreducible water and gas saturations, reshape relative permeability curves, and thereby influence capillary-driven fluid displacement, plume migration, and total storage capacity in geostorage and gas-injection EOR settings.Methods\/Procedures\/Process: Experiments were conducted using the captive bubble method, rather than the pendant drop technique, to better replicate in-reservoir gas injection with the rock surface fully submerged in brine. Gas\u2013brine\u2013rock systems were tested at 55,000 ppm NaCl and 122\u00b0F using two Vaca Muerta shale samples from different depths, each evaluated in unsaturated and pre-equilibrated (saturated) states. Pressure was varied stepwise at constant temperature, first increasing from 2,000 to 4,500 psi and then decreasing back to 2,000 psi. Contact angles were measured as a function of gas type, pressure, and saturation, and used to parameterize capillary pressure functions for reservoir-scale simulations.Results\/Observations\/Conclusions: Significant differences in cos \u03b8 were observed between unsaturated and saturated states, with variation magnitudes dependent on both rock type and gas species. For rock S1, the difference in cos \u03b8 ranged from 4.3% (CH4) to 10.3% (scCO2). For rock S2, differences were more pronounced, ranging from 31% (CH4) to 35% (scCO2). In general, scCO2 exhibited the largest variation in wettability alteration between saturation states. These changes in cos \u03b8 were propagated into capillary pressure functions and reservoir simulation, demonstrating measurable impacts on capillary-driven displacement, CO2 plume evolution, and storage efficiency in geostorage and gas-injection EOR scenarios.Applications\/Significance\/Novelty: The experimental findings demonstrate that variations in cos \u03b8 between unsaturated and saturated conditions with scCO2\u2014approximately 10% for rock S1 and 35% for rock S2\u2014translate directly into measurable changes in capillary pressure. Building on these laboratory insights, the corresponding Pc shifts (\u00b110% and \u00b135%) were incorporated into a two-phase (gas\u2013water) CMG reservoir model of a closed-boundary aquifer to assess their influence on CO2 plume migration and trapping behavior. Integrating laboratory wettability with reservoir simulation establishes a novel workflow linking pore-scale interfacial behavior to field-scale CO2 dynamics, improving predictions of storage efficiency, plume evolution, and long-term containment.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Wettability Alteration During Prolonged Exposure of Tight Sandstones to Boron Crosslinked Hydraulic Fracturing Fluid<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Garcia<sup>1<\/sup>, H. J. Khan*<sup>1<\/sup>, A. Othman<sup>1<\/sup>, M. Murtaza<sup>1<\/sup>, M. S. Kamal<sup>1<\/sup>, M. Al-Jawad<sup>1<\/sup>, R. A. Kalgaonkar<sup>2<\/sup> and B. Aldakkan<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. College of Petroleum and Geosciences, King Fahd University of Petroleum and Minerals; 2. EXPEC-ARC Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: After the hydraulic fracturing operation is completed, the well is shut in to allow the injected fluid to interact with the reservoir rock to prevent phenomena such as water blocking occurring. However, during this shut-in period, the fluid degrades, and it may affect the petrophysical properties of the rock. In this study we focus our attention on analyzing the changes in the wettability of tight sandstone formations when these are exposed to boron crosslinked hydraulic fracturing fluid.Methods\/Procedures\/Process: Forty-four (44) cylindrical samples (2.54 cm dia. x 0.5 cm thick) were made from tight Scioto sandstone blocks, which were divided into two groups: oil wet and water wet. The former were vacuum saturated with a light sweet crude oil and aged for 2 weeks at 90 \u00b0C to ensure uniform initial oil-wet conditions; the latter were cleaned with air plasma to remove contaminants and establish initially hydrophilicity. The contact angle was then measured using both captive bubble and sessile drop methods for the oil-wet and water-wet samples respectively, before aging in broken, filtered boron-crosslinked hydraulic fracturing fluid. Samples were removed after being aged for 0, 3, 6, 12, 24, 48, 72, 168, 360, 720, and 1440 hours, and the contact angles were measured again.Results\/Observations\/Conclusions: X-ray diffraction on the rock samples showed that it consists primarily of quartz with a small presence of albite. Preliminary results show that the boron crosslinked hydraulic fracturing fluid shows a rapid alteration in the wettability of the oil-wet samples, from 19.4\u00b0 to 99.8\u00b0 in the first 12 hours, after which it slows down to a maximum value of 164.2\u00b0 after 30 days. The change in the water-wet samples is significantly slower, from 15.1\u00b0 to 16.3\u00b0 after 12 hours, showing a continuous change.Applications\/Significance\/Novelty: To the best of our knowledge there are no studies showing the long-term chemo-mechanical damage caused by the extended interaction between the hydraulic fracturing fluid and the reservoir rock during the shut-in period. This work intends to bridge this knowledge gap by using meticulous measurements that would pave the way for designing improved hydraulic fracturing fluids that mitigate formation damage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6:  Subsurface Characterization, Applied Case Studies, and Business Implementation I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tYongshe Liu, Antonio Lazo\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Scalable Machine Learning Deployment for Automated Well Correlation and Model Building in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Tang*, J. Cassanelli, T. Watkins, J. Senison, H. Behzadi, X. Xie and T. Akpulat\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Produced water disposal is a key constraint on unconventional oil and gas field development across the Permian Basin. Effective disposal into shallow formations requires a strong understanding of reservoir connectivity and pore volume distribution. Additionally, accurate pressure and connectivity prediction is critical for casing design. For instance, switching from a 4-string to a 3-string design can save over $500,000 per well. Despite having well logs from more than 10,000 wells, the industry lacks a scalable, systematic stratigraphic framework due to the manual nature of traditional well correlation workflows\u2014often slow, error-prone, and non-scalable.Methods\/Procedures\/Process: Chronolog, a dynamic time warping\u2013based algorithm, was deployed in a Midland Basin pilot and later scaled enterprise-wide with a focus on automation, integration, and user experience. A cloud-hosted API on Azure integrates Chronolog with the Petrel database using Docker for scalable, multi-user access. An automated ETL pipeline cleans and standardizes raw log data, resolving duplicates, missing logs, and bad values. Optimized log indexing delivers up to 100\u00d7 speed improvement for datasets exceeding 2,000 wells. A custom Petrel plugin converts ML-generated well tops directly into static reservoir models and QC workflows. Large Language Models enabled rapid prototyping and agile iteration to quickly incorporate user feedback.Results\/Observations\/Conclusions: The solution has been deployed enterprise-wide. The plugin enables rapid, semi-automated correlation with minimal manual input. In testing, over 3,800 wells were correlated within hours. Smaller datasets (&lt;100 wells) process in minutes\u2014up to 100 times faster than manual workflows. More than 20 users have run the tool concurrently without performance degradation. Beyond speed, the tool significantly enhances geoscientist capability, enabling analysis of thousands of wells, anomaly detection, and iterative updates as new data arrives. The solution provides a fully integrated, scalable subsurface interpretation workflow previously unattainable with legacy methods.Applications\/Significance\/Novelty: The novelty is the first enterprise-scale, production-grade deployment of automated well correlation fully integrated with reservoir modeling. Chronolog runs through a Dockerized Azure API with automated ETL, 100\u00d7 performance gains from optimized log indexing, and a custom Petrel plugin that converts ML-generated tops directly into static models and QC workflows.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Well Spacing Impacts on Water Production in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Male*<sup>1<\/sup>,<sup>2<\/sup>, R. Dommisse<sup>3<\/sup> and K. Sathaye<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Penn State University; 2. Center for Subsurface Energy and the Environment, University of Texas at Austin; 3. Bureau of Economic Geology, University of Texas at Austin; 4. Novi Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: When planning new developments, well spacing is paramount to optimizing well recovery. Operators who build machine learning (ML) models to maximize profitability have to account for drilling and completion costs, geology, and expected three-phase production. The data for costs, geology, and oil and gas production are plentiful, but accurate water production data remains hard to find at the scale necessary to train good ML models.Methods\/Procedures\/Process: We combine high quality three-phase production, geology, completion, and spacing parameters for 7,000 Midland basin wells to train an ML model that can better predict water production. Geological parameters were extracted from a 2 billion cell three-dimensional geomodel incorporating 2.2 million formation tops, 42 3D Seismic volumes, and 16,000 quad combo petrophysical wells. The training workflow starts with Bayesian optimization to tune the hyperparameters for an XGBoost model predicting early and steady-state water production. Validation of the model is achieved through testing on held-out well pads. After achieving acceptable model fit, we calculate SHAP values to examine the interplay between geological and engineering parameters.Results\/Observations\/Conclusions: Predicting the first six months water production with XGBoost yields an R-squared of 0.47 and a Spearman rho of 0.71 on the validation data. Water cut models have an R-squared of about 0.4. The most important features impacting early water cut, after taking formation into account, are spacing parameters and how long parents have been producing. Optimizing the timing of child well completion can decrease early water cut by up to 0.05 bbl\/bbl, and multi-variate optimization can decrease water cut even further.Applications\/Significance\/Novelty: These results help guide operators to make more informed decisions in spacing and completion for wells when managing their water budgets. High water production has both direct economic consequences and later effects, such as increased pore pressure in upper disposal formations increasing the difficulty of future drilling and the risks of induced seismicity and zombie well blowouts. With this model, these consequences can be better managed and risks mitigated.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Machine Learning-Based Imputation and Classification of Missing Lithofacies for Enhanced Clastic Reservoir Characterization: A Spectral\u2013Sequential Approach<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tO. Khalaf<sup>1<\/sup>, W. J. Al-Mudhafar<sup>2<\/sup>, A. Alsubaih*<sup>3<\/sup> and K. Sepehrnoori<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Anton Oilfield Services Group; 2. Basrah Oil Company; 3. Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate lithofacies identification remains a fundamental challenge in reservoir characterization, particularly when dealing with missing core data and non-stationary well-log signals within highly heterogeneous clastic and carbonate reservoirs. While recent advancements in Automated Machine Learning (AutoML) have improved classification performance, such frameworks typically rely on raw, point-wise log values, which inherently fail to capture critical stratigraphic continuities and multi-scale geological features. To address these limitations, this study develops a robust multi-scale spectral\u2013sequential framework that integrates the Continuous Wavelet Transform (CWT) with deep sequential modeling to significantly enhance lithofacies prediction and impute missing data.Methods\/Procedures\/Process: The methodology employs four diagnostic well logs (Caliper, Gamma Ray, Density, and Neutron Porosity), which are first normalized and then transformed using CWT with a Morlet wavelet basis to generate depth-indexed scalograms, spatial-frequency representations of energy distribution. These multi-channel spectral images are subsequently processed through a deep Convolutional Neural Network (CNN) integrated with a Bidirectional Gated Recurrent Unit (Bi-GRU) to explicitly model vertical stratigraphic dependencies. To ensure scientific rigor, the proposed framework is systematically benchmarked against an industry-standard baseline established using the PyCaret AutoML library. Benchmarking results demonstrate that while the AutoML-optimized Gradient Boosting classifier achieves a high-test accuracy of 93.73%, the proposed spectral\u2013sequential (CWT-BiGRU) framework delivers superior validation accuracy of 94.44%, alongside a Kappa score of 0.938 and a weighted F1-score of 0.942.Results\/Observations\/Conclusions: These results indicate that spectral-domain transformation stabilizes feature extraction under non-stationary borehole conditions, while the sequential Bi-GRU architecture significantly improves classification consistency across complex lithological transitions, particularly in thin-bed and mixed lithology zones. Furthermore, by effectively reconstructing missing facies data with greater fidelity than conventional imputation methods, this study reduces geological uncertainty in static reservoir models.Applications\/Significance\/Novelty: The proposed methodology offers a scalable, high-fidelity, and geologically constrained alternative to traditional and AutoML-based workflows, providing a critical bridge between advanced signal processing and practical subsurface engineering.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Topicals\" style=\"border-top: 4px solid #01a3a4;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Topical Luncheon: Discovering and Commercializing Unconventional Reservoir Plays Larger and More Prolific than the Permian Basin and Marcellus Shale \u2013 The Promise of International Unconventionals<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        12:15 PM &#8211; 1:20 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Denise Benoit\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Topicals\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Leveille*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Tidal Wave Technologies)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Topicals\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Valleau*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Strategia Innovation and Technology Advisors, LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Topicals\" style=\"border-top: 4px solid #01a3a4;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Topical Luncheon: Tracking Ancient Groundwater on Mars: The Curiosity Rover&#039;s Exploration of Decameter-Scale Boxwork Patterns<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t382\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        12:15 PM &#8211; 1:20 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Fatick Nath\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Topicals\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Siebach*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Rice University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Panel\" style=\"border-top: 4px solid #fa709a;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: Theme 9: Lithium and Critical Mineral Extraction: Where is it Heading?<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:30 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Selin Erzeybek Balan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tW. Mutoru*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Equinor)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Yan*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Pacific Northwest National Lab)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Lebit*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Alma Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Collaboration Success Stories: How Collaborations Build Technology That Shape Our Industry<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:30 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Deepak Devegowda\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Newsham*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. T. Dang*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Oklahoma)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Heidari*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Texas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Clarkson*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Calgary)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: Emerging Geological Evaluations: Integrated Workflows for Prospectivity Assessment and Development Efficiencies I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJose Delgado, Aravind Nangarla\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integration of Geologic Mapping and Regional Geochemical Drainage Data for Successful Uinta Basin Development Planning<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Liu*<sup>1<\/sup>, K. Whitaker<sup>2<\/sup>, R. Newton<sup>2<\/sup>, H. Lipman<sup>1<\/sup>, Y. Liu<sup>1<\/sup>, J. Wu<sup>1<\/sup> and J. Bachleda<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. RevoChem LLC; 2. California Resources Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Geochemical drainage data has become an important tool for horizontal development planning allowing for quantifying drainage heights and key producing intervals with regard to landing zones. In this study, geochemical drainage profiles from a Uinta Basin database are integrated with regional geologic data to inform best landing zones in preparation for horizontal development. A new workflow is also presented utilizing oil-based mud (OBM) cuttings baseline for geochemical production allocation of the new development. New development drainage results are compared to the regional learnings and confirm the hypothesis based on regional drainage trends. This methodology can be used to plan for additional landing zones, optimizing development efficiency while maximizing drainage recovery.Methods\/Procedures\/Process: Drainage profiles of 104 wells from a Uinta Basin geochemical database are used for regional mapping of producing intervals. For the new development study, 67 OBM cuttings were analyzed covering a ~1,430\u2019 TVD interval in an area well. The OBM cuttings were cleaned using a proprietary 3-step solvent wash process to remove OBM contamination. Produced oils and formation oils extracted from the cleaned cuttings were analyzed using multi-dimensional gas-chromatography (GCxGC) and thousands of organic compounds were resolved in each of the samples. Production Allocation (PA), providing drainage heights and quantitative zonal contribution, was conducted by building a geochemistry-based model correlating the produced oils back to their contributing intervals represented by the cuttings.Results\/Observations\/Conclusions: 1) Regionally mapped production allocation data provides an effective monitoring of contribution from each formation through time and aids in identifying the best landing zone; 2) PA results show wells landed in the primary target zone (Fm B) initially drain more from the underlying formation (Fm A). Vertical fractures observed in core from Fm B appear to be filled with oils from underlying Fm A, suggesting early drainage is enhanced by the oil filled natural fractures; 3) Fm B oils are geochemically similar North and South of the Duchene fault, potentially indicating faulting occurred after migration of Fm B oils; and 4) A 3-step solvent wash process removes contamination from OBM cuttings, allowing them to be used for the creation of high-resolution baselines for quantitative PA.Applications\/Significance\/Novelty: To our knowledge, this is the first publication using OBM cuttings to build a high-resolution production allocation baseline using multi-dimensional gas-chromatography (GCxGC) data. Previously only water-based mud cuttings or core could be utilized to build a high-resolution baseline as OBM is a source of contamination. With OBM cuttings capable of being used, many areas drilled with OBM now have the ability to obtain high-resolution production allocation to aid in development planning, without the expense of collecting core. OBM cuttings extracts also reveal important information about the reservoir, indicating intervals with higher mobile oil saturation and matrix permeability.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Building a Permian Basin Regional Petrophysical Geomodel<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Dommisse*<sup>1<\/sup>, C. Kerans<sup>1<\/sup>, F. Male<sup>2<\/sup>,<sup>1<\/sup>, X. Janson<sup>1<\/sup> and C. Zahm<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Texas at Austin; 2. Penn State University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The result of this study is a first-of-its-kind Permian Basin-wide- 3D petrophysical model based on 3D geological, geophysical, and engineering parameters, guided by a newly developed stratigraphic framework. The 3D model contains over two billion cells, and is used to investigate basin-wide, regional, and reservoir-scale relationships between spatially valid petrophysical data and their influence on well productivity.Methods\/Procedures\/Process: Five tasks capture our approach: - The calculation of multi-zone petrophysics using multimineral inversion using 16,000 quad combo curves. - Calculation of lithofacies using artificial intelligence and machine learning methods conditioned to core data. - Construction of a stratigraphic framework based on BEG and proprietary operator well log top interpretations for more than fifty regional surfaces. - The distribution of petrophysical and lithofacies data using a variety of deterministic, geostatistical, and machine learning algorithms across more than 1,000 layers of the model. - The validation of the 3D attribute distributions using independent petrophysical, outcrop analogs, and core data from a variety of independent operators and published interpretations.Results\/Observations\/Conclusions: The 3D Permian Basin petrophysical geomodel covers the Delaware Basin, Northwest Shelf, Central Basin Platform, Midland Basin, and Eastern Shelf. The model\u2019s stratigraphy is based on 2.2 million well log tops from 360,000 vertical wells and 41,000 horizontal wells. The regional stratigraphy is supported by more detailed seismic interpretation from 42 3D seismic volumes and more than 2,500 2D seismic lines. Petrophysical calculations, including multimineral inversion and lithofacies estimation using machine learning, were performed on the 16,000 quad combo wells. The petrophysical results were distributed in multiple-scenario geomodels using forty-three formation zones, subdivided using cells sized 1000 x 1000 x 5 feet.Applications\/Significance\/Novelty: This unique multi-disciplinary model incorporates shelf, slope, and deep basin petrophysical and lithofacies distributions that are used to provide insights into well productivity performance based on geoscience and engineering parameters for unconventional and conventional reservoirs in the Permian Basin.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Comparative Geological Analysis of Mississippian Oil and Gas Resources of the Anadarko Basin (STACK and SCOOP Areas) and the Delaware Basin (Tobosa Sub-Basin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Sartika*, M. R. Becker, J. Laya and F. Marcantonio\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional reservoirs have been a significant boost to oil and gas production in the last decade, however, the key geologic drivers of highly productive areas are still under investigation. This study addresses this point by comparing the Mississippian unconventional resources of the productive Oklahoma Anadarko Basin (STACK and SCOOP plays) with the low - to non-productive West Texas-Southeast New Mexico Delaware (Tobosa) Basin. Both basins contain thick Mississippian intervals with favorable source rock properties, but their petroleum system performance differs markedly.Methods\/Procedures\/Process: To address this difference, the research integrated well logs, cutting descriptions, thin-section petrography, stratigraphic-structural cross-sections, and 2D PetroMod thermal maturity modeling. These multiple datasets were used to assess lithological (organic matter) variability, depositional environment, and basin tectono-thermal evolution.Results\/Observations\/Conclusions: Results of this study show that the Anadarko basin developed a mixed carbonate-siliciclastic ramp system with heterogeneous facies stacking, which promoted source-reservoir juxtaposition and enhanced hydrocarbon retention. Thermal modeling indicates that the Mississippian interval remains within the main to late oil window with kerogen transformation rate &gt;85%, coherent with the ongoing unconventional reservoir productivity. By contrast, the Mississippian carbonate ramp of the Delaware Basin though similar in thickness and organic content to the Oklahoma counterpart, contains more mud-prone facies and have reached the dry gas to overmature windows due to deeper burial, elevated heat flow, and possible significant exhumed overburden.Applications\/Significance\/Novelty: Overall, these findings highlight that long-term reservoir productivity can be governed not only by the richness of the source rock but also by tectonic stability and maturing evolution.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Diagnostics and Monitoring in Hydraulic Fracturing with Geomechanical Models I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAbdul Muqtadir Khan, Lili Xu\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Completion Best Practices: Fracture Diagnostics in Unconventional Resource (UCR) Fracturing &#8211; Capabilities, Insights, and Best Practices<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Cipolla*<sup>1<\/sup>, P. Huckabee<sup>2<\/sup> and K. E. Olson<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ResFrac Corporation; 2. AquaSmart; 3. Olson Turner Enterprises, LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper is the second installment in a series of white papers on current best practices and emerging insights. Focused on fracture diagnostics for multistage hydraulic fracturing in unconventional resource (UCR) horizontal shale wells, this paper synthesizes findings from field surveillance and recent SPE publications to present a concise overview of diagnostic technologies and their role in understanding stimulation distribution, fracture geometry, inflow profiling and properties towards the goal of reservoir-specific fracture design optimization and UCR development.Methods\/Procedures\/Process: Fracture diagnostics such as fiber-optic sensing (DAS, DTS, DSS), perforation imaging, microseismic, fracture driven interactions (FDI), pressure monitoring, geochemistry, and tracers etc., are essential for measuring the geometric and flow capability properties of generated hydraulic fractures through time and thereby optimizing fracture treatments. The industry has utilized these diagnostic techniques and resultant lessons learned to improve daily production, ultimate recovery, recovery factor, and efficiency for a range of reservoir types. Innovation in hydraulic fracture diagnostic techniques has produced a significant number of new technologies with differing capabilities in the last decade.Results\/Observations\/Conclusions: The innovations are not limited to development of these diagnostics for a single well but, for application methodologies associated with UCR developments such as slant observation wells, science pads, and specially designed trials to increase learnings. The UCR value creation opportunities are accelerated with good design of experiment methods and complementary diagnostics. This paper summarizes key lessons learned from field surveillance and integrated analysis that combine multi-modal data sources.Applications\/Significance\/Novelty: Designed for engineers and decision-makers new to the topic, this paper distills complex technical findings into actionable guidance for leveraging diagnostics to improve completion efficiency, ultimate recovery, and reservoir-specific fracture design. It provides a technically grounded summary of the state of the art in fracture diagnostics and highlights future opportunities for innovation. It also discusses the evolution of diagnostic practices, their limitations (e.g., cost, operational complexity), and provides best practices for implementation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Low-Frequency DAS for Cement Quality Monitoring in Horizontal Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Jin* and G. Jin\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Poor cement bonding compromises stage isolation, yet conventional diagnostics like CBLs are rare in unconventional wells due to cost. This work aims to validate a novel, quantitative method for evaluating cement quality using low-frequency distributed acoustic sensing (LF-DAS) in monitor wells, verified by external pressure gauges. The scope covers analyzing field data from a Bakken pad to identify strain patterns indicative of annular pressure communication. Furthermore, this study aims to quantify the hydraulic diffusivity of these behind-casing pathways by linking radial pressure to axial strain, establishing a physics-based framework for non-intrusive cement quality evaluation.Methods\/Procedures\/Process: We analyzed co-located LF-DAS and external pressure gauge data from a permanently instrumented Bakken monitor well. The LF-DAS optical phase was converted to mechanical strain rate and correlated with the temporal gradient of the pressure gauge data. This established a strong linear relationship, confirming our hypothesis that annular pressure induces axial strain via Poisson&#039;s effect. A 1D finite-difference numerical model was then developed to simulate pressure diffusion along the annular pathway. The model was history-matched to the observed DAS and gauge responses to quantify the hydraulic diffusivity of the leakage path, thereby validating the pressure communication mechanism.Results\/Observations\/Conclusions: We observed heel-ward-migrating LF-DAS strain patterns synchronized with pressure gauge increases, indicating annular fluid migration. Our 1D numerical model successfully replicated these observations, confirming the pressure communication along the monitor well. This successful history-match validates the overall method&#039;s feasibility for quantitatively diagnosing cement quality. We also conclude that poor cement can corrupt long-term production pressure monitoring measurements, which can be resolved by integrating RFS-DSS measurements to confirm the true fracture connectivity.Applications\/Significance\/Novelty: This study presents a novel method for quantitatively evaluating cement quality by using LF-DAS, offering a cost-effective alternative to conventional cement bond logs. The significance of this approach is twofold. First, it provides actionable data for pre-stimulation diagnostics, allowing operators to make proactive adjustments to completion designs (e.g., cluster spacing) to mitigate poor isolation and enhance stimulation efficiency. Second, it resolves a critical interpretational ambiguity in long-term pressure monitoring; it demonstrates how poor cement can corrupt pressure gauge readings and lead to misinterpretations of reservoir connectivity, a pitfall this integrated method can overcome.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Characterization of Shearing Deformation from Cross-Well Fiber Strain Measurements: Insights for Casing Deformation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Han*<sup>1<\/sup>, K. Wu<sup>1<\/sup> and G. Jin<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas A&amp;M University; 2. Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Shearing deformation commonly occurs along natural fractures, bedding planes, or faults during hydraulic fracturing treatments. However, shearing may trigger induced seismicity or cause casing deformation. Understanding and characterizing shearing deformation is therefore essential for optimizing hydraulic fracturing treatments. Distributed optical fiber sensing has become an increasingly important tool for monitoring such subsurface events. In this study, the characteristics of shear deformation were identified and applied to the analysis of cross-well fiber strain data.Methods\/Procedures\/Process: The shearing deformation is characterized by combining forward geomechanics modeling and cross-well fiber data analysis. Strain rate, strain change, and displacement are investigated to understand the key features of shear-induced strain responses. The differences in strain response between shearing and tensile opening are highlighted, and a sensitivity analysis is conducted to evaluate how variations in fracture geometry influence the shearing deformation. Subsequently, shearing deformation in the Eagle Ford is identified and matched using an in-house forward geomechanics model.Results\/Observations\/Conclusions: Compression\/extension (C\/E) pair is the key feature in shear-induced strain-rate and strain-change waterfalls, which can be quantitatively evaluated using several parameters, including size, the absolute positive-to-negative strain-rate ratio, maximum strain change and maximum displacement. After shut-in, C\/E pair doesn\u2019t reverse, indicating that the shearing deformation itself does not reverse. In the analyzed EGS dataset, the size of C\/E pair is over 900 ft and the average strain rate in the extension band is 3.0 nanostrain\/s. The maximum strain change and displacement within C\/E pair are up to 154 microstrain and 14.1 mm, respectively. Sensitivity analysis demonstrates that the size of the C\/E pair along the measured depth decreased with fracture height decreasing, while its symmetry is influenced by fracture strike angle and dip angle. In addition, the accumulated shear-induced strain change and displacement along the fiber are linearly dependent on both fault shear velocity and shearing duration. Finally, a complex strain response from a stimulation stage in the Eagle Ford is interpreted as a pre-existing shearing fault, producing a C\/E pair with a total size of 179 ft and an absolute positive-to-negative strain-rate ratio of 0.39, while also being influenced by nearby tensile reopening fractures.Applications\/Significance\/Novelty: These findings improved our understanding of the mechanical behavior of faults during hydraulic fracturing and provided a framework for identifying shearing deformation from distributed fiber-optic measurements. This method can be applied to quantitatively predict casing deformation in the future. The results further highlighted and extended the value of fiber diagnostics for subsurface characterization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Developments in NMR Logging, Interpretation, and Core Analysis I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tYulia Faulkner, Kanay Jerath\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">2D NMR and Pyrolysis to Determine the Residual Fluid Type in Unconventional Source Rock Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Althaus*<sup>1<\/sup>, J. Chen<sup>1<\/sup>, J. Broyles<sup>1<\/sup>, J. Gaytan<sup>1<\/sup>, Q. Sun<sup>1<\/sup> and M. Boudjatit<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Aramco Americas; 2. Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This residual fluid in a reservoir sample can give insight into the fluid in the reservoir and help determine the best course to produce the hydrocarbons. NMR spectroscopy is routinely used to evaluate the fluid types, such as water and hydrocarbon, and conditions, bound or movable, in conventional oil and gas reservoirs. Unconventional source rock shales are known for having residual hydrocarbon remaining in the cores, however the tight pore spaces lead to shortening of relaxation times making it difficult to separate the components. T1-T2 2D NMR can be combined with pyrolysis for better fluid typing in unconventional reservoirs.Methods\/Procedures\/Process: The samples were all measured using NMR and pyrolysis to determine the types of hydrocarbons in the samples. NMR time-domain data is collected using an inversion-recovery CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence. This data must then be inverted to a T1-T2 2D spectrum for fluid typing and quantification using MUPen2D (Multiple Uniform Penalty). The Organic content of the sample was analyzed via a pyrolysis technique (ROCKEVAL or HAWK) in order to determine the TOC (total organic carbon) and free\/adsorbed hydrocarbons.Results\/Observations\/Conclusions: The NMR data was processed to examine the residual fluid in the source rock. The fluid was divided into 4 categories: water, heavy hydrocarbon, confined light hydrocarbon, and free oil. The pyrolysis data was examined, and the organic matter was separated into kerogen, free, and adsorbed hydrocarbons. The results were then compared. It was determined that a better understanding of the fluid types can be achieved through a combination of NMR and pyrolysis. These interpretations can be used to better understand how to produce the reservoir.Applications\/Significance\/Novelty: 2D NMR is a popular technique to determine the fluid types in unconventional source rock reservoirs. The cutoffs in unconventional reservoirs are often debated due to the change in relaxation from the tight pore spaces. This study combines NMR and pyrolysis to type the residual fluid in unconventional shale samples.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">The Magnetic Resonance T2 Distribution Can Be More Than a Proxy for the Pore Size Distribution. It Can Be a Direct Measure of the Pore Size Distribution<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Yan<sup>1<\/sup>, M. J. Dick<sup>2<\/sup>, D. Veselinovic*<sup>2<\/sup>, D. Green<sup>2<\/sup> and B. J. Balcom<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. UNB MRI Centre; 2. Green Imaging)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: It is widely recognized that the magnetic resonance (MR) T2 distribution serves as a proxy for the pore size distribution. Direct conversion to pore size remains difficult because the key parameter, surface relaxivity, must be independently calibrated. Our recent work eliminates this requirement by exploiting the temperature dependence of the T2 lifetime in paramagnetic sandstones. This temperature dependence enables simultaneous determination of the dominant pore size and surface relaxivity. The measured surface relaxivity can then be used to quantitatively convert a T2 distribution into a pore size distribution without external calibration. Our work reveals that the T2 distribution is no longer just a proxy but can be a direct measurement of the pore size distribution.Methods\/Procedures\/Process: The method is based on the intermediate regime MR relaxation behavior described by Brownstein-Tarr (BT) theory, in which the T2 relaxation rate scales with the fluid self-diffusion coefficient. Rapid one-dimensional CPMG measurements were performed over a range of temperatures to obtain the dominant T2 time at each temperature. The measured T2 values were then fit to the known temperature dependence of the fluid self-diffusion coefficient. This procedure yields the dominant pore size and the surface relaxivity. With the measured surface relaxivity, the MR T2 distribution is subsequently converted into a pore size distribution following the intermediate regime of BT theory.Results\/Observations\/Conclusions: Temperature-dependent T2 measurements were conducted on brine saturated paramagnetic sandstones including Berea, Buff Berea, and Nugget. All samples showed a characteristic decrease in T2 lifetime with increasing temperature, consistent with the BT intermediate MR relaxation behavior. The non-linear fits provided pore size and surface relaxivity assuming a spherical pore geometry. The pore sizes determined agreed well with independent imaging measurements, confirming the validity of the approach. Using the measured surface relaxivity, the T2 distributions were successfully converted into pore size distributions, which were more accurate than those obtained using the traditional rapid-exchange MR assumption.Applications\/Significance\/Novelty: Traditional MR methods for converting a T2 distribution to a pore size distribution remain challenging in practice. Our work overcomes this limitation. A rapid CPMG measurement over variable temperatures, combined with a simple non-linear fit, estimates pore size and surface relaxivity simultaneously, which traditional MR techniques cannot achieve. The measured surface relaxivity enables direct conversion of the T2 distribution to a pore size distribution, and the resulting pore size distribution is more accurate than that obtained with the traditional MR rapid-exchange assumption. The most significant contribution is showing the T2 distribution is not just a proxy for pore size but can indeed be a direct measurement for the pore size distribution.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Novel NMR-Based Framework for Quantifying Gas Transport During Huff-n-Puff in Organic-Rich Shale Formations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. A. Oshaish*, Z. Heidari and K. Mohanty\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Hildebrand Department of Petroleum and  Geosystems Engineering, The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Reliable quantification of gas transport in organic shales is essential for evaluating gas huff-and-puff (HnP) EOR performance. Under lab conditions, HnP is dominated by diffusion, whereas in field conditions, it is dominated by convective mechanisms, creating challenges in modeling and scale-up. This study provides a comparative evaluation of C2H6 and N2 transport during HnP in shale cores. In addition, a novel approach is used for real-time monitoring of gas propagation using NMR during the huff, soak, and puff stages.Methods\/Procedures\/Process: Three Mancos shale core samples (M-A, M-B and M-C), with nano-Darcy permeability and porosity ranging between 3.5\u20135%, are subjected to HnP cycles using C2H6 and N2, with NMR bulk T2 and 1D saturation profile measurements acquired under both offline (for M-A with C2H6 and for M-B with N2) and real-time conditions (for M-C with C2H6). Before each HnP cycle, cores are fully saturated with oil and subjected to different HnP cycles, with a huff at 3,000 psi for the offline experiment and 2,000 psi for the online experiment, applied at the sample inlet. In phase I, two HnP cycles are performed under different soaking times; 4 hrs and 12 hrs for M-A and 12 hrs and 24 hrs for M-B. In phase II, ZnO2 core holder enabled real[1]time NMR acquisition during a single C2H6 HnP on M-C, which was conducted for 48 hrs while acquiring several NMR T2 and saturation profile scans throughout all the stages of the HnP.Results\/Observations\/Conclusions: The results show that C2H6 achieves higher oil recovery and faster gas propagation compared to N2. NMR T\u2082 measurements show recovery factors of 29% and 53% for C2H6 at 4 and 12 hrs soaking, respectively, while N2 yields 31% at 12 hrs and 52% at 24 hrs. T2 profiles indicate that C2H6 exhibits diffusion-dominated transport, producing a pronounced concentration gradient with ~90% recovery near the injection side. In contrast, N2 shows a more uniform saturation reduction with overall lower efficiency, governed by pressure-driven convection and multiphase flow. Real-time NMR-while-C2H6 HnP further reveals that approximately 20% of recovery occurs during soaking prior to puffing, with progressive depletion across both large and small pores and an overall recovery of 50\u201360% after 48 hrs of HnP. The saturation profiles confirm that ethane accumulates near the inlet during injection, diffuses deeper during soaking, and is produced with mobilized oil during puffing, resulting in a steep saturation gradient along the core.Applications\/Significance\/Novelty: This paper presents a novel experimental framework for quantifying gas transport in ultra-tight organic shales under HnP conditions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Decision\u2011Grade Insights for Unconventional Development: Models and Metrics that Scale<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tGonzalo Garcia, Thomas Johnston\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Quantifying Parent-Child Depletion Interference With a Calibrated Numerical Model-Based Proxy<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Paryani* and A. Tucker\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Apache Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work develops a practical proxy model to predict parent-child well interference driven primarily by reservoir depletion. Using a fully calibrated fracture-reservoir simulation workflow that captures hydraulic fracture geometry and pressure evolution, the study quantifies how different levels of parent-well depletion influence child-well productivity. The objective is to provide a fast, reliable surrogate for evaluating spacing, depletion duration, and vertical offset effects without requiring a large number of full-physics simulations.Methods\/Procedures\/Process: A calibrated numerical model was constrained to field diagnostics and production behavior to reproducefractureconnectivityand depletion response.Thecalibration incorporated fiber-observed frac communication together with production and rate-transient-response benchmarks to increase confidence in the modeled depletion behavior. Controlled simulations then varied key drivers such as lateral spacing, parent production time, and vertical stagger. Child-well degradation was defined relative to a non-depleted reference case. Linear and quadratic regression-based proxy models were developed, with the quadratic form used to capture the nonlinear depletion response and validated against simulation results not used for fitting.Results\/Observations\/Conclusions: The simulations show strongly nonlinear depletion effects. Wide spacing and short parent histories producelimited child-welldegradation,whereastightspacingcombinedwithlong parentproduction histories can cause severe performance loss. Most degradation develops within the first few years as the pressure sink matures. The quadratic proxy reproduces the full-physics depletion trends with good accuracy across the tested design space. Lateral spacing and parent production duration are the dominant controls, while vertical offset has a smaller secondary influence.Applications\/Significance\/Novelty: The resulting workflow provides a fast,physics-grounded tool for evaluating depletion-driven parent-child interference and supporting spacing, infill timing, and completion-design decisions. By combining a calibrated numerical model with an efficient reduced-order surrogate, the approach enables rapid what-if assessments while minimizing simulation burden.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Correlation of Acoustically Derived Perforation Efficiency to Early-Time Oil Production Using High-Resolution Surface Pressure Analysis<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Schult*<sup>1<\/sup>, S. Gabel<sup>2<\/sup>, A. Majer<sup>1<\/sup>, M. Khan<sup>2<\/sup>, M. Mullett<sup>2<\/sup>, J. Klostermann<sup>2<\/sup> and M. Aghababa<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Diamondback Energy, Inc.; 2. Seismos Inc.; 3. ArtMapAI)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: A Permian operator evaluated 85 recently completed Midland Basin wells to quantify the relationship between near-wellbore stimulation quality and early-time production. High-frequency surface-pressure diagnostics were used to calculate perforation efficiency and stage uniformity at scale. Multiple linear regression indicates that perforation efficiency is independently associated with 9-month oil production. Using public production data, each 10% increase in well average perforation efficiency corresponds to approximately +1,600 bbl per 1000 ft of additional 9-month oil, supporting its use as a quantitative diagnostic metric. Analysis of 3- and 6-month production showed that perforation efficiency becomes increasingly significant over longer production horizons.Methods\/Procedures\/Process: A controlled water-hammer, induced through a rapid rate change, was captured using a high-frequency surface-pressure transducer. Acoustic friction analysis separated pipe friction from perforation friction at both pre-sand and post-sand conditions. Initial EHD was calculated from the pre-sand rate drop and combined with a proppant-erosion model to estimate stage-level perforation efficiency from the end-of-stage perforation friction response. These measurements were merged with production data normalized by lateral length and analyzed using linear regression to quantify the impact of perforation efficiency while controlling for formation and geographical proximity.Results\/Observations\/Conclusions: Multiple linear regression analysis demonstrates that perforation efficiency is a statistically significant driver of first 270-day cumulative oil production, along with independent variables of formation and area grouping. Model results indicate that perforation efficiency contributes independently to production performance after accounting for formation and geographic distance. The model structure incorporated formation groupings and area-based grouping to control for geological and spatial variability, allowing the independent effect of perforation efficiency to be clearly isolated. Field-scale trends demonstrate that near-wellbore stimulation quality remains a material contributor to production performance across diverse development areas.Applications\/Significance\/Novelty: Integrating surface-acoustic diagnostics with production modeling provides a scalable method for quantifying stimulation quality and guiding design improvements. Because the workflow relies solely on surface data, it can be applied universally across large development programs. Field results show that optimizing perforation efficiency delivers meaningful incremental production impact. These findings position acoustically derived perforation efficiency as a scalable surface-derived metric for characterizing cluster performance, fluid distribution, and overall development consistency.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: From Design to Performance: Gas Injection Field Case Studies<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tHosein Kalaei, Ali Habibi\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Maximizing Value in a Miscible Gas Huff n Puff Enhanced Oil Recovery Pilot \u2013 A Case Study<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Zaghloul*<sup>1<\/sup>, S. Thomas<sup>1<\/sup>, L. Evans<sup>1<\/sup>, D. Ratcliff<sup>1<\/sup>, S. Perry<sup>1<\/sup>, C. Talley<sup>1<\/sup>, D. Tarar<sup>1<\/sup> and M. McClure<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Continental Resources; 2. ResFrac Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents a technical evaluation of a large-scale miscible gas huff n puff pilot project, highlighting the reasons for its success over four years of Enhanced Oil Recovery (EOR) operations. Field results show a notable 37% uplift to-date compared to the scenario without EOR. We built and calibrated a compositional, fully coupled fracture-reservoir model to assess and guide pilot operations. This model was calibrated to completion data, primary production data, and the results of multiple huff n puff cycles, all on a unified and consistent platform. Key model objectives included: 1. characterizing the stimulated rock volume (SRV), 2. properly capturing all mechanisms involved in the complex Huff n Puff process, 3. optimizing huff n puff cycling operations, and 4. estimating uplifts and incremental oil recoveries.Methods\/Procedures\/Process: The integration of geomechanics and reservoir models facilitated history matching completion data, primary production, and EOR response. By modeling the hydraulic fracturing process, we effectively estimated well injectivity and captured the behavior of hydraulic fractures, which are influenced by net effective stress on the proppant. Our findings indicate that unpropped portions of the hydraulic fractures may open and close during gas cycling, potentially leading to inefficient gas recovery. This modeling approach helped us avoid such issues. The calibrated model was used to perform a parametric evaluation to optimize injection cycling. Sensitivity analyses allowed us to estimate target injection pressures, volumes, and flowback strategies, guiding us into maximizing economic value.Results\/Observations\/Conclusions: Modeling results indicate that greater gas volumes do not always translate into increased value. While oil rate uplift improves with higher gas injection volumes, diminishing economic returns may be observed beyond certain thresholds. Field observations and the modeling study also indicate that Gas Utilization Factors (GUFs) are expected to rise in every cycle. Consequently, gas injection requirements significantly increase in subsequent cycles to achieve similar well responses, while oil production peaks decline with each cycle due to depletion in the near-fracture SRV.Applications\/Significance\/Novelty: This study presents a robust modeling framework for the design, evaluation, optimization, and economic enhancement of Miscible Gas Huff n Puff pilots in unconventional reservoirs. The insights gained are critical for informing future EOR projects and maximizing recovery efficiencies. As oil production from unconventional oil basins in the United States continues to experience decline, EOR becomes increasingly critical. EOR will have a substantial impact on increasing recoveries, accelerating production, and extending asset life for the unconventional plays in the United States, unlocking billions of barrels in additional recoverable resources.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Field\u2013Laboratory Evaluation of a Natural Gas Liquids Injection EOR Pilot in the Bakken Formation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Badrouchi*<sup>1<\/sup>, L. Jin<sup>1<\/sup>, A. Buck<sup>2<\/sup>, R. McGuigan<sup>2<\/sup>, S. Smith<sup>1<\/sup>, B. Kurz<sup>1<\/sup>, M. Kurz<sup>1<\/sup>, D. Schmidt<sup>1<\/sup> and J. Sorensen<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of North Dakota  Energy &amp; Environmental Research Center; 2. Chord Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work presents a detailed laboratory-based evaluation integrated with field pilot test results of a cyclic natural gas liquids (NGL) injection in the Middle Bakken. The study focuses on experimentally quantifying how injected NGL modify produced oil density, molecular weight, phase behavior, and compositional structure and how these laboratory observations correlate with field production and pressure trends. The objective is to identify the dominant fluid-level mechanisms driving the enhanced oil recovery (EOR) response observed in the field and to establish a mechanistic foundation that complements field observations and informs future larger-scale miscible gas EOR development in unconventional reservoirs.Methods\/Procedures\/Process: Along with field production data, produced oil, gas, and water samples were collected before and after NGL injection. Laboratory analyses included density measurements at controlled temperature, molecular-weight determination, swelling and solubility tests with injected NGL, and detailed gas chromatography (GC) compositional profiling of both produced oil and produced gas. Water samples were analyzed for total dissolved solids evolution to evaluate mixing and flow-path activation. Laboratory observations were integrated with daily production data to correlate fluid-property shifts with field behavior during and after the injection cycle.Results\/Observations\/Conclusions: NGL injection resulted in clear evidence of substantially increased oil production, accompanied by a 25\u00b0API density decrease. GC analysis of oil and gas samples collected over several weeks of production provided insight into oil and NGL mixing, highlighting that oil enrichment in light components continued beyond the initial flowback period. The positive reservoir response, combined with changes in brine chemistry, was evaluated to assess the potential reactivation of distal fracture\u2013matrix regions. Integrating experimental results with daily field production trends confirm that injected NGL penetrated beyond the near-wellbore zone and reengaged previously nonflowing fracture\u2013matrix volumes. These findings showcase the effectiveness of NGL injection in tight formations.Applications\/Significance\/Novelty: This study delivers the first comprehensive field\u2013laboratory dataset capturing the fluid-level mechanisms underlying an NGL EOR pilot in the Bakken. The results clarify how NGL interactions with reservoir fluids change production response and crude oil properties. The findings confirm that experimentally observed NGL\u2013oil interactions are directly reflected in the field response and represent a key step for predicting and optimizing NGL EOR performance in tight formations, supporting the design of future larger-scale NGL\/produced-gas projects.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Field Evaluation of Natural Gas Liquid (NGL) Injection for Enhanced Oil Recovery in The Bakken Formation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Jin*<sup>1<\/sup>, N. Badrouchi<sup>1<\/sup>, J. Sorensen<sup>1<\/sup>, A. Buck<sup>2<\/sup>, R. McGuigan<sup>2<\/sup>, X. Wan<sup>1<\/sup>, D. Schmidt<sup>1<\/sup>, M. Warmack<sup>1<\/sup>, A. Assady<sup>1<\/sup>, M. Kurz<sup>1<\/sup> and M. Hillix<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Energy &amp; Environmental Research Center; 2. Chord Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study investigates the enhanced oil recovery (EOR) response of a Bakken reservoir through miscible injection of natural gas liquid (NGL). The pilot aims to provide critical field evidence and data to inform the design and operation of a future large-scale produced gas EOR test. The work integrates laboratory investigations of NGL\u2013fluid interactions, a single-well field injection test, and modeling-based evaluations. The pilot was designed to evaluate injectivity, pressure response, and EOR effectiveness, providing foundational insights into cyclic miscible gas EOR feasibility in tight Bakken reservoirs.Methods\/Procedures\/Process: The pilot was conducted in a single Middle Bakken well in Mountrail County, ND, using 60% of the 4,524-ft lateral for injection. A total of 20,658 barrels (bbls) of NGL was injected over seven days, with daily injection rates up to 4600 bpd and periodic shut-ins to ensure operational safety. Comprehensive pressure and production monitoring was complemented by oil, gas, and water sampling before and after injection. Laboratory analyses evaluated changes in oil and gas composition, and water salinity. These measurements, together with model calibration, were used to assess miscibility, NGL recovery, and key EOR mechanisms.Results\/Observations\/Conclusions: Following the NGL injection, the well demonstrated a strong EOR response, with peak oil production reaching a 470% increase relative to pre-injection rates. Produced oil exhibited 2.4-11.8% lower density, indicating significant NGL dissolution. Produced gas was enriched in C3\u2013C5 components, confirming active vaporization and mass transfer between NGL and reservoir fluids. Water salinity increased slightly after injection, consistent with interfacial and capillary changes. Despite rapid pressure dissipation during shut-ins, the results confirm that NGL injection effectively mobilized additional oil and established key parameters for scaling up to multi-cycle gas EOR.Applications\/Significance\/Novelty: This pilot represents the first documented NGL EOR field test in the Bakken and provides unique compositional and pressure data essential for designing future cyclic miscible gas projects. The findings validate NGL as a viable EOR agent for tight oil reservoirs, offering an alternative to CO\u2082 and rich gas where infrastructure or sourcing is limited. The results emphasize the need for continuous injection or multi-cycle huff \u2019n\u2019 puff operation with the recovery of the NGL products to sustain miscible recovery efficiency and to minimize the cost of the NGL injectant. The study demonstrates how integrated field\u2013lab\u2013model workflows can de-risk large-scale implementation of gas-based EOR in unconventional plays.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 5: Examples of Geochemical Monitoring Integration with Multi-Disciplinary Datasets<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJason Jweda, Shawn Wright\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Cost-Effective Mud-Gas and Cuttings Geochemistry Workflow for Reservoir Connectivity, Geosteering, and Production Allocation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Carvajal-Ortiz<sup>1<\/sup>, T. U. Garlichs<sup>2<\/sup> and I. Easow*<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. GEOLOG Americas; 2. GEOLOG International.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Static modeling in unconventional plays (shale gas, tight oil, and coalbed methane reservoirs) aims to build a geological representation of the subsurface to characterize reservoir properties, estimate hydrocarbons in place, and optimize development strategies. However, the extreme heterogeneity, complex mineralogy, nanodarcy-scale permeability, and subtle structural compartmentalization of these reservoirs pose significant challenges to conventional modeling approaches.Methods\/Procedures\/Process: Mud-gas isotope geochemistry (typically \u03b4\u00b9\u00b3C and \u03b4D of methane, ethane, and propane) and molecular composition of hydrocarbons extracted from drill cuttings offer a cost-effective solution. Acquired during routine drilling of vertical and horizontal wells, these high-density datasets serve as powerful fingerprints of gas- and oil-generating formations. Variations in isotopic signatures (e.g., \u03b4\u00b9\u00b3C-CH\u2084, \u03b4\u00b9\u00b3C-C\u2082H\u2086, \u03b4D-CH\u2084) and molecular ratios can reveal reservoir compartmentalization, cross-formational migration, sealing faults, or hydraulic communication between zones. Uniform isotopic and molecular profiles suggest connectivity, whereas abrupt shifts commonly indicate flow barriers. Integrating these data into static models therefore refines compartment boundaries, improves estimates of connected hydrocarbon volumes, and guides optimal well placement and completion design.Results\/Observations\/Conclusions: Main objectives of this study are: Map subsurface variations in stable carbon (\u03b4\u00b9\u00b3C) and hydrogen (\u03b4D) isotopes across target formations to infer reservoir connectivity and flow units; Monitor temporal changes in chemical and stable isotopic composition of produced hydrocarbons and relate them to production behavior and reservoir dynamics; Develop and validate a comprehensive, cost-effective workflow that integrates isotope and molecular data from drill cuttings and produced fluids for production allocation, compartmentalization assessment, and static model refinement.Applications\/Significance\/Novelty: Exploratory data analysis (EDA) and unsupervised multivariate statistical techniques applied to mud-gas isotope and cuttings-derived molecular data successfully identified unique geochemical fingerprints for individual reservoir compartments. These fingerprints enabled accurate production allocation and confirmed the presence of previously unrecognized flow barriers. The proposed workflow has been validated in multiple unconventional fields and has also proven successful in conventional settings when high-resolution data are required under budget constraints. It provides a rapid, low-cost alternative (or complement) to traditional pressure transient analysis, tracer studies, and 3D seismic interpretation for assessing reservoir connectivity and monitoring production performance. This integrated geochemical approach significantly enhances the reliability of static and dynamic models in resource plays, especially in early appraisal or brownfield redevelopment phases where capital efficiency is critical.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Making the Most of Your Gas Data in Unconventional Plays:   Case Studies of Quantitative, Semi-Quantitative Allocation, and Time Lapse Programs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. D. Barrie*, E. Straughan and E. Michael\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Applied Petroleum Technology (APT))<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Production allocation in unconventional plays has been successfully applied using all three fluid phases. There is a litany of studies on production allocation of produced oil and water samples, but less so on the equivalent work using gas samples. One of the consistent challenges in unconventional allocation workflows is like-for-like end member samples are rarely available as downhole samples are not possible, as in conventional allocation. However, gas end member baseline samples (C1-5) can be taken from mud gases while drilling to utilize as end members. This paper, through discussion of case studies in the Eagle Ford and Montney plays, will outline the unique opportunities, challenges and considerations which need to be made when deploying gas allocation and time lapse programs.Methods\/Procedures\/Process: For quantitative production allocation programs samples were collected at a spacing dictated by a combination of planned landing zones, stratigraphic thickness and budget. For semi-quantitative allocation programs, representative, single zone produced gases were collected. Production samples were collected on a weekly, biweekly and monthly basis, decreasing through time. All gas samples, mud gas and produced gas, were analysed via the same program, initially via gas chromatography for composition, followed by stable isotopes of carbon (\u03b413C C1 to C4) and hydrogen (\u03b42H C1). Data were compared via both standard geochemical depth- and cross-plots as well as statistically using principal component (PCA) and hierarchical cluster (HCA) analyses and a range of machine learning (ML) models.Results\/Observations\/Conclusions: Gas allocations tend to be lower resolution, in terms of the thickness of interval, that can be allocated. This lower resolution is a function of the degree of homogenization of C1-5 gases and fewer components available to constrain solutions. The highest confidence data comes from isotopes, compositional differences can be affected between mud gas and production gases. Successful allocation case studies will be discussed from the Eagleford and the Montney. Gas allocation projects offer insight into drainage, helping to inform future development, and can be complimentary to oil results. Gas allocations may be similar to oil allocations but can also demonstrate higher drainage height and larger drained rock volume, especially in early flow back.Applications\/Significance\/Novelty: There is a paucity of papers \u2013 and studies \u2013 published on gas allocation and time lapse programs which are increasingly important in both the L48 and Canadian Unconventional plays as well as in international settings. The case studies discussed in this paper highlight the value in such work and the various approaches which can de adopted depending upon acreage and operator constraints. This paper will also challenge some of the perceptions in the industry around the veracity of gas allocation workflows, emphasising project design and approaches which can be adopted to make the most out of the data and interpretations generated.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Overcoming the \u201cSurvivorship Bias\u201d Of Surfactant Use. A Practical Surfactant Geochemical Optimization Methodology to Improve Child Well Production and Recovery<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Dalamarinis* and S. Fusselman\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(DG Petro Oil and Gas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Surfactant use and optimization has become prominent and integral part of hydraulic fracturing operations in unconventional reservoirs. Although many studies focus on tailored nano-surfactant applications based on drill cuttings and\/or offset oil\/water analysis, a different optimization process is presented as was applied in Wolfcamp A &amp; B wells in Delaware Basin. The method that was applied is presented in this work alongside production performance of the test wells to offset wells production results.Methods\/Procedures\/Process: Cutting samples as well as oil\/water samples were collected from offset wells targeting the same zones, and the optimum nano-surfactant was selected based on chemical compatibility, contact angle and interfacial tension (IFT). During drilling, mud-gas geochemistry was used, gas showings were collected and analyzed (Helium, CO2, H2S, C1 \u2013 C10). Based on these results the geochemical profile of each well was generated. In the parts of the lateral where good hydrocarbon showings were present the dosage used was the one recommended from the chemical company. In parts of the laterals with reduced hydrocarbon content surfactant dosage was engineered to enhance hydraucarbon production from these zones.Results\/Observations\/Conclusions: The wells at which we tested this surfactant usage methodology were completed in November of 2023. Initial flowback data (3 months), although they were infill child wells, demonstrated an increased oil production of ~50% per lateral foot for wells targeting Wolcamp A, and ~15% for wells targeting Wolfcamp B when compared to offset parent wells drilled in the same unit. Long term production data continued to outperform parent wells (~80% in bbls of oil per lateral foot for the Wolfcamp A wells, ~ 10% for Wolfcamp B). When compared to the offset operators\u2019 production performance they rank as the top producers for wells drilled and completed since 2020 (BOE\/lateral foot). Most importantly these wells produced without the need of artificial lift, for 11 months since being brought online.Applications\/Significance\/Novelty: Engineered usage of nano-surfactant in conjunction with the hydrocarbon content properties of the reservoir along the lateral of a well, on a stage-to-stage basis demonstrated superior production when compared to legacy wells of DG Petro and offset operators in Reeves County. Engineered surfactant volumes in zones with reduced hydrocarbon content, based on information provided by gas geo-chemistry helped to improve production performance and hydrocarbon recovery volumes.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"New Technology Showcase\" style=\"border-top: 4px solid #f093fb;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: New Technology Showcase &#8211; From Pilot to Profit: Navigating the Tech Adoption Gauntlet<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        2:00 PM &#8211; 2:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Henry*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Eunike Ventures)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Bahorich*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Cloudbreak Enterprises)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Gottfried*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Elysium Geoscience)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Kearns*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Greentown Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Coplon*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(4Cast)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"New Technology Showcase\" style=\"border-top: 4px solid #f093fb;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: New Technology Showcase &#8211; The Edge of Innovation: The Women Driving Energy Tech\u2019s Next Wave<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        2:30 PM &#8211; 3:00 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Siebach*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Rice University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Vielma*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petricore)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Ben*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy Applied AI Center of Excellence)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panellst<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. M. Kingham*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(GSI Environmental Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Wall*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Stratochem)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Student Poster\" style=\"border-top: 4px solid #ff6348;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Student Posters: Proppant Dynamics and Conductivity Optimization<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Booth 121\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        2:30 PM &#8211; 3:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tKaterina Yared, David Hume\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Experimental Investigation and Predictive Modeling of Conductivity in Discretely Propped Fractures<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tW. Rui*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Post-fracturing observations indicate that many shale fractures are only partially supported by proppants rather than fully packed. This study aims to develop an experimental approach and a predictive model to evaluate fracture conductivity under discrete or non-uniform proppant placement conditions.Methods\/Procedures\/Process: A split-core technique was used to create controlled fractures, which were then immersed in fracturing fluids containing varying proppant concentrations. The cores were closed under confining stress to allow natural proppant retention. Conductivity tests were performed after encapsulation, while CT imaging quantified proppant distribution patterns and density. Conductivity\u2013concentration relationships were analyzed to derive an empirical and mechanistic predictive model for discrete proppant-supported fractures.Results\/Observations\/Conclusions: Results reveal that discrete particle clustering dominates at low concentrations, forming irregular flow channels. Conductivity increases with concentration in a nonlinear trend and approaches a plateau beyond a critical sand loading. CT analysis confirmed the formation of discontinuous, semi-contacted proppant patches that sustain partial conductivity even at low coverage.Applications\/Significance\/Novelty: The study introduces a novel framework to quantify conductivity in non-ideal fracture systems. The proposed method bridges laboratory observation and field-scale interpretation, providing mechanistic insights and practical guidelines for optimizing proppant concentration and placement strategies in shale hydraulic fracturing.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Optimization of Proppant Size Combination Considering Fracture Conductivity and Fluid Distribution During Re-Fracturing of Shale Oil Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Li*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Post-fracturing production decline in shale oil wells often necessitates repeated fracturing. Since residual oil remains within previously stimulated fractures, this study aims to quantify the effect of oil-phase presence on fracture conductivity and to optimize proppant size combinations by coupling hydraulic and fluid-distribution performance.Methods\/Procedures\/Process: Artificially fractured shale cores were packed with different proppant size combinations (20\/40, 40\/70, 70\/140 mesh) and ratios. Conductivity tests were conducted under combined crude-oil and fracturing-fluid flow. A dual-criteria evaluation model integrating fracture conductivity and fluid occupancy characteristics was developed to determine the optimal proppant gradation under oil-bearing conditions.Results\/Observations\/Conclusions: Results show that oil presence alters fracture flow behavior and reduces effective conductivity. Mixed proppant systems achieve better oil mobility and sustained conductivity compared to single-size systems. Coarse\u2013fine blending balances flow channel stability and fluid distribution, leading to improved hydrocarbon displacement efficiency and reduced conductivity loss.Applications\/Significance\/Novelty: This study establishes a coupled evaluation framework linking fracture conductivity with in-situ fluid distribution, providing new insights for proppant design in repeated hydraulic fracturing of shale oil reservoirs. By accounting for oil-phase occupancy effects, the proposed method improves prediction accuracy of conductivity retention and guides data-driven optimization of proppant size ratios for enhanced stimulation efficiency.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mechanistic and Experimental Evaluation of Proppant Entry Efficiency in Ultra-Deep Fractured Tight Sandstone Reservoirs \u2014 Implications for Conductivity Retention and Formation Damage Control<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Ye*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(CUP)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In ultra-deep tight sandstone reservoirs, inadequate proppant entry into natural fractures often leads to poor fracture conductivity and localized formation damage. This study investigates the coupling between particle size, fracture width, and entry efficiency, aiming to predict proppant behavior under extreme closure and stress conditions.Methods\/Procedures\/Process: A fracture transport and entry simulation device was developed to evaluate proppant behavior for multiple size combinations (30\/50 to 100\/200 mesh) under varying width and concentration conditions. CT scanning and image-based quantification were employed to determine effective proppant coverage and entry ratio. A predictive model correlating size\/width ratio with proppant count and support area was developed.Results\/Observations\/Conclusions: Results demonstrate an exponential decline in proppant entry capability as the size-to-width ratio increases. When fracture width is below five times the median particle diameter, bridging becomes dominant, reducing in-fracture support and conductivity. Mixed-size proppant systems exhibit improved penetration and reduced blockage, enhancing conductivity retention.Applications\/Significance\/Novelty: This work provides mechanistic understanding of proppant entry efficiency in ultra-deep fractured formations. The developed model links micro-scale particle transport behavior with macro-scale conductivity and damage evolution, guiding optimal proppant sizing, blending, and fracturing fluid design to mitigate formation damage and enhance stimulation effectiveness in deep, high-stress environments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Impact of Proppant Placement Morphology on Fracture Conductivity and Flow Capacity \u2014 Experimental and Modeling Insights for Non-Ideal Proppant Distribution<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Ye*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(CUP)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Non-uniform proppant placement often leads to heterogeneous flow channels and localized damage in fractured formations. This study investigates the effect of proppant placement morphology and size distribution on fracture conductivity, providing a basis for improved evaluation and mitigation of conductivity loss in complex fracture systems.Methods\/Procedures\/Process: A multi-branch fishbone fracture apparatus was developed to simulate dynamic proppant transport and deposition. Real-time pressure and flow data were used to calculate effective fracture conductivity. Two equivalent flow evaluation methods\u2014rectangular planar and semi-circular radial\u2014were proposed to compare different packing geometries. CT-based post-experiment imaging was used to visualize proppant placement and verify model assumptions.Results\/Observations\/Conclusions: Results reveal that fracture conductivity is highly sensitive to placement morphology and particle-size proportion. Non-uniform packing causes channelized flow and localized pressure loss, while mixed-size systems improve packing density but introduce variable flow pathways. The rectangular area model underestimates conductivity compared with the semi-circular model, though both capture the same morphological dependence.Applications\/Significance\/Novelty: This work provides the first integrated experimental and modeling framework for evaluating fracture conductivity under realistic, morphology-dependent conditions. The findings enhance understanding of non-ideal proppant placement effects on flow performance and offer quantitative tools for optimizing proppant blending, pumping schedules, and fracture design to minimize conductivity loss and formation damage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Optimization of Proppant Size Combination Considering Fracture Conductivity and Fluid Distribution in Shale Oil Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Ma*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Proppant size and gradation strongly influence fracture conductivity, fluid retention, and overall stimulation effectiveness. This study aims to optimize proppant size combinations by coupling fracture conductivity performance with in-fracture fluid distribution characteristics under realistic shale oil production conditions.Methods\/Procedures\/Process: Core-splitting experiments were performed using artificial fractures filled with different proppant sizes (20\/40, 40\/70, and 70\/140 mesh) and mixing ratios. A coupled fracturing-fluid\u2013crude-oil flow system was applied to measure fracture permeability and conductivity. Nuclear magnetic resonance (NMR) T2 spectroscopy was used to quantify bound water and movable oil in the proppant pack, enabling simultaneous evaluation of hydraulic performance and fluid retention. A dual-criteria optimization method integrating conductivity and fluid occupancy characteristics was developed.Results\/Observations\/Conclusions: Results indicate that coarse proppants ensure higher initial conductivity, while fine particles improve fluid retention and oil displacement efficiency. Mixed proppant systems with balanced size ratios achieve better coupling between conductivity and effective hydrocarbon flow. The integration of NMR-derived fluid occupancy with conductivity testing reveals that the most efficient proppant combination optimizes both flow capacity and oil mobilization.Applications\/Significance\/Novelty: This study introduces a coupled evaluation approach that accounts for both fracture conductivity and fluid distribution in selecting proppant combinations. The findings advance the understanding of fracture flow dynamics and provide a more scientific basis for proppant design in hydraulic fracturing of shale oil reservoirs. The developed methodology enables a shift from single-parameter to multi-factor optimization, improving the long-term conductivity and production sustainability of stimulated fractures.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Linking 3D Fracture-Surface Morphology to Conductivity Retention \u2014 An Upscaling Approach with Implications for Damage Evaluation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Ma*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: To evaluate how morphology-controlled contact and aperture evolution affect conductivity retention and apparent damage, and to provide a morphology-informed framework for post-fracture performance prediction.Methods\/Procedures\/Process: Fracture types were identified from post-fracturing cores. 3D laser scanning quantified roughness (Ra), geometric metrics, and elevation range; D2 captured complexity. A morphology-to-flow upscaling model mapped roughness to contact ratio and stress-dependent equivalent aperture, yielding conductivity\u2013stress curves for each fracture type.Results\/Observations\/Conclusions: Higher D2 and elevation range enhance near-contact tortuosity and reduce continuous contact growth, improving conductivity retention under closure but introducing piecewise degradation. Mirror-like surfaces rapidly lose aperture and display larger \u201capparent damage\u201d under stress. Structural fractures remain intermediate. The framework differentiates morphology-induced conductivity loss from fluid-induced damage.Applications\/Significance\/Novelty: By isolating morphology-driven mechanisms from fluid\/solid damage, the method refines formation damage diagnostics and supports morphology-aware decisions on fluid viscosity, proppant selection, and re-fracturing targets. It offers a practical route to predict stress-dependent conductivity retention directly from 3D surface measurements.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Interwell Interference Identification for Multi-Layer Shale Development Using Multi-Source Data and LSTM-Based Dynamic Indices<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Pan* and S. Wang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Dense well patterns and multi-layer development in shale oil reservoirs make interwell interference a key constraint on stimulation effectiveness and long-term recovery. Conventional diagnosis based on single data sources or stand-alone simulation often fails to determine when interference starts, how strong it is, and what type of flow communication dominates. This study proposes a quantitative multi-source framework that integrates pressure and water-rate dynamic response indices, an Unconventional Fracture Model (UFM), and Long Short-Term Memory (LSTM) networks. The objective is to distinguish matrix-, fracture-, and hybrid-type interference, quantify intensity, and determine onset and termination times to support interpretable, real-time reservoir management.Methods\/Procedures\/Process: This study develops an integrated workflow for identifying inter-well interference in multi-layer shale oil development. LSTM models are trained on historical production data to predict baseline pressure and water rates. Dynamic response indices are then constructed by comparing these predictions with actual measurements. Concurrently, an UFM simulates hydraulic fracturing to characterize fracture propagation and connectivity, identifying wells and stages with high interference potential. Finally, the dynamic indices are integrated with the simulated fracture networks to pinpoint interference locations, determine its timing, and classify the interference modes (matrix-, fracture-, or hybrid-dominated). The method is validated using field data from a typical multi-layer shale oil block.Results\/Observations\/Conclusions: Application to the typical block demonstrates that the workflow can reliably distinguish three interference modes. Fracture-type interference is characterized by strong water-rate response with limited pressure response, whereas matrix-type interference shows pronounced pressure response but weak water-rate change; hybrid cases exhibit high responses in both indices. For typical wells, calculated water-rate impact for fracture-type interference exceeds 15% while pressure impact remains below 6%, whereas matrix-type cases display pressure impacts above 20% and water-rate impacts commonly below 10%. The method captures interference onset and termination times consistent with fracture-network simulations, and pinpoints the dominant communication pathways among stacked wells.Applications\/Significance\/Novelty: The proposed approach provides a practical and interpretable framework for diagnosing complex interwell interference in shale oil fields under multi-layer development. By jointly exploiting multi-source information from fracture simulations and production time series, it overcomes the limitations of single-indicator or purely numerical methods and enables rapid quantitative assessment of interference type, strength, timing, and spatial location. The dynamic response indices offer field-deployable metrics that can be updated as new data become available, supporting well-level surveillance, infill and refracturing decisions, and optimization of well spacing and layer selection to mitigate adverse interference and enhance ultimate recovery.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Panel\" style=\"border-top: 4px solid #fa709a;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: Inventory and Capital Allocation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:20 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Sean Kimiagar\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Folz*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chord Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Mercer*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Houlihan Lokey)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Panel\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Cander*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Marathon Oil (Former))<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: Geophysical Methods for Reservoir Characterization: Pore Pressure Prediction, Seismic Inversion, and Fracture Characterization<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tEmily Guidry, Tomasz Ochmanski\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Estimation of Reservoir Properties from Elastic Properties Using Empirical Equations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. J. Ruiz*<sup>1<\/sup>, M. Yang<sup>2<\/sup>, G. Deng<sup>2<\/sup>, Z. Liu<sup>3<\/sup>, J. Yu<sup>1<\/sup> and Y. Chen<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Sinopec Tech Houston; 2. Sinopec Northwest Oilfield Branch; 3. Sinopec Petroleum Exploration &amp; Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Reservoir properties, including water saturation (SW), effective porosity (Phie), and shale volume (Vsh) can be estimated with similar accuracy using any two independent elastic parameters together with density. Cross plots of these parameters, combined with solid rotation (R) in the elastic plane, provide consistent results. These approaches, including R, can be simplified through a rescaled Poisson Impedance (RPI) formulation, which incorporates three free parameters optimized numerically. The method\u2019s effectiveness was demonstrated on Xijiahe tight sand gas and oil reservoir datasets, with results validated against standard petrophysical estimates.Methods\/Procedures\/Process: The procedure consists of cross plotting any two independent elastic properties and then making the appropriate solid rotation of the data in the elastic plane to produce a maximum correlation between the rotated elastic properties and a desired reservoir property, such as a fluid term (FT) or Phie or Clay, or fracture intensity (Fi). The chosen elastic properties can be, e.g., a) any two elastic moduli, b) P and S Impedance, Ip and Is, respectively, c) P and S wave velocity, Vp and Vs, respectively, or d) Ip and PR (Poisson\u2019s ratio). A reservoir quality attribute (RQ), is defined here, as follows, RQ = FT*(1-Vsh )*Phie*e^(b*Fi ) If the chosen elastic properties are Ip and Is, then, the combined properties produced by the solid rotation is: Rfl = Ip*cos(\u03b8)-Is*sin(\u03b8)Results\/Observations\/Conclusions: Applying rotation (R(\u03b1)), the extended elastic impedance formula (EEI(\u03c7i)), or the rescaled Poisson impedance (RPI) yields reservoir property estimates with comparable accuracy. These approaches are mathematically equivalent, expressed as: R(\u03b8i) \u2248 EEI(\u03c7i) \u2248RPI = a+b cos(\u03b8) (Ip-Is*tan(\u03b8) ) \u2248 d +e(Ip -CIs) where , a, b, and C, and are constants determined through optimization. Once these constants are calibrated at well locations, kriging can interpolate them across CDP positions. Using any of the three techniques, water saturation (SW), effective porosity (Phie), shale volume (Vsh), and fracture intensity can be estimated independently, and subsequently combined to derive the RQ attribute.Applications\/Significance\/Novelty: In reservoir characterization, data for theoretical rock physics modeling are often unavailable, so empirical relationships between elastic and reservoir properties are used instead. We show that combining any two independent elastic parameters with density yields reservoir property estimates of comparable accuracy across three empirical techniques. The method can be calibrated with well log elastic data and then applied to seismic inverted properties to generate 3D reservoir volumes. This simple, efficient approach is well suited for tight sand gas and oil reservoirs and was successfully validated in the Xujiahe field, China\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Characterization of Hydraulic Fracture Propagation and Closure Using Cross-Well LF-DAS Measurements<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Almoagal*<sup>1<\/sup>, M. Han<sup>1<\/sup>, G. Jin<sup>2<\/sup> and K. Wu<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas A&amp;M University; 2. Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper is to characterize fracture propagation and geometry in a reservoir where two lateral sections are vertically offset by a fault, using Low-Frequency Distributed Acoustic Sensing (LF-DAS) data. The dataset was acquired during zipper hydraulic fracturing of two wells, with a fiber-optic-equipped monitoring well located between the two treatment wells. A fault divides the laterals into a deep and shallow section with approximately 350 ft vertical separation. This study examines whether the fault and the zipper-fracturing sequence influence fracture propagation and closure behaviors from cross-well strain data during fracturing.Methods\/Procedures\/Process: LF-DAS measurements from 62 stages were analyzed. A workflow integrating strain rate and other relative strain measurements was developed to identify fracture-hit counts, timing, and depths. This workflow was used further to evaluate fracture propagation behavior, including comparisons with expected hit locations, average propagation velocity, and volume to first response (VFR). In addition, it was applied to characterize fracture closure behavior, including fracture width, retained-width percentage, and post-treatment width decline.Results\/Observations\/Conclusions: Key findings show that the fault impact on fracture propagation was localized within the fault zone, with no observable changes in fracture azimuth, propagation velocity, or width on either side of the fault. Fractures generally propagated parallel to each other and perpendicular to the horizontal laterals. The incomplete fracture closure observed at the monitoring well indicates effective proppant transport, with confirmed propped fracture lengths exceeding 300\u2013400 ft. The zipper-fracturing sequence significantly influenced fracture geometry: fractures from Well 3H were longer, wider, and had slower closure than those from Well 1H, likely because Well 1H was stimulated after Well 3H and interacted with reservoir rock that had already been fractured and pressurized.Applications\/Significance\/Novelty: This study applies the workflow to a challenging case involving a fault intersecting the laterals, providing new insights into LF-DAS signals and their interpretation in faulted formations. The results illustrate fracture speed and width decline behavior in Eagle Ford. The study demonstrates the potential of LF-DAS for real-time diagnostics in complex geologic settings.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fourth-Mode Amplitude Variation with Azimuth Indicates Fluid-Filled Conjugate Fractures: Model and Geothermal Field Data Examples<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Cook*<sup>1<\/sup>, C. Sayers<sup>2<\/sup> and M. Chapman<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Tricon Geophysics, Inc.; 2. University of Houston; 3. University of Edinburgh)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Single sinusoid per 180 degrees of azimuth anisotropy is proportional to fracture density, provided the fractures are vertical, parallel, and evenly distributed. If there is more than one set of fractures (e.g. a conjugate fracture set), and the fractures are filled with fluid, another signal occurs. This second signal will vary as a double sinusoid per 180 degrees of azimuth. The strength of the higher mode anisotropy is proportional to the incompressibility of the fluid (Sayers and Dean, 2001). Strong second and fourth mode anisotropy was detected in an Eastern European geothermal project. The fourth mode anisotropy adds critical information for well placement as water-filled, conjugate fracture sets may better flow fluid for heat transfer than a single or fizz-water-filled fracture set.Methods\/Procedures\/Process: To preserve azimuth and offset information during migration, the data was binned into offset and azimuth ranges, with each bin PSTM migrated separately. After migration, a time-varying trim static was applied to perfectly flatten the reflections. Next, inner and outer mutes were applied to preserve only the high-quality mid-range offsets. Finally, the traces per azimuth are stacked, leaving ring gathers with one trace per 20 degrees of azimuth. A sinusoid was fit at every sample for every CDP, solving for G2 and \u03a6 2, the 2nd mode magnitude and bright azimuth, respectively. Additionally, a double-sinusoid was fit at every sample in order to solve for G4 and \u03a6 4 (4th Mode Anisotropy) (see Eq. 1 from Sayers and Dean, 2001). Eq. 1) Rpp(\u03b8, \u03a6) = D + G2cos(2 \u03a6 \u2013 \u03a6 2) + G4cos(4 \u03a6 \u2013 \u03a6 4)Results\/Observations\/Conclusions: Figure 2a COCA gather exhibiting strong G2 and VVAz, suggesting strong fracturing above the target (red line). 2b. Profile view of 2nd mode anisotropy and seismic data (corendered). Gather from 2a is located at vertical red line. 2c. 4th mode AVAz. Figure 3a COCA gather exhibiting strong G4 and VVAz, suggesting strong fracturing above the target (red line). 2b. Profile view of 2nd mode anisotropy and seismic data (corendered). 2c. 4th mode AVAz. G2-only AVAz analysis finds only some of the fracture network. By adding G4 to the analysis, a more complete fracture picture is seen. Additionally, because the G4 signal is sensitive to incompressibility of the fracture fluids, insight into the fluid type is obtained (i.e. pure brine vs. fizz water).Applications\/Significance\/Novelty: While prior papers propose the existence of higher mode (G4) anisotropy, they fail to show the phenomenon on real data. Also, because earlier papers claimed that higher modes would only exist on farther offsets, few were looking for such signals on real data, which may be why they went undetected until now. It would seem that such claims underestimated the effect of incompressible fluid in the fractures. In light of these new findings, other analysists may decide to go back through previous datasets, looking for missed fracture networks. Also, due to the sensitivity to fracture fluid incompressibility, they may reassess the presence or absence of fizz water and\/or oil.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: AI-Driven Hybrid Modeling for Intelligent Drilling, Cementing Operations, and Failure Diagnostics<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tDave Symmons, Zhuoran Li\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">CFD-Machine Learning Hybrid Modeling for Horizontal Wellbore Temperature Prediction Considering Drillstring Eccentricity<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Gou*, Z. Xu, Z. Yuan, M. Zhang, M. Zhou and X. Song\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Geosciences (Beijing))<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate prediction of wellbore temperature in deep and horizontal drilling is essential for the reliability of downhole tools and drilling fluids. However, conventional annulus heat transfer models often make inaccurate assumptions of a concentric drillstring and single-wall heating, ignoring the pronounced eccentricity and dual-wall heating typical of horizontal sections. This study develops a CFD-machine learning hybrid framework for rapid prediction of annular convective heat transfer and its application to wellbore temperature simulation.Methods\/Procedures\/Process: First, a three-dimensional CFD database comprising 850 cases was generated for concentric and eccentric annuli, covering eccentricities from 0 to 0.8, diameter ratios from 0.3 to 0.7, and both laminar and turbulent flow regimes under dual-wall heating conditions. Benchmark comparisons for concentric annuli showed good agreement with analytical and literature data, confirming the reliability of the CFD database.Results\/Observations\/Conclusions: Increasing eccentricity to e=0.8 severely weakens annular convection. Depending on the diameter ratio, the inner-wall Nusselt number decreases to approximately 2%~20% of the concentric value in laminar flow and 2%~22% in turbulent flow, while the outer-wall Nusselt number decreases to approximately 2%~20% and 3%~30% of the corresponding concentric value, respectively. Based on the CFD database, feedforward neural-network surrogate models were trained in MATLAB to predict the inner- and outer-wall Nusselt numbers as functions of the governing geometric and flow parameters. The trained models reproduced the CFD results with high accuracy, with R2 values exceeding 0.99 for the demonstrated turbulent cases. The surrogate model was then embedded into a transient 1D wellbore-2D formation temperature simulator, enabling fast prediction of wellbore temperature profiles. Compared with conventional heat-transfer correlations, the proposed hybrid model yields temperature differences of up to 12.6 \u2103, highlighting the importance of eccentricity and dual-wall heating in horizontal wellbore temperature analysis.Applications\/Significance\/Novelty: The proposed approach provides a fast, physically consistent tool for real-time temperature prediction, with potential applications in downhole tool selection, drilling-fluid design, and thermal management during drilling operations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Leveraging Generative AI for integrated Deep Learning Models in Drilling Hazard Prediction<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Moncayo<sup>1<\/sup>, I. H. Wang*<sup>1<\/sup>,<sup>2<\/sup>, F. Cardona<sup>1<\/sup>, M. Castrillon<sup>1<\/sup>, C. Coletta<sup>1<\/sup>, J. Courtier<sup>1<\/sup> and C. Sierra<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Ecopetrol Permian; 2. University of Houston)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Drilling hazards such as stuck pipe, mud losses, and wellbore instability pose significant operational risks, often resulting in costly sidetracks. Existing tools largely focus on drilling parameters, with limited incorporation of geological and geomechanical context, which can limit robustness across variable well conditions. Our objective is to develop an integrated deep-learning model that combines geological understanding, drilling data, and operation parameters for improved hazard prediction. Generative AI (GenAI) is leveraged to streamline data preparation and feature engineering, enabling scalable integration of unstructured drilling reports with subsurface dataMethods\/Procedures\/Process: We integrated drilling reports from a data lake with additional reservoir, and geomechanical properties, including well logs and seismic attributes, to build a comprehensive dataset. The workflow followed three key steps: (1) Utilized GenAI agents to automate extraction and organization of complex drilling report data, enabling rapid feature engineering and metadata creation. (2) Extracted geological and geomechanical properties along horizontal wellbores at a designed sample rate to match the resolution of drilling events. (3) Used GenAI to generate scripts that apply deep learning models for predicting drilling hazards, including stuck pipe and mud losses.Results\/Observations\/Conclusions: GenAI agents enabled rapid analysis of large volumes of drilling reports, reducing processing time by 50\u201380%. The deep learning model for drilling hazard prediction achieved over 80% accuracy when validated against post-drill events. For pre-drill assessments, planned directional surveys were used to evaluate the likelihood of drilling risks, and model predictions are subsequently compared with observed drilling challenges to assess performance. The results show that the model successfully identified challenging intervals at the predicted depths, demonstrating strong predictive capability and supporting its potential application for both planning-stage assessment and execution-phase hazard forecasting.Applications\/Significance\/Novelty: This initiative delivers a fully integrated AI\/GenAI workflow that transforms subsurface and operational processes, enabling rapid, data-driven decision-making. GenAI automates extraction from complex drilling reports and assists with model design and coding, reducing manual effort from weeks to days. The solution provides scalable, adaptive hazard prediction across different conditions. Its novelty lies in automating siloed processes, achieving up to 80%-time savings, and setting a new standard for intelligent asset management. Predictive models and GenAI agents improve efficiency, forecast accuracy, and capital performance, establishing a benchmark for digital transformation in upstream operations.Interdisciplinarity (Team Presentation\u2019s only): a. Geology &amp; geophysics b. Data management and data science c. Drilling\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Big-Data View of Drilling and Geology from 80,000 Texas Horizontal Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tI. Kuvaev<sup>1<\/sup>, D. Gibson*<sup>2<\/sup> and D. Balpreet<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ROGII; 2. Gibson Reports)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Since 2010, more than 80,000 horizontal wells have been drilled across Texas, primarily within the Eagle Ford, Midland, and Delaware Basins. This study aims to evaluate how drilling performance has evolved as a function of both the geology encountered and the drilling technologies applied, and to assess whether industry performance is beginning to reach a plateau. The analysis examines how key drilling practices\u2014such as BHA design, casing strategies, well length, rate of penetration (ROP), drilling days, and well trajectory - have changed over time. In addition, the study investigates how horizontal wells have been placed within different geological formations, the spacing strategies utilized, and the anticipated challenges associated with further in-fill development.Methods\/Procedures\/Process: Trajectories, GR, and ROP logs from 80,000 wells were digitized from PDF sources, with drilling days and casing data gathered from public databases, producing a 100-GB dataset. AI and computer-vision tools were used for log-header recognition and digitization. An ML model classified wells by BHA type (RSS vs. motor) with 97% accuracy validated on 1,000 wells. Formation tops were picked for all wells to identify target zones. Additional attributes - depth, lateral length, DLS, tortuosity, azimuth changes, drilling days, ROP, operator, DD company, and rig - were computed. Analytical outputs include density maps, depth maps, lateral maps by formation, nose plots, and yearly histograms of wells, ROP, BHA usage, and drilling days.Results\/Observations\/Conclusions: Most wells were drilled in the Midland and Delaware basins, followed by the Eagle Ford. Average ROP has doubled since 2010, reaching ~250 ft\/hr in 2026, while drilling days per well declined. ROP varies significantly by formation. Lateral lengths increased from ~3,000 ft in 2010 to ~10,000 ft in 2025, with many laterals approaching four miles. Most Texas horizontals use conventional motor-based BHAs, including the deepest and longest wells. Operators are pushing density limits, drilling multi-layer pads with up to 14 stacked targets; parts of the Delaware now exceed 30 wells per square mile. The deepest horizontals reach ~18,000 ft TVD, though most drilling occurs between 7,500\u201310,000 ft TVD.Applications\/Significance\/Novelty: With global shale development emerging in Argentina, the UAE, Saudi Arabia, China, Australia, and Mexico, most projects remain in early stages where benchmarking against Texas is essential. Texas drilling performance - reflected in rising ROP, longer laterals, refined well placement, and optimized BHAs - provides a mature reference point unmatched worldwide. This analysis offers operators a data-driven framework to evaluate their drilling efficiency, identify geological and technological constraints, and set realistic, achievable goals for future shale development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Geomechanics in Production Simulation, Forecasting, and Optimization<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJiehao Wang, Jonathan Ortiz\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Impact of Stress Creep on Well Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Vaidya*<sup>1<\/sup>, H. Li<sup>1<\/sup>, S. M. Kholy<sup>1<\/sup>, K. Patel<sup>2<\/sup> and V. Muralidharan<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Occidental; 2. CMG LTD)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional reservoir models often assume static rock properties, ignoring time-dependent deformation such as creep. Laboratory evidence shows creep in ductile, low-permeability formations can reduce matrix permeability and fracture conductivity, impacting well performance and recovery. This study investigates the combined effects of stress induced elastic compaction and time dependent viscoelastic creep on oil production, aiming to quantify their influence on long-term reservoir deliverability and forecasting hydrocarbon recovery.Methods\/Procedures\/Process: Using a simplified reservoir simulation workflow, we analyze how creep behavior, that may be driven by rock mineralogy, stress state, and pressure depletion, can gradually reduce permeability, and impair fracture conductivity over time\u2014ultimately lowering hydrocarbon recovery. Utilizing available published data and internal measurements, we examine the role of conventional elastic compaction and contrast its impact with that of time-dependent creep, showing that ignoring creep may lead to overly optimistic forecasts.Results\/Observations\/Conclusions: Simulation results show creep accelerates permeability loss and fracture conductivity degradation beyond elastic compaction. In ductile shale, cumulative oil recovery after 5 years can be substanitially lower when creep and compaction are considered. Ignoring creep gives overly optimistic forecasts, underscoring its critical role in unconventional reservoir evaluation. Oil rate reduction upon restart occurs when wells are shut in after aggressive drawdown, mainly due to time-dependent fracture conductivity loss, and effect is more pronounced with initially low conductivity fractures. These findings highlight the need for time-dependent modeling and joint consideration of creep and compaction in long-term production strategies.Applications\/Significance\/Novelty: This work addresses a major gap in current industry workflows by integrating creep and compaction into unconventional reservoir modeling. The approach enables more realistic production forecasts, improved well spacing, and informed fracture re-stimulation strategies. Novelty lies in quantifying creep\u2019s long-term impact on permeability and fracture conductivity degradation, which is often overlooked in field planning. Incorporating these mechanisms is essential for optimizing recovery in ductile shale formations and mitigating risks of underperforming assets. By bridging this knowledge gap, the study provides actionable insights for reservoir engineers to enhance development strategies and maximize hydrocarbon recovery.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Early-Time Flowback Diagnostics: Linking Cleanup Efficiency to Well Productivity<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Moussa* and H. Dehghanpour\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Alberta)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Flowback drawdown is a controllable operating variable, but its impact on well productivity is often masked by variations in reservoir quality and completion design. This motivates two key questions: Can flowback data reliably predict well performance? Does drawdown management during flowback influence long-term productivity? This research introduces a drawdown\u2013productivity type curve to identify the optimum drawdown rate as a function of rock type, in-situ stress, and fluid chemistry to maximize well productivity.Methods\/Procedures\/Process: We analyze 720 oil and gas MFHWs in the Montney formation integrating five datasets: flowback reports, FracFocus fluid chemistry, monthly production, completion reports, and sonic logs. A seven-step normalization workflow controls for reservoir quality, in-situ stress, completion design, and fluid chemistry. Flowing bottomhole pressure during flowback is utilized to develop a dimensionless drawdown normalized by the stress window and described by an exponential model, yielding four coefficients that characterize each well&#039;s drawdown strategy. An average dimensionless drawdown rate is then derived for each well and correlated with first-year normalized productivity to propose a drawdown\u2013productivity type curve and define cluster-specific optimum drawdown rates.Results\/Observations\/Conclusions: After normalization, a significant productivity variation remains within each rock-completion cluster, confirming that flowback drawdown is a primary performance driver. The drawdown-productivity correlation is a function of rock type, in-situ stress, and fluid chemistry: in surfactant-treated wells, especially in brittle rock, aggressive drawdown consistently reduces well productivity, whereas conservative drawdown yields the highest performance. In wells without surfactant, aggressive drawdown improves productivity only in high-stress ductile rock and low-stress brittle rock, but hurts performance otherwise.Applications\/Significance\/Novelty: This study provides a framework that directly links flowback drawdown to well productivity, and offers cluster-specific drawdown envelopes for choke-size management.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Geomechanics for Optimized Drilling and Completion Design in High-Risk Tier 3 Williston Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Do*<sup>1<\/sup>, A. Chakhmakhchev<sup>2<\/sup> and M. Madison<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. InDZone Consulting LLC; 2. SBC Global; 3. Green Mountain Exploration)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Development of Tier 3 reservoirs in the Williston Basin is challenged by thin net pay, less thermally mature rock, elevated water cut, and high risk of H2S production. These risks introduce significant safety concerns associated with sour gas production and often render such acreages uneconomic to develop. This paper presents a collaborative case study with a Bakken operator demonstrating how an integrated geomechanics-driven approach was applied to develop Tier 3 rock. The objective is to show how understanding rock mechanical behavior and stress relationships can enable optimized drilling and completion strategies that mitigate H2S and water production risks while maximizing oil recovery.Methods\/Procedures\/Process: The study integrated geologic interpretation, petrophysical analysis, and geomechanical modeling to characterize stress state, mechanical stratigraphy, fracture containment, and H2S production risks. Rock mechanical properties were derived from logs and calibrated with regional data to identify intervals susceptible to unwanted fracture growth into mature, H2S-bearing formations. Drilling trajectory, landing targets, stage spacing and perforation strategy were optimized to strategically target thin pay zones including the Middle Bakken and first Three Fork bench. Completion designs were tailored to control fracture height growth and reduce connectivity with high-water-cut and sour zones.Results\/Observations\/Conclusions: The optimized completions design with geomechanics-based strategy successfully mitigated H2S production and significantly reduced water cut compared to offset wells developed using conventional approaches. Studied wells achieved improved oil production and higher reservoir deliverability, demonstrating effective fracture containment within the target interval. The results confirm that Tier 3 rock previously considered marginal can be economically developed when drilling and completion decisions are guided by understanding rock mechanics.Applications\/Significance\/Novelty: This study demonstrates the value of integrated geomechanics as a practical platform linking geoscience and completion engineering to address development challenges in mature unconventional basins. The presented workflow is repeatable and scalable for de-risking Tier 3 reservoirs affected by sour gas and high water cut. To date, the methodology has been implemented across 13 wells in high-risk areas in the Williston basin with no observed H2S production. The novelty lies in proactively applying rock mechanical understanding to control fracture growth and fluid connectivity, enabling economic development of previously marginal acreage. This approach has broad applicability across unconventional plays facing similar operational and reservoir risks.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: From Molecules to Unconventional Field Simulation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSelin Erzeybek Balan, Haiwen Zhu, Vincent Artus\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Enabling Capillary-Corrected Phase Behavior in Commercial K-Value Compositional Simulators<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Deng, H. Amer and R. Okuno*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The nanoscale pores in unconventional reservoirs generate high capillary pressures, which greatly impact oil and gas production. However, traditional compositional simulators do not consider the effect of capillarity on phase behavior, except for a few academic simulators like UTCOMP. This research aims to enable a K-value-based compositional simulator (e.g., CMG STARS) to account for capillary-pressure effects on phase behavior without sacrificing computational efficiency and robustness.Methods\/Procedures\/Process: A novel procedure was developed to generate capillary-corrected K-values prior to simulation. First, an isothermal-isobaric (PT) flash was performed over a pressure range to obtain baseline K-values without capillarity. Next, an isothermal-isochoric (TV) flash, formulated to minimize the Helmholtz free energy, was performed to compute K-values with capillarity using a pore-size distribution within the same pressure range. Then, a component-specific scaling factor was calculated as the ratio of K-values with and without capillarity to set up the simulation. Finally, the procedure was demonstrated using a K-value compositional simulator for a Middle Bakken oil reservoir case and was benchmarked against UTCOMP.Results\/Observations\/Conclusions: The capillary-free bubble-point pressure (Pb) of the Middle Bakken oil was 1940 psia at 240\u00b0F. For a mean pore size of 10 nm, the capillary-corrected Pb indicated a Pb suppression of 130 psi. Scaling factors were calculated as a function of pressure. The oleic phase became richer in lighter components when capillarity was included, and the vapor phase became richer in the plus fractions. Therefore, under the capillary effect, the liberated vapor became slightly heavier, whereas the GOR still decreased. Simulation results after 10 years of primary depletion using capillary-corrected K-values confirmed observations in the literature. Cumulative oil production increased by 12.2% and cumulative gas production decreased by 27.5% with Pb suppression delaying gas exsolution by 60.0 days.Applications\/Significance\/Novelty: The novelty of this research lies in enabling commercial simulators to model the impact of capillarity on phase behavior using K-values for shale oil production. Previously, this capability was limited to EOS-based academic or in-house simulators, which were computationally difficult and expensive. As a result, this approach maintains the benefits of commercial simulators, offering improved computational efficiency, since a commercially available K-value compositional simulator runs at least ten times faster than those based on cubic EOS.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Application of a Dissipation-Based Continuation Nonlinear Solver for Unconventional Reservoir Simulation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Li<sup>1<\/sup>, R. Hasanzade*<sup>2<\/sup>, J. Natvig<sup>1<\/sup>, P. Tomin<sup>2<\/sup>, D. Bakkejord<sup>1<\/sup>, A. Kozlova<sup>1<\/sup> and D. Dias<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. SLB; 2. Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Maximizing productivity from unconventional oil and gas reservoirs relies on accurate simulation to understand complex flow dynamics and guide operational decisions. Unconventional reservoirs present unique challenges for modeling due in part to significant differences in pore volume and permeability between fracture and matrix regions. This work focuses on improving nonlinear convergence in fully implicit simulations to address these issues.Methods\/Procedures\/Process: We apply a dissipation-based continuation (DBC) method, originally developed for two-phase flow in heterogeneous formations and later extended to multiphase flow in discrete fractured media. The DBC method introduces dissipation terms into the mathematical model, which smooths the nonlinear equations and improves solver robustness. A central aspect of the method is controlling the amount of dissipation to ensure it aids convergence without compromising solution accuracy. While previous work recommends gradually reducing the dissipation during Newton iterations to maintain accuracy, our work adopts an alternative strategy: we use simple, observation-based rules to select the appropriate amount of dissipation.Results\/Observations\/Conclusions: Through multiple simulation examples involving unconventional reservoirs, we demonstrate that the DBC method can significantly enhance nonlinear convergence in fully implicit frameworks. Careful treatment of dissipation terms enables the use of larger time steps than standard solvers, while maintaining acceptable accuracy in the simulation results.Applications\/Significance\/Novelty: These results highlight the value of the DBC method in overcoming convergence issues in complex reservoir simulations. By supporting larger time steps, the DBC improves computational efficiency and practical applicability, offering particular advantages for modeling fractured and heterogeneous reservoir systems typical of unconventional assets.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Reconciling Performance Differences During Primary Production and Enhanced Oil Recovery for Two Collocated Wells Using a Coupled Reservoir Simulator<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Ratcliff*<sup>1<\/sup>, J. Zaghloul<sup>1<\/sup>, S. Thomas<sup>1<\/sup>, S. Perry<sup>1<\/sup>, C. Abbott<sup>1<\/sup> and M. McClure<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Continental Resources; 2. ResFrac Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents a modeling lookback of a two-well Enhanced Oil Recovery (EOR) miscible gas Huff-n-Puff project focused on reconciling differences between observed field behavior and initial model predictions. This project consisted of two horizontal parent wells drilled from the same pad in opposite directions. The incremental uplift of oil in these projects can be substantial and is necessary to extend the economic life of the well. However, the field response differed from the original modeling forecasts, motivating a comprehensive modeling investigation to better understand the controlling mechanisms.Methods\/Procedures\/Process: The original Huff-n-Puff pilot modeling was performed using a non-coupled reservoir simulator and focused solely on the production and gas injection processes. This model proved to be overly optimistic, and it was determined that there were more processes occurring than could be fully captured. Two new models were independently developed for each well using a fully coupled fracture and reservoir model whereby the entire lifecycle of the wells were captured in one continuous simulation, including the fracturing, primary production, gas injection and post injection processes. Finally, a third model was created to reconcile all history matching parameters from both wells into one overall model, simultaneously matching the entire lifecycle of each well.Results\/Observations\/Conclusions: The outcome of the modeling revealed several major considerations. First, although the primary production of each well could be history matched independently, it was difficult to match historical production from both wells with the same set of \u2018reasonable\u2019 petrophysical and reservoir parameters. Second, the injection pressures, rates, and subsequent production of the Huff-n-Puff could not be matched with the primary production reservoir parameters until the injection capacity of the lateral was limited. Finally, it was found that limiting lateral contribution during the injection and post injection production phases could also be used to match the primary production phase, allowing the entire lifecycle of the well to be successfully modeled for both wells.Applications\/Significance\/Novelty: The use of a fully coupled fracture and reservoir simulator was paramount in understanding the processes that make a Huff-n-Puff project successful and highlights the importance of well selection. It was learned that the injection capacity of the well was directly related to the primary production, and the original stimulation size, primary production depletion considerations, and lateral contribution are factors that need to be considered when choosing a pilot.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Scaling Unconventional Success: Global Case Studies in Appraisal and Early Development<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMatthew Adams, Matthew Poole\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pioneering Integrated Fracture Diagnostics to Derisk the First Multi-Well Pad Unconventional Development in the UAE Shilaif Formation.<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tO. A. Alvarado Sosa*<sup>1<\/sup>, O. Bustos<sup>1<\/sup>, T. Itibrout<sup>1<\/sup>, F. Silva<sup>1<\/sup>, S. A. Elazab<sup>1<\/sup>, S. Kelkar<sup>2<\/sup>, J. Mason<sup>2<\/sup>, R. Alhameli<sup>3<\/sup>, N. A. Alharbi<sup>3<\/sup>, B. Hunziker<sup>4<\/sup>, Y. Wu<sup>4<\/sup> and P. Lynch<sup>5<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ADNOC Onshore; 2. ADNOC Upstream; 3. ADNOC Drilling; 4. SILIXA; 5. Well-SENSE)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper presents how advanced hydraulic fracture diagnostics and adaptive operational strategies mitigated risk and optimized cost in the unconventional development of the Shilaif Formation, characterized by staggered laterals and multi-target zones. The study integrates real-time monitoring with iterative design modifications, where varied pump schedules and fracturing designs were implemented to evaluate their impact on fracture geometry, stimulated reservoir volume, and cross-well interactions. These adaptive strategies were deployed to maximize stimulation efficiency while reducing non-productive time and controlling operational expenditure.Methods\/Procedures\/Process: A comprehensive multi-well diagnostic program was executed during zipper fracturing operations, combining disposable fiber-optic strain measurements, microseismic monitoring, post-closure analysis, and offset well WHP surveillance. Fiber-optic sensing enabled real-time identification of fracture-driven interactions, while microseismic data provided insights into hydraulic fracture azimuth and fracture propagation trends. DFIT results calibrated in-situ stress magnitudes and revealed distinct pressure-decline behaviors across the formation. The operational flexibility inherent to multi-well pad development allowed real-time adjustments to pumping schedules and fracturing sequences, minimizing inefficiencies while maximizing diagnostic data acquisition.Results\/Observations\/Conclusions: The integration of fiber-optic diagnostics, microseismic mapping, and offset well WHP monitoring significantly improved fracture event detection and interpretation, delivering reservoir-focused actionable insights essential for informed field development. DFIT evaluations confirmed the presence of multiple zones of interest across the Shilaif Formation, validating targeted landing strategies. These combined findings supported optimization of well spacing, staggering pattern, cluster placement, and fluid\/proppant allocation\u2014ultimately reducing subsurface uncertainty and operational cost. The results contribute directly to sustaining the UAE\u2019s unconventional development objectives and supporting long-term energy security.Applications\/Significance\/Novelty: This study represents the first integrated application of DFIT, disposable fiber-optic sensing, microseismic monitoring, and offset well WHP surveillance during zipper fracturing in a Middle Eastern unconventional play. The methodology establishes a robust, data-driven foundation for future stimulation designs and sets a regional benchmark for derisking unconventional resources, optimizing cost, and enhancing stimulation performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluation of a New Unconventional Play in the Northern Flank of the Golfo San Jorge Basin, Argentina<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Agustino*, A. Vega, M. Zubiri and M. Cohen\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Pan American Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Golfo San Jorge Basin is one of Argentina\u2019s most mature hydrocarbon provinces; however, recent advances in unconventional resource development have renewed interest in the shale potential of the Pozo D-129 Formation. This study presents an integrated subsurface evaluation of the Basal Pelitic Section (SPB), a lower interval of the Pozo D-129 Formation within the Cerro Drag\u00f3n area, in the North Flank of the basin. The objective is to assess the viability of the SPB as an unconventional shale reservoir, identify the key geological and operational controls on productivity, and evaluate how geomechanical behavior, structural heterogeneity, and fracture containment influence reservoir performance.Methods\/Procedures\/Process: A staged workflow was applied, including regional screening using legacy wells and seismic data, tectonostratigraphic definition, vertical and horizontal pilot drilling, advanced petrophysical and geomechanical modeling calibrated with laboratory measurements and Diagnostic Fracture Injection Tests (DFITs), and post-fracture evaluation using microseismic monitoring and chemical tracers. Seismic interpretation defines a rift\u2013sag tectonostratigraphic framework, with the SPB representing the basal Chubut Group deposited under lacustrine conditions and structurally controlled by inherited Neocomian half-grabens. Based on this integrated and attribute-driven approach, a second vertical pilot well was drilled to test improved targeting and subsurface characterization strategies.Results\/Observations\/Conclusions: Petrophysical results from the first vertical pilot well indicate a relatively homogeneous organic-rich shale interval (~70 m thick) with average total organic carbon (TOC) of ~2.2%, clay contents of 30\u201335%, porosities of 11\u201312%, nanodarcy-scale permeability, and thermal maturity within the wet gas window (Ro ~1.6). DFIT analysis reveals significant overpressure, with pore pressure gradients around 0.65 psi\/ft (approximately 50% above hydrostatic). A horizontal pilot well (1,500 m lateral) was successfully geosteered within the target interval and hydraulically fractured in 25 stages. Gas production stabilized during the first month, with transient peaks of up to 80,000 m\u00b3\/d following shut-ins, confirming hydrocarbon charge and producibility but indicating limited effective stimulated reservoir volume (SRV). The second vertical pilot well encountered a significantly thicker SPB interval (~146 m) with improved preliminary petrophysical properties, including higher TOC, higher resistivity, lower water saturation, and sustained overpressure (0.61\u20130.74 psi\/ft). Results confirm that while reservoir quality is generally sufficient, productivity is primarily controlled by geomechanical behavior, structural heterogeneity, and fracture containment.Applications\/Significance\/Novelty: Microseismic and tracer analyses show heterogeneous fracture development, with clustering of events and significant fracture propagation outside the target interval (~40\u201345%), interpreted to result from interaction with pre-existing natural fractures and local structural complexity. These effects led to uneven stage contributions and reduced stimulation efficiency. Seismic attributes, particularly acoustic impedance calibrated with well data, proved effective in mapping SPB thickness, TOC trends, and areas of lower structural risk. The study demonstrates that successful development of the SPB unconventional play will depend on optimized well placement, landing zone selection, and completion designs that explicitly account for the interaction between hydraulic fractures and the inherited structural framework. The integration of seismic attributes with petrophysical and geomechanical data proved critical for reducing uncertainty and improving reservoir targeting, highlighting the value of a multidisciplinary approach in unconventional resource development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 5:  Fluid Geochemical Impacts on Production Performance and Hydrocarbon Recovery<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAlexandra Hakala, Wei Wang\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Allocation of Eagle Ford Oil and Y-Grade NGL Mixtures Collected During an EOR Huff-n-Puff Pilot Project<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Kornacki*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Stratum Reservoir)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Various kinds of EOR techniques are being evaluated to try to increase the amount of oil recovered from source-rock reservoirs. The Huff-n-Puff (HnP) method involves injecting a miscible fluid into the reservoir and then immediately allowing the oil+fluid mixture to flow back. If NGL is used as the injectant, it is crucial to determine how much of the produced mixture is native oil and solution gas vs. liquefied or gaseous NGL. During this EOR pilot project, BlackBrush injected a large amount of Y-grade NGL into a stimulated horizontal well landed in the Eagle Ford reservoir during three cycles and collected gaseous and liquid samples at a LP separator, the stock tank, and HP gas export pipeline. Geochemical methods were used to estimate the amount of oil and natural gas in these samples.Methods\/Procedures\/Process: High-resolution GC (HRGC) analyses were performed on end members (2 depressurized Y-grade NGL samples and a native Eagle Ford oil sample collected from the pilot well before NGL was injected) and on 14 STO and 14 separator oil samples collected periodically during the 3 HnP cycles. Hierarchical Cluster Analysis and Principal Components Analysis results using HRGC peak-height ratios indicate the relative amount of NGL decreased systematically with time in oil samples collected during each HnP cycle. Y-grade NGL contains only a small amount of the C8+ HC compounds typically used to allocate commingled oils. Good allocation results were identified using the abundance of C6-C9 compounds. The density of end-member, STO, and separator oil samples also was used to allocate the commingled samples.Results\/Observations\/Conclusions: Good allocation results using HRGC data indicate the vol% Eagle Ford oil in STO samples collected during the 1st and 2nd HnP cycles increased systematically with time from 18.4 \u00b1 1.5 to 93.7 \u00b1 3.8, and from 67.3 \u00b1 2.9 to 90.5 \u00b1 2.3 respectively. (Uncertainties are shown for the 95% confidence level.) Similar HRGC allocation results were obtained for separator oil samples. Allocation results using fluid densities are less satisfactory. Allocation results with larger uncertainties indicate that during the 3rd HnP cycle the amount of Eagle Ford oil in STO and separator oil samples increased less dramatically from \u223c87-89 vol% to \u223c92 vol%. These results agree with reservoir simulation models predicting that mixtures initially flowing from the reservoir would contain the largest amount of NGL.Applications\/Significance\/Novelty: HRGC data can be used to accurately estimate the amount of native crude oil present in samples collected from wells injected with Y-grade NGL if appropriate end-member samples previously were collected. This commerical product contains a large amount of very light HC compounds that also may have extracted some of the non-producible petroleum that is sorbed in the kerogen present in the Eagle Ford Formation. This raises the interesting possibilty that the oil produced during this EOR pilot project includes some amount of immobile petroleum -- as well as the oil that naturally flows from the reservoir. In that case, the STO and separator oil samples will contain more resins and aromatic compounds than does the native oil sample that was collected from the pilot well before NGL was injected.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Unique Properties of Low Gas to Oil Ratio (GOR) Black Shale Oil<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Wu*<sup>1<\/sup>, H. Ostera<sup>2<\/sup> and A. Sneddon<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Power Energy and Environmental Research Institute; 2. University of Buenos Aries; 3. Midcon Well Logging (MCWL))<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In the past 6 years, Low GOR Black Shale Oil (LGBSO, API&lt;35\u00b0, GOR&lt;350scf\/bbl) have emerged as superior shale producer compared to High GOR Light Tight Oil (HGLTO). The objective is to find what are the unique LGBSO properties that enable the high productivity, given the conventional wisdom that they are too thick with low API &amp; GOR to produce. We report surpising observations of the difference between LGBSO and conventional black oils i.e. low Asphaltene (ASP) level, and high wetness gas and high gasoline content C6-C11, and these two results in much lower viscosity and higher compressibility than previously estimated based on conventional black oils paradigms. In another paper, we discuss the main driving forces that take advantage of these unique features resulting extra large recoveries.Methods\/Procedures\/Process: These parameters we extensively investigated here include, viscosity both live and dead oil, SARA composition especially ASP, dissolved gas composition, GOR and API, as well as ratios of C1-5,C6-9,C10~25, and C25+. The conventional black oils with low GOR and API from shallow to deep &gt;4km, e.g. deep water Gulf of America, and LGBSO from Vaca Muerta, Unita and saline lacustrine shale basins in China. The data for ASP levels and gas wetness, along with viscosity under reservoir temperatures and other parameters from several LGBSO basins\/blocks are collected and measured in our labs, including reported by open sources. Compressibility measurement are also carried out on several LGBSO samples in our lab.Results\/Observations\/Conclusions: Conventional black oils often have very large amount of ASP, and especially high for black and heavy oil, i.e. &gt;10%; while the dissolved gas wetness is below 20%; and also LGBSO Shale oil or near-source tight oil has very low amount of ASP, even for LGBSO high density shale oil with API&lt;25deg and GOR &lt;100scf\/bbl, ASP often close to 1% or less; while dissolved gas wetness is over 35% up to 50%. ASPs are a primary cause of increased viscosity, and this difference alone can easily account for a 10X or greater viscosity differences. Many people argue that LGBSO is not shale oil but rather migrated oil produced from conventional reservoirs; this uniquely low ASP content and high-wetness gas indicate easily differentiate them from conventional migrated oils.Applications\/Significance\/Novelty: The unique properties for LGBSO detailed here could explain the production type curve and also help optimize production. For example, the low ASP and high wetness gas could contribute to foamy or emulsion formations, which could help or deter LGBSO production; they could also explain the compressibility uniqe to LGBSO. The LGBSO viscosity has to be actually measured under reservoir conditions, and other possible mechanism of transportation, i.e. foamy oil, has to be tested, and conditions to optimize them has to be verified experimentally. In light of the surprising find that ASP is so low in LGBSO, the conventional conjecture that &quot;high ASP&quot; correlate with &quot;low maturity&quot; probably is not correct, instead, most of the ASP in conventional oil is generated later during migration and storage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Reactive Transport Modeling of the Dissolution Front in Siliciclastic Mudrocks During the Post-Fracturing Shut-In Period<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. J. Khan*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(College of Petroleum &amp; Geosciences, King Fahd University of Petroleum &amp; Minerals)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Previous experimental work has tracked the movement of the dissolution front in siliciclastic mudrocks during the shut-in period. In this work we model the movement of the reaction front in these siliciclastic mudrocks by setting up a reactive transport simulation to mimic the post-fracturing shut-in period to observe the chemical evolution of the rock and fluid phases.Methods\/Procedures\/Process: A 1-D reactive transport model has been set up in CrunchFlow, an open-source geochemical reaction software package, to model the reactive imbibition experiments in the mudrocks. Replicating the experimental conditions, the domain has been discretized into 90 grid blocks (100 mm rock and 550 mm of equilibrated brine) and its initial conditions are derived from the experimental work. Acidized equilibrated brine is diffused into the rock matrix, and the model simulates carbonate, pyrite, and apatite dissolution, clay solubility, iron (III) hydroxide precipitation, and organic matter reaction. Sensitivity analysis is conducted to highlight the impact of physical (e.g. temperature) and chemical parameters (e.g. pyrite concentration) on the movement of the reaction front.Results\/Observations\/Conclusions: In the base simulation, replicating the experimental results, the equilibrated system moves towards equilibrium with the fluid pH increasing rapidly to 6.6, which occurs after 40 days of reaction. Calcite dissolution is the primary reaction, with the calcite volume reducing up to 50% at the interface. Higher temperature results in increased dolomitic dissolution at the inlet, whereas calcite precipitation is observed. An increase in the tortuosity and decrease in the effective diffusion coefficient results in a slower progression of the reaction front and a rapid increase in the porosity at the interface. The presence of pyrite enhances dolomitic dissolution and results in faster progression of the reaction front.Applications\/Significance\/Novelty: This work highlights the parameters governing the movement of the reaction front and quantifies the depth of penetration of the reactive fluid during the post hydraulic fracturing shut-in period. The results help explain the evolution of hydraulic fracturing fluid as it is trapped in the near-fracture zone and ultimately identify pathways to remove this blockage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Current and Forward Opportunities De-Risking for Hydrogen Storage and Production<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSandrabh Gautam, Juliette Pearson\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Transforming Depleted Unconventional Wells into Distributed Subsurface Hydrogen Storage Vessels<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. T. Alfaraj* and M. J. AlTammar\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional drilling has expanded significantly over the past two decades, creating a large inventory of wells that reach economic limits within a relatively short production life. These wells, equipped with high-integrity casing designed for high-pressure environments, present an opportunity for repurposing rather than abandonment. This study investigates the feasibility of converting depleted unconventional wells into distributed hydrogen storage systems using a wellbore-based containment approach.Methods\/Procedures\/Process: The concept retains the existing production casing as the primary storage vessel following isolation of the fractured interval using mechanical and cement barriers. Storage capacity is estimated using representative wellbore geometry and assumed operating conditions, with hydrogen density evaluated using real gas relationships. Additional evaluation considers hydrogen\u2013natural gas blending and alternative carriers such as ammonia and formic acid.Results\/Observations\/Conclusions: Results indicate that unconventional wells can provide practical storage capacity at moderate pressures while leveraging existing infrastructure with limited intervention.Applications\/Significance\/Novelty: This approach offers a scalable, geographically distributed solution that reduces surface footprint and transforms abandoned wellbores into valuable energy storage assets supporting future hydrogen economy.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Field-Scale Evaluation of Operational Strategies for Hydrogen Storage in a Heterogeneous Deep Saline Aquifer: Impacts of Cushion Gas, Well Configuration, and Cyclic Operation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Baru*<sup>1<\/sup>, S. I. Eyitayo<sup>1<\/sup>, C. Okere<sup>2<\/sup> and M. Watson<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas Tech University; 2. Cullen College of Engineering, Department of Petroleum Engineering, University of Houston)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The objective of this work is to evaluate field-scale hydrogen storage performance in a heterogeneous deep saline aquifer and to quantify how operational design choices (cushion gas strategy, well configuration, and cyclic injection\/withdrawal schedules) influence hydrogen plume evolution, recoverability, and loss mechanisms. The study aims to provide operational guidance for pilot deployment and techno-economic assessment by linking realistic reservoir heterogeneity and boundary conditions to deliverability and storage efficiency.Methods\/Procedures\/Process: A three-dimensional, field-scale geological model with spatially distributed porosity, permeability, layering, and caprock geometry is used to perform compositional multiphase reservoir simulations. The model represents a typical deep saline aquifer target for seasonal storage. Scenarios include alternative cushion gas compositions and volumes (N2, CH4, partial H2 cushion), multiple well placement strategies (vertical vs. horizontal, injector\/producer spacing), and cyclic operational schedules (annual and sub-annual cycles). The simulation physics account for real-gas behavior, capillary pressure and hysteresis, gas\u2013brine partitioning (dissolution), and diffusive losses. A systematic sensitivity analysis quantifies uncertainty drivers.Results\/Observations\/Conclusions: Results indicate that reservoir heterogeneity and mobility contrasts drive viscous and capillary fingering that materially reduce sweep and increase residual trapping. Cushion gas placement and volume emerge as primary controls on pressure support and working-gas fraction: optimized N2 cushion volumes reduced working-gas losses by up to ~15% relative to baseline cases, while CH4 cushions improved deliverability but increased mixing contamination. Well configuration significantly affects sweep efficiency, with horizontal producers improving recovery by 8\u201312% in layered systems. Cyclic operation increases hysteresis effects, driving an incremental residual loss of 5\u201310% over multiple cycles; dissolution and diffusive losses contribute an additional 2\u20136% depending on pressure regime.Applications\/Significance\/Novelty: This work provides one of the first comprehensive field-scale sensitivity studies that couples realistic geological heterogeneity with operational design for hydrogen storage in DSAs. The findings demonstrate that (1) cushion gas strategy and well design are the most actionable levers to improve recoverability, (2) heterogeneity-informed well placement can materially reduce losses, and (3) cyclic hysteresis and dissolution must be included in pilot and economic design to avoid overestimated working capacity. The results provide direct, implementable guidance for pilot planning and risk-informed scale-up of underground hydrogen storage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Microwave-Assisted Hydrogen Generation from Hydrocarbon-Bearing Reservoir Rocks: Stage-Dependent Thermal Runaway and In-Situ Carbonate Engineering<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. B. Demir<sup>1<\/sup>, Q. Yuan<sup>2<\/sup> and B. Hascakir*<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Harold Vance Department of Petroleum Engineering, Texas A&amp;M University; 2. Bob L. Herd Department of Petroleum Engineering, Texas Tech University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Microwave-assisted hydrogen generation from hydrocarbon-bearing reservoir rocks is strongly influenced by mineralogy, methane activation, carbonate reactions, and thermal runaway behavior. This study investigates a new approach in which carbonate phases are generated in-situ through the reaction of internally produced CO2 with Ca(OH)2 under microwave heating conditions. The objective is to evaluate how rock mineralogy, methane injection, and Ca(OH)2 addition influence hydrogen generation, carbon redistribution, and stage-dependent reaction pathways during microwave exposure.Methods\/Procedures\/Process: Microwave heating experiments were conducted using Permian Basin reservoir rocks under three experimental conditions: rock-only experiments under Ar atmosphere, CH4\u2013Ar experiments without additive, and CH4\u2013Ar experiments containing 5 wt% Ca(OH)2. Methane-assisted experiments were performed under continuous injection of 30 standard cubic centimeters per minute (sccm) CH4 and 30 sccm Ar. Based on thermal runaway behavior, each experiment was divided into three operational stages: Before Thermal Runaway (BR), After Thermal Runaway\u2013Decrease in Microwave Power (ARD), and After Thermal Runaway\u2013Increase in Microwave Power (ARI). Temperature and gas composition were continuously monitored throughout the experiments.Results\/Observations\/Conclusions: The rock-only experiments demonstrated that hydrogen generation can occur intrinsically from hydrocarbon-bearing rocks under microwave heating, even without externally injected methane. However, hydrogen production did not correlate solely with kerogen content, indicating that mineralogy strongly influences hydrogen-generation pathways. Correlation analyses suggested that kerogen decomposition initially generated CH4, CO, and CO2, followed by secondary hydrocarbon reactions associated with H2 and C2 hydrocarbon formation. Methane-assisted experiments substantially increased hydrogen production; however, identical methane injection rates produced significantly different hydrogen yields among the rock samples, confirming that mineralogical composition controls methane-conversion behavior under microwave heating conditions. The addition of Ca(OH)2 significantly altered carbon evolution behavior in a stage-dependent manner. During the BR stage, Ca(OH)2 reduced gas-phase CO2 production, particularly in carbonate-rich rocks, indicating favorable conditions for in-situ carbonation and carbonate deposition prior to extensive thermal decomposition. The suppression of CO2 during BR became more pronounced with increasing carbonate content of the rock system. After thermal runaway, carbonate-containing systems exhibited enhanced hydrogen generation behavior, suggesting that carbonate-derived mineral transformations and carbonate-mediated reactions contribute to high-temperature hydrogen-generation pathways. In carbonate-poor rocks, Ca(OH)2 addition enabled simultaneous enhancement of hydrogen production and partial suppression of CO2 release during the post-runaway stages.Applications\/Significance\/Novelty: Overall, the results demonstrate that microwave-assisted hydrogen generation is governed by dynamically evolving interactions among kerogen decomposition, methane activation, mineral transformations, carbonate formation\/decomposition, and thermal runaway behavior. This work introduces in-situ carbonate engineering with Ca(OH)2 as a strategy for coupling hydrogen generation with partial in-situ carbon management under microwave heating conditions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"scholarone-tab-content\" id=\"day-1\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Engineering the Future of UAE Unconventionals- Lessons from ADNOC\u2019s Diyab Journey<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:50 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Matthew Poole\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Al Blooshi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Itibrout*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Silva*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: International and Emerging Challenges of Unconventional Resources<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAndrew Keene, Yitian Xiao\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Utilizing Acoustic Pipe and Perf Friction Measurements for Completion Design Optimization in the Vaca Muerta Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. De La Garza*<sup>1<\/sup>, M. Khan<sup>1<\/sup>, R. Holland<sup>1<\/sup>, M. D. Pellicer<sup>2<\/sup> and J. Baratcabal<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Seismos, Inc.; 2. Pan American Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As many advancements and technologies in hydraulic fracturing continue to make their way to the Vaca Muerta basin, Argentina has become home to one of the fastest-growing shale plays globally. To leapfrog on the success of US unconventional, an Argentinian operator opted to incorporate real-time acoustic friction analysis as a tool for design evaluation and optimization. Real-time measurements during fracturing treatments have given the operator the ability to evaluate changes to their design. This paper presents workflows that incorporate live surface-based measurements of pipe friction, perforation friction, and cluster distribution, to enable informed decision-making for improving completion strategies both in performance and economics.Methods\/Procedures\/Process: A simple surface set-up is used via high-frequency acoustic sensors to capture rapid rate changes (planned and unplanned). Induced tube-wave responses are decomposed to quantify pipe and perforation friction present in the system. Additional downhole performance metrics are calculated, such as perforation efficiency, uniformity index, and effective flow area. The data was further validated by a pressure gauge frac ball to measure the bottomhole pressure. These metrics are displayed through a local or operator-integrated dashboard and incorporated into engineered workflows tailored to operator-specific formations, designs, or other variables.Results\/Observations\/Conclusions: Real-time measurement of pipe friction, perf friction &amp; cluster distribution enabled a robust evaluation of 4 different completion designs. This study demonstrated the importance of the relationship between rate and effective flow area in achieving and maintaining optimal treatment distribution across the clusters. Targeting a more aggressive limited-entry approach increased perforation efficiency by 30%. As pipe friction decreases as one moves up the lateral, and rate increases are possible, proactively optimizing the effective flow area by adjusting the perforation scheme is imperative to maintaining uniformity in the optimal zone throughout the lateral.Applications\/Significance\/Novelty: This study highlights that real-time measurements-derived solely from surface-mounted acoustic sensors \u2013 provide a practical pathway toward data driven hydraulic fracturing. This modern workflow enables operators to enhance stage performance and avoid unnecessary capital exposure to under-performing stages, advancing real-time optimization beyond the conventional surface-pressure-only approach in the Vaca Muerta Basin.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Delineation of the High Production Potential Areas from Regional to the Well Pad Scale in Unconventional Gas Play in UAE<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Mykhaylenko, C. F. Burgess, C. Wells, O. Nielsen, A. Alharthi, T. Brooks, I. Sukhodoev, A. Al Blooshi and M. Al Braiki*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper builds upon a case study of an exploration and appraisal program in the UAE designed to initiate commercial production of unconventional gas. It focuses on subsurface data analysis, integration, and application of the techniques for identifying natural fractures within carbonate reservoirs, utilizing large-scale onshore seismic data, borehole imaging, advanced mud logging, and seismic inversion results. The primary objective is to evaluate how dependable, adaptable, and precise this approach is across various geological settings and reservoir types, with the ultimate aim of employing it as a strategy to pinpoint optimal drilling sites.Methods\/Procedures\/Process: This workflow integrates seismic inversion, structural attributes interpretation, and borehole image log calibration to identify fracture-prone zones. It adapts to field-wide seismic changes using well test and new image log data, with key inversion parameters cross-analysed against structural attributes. Calibration from advanced mud logging improves the fracture detection algorithm, while comparative analysis assesses the transferability of fracture indicators to new areas. Production data from exploration horizontal producers was used together with the pilot production well pads to validate the methodology and come up to the reliable level of the delineation of the high grade reservoir areas considering uncertainties and data limitations.Results\/Observations\/Conclusions: Applying this methodology in new areas revealed positive correlation between predicted fracture zones, high-porosity anomalies, and dynamic well tests. Wells intersecting seismic-identified fractures showed higher productivity, stable wellhead pressures, and lower decline. Low-impedance regions reliably marked sweet spots, especially when fracture anomalies indicated connected networks. Optimizing thresholds for fracture attributes improved prediction across reservoir conditions. Long-duration well tests confirmed spatial continuity and hydraulic connectivity of interpreted fractures. Overall, seismic inversion data significantly enhances fracture prediction for unconventional carbonates, enabling precise infill drilling and better reservoir management.Applications\/Significance\/Novelty: This work demonstrates the effective exploration &amp; appraisal strategy applied in for unconventional gas reservoir in UAE. Subsurface data integration in regional and local scale, such as fracture identification, porosity prediction and production analysis allowed to initiate the expansion of the pilot development project. Proposed methodology contributes to effective and evidence based decision-making in reservoir development, especially during production drilling of the unconventional assets where new wells and updated seismic attributes offer opportunities for model calibration and optimization. The work aligns with ADNOC focus on maximizing recovery through innovation, data integration, and applied geoscience in complex subsurface environments.Interdisciplinarity (Team Presentation\u2019s only): Successful implementation of the exploration and appraisal strategy required multidisciplinary efforts, including geological and geochemical studies, large-scale seismic data collection, drilling, coring, petrophysical and geomechanical analysis, production, surface facilities management, HSE, and coordination with government agencies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Geological Insights into Lacustrine Shale Oil and Gas Enrichment and Prospect Evaluation: The Lianggaoshan Formation in Northeastern Sichuan Basin, China<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tQ. Wang*, Z. Hu, D. Feng and S. Xu\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Jurassic lacustrine shale in the Sichuan Basin is a key emerging target for shale oil and gas exploration in China. This study focuses on the Lianggaoshan Formation in northeastern Sichuan to investigate its complex lithofacies, strong heterogeneity, and enrichment controls. The formation shows a tripartite structure: variegated shore\u2013shallow lake facies in the lower and upper parts, and dark shale in shallow to semi\u2013deep lake settings in the middle member (Liang\u20132). Frequent lithofacies changes and diverse organic matter complicate pore development and hydrocarbon enrichment. The research aims to improve understanding of accumulation mechanisms and support exploration in this and similar lacustrine shale plays.Methods\/Procedures\/Process: This study employed an integrated sedimentological and reservoir characterization approach. Core analysis, well\u2013log interpretation, and geological statistics were combined to map lithofacies distribution and identify high\u2013quality shale intervals. Petrophysical tests, organic geochemistry, and field\u2013emission scanning electron microscopy were used to quantify pore structure and identify key controlling factors. Diagenetic evolution and hydrocarbon generation processes were systematically analyzed, revealing the sediment\u2013diagenesis coupling mechanisms in lacustrine shale systems. This multidimensional methodology enhances understanding of geological\u2013reservoir interactions and establishes a sweet\u2013spot evaluation framework for the Lianggaoshan lacustrine shale.Results\/Observations\/Conclusions: This study established a stratigraphic framework for the Lianggaoshan Formation in northeastern Sichuan, identifying six lithofacies dominated by laminated argillaceous and silty shales. High\u2013quality shale (TOC &gt; 1%) concentrates in semi\u2013deep facies of the upper Liang\u20132 Lower Submember, primarily in the Fuxing and Qijiang areas. Pores are mainly inorganic meso\u2013 to macropores, controlled by clay minerals, organic matter, and thermal maturity. Key enrichment factors include high\u2013quality shale, preservation conditions, and silty laminae. The upper Liang\u20132 Lower Submember is the sweet spot. Based on a &quot;four\u2013property&quot; system, three exploration zones, including southern northeastern Sichuan, were selected.Applications\/Significance\/Novelty: This study systematically elucidates the enrichment mechanisms of shale oil and gas in the Lianggaoshan Formation for the first time. The innovation is reflected in establishing a ternary evaluation model of &quot;lithofacies association\u2013reservoir property\u2013controlling factors,&quot; which identifies the upper Liang\u20132 Lower Submember as the sweet\u2013spot interval. The research significance lies in constructing a &quot;four\u2013property&quot; evaluation system applicable to continental shale, further advancing the understanding of hydrocarbon enrichment patterns. Its practical value is demonstrated through guiding the selection of favorable exploration zones, such as the southern northeastern Sichuan Basin, and providing theoretical and technical support for developing similar lacustrine shale reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Geomechanics in Well Design, Construction, and Drilling<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tFatick Nath, Dharmendra Kumar, Yuanbo Lin\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Stress Profiling Minimum and Maximum Horizontal Stress in Emerging Tight Gas Horizontal Plays: A Uinta Basin Case Study<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Morales*<sup>1<\/sup>, E. M. Kias<sup>2<\/sup> and M. Mohiuddin<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Enbridge Inc; 2. WD Von Gonten Engineering LLC; 3. K&amp;M Technology Group)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In a vertically transverse isotropic (VTI) medium, accurate prediction of the vertical and horizontal Young\u2019s moduli and Poisson\u2019s ratios is crucial to predicting minimum horizontal stress and hence selecting drilling mud, cement weights, perforation locations and fracture containments. Accurate stress profiles help prevent mechanical integrity failures during the transition from vertical to horizontal drilling and guide the selection of productive landing intervals where fractures remain within target zones. The main objective of this study is to characterize the anisotropy of the rocks, predict a reliable stress profile and minimize the risks while transitioning from vertical to horizontal wells.Methods\/Procedures\/Process: A comprehensive field program included anisotropic velocity measurements on 300+ ft of core slabs, triaxial tests on vertical\/horizontal samples for elastic, strength, and failure properties, Biot coefficient estimation, depth-corrected DFITs, and maximum horizontal stress interpretation from FMI via drilling-induced fractures, breakouts, and friction analysis. M-ANNIE parameters came from velocity data; horizontal stresses were estimated using calibrated elastic moduli. Core and log data were integrated to build stress profiles, followed by fracture geometry simulations. Rock properties, pore pressure, and stresses were then used in wellbore stability studies for planned horizontals.Results\/Observations\/Conclusions: Key observations include the value of multiple DFITs in stress barrier layers. Sonic-to-elastic correlations and Biot calibration strongly influence stress profiles. Comparisons of isotropic, ANNIE and M-ANNIE 1 show lithology based M-ANNIE 1b (introduced here) yields higher stress contrast. Transition of static Poisson\u2019s ratio (horizontal vs. vertical) from &gt;1.0 to &lt;1.0 offers insight for fracture modeling and completion design, as well as predicted rock failure matched breakouts and tensile failures on caliper\/FMI logs.Applications\/Significance\/Novelty: The workflow improves stress profile estimation by integrating an FMI derived maximum horizontal stress calibration point and comparing multiple sonic interpretation methods. Novelty lies in establishing a lithology based M-ANNIE 1b parameters from anisotropic velocity measurements of core slabs, combining column DFITs (minimum horizontal stress, Shmin), FMI-derived stress (maximum horizontal stress, Shmax), empirical fracture barrier observations, and core-based triaxial and Biot measurements. Results show that lithology based M\u2011ANNIE 1b yields higher stress contrast between reservoir and barrier layers, improved agreement with observed wellbore failures, and more reliable fracture containment predictions. Wellbore stability analysis indicates a narrow mud\u2011weight window for future horizontal wells, emphasizing the importance of accurate stress modeling and ECD control. The integrated workflow reduces uncertainty in stress profiles, lowers drilling and completion risk, and supports confident horizontal development in data\u2011limited tight\u2011gas reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Geomechanics Meets Machine Learning: Drilling Risk Prediction for Challenging Infill Wells in Mature Unconventional Play<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. P. Castagnoli<sup>1<\/sup>, T. Tomberlin*<sup>2<\/sup>, P. Shukla<sup>1<\/sup>, A. Rodriguez-Herrera<sup>1<\/sup>, S. Rivera Barraza<sup>2<\/sup>, A. F. Cadena<sup>2<\/sup>, H. Loa<sup>2<\/sup> and J. Wong<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. SLB; 2. Murphy Oil Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work aims to improve infill drilling performance in the Eagle Ford Shales by accounting for in-situ stress changes caused by depletion and fracturing history. Costly and prohibitive failures in early infill programs threatened redevelopment inventory and resource recovery, driving the creation of a workflow that auto-classifies drilling events, applies a friction-fault approach as a constraint on stress inversion results, and calibrates a multiwell Mechanical Earth Model (MEM). Machine-learning models, trained on the enriched drilling-geoscience database, predict failure risks along planned well paths. These enable risk mitigation through a targeted drilling playbook benchmarked against field performance, resulting in improving economics and recovery of the resource.Methods\/Procedures\/Process: Infill and redevelopment wells in Karnes County experienced elevated non-productive time (NPT), prompting a geomechanics-driven risk-mitigation workflow. Historical drilling reports were processed with LLM and oriented-expression algorithms to extract events, classify, and ensure data quality using a Business Intelligence system. This structured database was enriched with geological, petrophysical, and rock-mechanics data to calibrate 1D MEM across 34 offset wells. The geomechanical model was integrated with the database to distinguish problematic from non-problematic drilling intervals and define instability drivers. Machine learning techniques were then applied to predict drilling failures and quantify risk to optimize well design, generating actionable guidance for a drilling playbook.Results\/Observations\/Conclusions: The integrated workflow delivered transformative drilling performance, with non-productive time reduced by 78%, and the lost-in-hole events were eliminated entirely. This confirmed the effectiveness of the integrated workflow. The enriched dataset and ML-driven analysis identified the primary drivers of drilling failures, reduced geomechanical uncertainty across 34 offset wells, and validated the time-dependent stress path and drilling window. Consequently, mud weight selection was optimized, casing schemes refined, and targeted lost-circulation material (LCM) strategies deployed that directly improved drilling efficiency and operational performance.Applications\/Significance\/Novelty: This workflow offers a practical application for operators seeking to proactively manage drilling risks by integrating automated event extraction, geomechanics, and machine learning into a single decision-support system. Its significance lies in demonstrating that high-impact performance improvements can be achieved even in rock mechanics data-limited environments through intelligent data structuring and multi-well calibration. The approach is novel in its use of LLM-based event recognition combined with stress inversion modeling and ML-driven risk prediction to guide well design. By translating complex subsurface behavior into a prioritized drilling playbook (or how to act), the method provides a repeatable and scalable framework for future infill and redevelopment campaigns.Interdisciplinarity (Team Presentation\u2019s only): Initial attempts to thread tight infill wells through densely drilled unconventional reservoirs encountered non productive time, cost overruns, and other failures putting viability of redev programs in question. An interdisciplinary planning team was formed to integrate available geology, engineering, geomechanics, and drilling data to define and mitigate hazards, depletion, well construction parameters, and other conditions contributing to failures. Through collaboration and data integration across disciplines, solutions were defined and implemented such that infill drilling is now routine. And, by applying current understanding of the reservoir, targeting, and completion design, infill wells are being delivered that often exceed performance of primary wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Hydromechanical and Strength-Informed Mechanical Specific Energy Model for Real-Time Drilling Optimization<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Ahmed* and K. Sepehrnoori\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Mechanical Specific Energy (MSE) is widely used for drilling optimization but is highly distorted by friction losses, hydraulics, dysfunction, and formation strength variations. This work develops an advanced hydromechanical, strength-informed MSE model that isolates true rock-cutting energy and normalizes it by in-situ strength as a robust real-time Key Performance Indicator (KPI). The scope covers derivation of the new formulation and its validation on multiple field intervals in sandstone, shale, and limestone using conventional surface drilling data.Methods\/Procedures\/Process: We formulate net cutting energy by subtracting torque-and-drag and axial friction from surface WOB and torque and by adding a scaled bit-jet power term from bit pressure drop and flow rate. Dynamic dysfunction is represented through stick-slip, RPM variability, and axial-shock indices. Rock strength is computed from a Mohr-Coulomb model using UCS, friction angle, and effective stress at bit depth. Model coefficients are calibrated on steady drilling intervals per bit and lithology using cleaned field data.Results\/Observations\/Conclusions: The enhanced MSE terms significantly reduce intra-lithology scatter compared with classical MSE by removing frictional losses and accounting for jet power and dynamics. Strength normalization yields a dimensionless performance indicator that remains near unity during efficient cutting and rises systematically with dysfunction, bit wear, or strength contrast. Field applications in sandstone, shale, and limestone show improved alignment with log- and MEM-derived strength profiles and clearer identification of lithology transitions.Applications\/Significance\/Novelty: The strength-informed MSE model enables more reliable real-time optimization of WOB, RPM, and flow, supports bit selection and run benchmarking, and improves detection of drilling dysfunction and inefficient operating windows. Because the indicator is referenced to formation strength, the resulting KPI can consistently compare performance across BHAs, wells, and campaigns in mixed lithologies. The framework is suitable for integration into digital drilling advisors and autonomous control systems using only surface data and basic geomechanics.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Technology Transfer from the Oil and Gas Industry for CCUS Applications<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tScott Singleton, Pouria Mousavi\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Study of Simultaneous Hydrogen Gas Stream Purification and CO2 Sequestration in Deep Coal Seams using the RTAPK Method Applied to an Artificial Coal Core Plug<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Song* and C. Clarkson\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Department of Earth, Energy and Environment, University of Calgary)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Steam methane reforming (SMR) is a widely used process for hydrogen (H2) production; however, it produces CO2 as a by-product. A potential solution is to simultaneously purify the H2 component, and permanently sequester CO2, by injecting mixtures of H2 and CO2 into depleted subsurface coal, followed by production of the gas mixtures. This process was recently validated experimentally using a huff-n-puff (HNP) approach applied to a real coal core plug sample. Given the challenges associated with extracting real coal core plugs, the present study replicates the same experimental procedures using an artificial (compressed powder) coal core plug, with the results compared to those obtained from the real core plug.Methods\/Procedures\/Process: In this study, an artificial coal core plug sample was prepared by hydraulically compressing fine coal powder to 9000 psi prior to the HNP experiments. Synthetic SMR gases (75%H2+15%CO2+10%CH4) were injected into the coal sample, allowed to equilibrate, and then produced from the coal sample. Two cycles of injection\/equilibration\/production of the synthetic SMR gas were performed at reservoir temperature; a third cycle was carried out using the purified SMR gas after cycle 2. Additionally, the permeability of the coal to gas mixtures during H2 purification\/CO2 sequestration experiments was determined using the RTAPK method.Results\/Observations\/Conclusions: After the injection stage of cycle 1, the gas composition stabilized to ~92%H2\/3%CO2\/5%CH4, demonstrating an increase in H2 concentration. Cycle 2 yielded a similar result. The produced gas of cycle 1 closely matched the equilibrium composition. However, late-stage produced gas composition for cycle 2 decreased to ~85%H2\/5%CO2\/10%CH4. This gas was reinjected for cycle 3, whose late-stage produced gas (~83%H2\/5%CO2\/12%CH4) suggested further H2 purification was not possible. Cycles 1, 2, 3, yielded a maximum H2 recovery of 59%, 61%, 63%, and CO2 storage of 92%, 84%, 60%. Coal permeability decreased from ~0.4 to ~0.1 md from cycle 1-3. These trends align with the previous real coal core plug test, although the artificial coal core plug exhibited higher H\u2082 recovery and lower CO\u2082 storage due to its higher permeability.Applications\/Significance\/Novelty: This proof-of-concept study demonstrates that simultaneous H2 purification and CO2 storage can be achieved through injection of H2-CO2-containing SMR gases into coal samples and subsequent production of the gas mixture. Importantly, it demonstrates that an artificial coal core plug can achieve similar H2 purification performance to a real coal core plug, which is of practical value due to the difficulty of obtaining intact coal core plug samples.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Rapid CO2 Capture by Alkaline Hydroxides under Multiphase High-Temperature Conditions: Insights from Non-Isothermal Analysis<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Karabayanova and B. Hascakir*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Harold Vance Department of Petroleum Engineering, Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Rapid carbon dioxide capture under elevated-temperature subsurface conditions remains poorly understood, particularly in multiphase systems where reactive solids are simultaneously exposed to CO2, water, brine, and hydrocarbons. This study investigates the carbonation behavior of alkaline hydroxides, NaOH, Mg(OH)2, and Ca(OH)2, under conditions representative of subsurface thermal processes.Methods\/Procedures\/Process: A total of 11 experiments were conducted under 100% CO2 at a constant heating rate of 15 \u00b0C\/min using thermogravimetric analysis coupled with differential scanning calorimetry (TGA\u2013DSC). End temperatures of 600 \u00b0C and 1,000 \u00b0C were selected to distinguish the carbonation stage from subsequent high-temperature carbonate decomposition. Carbonate formation was evaluated through a multi-scale analytical framework combining TGA\u2013DSC (quantitative bulk analysis) with FTIR, Raman spectroscopy, and SEM\u2013EDS (qualitative and semi-quantitative characterization).Results\/Observations\/Conclusions: Among the tested materials, Ca(OH)2 exhibited the highest and most consistent reactivity. Carbonation initiated at ~350\u2013400 \u00b0C and reached a maximum near 600 \u00b0C, defining a distinct temperature window relevant to subsurface thermal systems. In contrast, Mg(OH)2 showed no measurable carbonation under the applied non-isothermal conditions, while NaOH exhibited limited low-temperature uptake (3.41 g CO2 per 100 g). Maximum CO2 uptake for Ca(OH)2 ranged from 32.82 to 42.47 g per 100 g, with carbonation conversion reaching up to 72% in the formation brine system, compared to 60% in distilled water, 55% under dry conditions, and 28% in crude oil. These results demonstrate that aqueous phases significantly enhance carbonation, while hydrocarbons inhibit reaction pathways. FTIR and Raman analyses confirmed carbonate formation prior to 600 \u00b0C, while SEM\u2013EDS revealed carbon incorporation and the development of predominantly crystalline CaCO3 morphologies in aqueous systems. In contrast, crude oil systems exhibited incomplete surface coverage and carbonaceous interference.Applications\/Significance\/Novelty: Overall, the results establish Ca(OH)2 as the most effective hydroxide for rapid CO2 capture under high-temperature, multiphase conditions. The identified carbonation temperature window and strong dependence on fluid environment provide new insights into how CO2 capture may initiate and evolve in subsurface systems, including in-situ combustion (ISC) and other thermal enhanced oil recovery (EOR) processes.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Hydro-Mechanical Coupling During CO\u2082 Injection in Entrada Sandstone: Linking Pore-Pressure Diffusion, Fault Dilation, and Acoustic Emission Energy Release<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Akpabli* and H. Rahnema\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(New Mexico Institute of Mining and Technology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to investigate how CO2 injection influences fault stability in Entrada Sandstone through coupled hydro-mechanical processes. Specifically, we examine how pore-pressure diffusion, fault dilation, and acoustic emission (AE) energy release evolve under different CO2 injection rates and stress conditions to determine the onset of shear slip. By integrating real-time AE monitoring with controlled laboratory injection experiments, the work seeks to identify mechanical and seismic precursors to instability and provide experimentally grounded insight relevant to safe CO2 sequestration, injection design, and seismic risk mitigation.Methods\/Procedures\/Process: Entrada Sandstone cores (38.1 mm \u00d7 76.2 mm) were fully brine-saturated, CO2 equilibrated, and loaded in a true-triaxial cell under 25\u201330 MPa confining pressure and 110\u2013130 MPa differential stress to simulate near-critical reservoir conditions. CO2 was injected at controlled rates ranging from 0.5\u20132.0 PV\/h using a high-pressure syringe pump, with injection pressure ramped from 4 to ~20 MPa at 0.1 MPa\/s. Real-time measurements of axial\/radial strain, pore pressure, and fracture dilation were captured using LVDTs and high-precision pressure transducers, while a 12-sensor broadband acoustic emission (AE) array recorded microseismic activity throughout injectionResults\/Observations\/Conclusions: CO2 injection into Entrada Sandstone produced a rapid pore-pressure diffusion front that reached the fracture plane within 30 seconds, roughly three times faster than brine, leading to an effective normal stress reduction of 8\u201312 MPa and early onset of shear slip. Pre-slip dilation of 6\u201312 \u03bcm evolved into a dynamic dilation of 110\u2013140 \u03bcm, increasing fracture aperture by more than 50% and sharply enhancing permeability. Acoustic emission activity showed a clear transition from aseismic behavior (\u22642 hits\/min) to an intense seismic burst during dynamic slip, providing a measurable precursor 10\u201315 seconds before failure. Slip velocities increased from 1\u20132 \u03bcm\/s (quasi-static) to 80\u2013120 \u03bcm\/s (dynamic), and higher injection rates produced larger stress drops and more energetic AE events.Applications\/Significance\/Novelty: This study provides experimentally grounded insight into how CO2 injection alters fault stability in Entrada Sandstone through the coupled evolution of pore pressure, dilation, and acoustic emission behavior. This work identifies measurable mechanical and acoustic precursors to slip, demonstrating clear rate-dependent controls on fault reactivation. The novelty of the study lies in its integrated use of high-pressure CO2 injection, real-time AE monitoring, and precise displacement tracking to reveal previously unquantified relationships between pressure diffusion, fracture dilation, and seismic energy release providing a new foundation for scaling laboratory observations to field-scale carbon storage operations\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Developments in NMR Logging, Interpretation, and Core Analysis II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJose Dugarte, Giselle Garcia Ferrer\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Shale Characterization Using Magnetic Resonance T1-T2* Relaxation Correlation Method<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Zamiri<sup>1<\/sup>, M. J. Dick<sup>2<\/sup>, D. Veselinovic*<sup>2<\/sup>, D. Green<sup>2<\/sup> and B. J. Balcom<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. UNB MRI Research Centre; 2. Green Imaging)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unlocking reserves from shale formations has led to energy independence in the US. Accurate evaluation of fluid content and kerogen assessment are essential to sustain production. 1H magnetic resonance (MR) has proved extremely useful for shale characterization. Currently, the T1-T2 method is used to resolve the 1H MR signal for various 1H-bearing shale species namely water, oil, gas, and kerogen. However, the low signal resolution of T1-T2 limits its applicability to simple fluid typing. We have developed a new method, T1-T2* correlation, which is an analytical technique allowing high signal resolution. This method is well suited for shales and extends MR analysis beyond simple fluid typing. In this work, we show T1-T2* can quantify fluid content in shale samples and perform kerogen assessment.Methods\/Procedures\/Process: The applicability of the T1-T2* method was demonstrated using outcrop shale samples from the Marcellus and Eagle Ford Formations. Measurements were performed at three static magnetic fields, equivalent to 1H frequency of 12, 33, and 127 MHz. The T1-T2* response of shale samples was compared with T1-T2 measurements. Various processes relevant to the study of shale formations were examined. Water uptake and pyrolysis processes were monitored with the T1-T2* relaxation correlation technique. The high signal resolution of T1-T2* allowed an accompanying reference sample, with a known hydrogen content, to be used to calibrate the resolved MR signals in the relaxation correlation plots. These methods require minimal sample preparation.Results\/Observations\/Conclusions: T1-T2* method was used to resolve MR signal for water, oil, and kerogen. This allowed oil and water quantification in shale core plugs and kerogen assessment. Water and oil content in shales were manipulated using pyrolysis experiment, which allowed assignment of signal peaks to shale species. The pyrolysis experiment showed that the resolved kerogen peak in the T1-T2* response can quantify 1H in kerogen and thus allow kerogen assessment. The T1-T2* response of shales has been employed to understand and invoke contrast in an MR imaging experiment, allowing selective imaging of oil and water in shales. In addition to 1H, 13C MR measurements were conducted at 3.1 T to quantify oil content and determine the H\/C ratio of kerogen, indicating kerogen type and maturity.Applications\/Significance\/Novelty: Traditional methods for shale characterization are slow, lack accuracy, and require extensive sample preparation. They rely on material extraction followed by measurement. In contrast, the methods presented here can be applied in core analysis laboratories with minimal sample preparation, permitting faster and more accurate characterization. Additionally, these methods can be very beneficial at the wellsite to provide fluid content and kerogen assessment of drill cuttings. The T1-T2 method for shales requires high sensitivity which is achieved at high field. However, rock analysis MR instruments commonly operate at low field. At low field, signal peaks lie closer together in the relaxation correlation maps. The higher resolution from the T1-T2* method addresses these issues and improves the analysis.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">NMR Evaluation of Tight Oil Mobility by Supercritical Carbon Dioxide Displacement<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Zhu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The evaluation of tight oil mobility, particularly for shale oil, currently relies on limited methodologies. The primary approach is based on the geochemical parameter S1\/TOC, which requires extensive coring data. Supercritical carbon dioxide displacement for mobilizing shale oil represents a promising development technique and a significant technological trend. Concurrently, Nuclear Magnetic Resonance (NMR) technology offers the advantages of being rapid, non-destructive, and capable of in-situ measurement. This study integrates supercritical carbon dioxide displacement with laboratory NMR analysis and links it to NMR well logging to establish a methodology for Identifying potential zones with moveable oil in tight reservoirs.Methods\/Procedures\/Process: Firstly, multiple NMR sequences\u2014including GR-HSE, SE-SPI, SR-CPMG, CPMG, and imaging were employed to experimentally evaluate tight oil mobility within cores during supercritical carbon dioxide displacement. This investigation examined crude oil migration behavior, thereby enriching the suite of evaluation parameters. Simultaneously, the T2 cutoff value for core samples from the study area under supercritical carbon dioxide displacement was determined. Finally, from an experimental perspective and in conjunction with NMR logging, oil content index and movable oil index were defined. These were integrated with geochemical maturity parameters from different depths to establish a Comprehensive Movable Potential Index (IMP) specific for tight oil evaluation in the study area.Results\/Observations\/Conclusions: This methodology was applied to assess tight oil in the Yanchang Formation, Ordos Basin, China. Laboratory measurements on core samples from Well R20X yielded a T2 cutoff value of 60 ms. Initial evaluation based on field pyrolysis S1 data identified two potential &quot;sweet spot&quot; intervals (upper and lower) in this well. However, application of the Comprehensive Movable Potential Index (IMP) suggested that only the crude oil within the upper sweet spot interval possesses significant mobility. Consequently, this upper interval was selected as the target window for the horizontal drilling section and subsequent development.Applications\/Significance\/Novelty: The primary novelty of this study lies in developing an NMR-based evaluation method for tight oil cores subjected to supercritical carbon dioxide displacement. By linking laboratory data with well logging, a continuous index curve was established, which proves highly effective in identifying intervals with movable oil potential. Beyond the specifically defined IMP index, the continuity of the IMP curve allows for the application of empirical values even in wells within the same study area that lack core-derived laboratory data. This presents a simple and practical method. In practical application, it is recommended that explorationist further validate this method based on ultimate crude oil production.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Teaching NMR to Think: AI-Driven Prediction Of NMR T2 Cutoff Values<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Vaisblat<sup>1<\/sup>, A. Ganesh<sup>2<\/sup>, M. J. Dick<sup>3<\/sup>, D. Green<sup>3<\/sup>, D. Heagle<sup>1<\/sup> and D. Veselinovic*<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. CanmetEnergy Natural Resources; 2. Chandramouli, Independent Consultant; 3. Green Imaging)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The objective of this study is to develop a data-driven workflow to predict NMR T2 cutoff values directly from relaxation spectra, reducing reliance on laboratory calibration and fixed, lithology-based assumptions. The work focuses on capturing pore-system variability using a multi-lithology dataset of 214 core samples and evaluating model performance across distinct pore-system classes. A key component of the study is assessing applicability beyond laboratory conditions through deployment on subsurface CMR data from a CO2 storage reservoir, with emphasis on implications for irreducible water saturation and effective storage-capacity estimation.Methods\/Procedures\/Process: Each dataset included NMR signals from fully saturated and centrifuged samples at irreducible water saturation. T\u2082 cutoff values were reassigned and outliers removed. The fully saturated T\u2082 distributions were standardized into a normal distribution to eliminate bias and ensure equal feature weighting across the machine learning models. Principal component analysis reduced the dataset to 8 components capturing over 90% of total variance. A random forest regressor was then trained on 60% of the data to identify the relationship between the reduced T\u2082 features and the corresponding T\u2082 cutoffs while the remaining 40% was used for validation. Downhole NMR data were processed through the same workflow to form an equivalent 8 feature matrix to which the trained model was applied to predict cutoffs.Results\/Observations\/Conclusions: Cluster-specific models show strong agreement between predicted and laboratory-derived T2 cutoff values, with best performance in clusters characterized by narrower cutoff distributions. Larger uncertainty is observed in clusters with broader cutoff ranges and limited high-value samples. Application to Aquistore downhole CMR data reveals substantial vertical variability in predicted cutoff values (0.6\u201374 ms; average ~11 ms), with ~88% of the interval deviating from the conventional 33 ms assumption by more than 10 ms. This variability results in revised estimates of irreducible water saturation and increases effective CO2 storage capacity by ~43% over the study interval.Applications\/Significance\/Novelty: The proposed workflow provides a scalable, data-driven alternative to fixed T2 cutoff approaches and reduces long-term reliance on laboratory calibration. By capturing pore-system variability directly from T2 distributions, the method improves estimation of irreducible water saturation and effective storage capacity in heterogeneous reservoirs. Application to subsurface CMR data demonstrates field-scale applicability and highlights the limitations of conventional fixed cutoffs. The flexible dashboard developed enables rapid integration of new data and continuous model refinement, with predictive performance expected to improve as additional samples are incorporated. The approach is broadly applicable to reservoir characterization, CO2 storage assessment, and NMR log interpretation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Lowering Operational Uncertainty: Diagnostics and Integration that Improve Completion Outcomes<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tCraig L. Cipolla, Annie Shen\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Comparison of Near Field and Far Field Uniformity Metrics in the Alberta Montney<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Van Dijken*, S. Genoway, B. Gaffney and B. Hepburn\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Whitecap Resources Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The optimization of hydraulic fracture networks is critical to unlocking value in unconventional resource plays. Understanding fracture growth behavior in both the near and far field is important in this endeavor. We summarize the results of recent case studies in the Alberta Montney that compare field observations from various diagnostic techniques in both domains. These insights are used to better understand production drivers and identify opportunities for further improvement.Methods\/Procedures\/Process: Our first case study evaluates the ability of a plug-and-perf (P&amp;P) completion design to replicate the results of a single-point-entry (SPE) system in a single-bench scenario oriented on-grid. Various stage designs are investigated, from a single cluster up to five clusters. Our second case study considers the dependence of far field uniformity on well orientation (on-grid vs. on-azimuth), this time among P&amp;P completion designs in a multi-bench scenario. Our third case study investigates the role of well orientation as well, but in the context of a SPE completion design, and considers the role of operational sequencing at the same time. A combination of the following diagnostic technologies was employed to monitor stimulation uniformity in each study: 1) perforation imaging, 2) real-time frac analysis, 3) disposable fiber, and 4) microseismic.Results\/Observations\/Conclusions: The distribution of proppant in the near field, as inferred by imaging and real-time frac analysis, indicated good uniformity among the P&amp;P stages overall, with a modest reduction at higher cluster counts. However, the timing of frac hits for different stage designs in Case Study 1 formed a trend that suggests comparatively less uniformity in the far field, while also highlighting the influence of rate per entry point on fracture shape and width. A contrast was also evident in the microseismic, where SPE wells appear to achieve greater vertical penetration, which may be the product of operational sequencing but also rate per entry point. In Case Study 2, the frac hit timing data suggests that far field uniformity is influenced by effective fracture spacing, which is tighter on-grid than on-azimuth for a given stage design along the wellbore. Case Study 3 makes similar observations on wells completed with SPE, while also noting asymmetry that arises on some wells due to their order in the sequence.Applications\/Significance\/Novelty: We observe apparent discrepancies between near and far field uniformity trends, which suggests that rate per entry point, entry point spacing, well orientation, and operational sequencing can each have a notable impact on fracture growth behavior in the far field. The resulting differences in fracture shape and symmetry present opportunities for further well design optimization to effectively stimulate the far field by accounting for these effects. Depending on the benching strategy involved, this has implications on stage design and frac order when considering how to best stimulate a given reservoir both vertically and horizontally.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Emerging Visean Unconventional Play within the Dnieper Donets Basin, Eastern Ukraine: Multidisciplinary Approach from Exploration to Pilot Testing<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Levoniuk*<sup>1<\/sup>, S. Orynchak<sup>1<\/sup>, N. Garcia<sup>2<\/sup>, N. Roberts<sup>3<\/sup>, B. Kruhlov<sup>4<\/sup>, J. Hejnar<sup>5<\/sup>, V. Karpyn<sup>1<\/sup>, A. Ficarra<sup>1<\/sup> and M. Vityk<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. JSC UkrGasVydobuvannya (UGV) of NaftoGaz Group; 2. Notio Strategic Consulting; 3. Halliburton; 4. Taras Shevchenko Kyiv National University; 5. Wellfield Geoscience)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Ukraine\u2019s Dnieper Donets basin (DDB) is a first-rank Super basin with total discovered conventional reserves exceeding 11 billion barrels of oil equivalent (BBOE). Integration of extended surface &amp; wireline logging, laboratory tests from core, cuttings and reservoir gas, and regional seismic stratigraphic studies collectively point to new promising unconventional play within a thick succession of Visean marine source rocks. The sweet spot has estimated mean recoverable resources of ~9.4 BBOE. This paper aims to describe the efforts and learnings from five years of an active multidisciplinary investigation of Eastern Ukrainian emerging unconventional play that is extremely close to the full-scale war line including geology, geochemistry, geomechanics, completions and reservoir engineering.Methods\/Procedures\/Process: Since 2021, UkrGasVydobuvannya (UGV) has undertaken an extensive exploratory effort across the DDB to characterize and test potential unconventional targets in both organic-rich shales and tight carbonates V-23, V-24-25 and V-26, which are the main source rocks in the basin. The study involves the special lab analysis of core, cuttings, and reservoir gas samples from more than 50 boreholes across the basin. The research is supported by seismic reinterpretation and extended surface &amp; wireline logging. It will be used to describe sweet spot mapping, reservoir quality and geomechanical rock properties. Additionally, drilling and stimulation results, production tests from offset vertical wells will be used to describe the technical limitations and potential production opportunities.Results\/Observations\/Conclusions: The most promising basin\u2019s part for Visean unconventional exploration is the deep shelf, which is mostly located in the source rocks\u2019 optimal depositional environment and thermal maturity window VRo\u22651.0%. Here, vertical depth range is between 2000 and 5000 m. Three organically-enhanced landing targets, which are characterized by different mineralogy and facies, were studied. Geochemical characterization shows good to excellent potential for hydrocarbon generation. After testing these targets in more than 10 vertical wells, reservoir hydrocarbon presence and transmissibility, as well as overpressure, have consistently shown very promising results. Additional complications to full horizontal development are high fracture gradient combined with near wellbore complexity observed during DFITs.Applications\/Significance\/Novelty: Unconventional reservoirs are fast becoming more vital to the supply and reliability of future world energy supply. North and South America have shown that unconventionals can be developed in a sustainable and profitable way. The presented multidisciplinary case study from the DDB Unconventional play provides valuable insights that can improve exploration strategies for unconventional hydrocarbon resources in different petroleum basins around the world. Shortage of services and staff experience due to extreme proximity of the full-scale war line forced us to use non-standard methodology for this play exploration. The findings also contribute to the broader body of knowledge regarding Eastern European unconventional plays, potentially offering new frontier for energy development in Ukraine.Interdisciplinarity (Team Presentation\u2019s only): The paper marks an integration of five years of UGV efforts and learnings from active multidisciplinary study of DDB Visean promising unconventional formations in the center of Eastern Europe. Starting from exploration including discussing for geological settings, depositional environments, reservoir quality, source rocks geochemical characterization combine to result in sweet spot mapping. And continuing with geomechanical rock properties, drilling, completions, stimulation results and production tests from the offset vertical wells have resulted in an assessment of the technical limitations and potential production opportunities for three unconventional targets.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Production Analysis in Unconventional Reservoirs<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tZhenzhen Wang, Leopoldo Matias Ruiz Maraggi\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Flowback Rate Transient Analysis for Gas-Condensate Shale Reservoirs with Skins<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Yang and H. Emami-Meybodi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Penn State)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Early-time performance of hydraulically fractured gas-condensate wells can be affected by damage that develops during hydraulic fracturing and flowback. As pressure declines below the dewpoint, condensate may accumulate near the hydraulic fracture (HF) face, reducing gas and water mobility and introducing condensate-induced HF-face skin. In addition, fracturing-fluid leakoff may damage the near-HF matrix permeability, introducing leakoff-induced HF-face skin. Furthermore, during flowback, near-wellbore HF damage can occur, reducing HF conductivity and restricting flow, leading to choked-HF skin.Methods\/Procedures\/Process: In this study, a semi-analytical model is developed to analyze three-phase flowback data in gas-condensate reservoirs by incorporating condensate- and leakoff-induced HF-face skin, with a preliminary coupled-relation extension for evaluating choked-HF skin. The workflow couples three-phase flow within the HF with two-phase gas and water influx from the matrix, and estimates the accumulated oil saturation in the matrix using a CCE\/CVD-derived oil-saturation\/pressure (So-P) path. The resulting water and gas influxes are corrected using dynamic HF-face skin factors and incorporated into a multiphase flowback rate transient analysis (RTA) framework. Straight-line analysis is then used to estimate key HF properties, including HF half-length and initial HF permeability.Results\/Observations\/Conclusions: The model is validated against numerical simulation cases representing three skin mechanisms. Results show that condensate-induced HF-face skin has a limited impact on the interpreted HF properties during the analyzed flowback period, whereas leakoff-induced HF-face skin produces a stronger effect and can significantly distort RTA interpretation if ignored. When HF-face skin is accounted for, the workflow estimates dynamic phase-dependent HF-face skin and interprets average HF half-length and initial HF permeability with relative errors of &lt;10%. The choked-HF skin analysis shows that ignoring choked damage leads to an underestimation of HF permeability.Applications\/Significance\/Novelty: This work provides a practical early-time diagnostic workflow for identifying HF-face damage using routine flowback data and demonstrates that neglecting influential skin mechanisms can significantly bias the interpretation of stimulation performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fracture-Controlled Evolution of the CO2-Crude Oil Miscible Zone During CO2 Injection in Fractured Low-Permeability Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Zhang<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>, J. Yao*<sup>2<\/sup>, Y. Chen<sup>2<\/sup>, Y. Zeng<sup>2<\/sup>, F. Zhang<sup>2<\/sup> and X. Li<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Hainan Institute of China, China University of Petroleum (Beijing); 2. College of Petroleum Engineering, China University of Petroleum (Beijing); 3. State Key Laboratory of Enhanced Oil &amp; Gas Recovery, Research Institute of Petroleum Exploration &amp; Development)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The dynamic evolution of the CO2-crude oil miscible zone directly affects CO2-oil contact, effective sweep area, and oil-recovery response, making it critical for evaluating and optimizing CO2 miscible flooding in low-permeability reservoirs. In fractured wells, however, fracture geometry and conductivity can significantly alter CO2 migration pathways and fracture-matrix mass transfer, leading to distinct fracture-controlled miscible-zone evolution. This study aims to reveal the fracture-controlled evolution of the CO2-crude oil miscible zone and clarify its relationship with oil-recovery response during CO2 injection in low-permeability reservoirs.Methods\/Procedures\/Process: The proposed workflow integrates flowback-based fracture-parameter inversion with quantitative miscible-zone characterization. Fracture half-length and fracture permeability were first estimated from dynamic flowback data and then used as key inputs for constructing the fractured-well model, while a non-fractured base model was established as a benchmark. The miscible zone was identified using dual criteria based on the CO2 mole fraction in the oil phase and oil-gas interfacial tension. The front sweep coefficient, rear sweep coefficient, dimensionless miscible-zone area, and miscible-front fingering coefficient were introduced as four characteristic parameters to quantify miscible-zone evolution. A miscible-front propagation model and a staged oil-recovery response model were further developed to evaluate the relationships among front advancement, rear-boundary expansion, miscible-zone development, and recovery performance.Results\/Observations\/Conclusions: Numerical validation showed that the relative errors of the inverted fracture half-length and fracture permeability were less than 10%, confirming the reliability of the proposed inversion method. The results show that high-conductivity fractures significantly accelerate miscible-front advancement and promote early miscible-zone development. However, fractures also enhance rear-boundary expansion during the middle-to-late injection stage, causing the dimensionless miscible-zone area to peak earlier and decline more rapidly after breakthrough. As a result, the displacement process changes from relatively uniform propagation in the base model to heterogeneous expansion dominated by fracture-controlled flow channels in the fractured-well model. Sensitivity analysis indicates that lower gas injection rates favor the formation and maintenance of the miscible mass-transfer transition zone, whereas oil recovery in the fractured-well model is less sensitive to injection-rate variation because fractures enhance CO2 transport and fracture-matrix exchange. Higher CO2 purity improves oil recovery in both models by promoting miscible-zone development and increasing the peak dimensionless miscible-zone area. In contrast, reduced CO2 purity weakens effective miscibility, although it may slightly promote rear-boundary expansion during the middle-to-late injection stage.Applications\/Significance\/Novelty: By linking flowback-derived fracture parameters with fracture-controlled miscible-zone evolution, this study integrates fracture-parameter inversion, miscible-zone characterization, and oil-recovery response analysis within a unified framework. The proposed approach quantitatively characterizes front advancement, rear-boundary expansion, miscible-zone development, and recovery response under fracture-controlled flow conditions, providing theoretical support for the design and optimization of CO2 gas-injection development schemes in fractured low-permeability reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Simple and Accurate Density-Based Method for Analysis of Production and Bottomhole Pressure Data from Multistage Hydraulically Fractured Horizontal Dry Gas Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Tanveer* and M. Onur\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Tulsa)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper presents a general density-based method to estimate formation properties and pore volume in multistage hydraulically fractured unconventional dry gas reservoirs using rate and bottomhole pressure data. A mass-rate normalized density function plotted against material balance time on a log-log scale reveals transient flow regimes (bilinear, linear, radial) and the unit-slope line in boundary-dominated flow. Straight-line analysis provides flow capacity and skin factor without complex superposition. A Cartesian plot of the same function versus material balance time yields pore volumes from the slope and productivity from the intercept. Unlike conventional flowing material balance methods, this approach avoids iterative reservoir pressure estimates and simplifies the workflow.Methods\/Procedures\/Process: Field BHP and rate data often contain errors, making mass-rate- or volumetric-rate-based material balance time non-monotonic and complicating log-log model identification. A monotonic ordering scheme enables derivative-based identification. Flow regimes are then analyzed using nonlinear regression to estimate formation properties, contacted pore volume, and productivity index from routine production and gas property data. Least absolute value (LAV) regression mitigates outliers, improving stability in noisy datasets. The density-based method is validated on synthetic and field unconventional gas reservoir data and compared with the conventional FMB approach using both rate-normalized pseudo pressure with material balance pseudo time and the proposed density-based formulation.Results\/Observations\/Conclusions: The density-based method provides a faster, simpler, and equally accurate alternative to conventional approaches, making it suitable for unconventional reservoirs with variable flow regimes. Analysis using density-based or real gas pseudo-pressure data against mass- or volumetric-rate material balance time for transient data avoids flow regime-specific superposition functions. In boundary-dominated flow, plotting a mass-rate normalized density function against mass-rate material balance time removes the need for iterative initial pressure estimation and yields pore volume estimates as accurate as conventional FMB analysis.Applications\/Significance\/Novelty: To the best of our knowledge, this is the first study proposes a general, yet simple rate transient analysis method for analyzing rate and bottomhole pressure data acquired from multistage hydraulically fractured wells producing in unconventional dry gas reservoirs. So, it is novel. It simplifies the analysis of data by elimating the use of flow regime specific superposition function time for transient data sets (bilinear, linear, or radial if it is observed), and the iterative procedure on average pressure unlike the Agarwal et al. method introduced in 1990s for boundary dominated flow data.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: AI-Enhanced Well Planning, and Completion Design and Optimization<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tPeyman Moradi, Prithvi Chauhan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">From Data to Design: MVA Modeling for Smarter Well Planning in the Delaware Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Li*, X. Zhao, A. Brehm, P. H. Nguyen, S. Esmaili and R. Gordillo\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Optimizing completion design is critical for enhancing unconventional reservoir performance, yet it remains challenging due to geological complexity and variability. This study introduces a novel, fracture\u2011informed multivariate analysis (MVA) model that enables virtual experimentation of diverse well spacing and completion strategies. The approach delivers actionable insights to maximize production and reduce costs, eliminating the need for costly, time\u2011intensive field trials. Applied to the Delaware Basin, particularly in undeveloped acreage, the model provides rapid, computation\u2011driven guidance to optimize well spacing and completion designs with greater precision and efficiency.Methods\/Procedures\/Process: Data-driven models are developed to predict 2-year cumulative oil production for 10,000+ wells in the Delaware Basin, integrating geological and completion parameters. Geological data originated from a basin\u2011wide model, with fracture geometry incorporated as a primary driver. A novel workflow incorporates fracture height data from a specialized identification tool, using weighted averages across height and along the lateral to reflect fracture\u2011driven production conditions. This approach delivers higher accuracy than landing\u2011bench data alone. The two\u2011phase workflow builds an initial public\u2011data model to analyze proppant loading, fluid loading, and spacing impacts, followed by an internal-data model with more granular completion design data for economic optimization.Results\/Observations\/Conclusions: Multiple machine-learning algorithms are tested, with targeted feature engineering and hyperparameter tuning applied to boost accuracy and robustness. Sensitivity analyses examined well spacing, proppant loading, and fluid loading. The model is then applied to undeveloped drilling spacing units (DSU) across various benches, with geological uncertainty captured by populating each DSU with stochastic wells. This workflow produces a range of predicted outcomes for each DSU, reflecting realistic scenarios. Cost optimization is performed by feeding the model varying completion designs, integrating economic parameters to support return on investment (ROI)\u2011focused decisions.Applications\/Significance\/Novelty: The novelty of this study lies in incorporating fracture height data to achieve a more comprehensive characterization of geological properties. Well performance is influenced not only by the landing bench but also by adjacent upper and lower benches intersected by fractures. A second innovation is the application of the model to undeveloped DSUs, enabling rapid, forward\u2011looking analyses to support business decisions. Geological uncertainty is quantified for each DSU by deriving property distributions from basin\u2011wide models, ensuring realistic, scenario\u2011based predictions. Beyond production forecasting, the model supports completion\u2011design economic optimization, providing fast, data\u2011driven guidance to maximize return on investment.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">SURPRISE Functions! Real-time Adaptive Completion Designs Based on Physics Informed Machine Learning<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. de Boer*<sup>1<\/sup>,<sup>2<\/sup>, T. Szilagyi<sup>1<\/sup>, I. Zaghmoot<sup>3<\/sup> and H. Merry<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Shear FRAC Group, LLC; 2. University of Toronto; 3. Arrington Oil &amp; Gas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work demonstrates how high-resolution time-series data with advanced machine learning techniques applied to surface treating pressure data, transforms hydraulic-fracture diagnostics from stage-level summaries into continuous, physics-informed measurements. The objectives are to quantify fracture behavior at one-second, or higher resolution, enhance predictive design capability, and support real-time operational adjustments. This paper contains an exemplary case study across a multi-pad ~70 well dataset in the Permian Basin with Arrington Oil &amp; Gas. The data is evaluated to assess accuracy, generalization, and impact on completion performance as the first step towards closed loop automations\u2026 in more simplistic terms this workflow removes the pumps from surface pressure measurement as allows us to \u201clisten to the rock\u201d fracturing via a surface pressure transducer; which is hydraulically coupled to each wellbore.Methods\/Procedures\/Process: A multi-pad dataset containing 70 wells and 4,612 assembled stage bundles was analyzed. Treating pressure, slurry rate, proppant information, stage context, 1Hz and 50Hz high-resolution pressure spectrograms, and joined production data were assembled into a single structured database. Stage-level design was first evaluated as a baseline. A compact neural forecasting model was then trained to predict near-future pressure-spectral response from prior pressure-spectral history and active pumping controls. Pressure based event-rate descriptors were compared with production outcomes in eligible joined subsets in order to remove pumping artifacts from the datasets and quantify the signal coming from the rock only.Results\/Observations\/Conclusions: Stage-level design summaries showed limited production-facing rank agreement, while pad groupings indicated that trends remained after design-only adjustment. The model improved on a persistence forecast, with an overall Root Mean Squared Error (abbreviated as RMSE within) ratio of 0.899 and a 19.1% squared-error reduction relative to persistence across held-out validation rows. By removing the predicted spectrogram from the actual spectrogram, the Authors generate a residual spectrogram, which they have termed the Surprise Function. In simple terms, this function displays how much the model deviates from the actual data. However, as only pumping rate and proppant is provided to the model, the Surprise function is a manifestation of the rocks response in real time, or the chaos measured by the system due to hydraulically coupled surface pressure gauges. Aggregate surprise showed a descriptive production trend: at 6 months, the top surprise third averaged about 1.2 times above the bottom third in 35 eligible wells. Design-adjusted high-surprise event rate also showed positive rank association with 6-month production residuals.Applications\/Significance\/Novelty: This approach provides a scalable method to integrate Machine Learning with physics-based signal analysis for both pre-job design selection and in-stage adaptive control. It increases dataset density from tens of points per well in stage level analytics to hundreds of thousands by analyzing time-series data, enabling robust predictive modeling previously unattainable with stage-level summaries. The workflow is the primary building block for Closed-Loop automated decision-making, optimized treatment allocation, and improved field-wide development planning, illustrating a significant step toward intelligent, data-driven completions. Surprise!\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">AI-Driven Fluid and Proppant Optimization for Unconventional Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Katterbauer and A. W. Alsmaeil*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents an AI-driven system for Fluid and Proppant Optimization designed to maximize estimated ultimate recovery (EUR) while minimizing stimulation cost in unconventional reservoir completions. Motivated by increasing development heterogeneity within the Permian Basin, the objective is to create robust, data-driven models that determine optimal fluid volumes, fluid blend types, and proppant loading strategies tailored to local geologic and completion contexts. The system seeks to outperform traditional rule-of-thumb engineering heuristics by integrating high-resolution operational and production data with advanced machine-learning workflows.Methods\/Procedures\/Process: The workflow integrates a Permian Basin dataset containing multi-operator completion designs, stage-level parameters, geologic attributes, and production metrics. Gradient-boosted trees, probabilistic regression models, and surrogate-physics networks were trained to capture nonlinear interactions between fluid selection, proppant intensity, and reservoir quality. A multi-objective optimization engine\u2014combining Bayesian optimization with cost-constrained search\u2014was then applied to identify optimal fluid- proppant combinations for specific geologic clusters. Model performance was validated using cross- basin blind wells and perturbation sensitivity analyses to quantify uncertainty and assess operational robustness.Results\/Observations\/Conclusions: The AI system consistently identified design strategies that delivered good predicted EUR uplift at equal or reduced stimulation cost compared with historical offsets. Optimization outputs showed clear spatial variations: in higher-pressure, higher-quality zones, the models favored increased proppant intensity with moderate fluid volumes, while in lower-quality intervals, reduced fluid loading and hybrid fluid blends produced superior cost-normalized returns. Sensitivity analyses demonstrated stable model behavior across a wide range of operational constraints, and blind-well tests showed strong predictive accuracy relative to engineering baselines.Applications\/Significance\/Novelty: This study introduces a unified, multi-objective AI framework that simultaneously optimizes fluid volumes, fluid types, and proppant loading\u2014dimensions typically evaluated independently in conventional completion design. By combining large-scale Permian Basin data, geologically aware machine-learning models, and automated optimization under cost constraints, the system enables more granular, pad-specific stimulation strategies. The approach advances data-driven completion engineering by offering an operationally deployable tool capable of quantifying uncertainty, reducing design bias, and uncovering non-intuitive interactions that influence well performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Student Poster\" style=\"border-top: 4px solid #ff6348;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Student Posters: Sustainability, CO\u2082 Sequestration, and Advanced Stimulation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Booth 121\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:00 AM &#8211; 11:00 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tKaterina Yared, Hosein Kalaei, David Hume\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Study on CO2 Miscible Displacement Mechanisms and Injection Optimization in Ultra-Low Permeability Reservoirs Using Integrated Core Flooding Experiments and Numerical Simulation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Sun*<sup>1<\/sup>,<sup>2<\/sup>, L. Li<sup>1<\/sup>,<sup>2<\/sup>, X. Wang<sup>3<\/sup>,<sup>4<\/sup>, Q. Liu<sup>1<\/sup>,<sup>2<\/sup>, X. Bian<sup>1<\/sup>,<sup>2<\/sup>, Z. Chen<sup>1<\/sup>,<sup>2<\/sup> and Y. Chen<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State Key Laboratory of Deep Oil and Gas, China University of Petroleum; 2. School of Petroleum Engineering, China University of Petroleum; 3. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum; 4. Institute of Unconventional Oil and Gas Science and Technology, China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This research aims to systematically investigate the oil recovery mechanism of carbon dioxide miscible flooding in ultra-low permeability reservoirs (with a permeability range of 0.1 - 1 mD) and the optimization methods of injection and production parameters. The research scope includes fine tube experiments for determining the minimum miscible pressure, a series of core oil recovery experiments under reservoir conditions combined with nuclear magnetic resonance testing, and numerical simulation at the core scale. The research results will provide theoretical basis and technical guidance for enhancing crude oil recovery in unconventional ultra-low permeability reservoirs.Methods\/Procedures\/Process: This study aims to systematically investigate the CO2 miscible flooding process in ultra-low permeability oil reservoirs (with a permeability range of 0.1 - 1 mD). The primary step is to determine the minimum miscible pressure of CO2 and crude oil through fine-tube experiments. Then, by combining core-scale experiments with numerical simulations, the oil recovery efficiency of pure CO2 flooding, single water flooding, water-alternating-gas flooding (WAG), and gas-alternating-water flooding (GAW) under the condition of miscibility is quantified; the injection and production parameters such as water injection sequence and segmental plugging size are optimized; and a core-scale numerical simulation model based on CMG-GEM is established to verify the experimental results.Results\/Observations\/Conclusions: The minimum miscible pressure for CO2 flooding in a certain oilfield of a very low-permeability reservoir is 20.6 MPa. Under reservoir conditions, miscibility can be achieved. The WAG recovery rates in the experiments were all between 55% and 70%. The simulation results corresponded to the experimental results, verifying the accuracy of the study. The water section plug in WAG inhibits gas migration, while the CO2 section plug improves the microscopic oil recovery efficiency, achieving synergistic enhancement. Under certain conditions, the number of section plugs is proportional to the recovery rate. The injection sequence affects the degree of crude oil recovery. Within a certain injection volume range, injecting gas first and then water is superior to injecting water first and then gas.Applications\/Significance\/Novelty: Clarify the oil recovery mechanism of CO2 miscible flooding in ultra-low permeability oil reservoirs and the gas-water synergy mechanism, and improve the theory of enhanced oil recovery in unconventional oil reservoirs. Provide optimized CO2 injection parameters for similar oil reservoirs, supporting pilot tests and industrialization promotion. By optimizing the injection parameters, the recovery rate of crude oil can be increased (expected to be improved by 15% - 25%), while reducing the risk of gas migration and operational costs. Realizing the synergy between efficient geological storage of CO2 and increased crude oil production can provide a key technical path for the green transformation of oil fields.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">CO2-Foam and Nanofluid Hydraulic Fracturing for Sustainable Unconventional Reservoir Production<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Kumar*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Rajiv Gandhi Institute of Petroleum Technology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In order to find sustainable alternatives to traditional water-based hydraulic fracturing in unconventional shale reservoirs, this study will examine CO2-foam and nanoparticle-enhanced fracturing fluids. The goal is to comprehend how CO2-foam can improve reservoir contact, minimize formation damage, and drastically reduce water consumption. Examining how well nanoparticles stabilize CO2-foam, enhance viscosity, lower leakoff, and facilitate proppant transport is part of the scope. Additionally, the study assesses how these advanced fluids can enhance fracture complexity, boost stimulated reservoir volume, and support more ecologically friendly hydrocarbon production.Methods\/Procedures\/Process: The performance of CO2-foam and nanoparticle-enhanced fracturing fluids is examined in this work through simulation studies and laboratory experiments. To comprehend fluid behavior under reservoir conditions, experimental data on foam stability, rheology, foam quality, and proppant suspension are examined. The ability of nanoparticles like silica, alumina, and iron oxide to stabilize CO2-foam, minimize fluid loss, and preserve structure during fracture propagation is assessed. Fracture geometry, fluid leakoff, flowback behavior, and production response are all modeled numerically. Performance gains and operational benefits are highlighted by comparison with slickwater designs.Results\/Observations\/Conclusions: .Results from literature and simulations show that CO2-foam can cut water use by up to 70 to 90%. It also improves fracture complexity and significantly lowers the risk of water blockage in shale formations. Nanoparticles improve foam durability by increasing film strength and slowing drainage. This leads to better proppant-carrying capacity and less leakoff. Simulations indicate a higher stimulated reservoir volume and better control over fracture height compared to traditional fluids. The study concludes that using CO2-foam with nanofluids provides a promising, efficient, and lower-impact stimulation technology. This method can enhance both early and long-term production.Applications\/Significance\/Novelty: This research shows a new fracturing method that supports sustainability by decreasing the need for freshwater, reducing chemical use, and allowing for some CO2 storage in the reservoir. This technique is especially helpful in areas with limited water supplies where slickwater fracturing is difficult. The innovation comes from combining nanoparticles with CO2-foam to form a more stable, thick, and effective fracturing fluid. This fluid improves fracture growth, proppant placement, and overall reservoir output. This method offers a fresh route for cleaner and more effective stimulation of unconventional formations and backs future low-carbon energy development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">1D Geomechanical Modeling for HPHT Onshore Wells Near a Salt Structure: Impacts on Stress Prediction and Drilling Optimization<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. E. Sanchez Zuniga*<sup>1<\/sup>, O. Castillo Castillo<sup>2<\/sup>, L. Sanchez Guillen<sup>1<\/sup> and H. J. Sanchez<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Universidad Nacional Autonoma de Mexico; 2. Pemex; 3. SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study develops a 1D Mechanical Earth Model (MEM) for HPHT onshore wells drilled near a salt dome, where narrow drilling windows and stress anisotropy introduce significant wellbore stability risks. The objective is to characterize pore pressure, stress magnitudes, and rock mechanical properties, and to assess how salt-related deformation affects critical drilling decisions. The work provides a transferable workflow for complex evaporite-influenced environments.Methods\/Procedures\/Process: A VCDSE workflow\u2014Validation, Calibration, Data Screening, Simulation, and Evaluation\u2014was applied to integrate petrophysical logs, offset-well behavior, drilling parameters, and real-time monitoring into a unified 1D geomechanical model. A full pre-drill, real-time geomechanics, and post-mortem sequence refined pore pressure, and horizontal stresses were constrained through poroelastic modeling and calibrated with breakouts, induced tensile fractures, image-log features, and instability indicators. Near the salt flank, iterative updates captured stress rotation, anisotropy, and thermoviscoplastic deformation. Salt characterization and fault-leakage analysis reduced uncertainty and improved the prediction of stability limits and mud-weight windows.Results\/Observations\/Conclusions: As the trajectory approached the salt body, the well response shifted markedly, revealing strong stress reorientation, sharp lateral pressure contrasts, and pronounced thermoviscoplastic creep driven by the advancing flank. These effects compressed the viable mud-weight envelope, increased the likelihood of stuck pipe, pack-off, and wellbore breathing, and forced revisions to casing depth and trajectory risk assessments. The updated MEM successfully reproduced the depth, severity, and timing of these instability manifestations, enabling proactive mitigation during drilling and confirming the model\u2019s value as a decision-critical tool for HPHT wells in dynamic evaporitic systems.Applications\/Significance\/Novelty: This work shows how an integrated geomechanical workflow strengthens decision-making in salt-affected HPHT wells by quantifying stress evolution, deformation mechanisms, and time-dependent creep essential for defining stable trajectories and safe mud-weight strategies. The resulting MEM improved planning and contributed to a 30% reduction in NPT by anticipating instability-prone intervals and guiding real-time adjustments. Beyond drilling optimization, the workflow also supports CCUS and H2 storage by characterizing salt sealing behavior and long-term deformation. Notably, this student-led approach is innovative in Mexico, where CO2 sequestration and advanced geomechanical evaluations are still emerging, highlighting its potential for future subsurface carbon-management initiatives.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Water-Avoidance Stimulation Design in Ultra-Deep Fractured Tight Sandstone Reservoirs \u2014 Integration of Water-Control Proppant Experiments and Fracture Simulation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Wang*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Water invasion from strong edge aquifers is a major factor reducing gas productivity in ultra-deep fractured tight sandstone reservoirs. This study develops an integrated water-avoidance stimulation methodology combining experimental evaluation of water-control proppants with numerical fracture propagation modeling to guide hydraulic fracturing design.Methods\/Procedures\/Process: Laboratory tests were conducted to determine water-control height, duration, and fracture conductivity using gas-permeable water-control proppants with different coating types and particle sizes. NMR spectroscopy characterized fluid distribution during gas\u2013water coflow. A 3D geomechanical model coupled with fracture propagation simulation was employed to analyze the effects of injection rate, fluid viscosity, and temporary plugging on stimulation performance and water suppression efficiency.Results\/Observations\/Conclusions: Results indicate that coated water-control proppants can maintain high gas-phase conductivity while effectively limiting water channeling. Moderate increases in fluid viscosity and optimized injection rates improve water-blocking efficiency without compromising fracture connectivity. Simulation results validated the experimentally derived water-avoidance parameter maps for different water-invasion stages.Applications\/Significance\/Novelty: This study establishes a novel water-avoidance fracturing framework integrating laboratory validation and numerical design. By coupling water-control mechanisms with fracture propagation behavior, it provides quantitative guidance for optimizing fracturing parameters, proppant selection, and stage design in ultra-deep gas reservoirs affected by water invasion.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Hydraulic-Fracturing Parameter Optimization in Ultra-Deep Tight Sandstone Reservoirs Based on Coupled Understanding of Fracture-Controlled Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Wang*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Ultra-deep tight sandstone reservoirs are predominantly characterized by natural fractures which critically serve as both primary storage spaces and essential flow pathways. This study aims to develop a systematic hydraulic-fracturing parameter design methodology that effectively integrates the geological understanding of these fracture-controlled reservoirs with advanced fluid-mechanical coupling principles. The ultimate objective is to significantly enhance stimulation effectiveness and optimize reservoir connectivity, thus significantly enhancing the recovery of crude oil from the formation.Methods\/Procedures\/Process: A three-dimensional geomechanical model of the Dibei Block (Tarim Basin, China) was established to simulate fracture propagation during hydraulic-fracturing. The effects of injection rate, fluid viscosity, fracturing liquid volume and proppant concentration on fracture extension and diversion were analyzed using numerical modeling. Additional simulations evaluated the combined application of gas-permeable water-control proppants and high-strength ceramics to improve gas production while suppressing water breakthrough.Results\/Observations\/Conclusions: Results indicate that fracture propagation behavior in ultra-deep wells is highly sensitive to fluid viscosity and rate balance. Moderate increases in injection rate and fracturing liquid volume, coupled with mid-to-high viscosity fracturing fluids, significantly enhance fracture penetration and network connectivity within fracture-dominated reservoirs. The optimized proppant system improves conductivity and selectively reduces water phase mobility.Applications\/Significance\/Novelty: This work establishes a parameter optimization framework for ultra-deep hydraulic-fracturing that moves beyond conventional conductivity-driven design. By coupling reservoir-scale fracture characterization with operational parameter simulation, it provides a comprehensive methodology for enhancing gas recovery and managing water production in fracture-dominated tight sandstone reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Student Poster\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Re-Fracturing Parameter Optimization in Ultra-Deep Tight Sandstone Reservoirs Based on Coupled Understanding of Fracture-Controlled Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Wang*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Ultra-deep tight sandstone reservoirs are dominated by natural fractures that serve as both storage and flow pathways. This study aims to develop a systematic re-fracturing parameter design methodology that integrates geological understanding of fracture-controlled reservoirs with fluid\u2013mechanical coupling to enhance stimulation effectiveness and reservoir connectivity.Methods\/Procedures\/Process: A three-dimensional geomechanical model of the Dibei Block (Tarim Basin, China) was established to simulate fracture propagation during re-fracturing. The effects of injection rate, fluid viscosity, and temporary plugging on fracture extension and diversion were analyzed using numerical modeling. Additional simulations evaluated the combined application of gas-permeable water-control proppants and high-strength ceramics to improve gas production while suppressing water breakthrough.Results\/Observations\/Conclusions: Results indicate that fracture propagation behavior in ultra-deep wells is highly sensitive to fluid viscosity and rate balance. Moderate increases in injection rate, coupled with mid-to-high viscosity fracturing fluids and staged temporary plugging, significantly enhance fracture penetration and network connectivity within fracture-dominated reservoirs. The optimized proppant system improves conductivity and selectively reduces water phase mobility.Applications\/Significance\/Novelty: This work establishes a parameter optimization framework for ultra-deep re-fracturing that moves beyond conventional conductivity-driven design. By coupling reservoir-scale fracture characterization with operational parameter simulation, it provides a comprehensive methodology for enhancing gas recovery and managing water production in fracture-dominated tight sandstone reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Beyond Assumed Fracture Geometry: Insights from Fracture Surveillance<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:45 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Jiehao Wang\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Design, Implementation, and Key Insights from the Qincheng Shale Oil Hydraulic Fracturing Test Field<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Mu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(CNPC Changqing Oilfield Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Design, Implementation, and Key Insights from the Qincheng Shale Oil Hydraulic Fracturing Test Field<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tW. Yu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SimTech)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Large-Scale Bedding Plane Slippage and Its Impact on Hydraulic Fracturing: Integrated Analysis from Field Observations in the Eagle Ford and Austin Chalk Formations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Jin*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Decoding the Fracture Flow Puzzle: Tracer-Based Insights from Utah FORGE Reveal Complex Flow-Path Behavior in Hydraulically Fractured System<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. N. Fredd*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Ignovis)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Emerging Unconventional Plays and Case Studies<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tHaijing Wang, Yalda Barzin\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Is the Western Haynesville a New Emerging Natural Gas Play in Texas?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tO. Popova*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(EIA DOE)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As U.S. demand for natural gas increases\u2014driven by the expansion of liquefied natural gas (LNG) export projects and the rapid growth of data centers\u2014the Western Haynesville has emerged as a potential key contributor to future supply. Early results from the region are promising: wells in the Western Haynesville have demonstrated productivity levels twice that of the median legacy Haynesville well. However, this performance comes at a cost\u2014drilling and completion (D&amp;C) expenses are approximately three times higher. The long-term viability of the play will depend on operators\u2019 ability to reduce costs while maintaining or enhancing well productivity. Located in Texas\u2019s Robertson, Freestone, and Leon counties, the area has seen approximately 60 wells drilled over the past four years.Methods\/Procedures\/Process: The Western Haynesville\u2019s combination of high costs and high productivity is largely driven by its unique geology. Well observation data show that in this far western extension of the Haynesville and Bossier shales, reservoirs are located at true vertical depths of approximately 17,000 feet or more, characterized by extreme bottomhole temperatures and significant overpressure. Although the region has a history of hydrocarbon development, the complex geological conditions at these depths previosly rendered it economically unviable until Comstock initiated its drilling program in 2021.Results\/Observations\/Conclusions: Type curves by vintage in the Western Haynesville reveal significantly higher initial productivity and shallower decline rates compared to those in the legacy Haynesville. The 2022 type curve displays temporary dips in output due to production exceeding local midstream capacity, which necessitated shut-ins. However, volumes quickly rebounded with the average 2022 well sustaining an impressive 15 Million cubic feed per day (MMcfd) over one year of production. The 2023 and 2024 vintages have continued to deliver strong performance, with initial production rates exceeding 25 MMcfd. To that end, 60 wells have been drilled in the Western Haynesville. Production has surged from zero in 2022 to 500 MMcfd as of August 2025. Output has increased by 50% since December 2024.Applications\/Significance\/Novelty: As with any emerging play, scalability and economic viability remain the central considerations. Although initial productivity has been strong, it is still uncertain whether such performance can be sustained. Assuming well performance remains in line with the 2022\u20132023 average type curve, a typical D&amp;C cost of $35 million would require a Henry Hub price of $4.60 per million British thermal units (MMBtu) to achieve breakeven at a 20% discount rate. Even with cost reductions to around $30 million per well, breakeven economics still demand natural gas prices above $4.00\/MMBtu.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Application of Multiple DFIT-FBA Tests to Evaluate Inter-Bench Stress Contrasts, Reservoir Properties and Communication Risk Between Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Haqparast* and C. Clarkson\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Calgary)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Inter-well communication between multi-fractured horizontal wells (MFHWs) is now commonly observed in unconventional reservoir development scenarios. DFIT-FBA is a new DFIT design that allows for rapid (typically 3-4 hours) estimation of in-situ stress and reservoir properties through application of rate-transient analysis to the flowback portion of the test. Historically, it has been demonstrated that DFIT-FBA can be applied at the toe of two adjacent wells completed in the same bench (reservoir layer). In this study, it is demonstrated (using simulation) that DFIT-FBA performed in laterals completed in separate benches can be used to evaluate inter-bench stress and reservoir properties and the risk of inter-well communication.Methods\/Procedures\/Process: A coupled hydraulic fracturing\u2013reservoir simulator is used to simulate two DFIT-FBA tests performed at the toe of two vertically-offset horizontal wells completed in separate benches. The test performed in the first well corresponds to a conventional single-cycle DFIT-FBA test (pump in to create and propagate a fracture followed by flowback); the test performed in the second well corresponds to a multi-cycle DFIT-FBA (multiple cycles of injection\/flowback with progressively increasing injection volumes). For the latter, each cycle results in greater height growth of the fracture. Each DFIT-FBA test is analyzed for stress, reservoir properties, and fracture dimensions, while vertical communication is assessed by monitoring pressure responses in the first well (with a single-cycle test).Results\/Observations\/Conclusions: The simulated two-test procedure demonstrates that inter-well communication can be detected in the observation well for certain (single- and multi-cycle) test designs, depending on stress contrast and\/or presence of stress barriers between layers. The poroelastic effect appears in the monitoring well for certain scenarios, while minimum in-situ stress and reservoir properties can be obtained from both tests. When the two tests indicate small stress contrast and\/or absence of stress barrier between layers\/benches, the risk of inter-well communication is deemed to be greater for the main stimulation stages in vertically-offset wells. The two-test approach using different DFIT-FBA designs is demonstrated to be an effective approach for evaluating inter-well communication risk between benches.Applications\/Significance\/Novelty: For the first time, it is demonstrated that DFIT-FBA, developed to provide rapid estimates of minimum in-situ stress and reservoir properties, can be used to evaluate the risk of communication between MFHWs completed in separate benches, which is frequently observed in modern multi-bench development scenarios. For this purpose, it is proposed that single- and multi-cycle DFIT-FBA tests be performed at the toe (or other sections of the wells) in vertically-offset laterals (within 1 day) before the main stimulation treatment of both wells. This approach can help operators optimize the main stimulation treatment design and mitigate communication between wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Unveil Gas Potential in Abnormally Low Resistivity Shales by NMR T1-T2 Measurements, Contributing to China&#039;s Shale Gas Production Record<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Hou<sup>1<\/sup>, L. Wang<sup>1<\/sup>, K. Li*<sup>2<\/sup>, S. Chen<sup>2<\/sup>, D. Yu<sup>2<\/sup> and L. Cai<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. PetroChina Southwest Oil and Gasfield Company; 2. SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The deep Cambrian Qiongzhusi shale with a burial depth of more than 3500m has emerged as a highly prospective target for future shale gas exploration and development within the Sichuan Basin, China. High-yield gas flows have been achieved in some early fractured wells. However, the pattern of shale gas enrichment remains ambiguous, and even low-resistivity shales have been drilled. This paper aims to demonstrate the direct assessment of the gas-bearing characteristics of Qiongzhusi shale using nuclear magnetic resonance (NMR) T1-T2 logging data and presents a comprehensive workflow for shale reservoir evaluation that integrates multiple wireline logging techniques.Methods\/Procedures\/Process: The water saturation estimation by Archie\u2019s equation has relatively high uncertainty in shale, due to the diverse mineralogy, intricate pore structures, and the presence of kerogen. NMR measurements are free from mineralogy and kerogen, therefore offering a more precise determination of porosity. Furthermore, two-dimensional T1-T2 NMR measurements enable the quantitative assessment of different fluid component volumes within pores and their respective saturations. As such, the gas-bearing properties of low-resistivity Qiongzhusi shale can be accurately determined based on 2D NMR logging data standalone.Results\/Observations\/Conclusions: The 2D NMR data showed high gas-bearing porosity and relatively low water saturation in the target shale with very low resistivity ranging from 0.5 to 8 ohm.m. The subsequent well-test performance set a record for China&#039;s shale gas production, which validates the reliability of NMR T1-T2 logs. By integrating nuclear spectroscopy and electrical image logs, the root cause of the abnormally low resistivity shale can be determined, which is the high maturity of kerogen. However, the gas-bearing capacity of the shale has not been significantly compromised, primarily due to its considerable burial depth and superior preservation conditions. This paper introduces a direct and robust deep shale evaluation workflow that combines multiple wireline measurements.Applications\/Significance\/Novelty: This manuscript underscores the innovative application of NMR T1-T2 logs in deep shale gas reservoirs, which provide more accurate gas-bearing property assessments when the resistivity-based method suffers from high uncertainties due to complex mineralogy and the presence of kerogen. The integrated evaluation workflow facilitates faster and more confident decision-making for operators engaged in shale gas exploration and development. Meanwhile, this technology can also be applied to other types of reservoirs, such as shale oil and tight sandstone reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: What\u2019s in Your Rock: Geological and Geophysical Integration that Impact Business Decisions and Capital Efficiency<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMarianne Rauch, Liwei Cheng\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fracture Modeling and Enhanced Oil Recovery, in the Chinguetti Offshore Reservoir: Mauritanian Coastal Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. M. Bah*<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Boumerdes University; 2. Nouakchott University (Faculty of Sciences and Technology); 3. School of Mining -Oil &amp; Gas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study investigates productivity challenges in the Chinguetti offshore reservoir caused by fracture-driven compartmentalization and complex fault\u2013salt interactions affecting pressure support and waterflood efficiency. It also evaluates seismic-to-well mismatches in the MCN5-S20 zones (A, B, C) that lead to inaccurate reservoir characterization and suboptimal well placement. Using integrated modelling, the study aims to explain production decline, pressure drop, rising water cut, and propose optimized strategies for enhanced oil recovery.Methods\/Procedures\/Process: This study uses data from the Chinguetti reservoir modeling project (2008\u20132014) based on three wells\u2014Chinguetti 4-5, 4-2, and 4-3\u2014compiled by PETRONAS and stored at the Ministry of Energy and Petroleum, including seismic, petrophysical, structural, and production datasets. Seismic sections generated in PETREL from SEG-Y data enabled visualization of faults and salt bodies, while fractures in Chinguetti-4-2 were characterized through core observations, imaging logs, FMI calibration, and goniometer measurements. Porosity and permeability were derived from GR, Caliper, Sonic, and Density logs, with correlations analyzed using Python scatter plots. Water-injection performance and inter-well connectivity were further assessed using chemical tracers and simulated through the GAP software.Results\/Observations\/Conclusions: Turbiditic channel sandstones dominate the reservoir, influenced by growth faults and Miocene toe thrusts above long-lived diapirs. Massive sands exhibit 24\u201330% porosity and permeability up to 4 Darcy (0.32\u20132660 mD core), while laminated sands show 16\u201324% porosity and &lt;10 mD permeability. Net-to-gross ratios range from 45\u201350% in massive intervals to 10\u201330% in laminated zones. Water saturation (Swi) ranges from 10\u201315%, with marked lateral heterogeneity. Fracture density reaches 22 fractures\/m at 2389.5 m (average ~0.4 fractures\/cm). Dominant sets trend SW\u2013NE and NNW\u2013SSE, with secondary NE\u2013E and ENE\u2013WSW orientations. DST tests yielded 1560 BOPD, 650 Mscf\/d, GOR ~1300 scf\/bbl, API 41\u00b0. Field production peaked at ~75,000 BOPD but declined rapidly....Applications\/Significance\/Novelty: This study provides the first integrated technical evaluation of the Chinguetti Offshore Field by combining 3D seismic interpretation, petrophysical modeling, fracture characterization, and dynamic EOR performance. Results demonstrate that a long-lived Miocene\u2013Pliocene salt diapir is the primary structural driver creating extensive radial faulting and strong reservoir compartmentalization, confirmed by a 59 m seismic\u2013to\u2013well depth mis-tie and multi-level Oil\u2013Water Contacts. Geological and petrophysical analysis identifies heterogeneous turbiditic channel sandstones with porosity ranging from 16\u201330% and permeability spanning millidarcy to multi-Darcy scales, while fracture analysis reveals that open natural fractures are largely absent within the MCN5\u2013S20 reservoir interval.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Repeatable Integrated Workflow to Differentiate Fault Activation Outcomes in Structurally Complex Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Stocking*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Ubiterra Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents a repeatable, multidisciplinary workflow for evaluating fault activation behavior in structurally complex reservoirs, with application to the Austin Chalk of South Texas. Development in these environments is challenged by the interaction between hydraulic fractures and pre-existing faults, which can either compromise wellbore integrity or enhance stimulation performance. The objective is to demonstrate how integrated geologic and completions diagnostics can be used to differentiate these outcomes and guide operational decision-making to maximize productivity while minimizing deformation.Methods\/Procedures\/Process: The workflow integrates pre-frac seismic interpretation, including symmetry volumes, with resistivity image-log-derived fracture characterization and geosteering interpretation to identify fault orientation, continuity, and shear potential relative to SHmax. These interpretations are combined with real-time frac diagnostics such as fracture-driven interaction (FDI), frac gradient trends, and poroelastic communication indicators, and validated through post-frac evaluation including pressure communication and casing deformation observations. Data were integrated within a centralized visualization environment to enable cross-disciplinary interpretation and stage-level decision support.Results\/Observations\/Conclusions: Two anonymized case examples demonstrate contrasting fault activation outcomes. In Case 1, faults moderately misaligned with SHmax created elevated shear potential, resulting in screen-out risk and potential casing deformation. Through integration of seismic attributes and real-time diagnostics, these risks were mitigated through adjustments to treatment design, preserving wellbore integrity. In Case 2, a more complex structural environment resulted in increased hydraulic and poroelastic interaction, including S-bend casing deformation. Under controlled conditions, this interaction contributed to increased fracture surface area and reservoir contact while remaining within operational limits. These results demonstrate that fault activation is not uniformly detrimental and can be classified and managed using integrated diagnosticsApplications\/Significance\/Novelty: This work highlights the critical role of seismic-derived structural understanding in completions design and execution. By linking geophysical interpretation directly to geomechanical behavior and stimulation response, the workflow provides a repeatable framework for managing fault interaction in unconventional reservoirs. The approach enables operators to move beyond avoidance of structural complexity and toward controlled interaction with geologic features to optimize performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Identification of Perforation Locations and Estimation of Production Profile in Multistage Hydraulically Fractured Horizontal Wells Using Distributed Temperature Sensing Data<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Yang* and M. Onur\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Tulsa)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Distributed Temperature Sensing (DTS) provides temperature measurements over time and along the length of a horizontal well, which can reveal how effectively fluid is flowing in multistage fractured horizontal wells. This study presents a framework to determine which clusters or perforation sites are effective by analyzing temperature deviations observed during warm-back and subsequent multiphase production, helping to differentiate between contributing and non-contributing perforations and to estimate production inflow profiles using permeability inversion. The full interpretation framework\u2014including cluster permeability estimation and production profile reconstruction\u2014is tested with both synthetic and field DTS datasets using a thermal simulator based on the compaction\/dilation method.Methods\/Procedures\/Process: We conduct thermal simulations with the commercial simulator for a full injection\u2013warm-back\u2013production stages mimicking stage-wise hydraulic fracturing and subsequent thermal recovery. Synthetic and field DTS samples were differentiated using a discrete three-point quadratic polynomial scheme to detect temperature troughs indicating perforations. Production-stage temperature deviations were then used to classify contributing versus non-contributing perforations. To estimate cluster and matrix permeability, temperature-response a polynomial-based response surface models were generated covering fracture permeability and within their realistic ranges, enabling joint inversion of both permeabilities from a single temperature deviation.Results\/Observations\/Conclusions: Differentiation of DTS data accurately recovers perforation locations in both synthetic and field datasets, confirming the physical basis of delayed warm-back as a perforation indicator. Production-stage temperature deviations reliably distinguished contributing from ineffective clusters, as validated through reduced-perforation thermal simulations replicating DTS-derived inflow patterns. Polynomial temperature\u2013permeability mappings produced stable estimates for fracture and matrix permeabilities. Reconstructed production profiles display strong agreement with multiphase simulation results, demonstrating that DTS temperature anomalies contain sufficient information to quantify perforation inflow contributions, even in tight reservoirs with complex thermal and hydraulic interactions.Applications\/Significance\/Novelty: This study shows that DTS temperature data can estimate production profiles in fractured horizontal wells, providing a cost-effective alternative to traditional logging tools. By combining warm-back cooling\/heating stage temperature shifts, and permeability estimation, the framework enables diagnosis of perforation efficiency and inflow imbalance. Key innovations include polynomial differentiation for cluster localization, 2D surfaces for fracture\u2013matrix permeability inversion, and validation on synthetic and field datasets. Overall, DTS emerges as a powerful tool for optimizing stage design, perforation placement, and well performance in tight reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: When Classical PVT No Longer Applies &#8211; Confined Fluid Phase Behavior<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJiajun He, Sardar Asadov\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Molecular Diffusion as the Main Driver for HC Gas, CO2, and NGL Enhanced Oil Recovery in Unconventional Shale<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Amini*, H. Kazemi, K. Dewanda, B. Mindygaliyeva and E. Ozkan\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: We present an easy-to-use analytical mathematical model as an extension of Sheikha et al. 2005 method for interpreting diffusion experimental data in very low-permeability porous media and for demonstrating that molecular diffusion is the main transport mechanism for enhancing oil recovery in very low permeability shale reservoirs.Methods\/Procedures\/Process: The analytical model is a linear model and relies on two physical concepts\u2014molecular diffusion for interphase mass transfer via Henry\u2019s solubility constant for gas solubilization into oil.Results\/Observations\/Conclusions: We also have validated the analytical results with a numerical model of the analytical formulation and a more elaborate compositional model which is non-linear in formulation and utilizes more elaborate phase behavior thermodynamics principles. The models produce comparable and consistent results that are included in the paper.Applications\/Significance\/Novelty: Additionally, the simpler analytical model solution is useful for other applications such as assessing the potential for gas diffusion leaks in gas storage applications.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Molecular Insights into the Miscible Behavior and Displacement Mechanisms between CO2-CH4\/C2H6\/C3H8 Gas Mixture and Tight Oil in Nanoslits<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Guo*<sup>1<\/sup>,<sup>2<\/sup>, K. Wu<sup>1<\/sup>, Z. Jin<sup>2<\/sup>, J. Xu<sup>2<\/sup>, D. Feng<sup>3<\/sup>, T. Wang<sup>1<\/sup>, L. Peng<sup>1<\/sup> and Y. Huang<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Petroleum; 2. University of Alberta; 3. China University of Geosciences)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: CO2 is an effective injection fluid for EOR, combining oil swelling, viscosity reduction, and miscibility advantages, especially in tight and shale reservoirs. However, the high cost of capture and purchasing CO2 limits pure CO2 projects. Tests show that CO2 injection in tight oil can trap medium to heavy fractions, plug pore throats and lower oil recovery. Co-injecting produced hydrocarbon gas with CO2 can reduce minimum miscibility pressure (MMP), enhance mass transfer, mobilize residual oil in smaller pores and mitigate formation damage. Yet miscibility and displacement differences among various hydrocarbon-CO2 mixtures in tight oil remain unclear. This work elucidates these mechanisms at the molecular scale to guide CCUS-EOR design and associated gas handling in tight oil reservoirs.Methods\/Procedures\/Process: Molecular dynamics simulations were applied to investigate miscibility and transport between tight oil and hydrocarbon gas-CO2 mixtures. Octane was chosen as a representative tight-oil component. First, interfacial-tension models of different hydrocarbon gas-CO2 mixtures against n-octane were built to quantify microscale miscibility. Second, miscible displacement models in a quartz nanoslit were constructed to compare how gas composition and concentration influence miscible behavior. Third, component transport under confinement to reveal molecular scale displacement mechanisms of different mixtures were analyzed. Finally, oil recovery and optimize injected gas composition were evaluated to support efficient and economic development of tight-oil CCUS-EOR projects.Results\/Observations\/Conclusions: (1) Solubility and molecular diffusion jointly control miscibility and sweep. Miscibility follows CO2-C3H8 &gt; CO2-C2H6 &gt; CO2-CH4, whereas sweep efficiency shows the opposite trend. Stronger miscibility leads to oil looser, larger swelling, lower viscosity and better mobility; stronger sweep enables deeper gas penetration and higher oil recovery. (2) Gas concentration strongly influences flooding performance. When CO2 fraction decreases, CO2-CH4 mixtures favor sweep over miscibility; in CO2-C2H6 mixtures both miscibility and sweep weaken, reducing recovery; CO2-C3H8 mixtures are less sensitive. (3) CO2-C2H6 and CO2-C3H8 gas mixture have similar miscibility and sweep efficiency on medium composition oil, suggesting field strategies that first maximize miscibility and then optimize displacement.Applications\/Significance\/Novelty: CO2-EOR with co-injected hydrocarbon gas can extend field life, improve resource utilization, cut emissions and lower costs. At the same time, associated gas makes hydrocarbon-CO2 separation challenging: CH4-CO2 and C2H6-CO2 separation technologies are mature but expensive, whereas C3H8-CO2 separation is rarely reported. Re-injecting suitable hydrocarbon-CO2 mixtures relaxes surface-separation requirements, reduces capital and operating costs, and offers a practical CCUS-EOR option for tight oil. The molecular scale insights from this study support selection of injected-gas compositions and design of associated-gas facilities.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Uncovering Confined CO2 Phase Behavior in Unconventional Reservoirs Through Novel Gravimetric Adsorption Experiments<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Qiasi* and X. Li\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Kansas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional resources are characterized by low to ultra-low permeability, making hydrocarbon recovery highly dependent on fluid behavior within nanoscale pore systems. The objective of this work is to critically examine and quantify the capillary condensation pressure in nanoscale pore spaces for the first time at temperatures approaching reservoir conditions. The experimentally unprecedented high-pressure measurements reveal fundamental deviations from bulk thermodynamics arising due to strong solid-fluid interactions, restricted molecular motion, and pore-size-dependent effects. Although bulk fluid phase behavior is well established by conventional EOS models, they fail for nanoconfined fluid. The study also bridges experiments and modeling through virial-based isotherm analysis.Methods\/Procedures\/Process: A high-precision gravimetric microbalance is employed to obtain the adsorption and desorption isotherms of pure CO2 in MCM-48 and SBA-16. Nanoporous materials are first characterized by N2 physisorption at 77K to obtain the pore size, surface area, and pore volume, yielding pore sizes of 2.97nm and 5.46nm, respectively. Samples are pre-treated at 350\u00b0C under vacuum to ensure accurate mass uptake. The effect of different pore sizes, pore geometry, and temperature on confinement-induced phase behavior is evaluated, and the isotherm inflection is used to identify condensation pressure. The virial isotherm model is adopted to regress experimental data, derive adsorption parameters, and predict the confined phase envelope, with results validated against published data.Results\/Observations\/Conclusions: Experimental isotherms reveal pronounced confinement effects and temperature-dependent shifts in condensation pressure. For CO2 confined in MCM-48 (2.97nm), its dew point pressures are suppressed by 44.12, 43.65, and 42.36% at 5, 10, and 15\u00b0C, respectively, compared with their bulk saturation pressures. Corresponding to an average suppression of 43.38% in MCM-48 and 14.92% in SBA-16 from the bulk saturation pressure of CO2. Furthermore, the virial-model predictions show excellent agreement with experimental measurements, with an AARD &lt; 5%. Therefore, experimental adsorption isotherms are modeled to leverage their utility in simulating performance at alternative temperatures, thereby minimizing the requirement for extensive experimental measurements. It is also deduced that the degree of suppression in capillary condensation pressure is higher as the pore size and temperature decrease, which is consistent with published articles.Applications\/Significance\/Novelty: The findings of this work will significantly deepen the understanding of confined fluid phase behavior, which will not only enable the reservoir engineers to comprehend unconventional reservoirs better but also will allow accurate predictions of hydrocarbon-in-place, fluid transport, and recovery efficiency. It will also provide the basis for the researcher to excel in the manipulation of phase transitions at the nanoscale, which is the foundation for many advanced materials, carbon storage, and separation processes. The study delivers rare high-quality experimental data that strengthens and benchmarks predictive models for confined fluids.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: Data-Driven Optimization and Decision Practices in Oilfields<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJudy Zhu, Yuxing Ben\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Data-Driven Framework Using Feature Space Optimization with Advanced Machine Learning and Explainable AI Methods for Performance Prediction in Multi-Stage Hydraulically Fractured Horizontal Wells.<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. He* and M. Onur\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Tulsa)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study introduces a data-driven framework for predicting well outputs, including bottom-hole pressures during the hydraulic fracturing stage, multiphase production rates (oil, gas, and water), and key performance indicators such as gas-oil ratio and water-oil ratio for both single-well and multi-well setups. Predicting these well outputs for uncertainty analysis and scenario evaluation using high-fidelity reservoir simulators is computationally very demanding. This study addresses this challenge by proposing a framework that uses a three-stage feature space optimization strategy combined with machine learning and explainable AI techniques. The framework achieves a speedup of 1,000 to 10,000 times while maintaining similar prediction accuracy.Methods\/Procedures\/Process: The framework combines three machine learning methods: kernel-based regression (Least-Squares Support Vector Regression and Semi-Supervised LSSVR), gradient boosting algorithms (XGBoost and CatBoost), and deep learning models (LSTM networks and Transformers). Training data is generated using a commercial simulator with Latin Hypercube Sampling across geologically realistic parameter ranges. A structured three-stage feature space optimization approach is applied: (1) filter-based methods for reducing dimensionality, (2) Grey Wolf Optimizer combined with Brute Force search to select the best feature subset, and (3) global sensitivity analysis using Sobol and Shapley methods to evaluate the contributions of individual parameters and their interaction effects on the well outputs.Results\/Observations\/Conclusions: Preliminary validation demonstrates exceptional proxy performance across both single-well (9 parameters) and multi-well systems (37 parameters). For single-well configurations, all proxy models achieved normalized RMSE below 0.05 and R**2 exceeding 0.97 for oil, gas, and water predictions. Multi-well proxies maintained R**2 values of 0.65-0.88 while capturing complex inter-well interference effects. Kernel-based LSSVR exhibited superior training efficiency, completing in seconds to minutes versus hours for gradient boosting methods. Deep learning models effectively captured temporal dependencies in production decline curves and pressure transient behavior during shut-in periods.Applications\/Significance\/Novelty: This study advances unconventional reservoir modeling by creating a systematic framework for quickly assessing the effectiveness of fracturing and production forecasting under geological uncertainty. The proposed feature optimization method, combined with global sensitivity analysis, is a novel approach for identifying key descriptive features in complex multi-well systems. The comparative evaluation of kernel-based, gradient boosting, and deep learning models offers practical guidance for selecting proxies based on computational limits and accuracy needs. The validated proxies support computationally efficient ensemble-based history matching and production optimization workflows. This enables quick scenario analysis, design assessment, and data-driven decisions for field development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Automated Geosteering in Shales: Can AI Take Over Real-Time Decisions?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. E. Aguilar and I. Kuvaev*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ROGII Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Over the past two decades, more than 200,000 horizontal wells have been drilled across the United States and Canada, primarily targeting tight and shale formations. Modern development is characterized by dense infill drilling in laterally continuous geology, where geosteering remains largely a manual process based on MWD gamma ray (GR) and survey data. Increasing rates of penetration (ROP) and higher well counts per geosteerer require automated workflows that maintain interpretation quality while improving the quality of real-time decision-making. This work presents a multi-well AI geosteering workflow designed to replicate human interpretation using offset-well data.Methods\/Procedures\/Process: The workflow combines a structure prediction algorithm with a multi-stage AI Geosteering interpretation process. Apparent dip is estimated from offset wells using local plane fitting with robust weighting. The well is segmented using the Pruned Exact Linear Time (PELT) algorithm, and GR correlation is performed segment-by-segment against type wells, offset laterals, and along the lateral itself. Multiple interpretation scenarios are evaluated using statistical and machine learning-based methods, with a Best Interpretation Selection algorithm retaining the most plausible candidates. Hyperparameter optimization is performed on ~5,000 Eagle Ford wells.Results\/Observations\/Conclusions: The workflow achieves agreement with human interpretation within expected variability. Offset-well-based apparent dip prediction provides a strong structural constraint, while multi-well conditioning and self-correlation improve interpretation consistency.Applications\/Significance\/Novelty: The system enables real-time, \u201cautopilot-style\u201d geosteering by continuously updating interpretation and flagging deviations from the target corridor. The workflow reduces manual workload while maintaining decision timing compatible with high-ROP drilling.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Driving Consistency and Capital Efficiency in Hydraulic Fracturing Through an Automated Real-Time Guided Decision Framework<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Williams*<sup>1<\/sup>, M. Watson<sup>1<\/sup>, J. Iriarte<sup>2<\/sup>, P. Dharwadkar<sup>2<\/sup>, R. Schick<sup>2<\/sup>, J. Rafiee<sup>2<\/sup>, M. L. Gosnell<sup>1<\/sup> and E. R. Davis<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ConocoPhillips; 2. Corva)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Inconsistent stage-to-stage design execution during hydraulic fracturing remains a major challenge when evaluating completions performance and optimizing assets. Variability from differing experience, personnel, toolsets, and equipment leads to unpredictable design deviations and cost fluctuations. To address consistency, the Permian team implemented an automated, guided decision application integrating real-time visualization, alerts, recommendations, and cost insights. This centralized, digital approach standardized frac design execution for the business, enhanced capital efficiency, and empowered engineers with real-time, data-driven optimization.Methods\/Procedures\/Process: The implemented solution provides a decision-support interface to assist frac supervisors in real-time operations both in field and Real-time Operations Centers (RTOC), reducing cognitive load and enabling oversight of multiple jobs concurrently and consistently in the RTOC. The framework integrates real-time data streaming, design overlays, adherence scoring, and smart alerts with prescriptive recommendations that trigger on deviations and track operator responses. Standardized KPIs, operating envelopes, and automated event detection form a \u201ccopilot\u201d that drives consistent, rapid decision-making across fleets and shifts. These components create a continuous optimization workflow connecting design intent, execution, and post-stage analytics for systematic performance improvement.Results\/Observations\/Conclusions: Deployment across multiple fleets in the Permian Basin improved alert relevance via operator feedback and consistent stage categorization. The framework transformed the completions team and RTOC into a center for continuous improvement, ensuring consistent execution across crews and improved design adherence. Resulting slurry and proppant execution accuracy increased by 39% and 22% respectively, and time to rate improved by 27%. Improved adherence decreased costs and compressed variance across fleets, enhancing operational predictability. Non-compliance increased stage costs by 10\u201330%. When annualized across the full operational volume, this represents a multi-million-dollar savings opportunity achievable through enhanced consistency, reduced deviations, and improved execution quality.Applications\/Significance\/Novelty: This work introduces a structured, data-driven workflow that standardizes real-time decision-making in hydraulic fracturing operations and provides a way to improve subsequent design. Unlike traditional monitoring systems, it operationalizes consistency through guided analytics and prescriptive decision support directly into execution workflows, transforming each stage into both an operational event and a data point for optimization. Future enhancements include expansion of data ingestion to other contextual sources, smart rerouting, and predictive modeling for proactive, automated control.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Scaling Performance at Pace: Digital Workflows, Factory Learnings, and Completion Effectiveness<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tLuis Baez, Amr Ramadan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Automating DSU Analysis with Interactive Geo GBV Visualizations for Faster Planning Cycles<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Gao*, R. Simms, D. Sutton, V. Muralidharan and Y. Askabe\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Drilling and Spacing Unit (DSU) analysis is critical for unconventional planning, yet manual workflows consume 20\u201340% of a reservoir engineer\u2019s time. Engineers previously planned wells on slides by adjusting arrow lengths, calculating distances, and copying data into a Parent-Child Degradation (PCD) tool\u2014a process that took hours and was error-prone. We present interactive DSU planning Geologic Gun Barrel View (Geo GBV) visualizations that reduce repetitive effort. Engineers can explore spacing and landing scenarios by observing dynamic Stimulated Reservoir Volume (SRV)\/frac shape changes, based on backend calculations of infill degradation and completion design. This visual approach accelerates pre-drill reviews and supports cross-functional decisions to optimize well spacing and improve economics.Methods\/Procedures\/Process: We developed an automated, Python-based application using Streamlit to optimize DSU planning. The app plots wells on a Geo GBV using inputs such as distance to the left lease line, bench name, and the ratio of landing-to-bench-top vertical distance to bench thickness. Illustrative fracture shapes are assigned to the wells. The backend automates parent\u2013child distance calculations and integrates with models to quantify impacts from parent proximity, infill spacing, and completion design. Scripts were validated against manual methods across multiple DSUs, ensuring accuracy, repeatability, and scalability. The front end displays rescaled SRV shapes based on infill degradation and completion design, enabling engineers to visualize spacing scenarios and optimize planning.Results\/Observations\/Conclusions: The workflow dramatically reduces processing times from hours or even days to mere seconds or minutes, delivering an efficiency improvement of over 99%. It consistently reproduces manual findings while enabling real-time \u201cwhat-if\u201d scenario reviews that empower decision-making. Teams can seamlessly add or remove wells, shift pads, or adjust landing zones and instantly visualize quantitative changes in frac growth and SRV overlap on the Geo GBV\u2014tasks that previously demanded hours of rework. Field pilots across multiple DSUs demonstrated repeatable outputs and fewer manual errors. Interactive visualizations transform GBV reviews into dynamic working sessions with cross-functional teams, accelerating planning cycles and delivering measurable operational and economic benefits.Applications\/Significance\/Novelty: This approach provides a scalable, operator-ready solution to streamline DSU analysis across diverse development areas. By pairing automated backend calculations with geo-aware visualizations, engineers shift time from repetitive tasks to interpretation and scenario design, reducing workload. Novelty lies in physics-informed frac representations to produce decision-grade visuals and enable real-time scenario planning. Traditional DSU analysis consumes 20\u201340% of a reservoir engineer\u2019s time; eliminating manual workflows can save a similar proportion\u2014potentially translating to a 20\u201340% cost reduction for DSU tasks. This tool improves efficiency, accelerates planning cycles, and drives economic performance through optimized well placement and standardized GBV reviews.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Building on Factory-Mode Learnings to Appraise Southern Fort\u00edn de Piedra: Integrated Geological Modeling and Pilot Results<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Bande*, P. Biscayart, E. Valeff, A. Laurora, P. Junco, D. Garcia Acebal, S. Olmos and L. A. Pons\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Tecpetrol SA)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Fort\u00edn de Piedra, located in the core sector of Vaca Muerta, produces 24 MM m3\/d of natural gas representing 15 % of total Argentina gas production. Since 2017, Tecpetrol has drilled more than 190 horizontal wells, targeting 3 landing zones in lower Vaca Muerta. The full development area is located in an area of 181 km2 north of the Neuqu\u00e9n River. However, Fort\u00edn de Piedra has a remaining area of 62 km2 located south of the river, where the available subsurface information is a legacy well and a newly merged seismic survey. Using the available data, an integrated assessment was performed with the aim of quantifying the development potential of the southern acreage and build an efficient development plan, where we expect important facies and fluid changes.Methods\/Procedures\/Process: A detailed stratigraphic framework was built correlating the well-known northern part with the southern appraisal sector. To achieve the task we correlate 3 vertical wells in the north with a legacy well and an updated seismic simultaneous inversion to predict facies changes in a progradation setting. The outcome of this first stage was the selection of pilot well location, to better characterize the variability of the S sector. In 2025 Tecpetrol drilled a vertical pilot well with a complete set of logs together with 140 m of core in the targeted section. Following the abandonment of the pilot a total of 3 lateral appraisal horizontal wells were drilled. Prior to the completion work, DFIT tests on each well were carried out in an offline mode. The pilot pad will also evaluate development effectivity across vertically condensed and expanded targets and assess potential inter-well interference to optimize future field development.Results\/Observations\/Conclusions: The petrophysical and geomechanical analysis defined the presence of 4 landing zones. The wells navigated the lower 3 zones, to the south. The upper target is still untested and should be drilled to the north since its located in foreset facies, prograding to the NW. Integrated data acquisition will support optimization of completion design, spacing, and sequencing for southern sector development. DFIT results of the uppermost drilled target shows a pre-closure PDL behavior of two sets of fractures suggesting a naturally fractured reservoir. This is consistent with a toe-set position and a more carbonate-rich zone confirming the model. Chronostratigraphic correlation show that the navigated 3 landing zones correspond to the lower 2 zones developed in the northern part of the block, clearly demonstrating facies condensation. Production testing will finally confirm facies variations identified and the engineering efficiency of full-field southern sector development.Applications\/Significance\/Novelty: The approach used in the appraisal project of Fortin de Piedra takes into account the lesson learned in the factory-mode full development of more than 190 wells drilled in the northern zone. This new model combines key unconventional technical disciplines, including geology, geophysics, reservoir engineering, and geomechanics. The main objective of the plan is to characterize the southern block targets searching for differences and similarities with the northern targets. A robust subsurface model was constructed and later confirmed with a vertical pilot well and 3 horizontal branches. Results from this integrated study enhance future factory-mode development by incorporating risk mitigation strategies, reducing vertical interference and parent-child effects, while maximizing recovery and capital efficiency.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Where\u2019s the Proppant? Plug Isolation Impacts on Treatment Uniformity and Implications for Production Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. R. Jones*, H. Li, T. Conner, B. Fryar, J. Wang, T. Gang and I. Krane\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Achieving optimal zonal isolation during hydraulic fracturing can be critical for maximizing well productivity and ensuring uniform proppant placement. This Permian Basin multi-bench study investigates the operational differences between composite and dissolvable plugs, focusing on their isolation efficiency, impacts on treatment uniformity, and subsequent production response. Simulation efforts complement field diagnostics to assess how well spacing and plug failure patterns impact production and economic outcomes.Methods\/Procedures\/Process: Advanced diagnostics\u2014including perforation erosion analysis, radioactive tracer surveys, and bottomhole pressure measurements\u2014were applied to wells with consistent completion designs. Plug isolation performance was evaluated using tracer-tagged stages and casing wall loss measurements. Early-time production response was analyzed through pressure and rate data, and the treatment uniformity was quantified at well and stage levels. Integrated simulation models explored sensitivity to plug failure scenarios and well spacing configurations.Results\/Observations\/Conclusions: Key findings indicate that, in this study, dissolvable plugs exhibited a significantly higher isolation failure rate than composite plugs, resulting in more frequent loss of isolation. When dissolvable plugs fail, multiple previously treated stages are affected, whereas composite failures typically impact only the preceding stage. This loss of isolation results in a reduction in well-level treatment uniformity. Despite these differences, early-time production response showed only minimal impact to the linear flow parameter (LFP) of the wells with composite plugs. Long-term production effects are still under evaluation, though ongoing simulation studies indicate that the production and economic impacts are contingent on well spacing and plug failure patterns across well in the DSU.Applications\/Significance\/Novelty: This study highlights the importance of robust plug isolation for maintaining treatment uniformity and optimizing production. Operational trade-offs between plug types are clarified, providing actionable insights for completions design and capital deployment. By integrating field diagnostics with simulation, this work advances understanding of near-wellbore completion strategies and demonstrates that production and asset value are sensitive to both well spacing and plug failure patterns, offering practical guidance for improving operational efficiency and long-term asset performance in unconventional reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 8: Connecting Forecasting with Reservoir Management Decisions<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAutumn Shannon, Hector Barrios, Eric Bryan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Produced Water Management in the Permian Basin to Accommodate the Arid West Texas Region: Historical Production, Forecast, and Water Composition<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Bechara*, M. Watson, T. Gamadi, H. Emadibaladehi, E. Hajiyev and A. Tiam\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Bob L. Herd Department of Petroleum Engineering, Texas Tech University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Permian Basin has been the US largest producing basin since 2018, contributing to 28% of the US hydrocarbon energy ever since. Large volumes of produced water (PW) are left to be managed as a result. If not used in oilfield applications, PW is injected into saltwater disposal (SWD) wells. SWD is undesirable for increasing seismic activity and expediting regulatory action that could restrict hydrocarbon production. New Mexico has limited SWD more than Texas, leading to 2 MMbwpd to be disposed of in West Texas. A total of about 16 MMbwpd is disposed of in West Texas, increasing seismic risk in a region faced with 22 MMbwpd in water shortage through 2070. This study evaluates the basin&#039;s PW quality and forecasts production to minimize SWD and repurpose PW for beneficial use in West Texas.Methods\/Procedures\/Process: Historical production was divided into 8 periods for each county with each period\u2019s decline forecasted. Drilling rig intensity and well lateral length per layer in a county is based on data from 2020 to 2025. Type Curves for future wells in each layer in a county were based on production data of wells drilled since 2019. We used a period of 18 days per well in the Midland Basin, and 22 days per well in the Delaware Basin, with rig count for each county estimated based on drilling activity since 2021. The incorporated extents of layers were determined by Enverus in 2024 for each layer, with well spacing based on existing distribution of wellbore density in developed areas. We then forecasted production &amp; fracking demand. Chemical properties were collected from different sources and mapped.Results\/Observations\/Conclusions: The forecast details 3 cases: low, base and high. The Permian Basin&#039;s PW from unconventional resources is set to peak at 28.8 MMbwpd in 2039 (base case) whilst producing 7.6 MMbopd and 42.1 bcf\/d. The PW from unconventional wells is often injected into SWD wells, if not used for hydraulic fracturing. But if treated at a 50% recovery rate, the PW from tight-oil wells in the TX part of the Permian would meet 18% to 45% of the West-Texas irrigation water shortage through 2070. This was done by considering recent PW recycling percentages in fracking and estimating future trends. The Delaware Basin in Texas seems to be the most convenient region to treat PW from beneficial reuse, due to low water demand for fracking and low salinity.Applications\/Significance\/Novelty: This study is novel as it provides a detailed rigorous forecast by county of the Permian Basin through 2070 and lays out the basis for future work to help evaluate reuse of PW in beneficial reuse throughout West Texas. The study also looks at geographical distribution in lithium and boron, amongst other chemical properties that can be provided on demand.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Making an Impact: Machine-Learning-Ready Metrics for Ultra-Tight Infill Development<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Davis*<sup>1<\/sup>, A. Qualls<sup>2<\/sup>, K. Sathaye<sup>1<\/sup> and G. A. Quintero<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Novi Labs; 2. Devon Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In this paper, we examine different approaches to assessing infill spacing impacts on machine learning model outputs. This work will focus on Devon-operated assets and will assess various methods of describing spacing impacts for the purposes of predicting future well performance. Our goal is to examine the benefits of a machine learning approach to infill well forecasting. In many cases, machine learning approaches handle spacing impacts properly for typically spaced wells. However, na\u00efve spacing metrics struggle with spacing designs that are much tighter than the basin average.Methods\/Procedures\/Process: As a source of actuals, we used an anonymized dataset of wells from Devon operated pads in the Delaware Basin, covering a variety of infill spacing scenarios. We then trained causal inference, random forest models using different feature sets. These models were used to predict production for a set of Devon operated wells which served as a blind holdout test set. Performance was measured against actual production for the hold out set. Our baseline method for this work was the pre-drill planning type-curves used for these wells. Our evaluation features were traditional point-to-point distances (tangent distance), distance weighted features (impact factors), area based features (Voronoi tiling), and combinations of these different approaches (Voronoi + Impact, Voronoi + Tangent, etc).Results\/Observations\/Conclusions: There is a significant improvement in accuracy and spacing sensitivity when combining causal inference, random forest models and distance weighted spacing features. While using the features alone (only Voronoi or only impact factors) yield good results, we find the best performance is a combination of these features. In our test set, type curve forecasting methods showed 20% median absolute percent error rates against actuals for 1-year cumulative production. In contrast, machine learning based forecasts showed a 15% error rate when judged on the same metric. SHAP explanation value analysis shows that while the more digestible untransformed features showed directionally correct trends, only the transformed features captured the expected non-linear spacing performance degradation.Applications\/Significance\/Novelty: Error metrics for machine learning models are one facet of tool evaluation. However, they don\u2019t present the complete picture. Different feature sets can have similar errors while also exhibiting different sensitivity to spacing changes. It can be difficult for engineers to consider all the interacting physical processes such as geology, completions, spacing, and depletion. Machine learning approaches, especially methods that are aligned with good engineering judgement, are an integral tool in evaluating development planning and optimization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Optimization of Multi-Well Productivity Through Production Sharing and Fracture-Driven Interactions: A Numerical Assessment<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Alhajjaj* and E. Ozkan\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Well interference is typically viewed as detrimental to productivity in unconventional reservoirs. This paper treats well interference in unconventional reservoirs as an optimization problem that balances productivity gains from reduced well spacing against productivity losses that may result from infill wells.Methods\/Procedures\/Process: It shows that a limited number of fracture-driven interactions cause only short-term disruptions in the productivity trends of the offset wells. An exception occurs when fracture-driven interactions cause permanent damage to offset-well completions or stimulated reservoir volumes, which are more difficult to model and less predictable.Results\/Observations\/Conclusions: In general, the challenge in optimizing well spacing in unconventional reservoirs lies in distinguishing the effects of natural pressure diffusion from those of fracture-driven interactions and evaluating their combined effect on multi-well productivity.Applications\/Significance\/Novelty: We conclude that if managed properly, fracture-driven interactions do not entirely negate the benefits of infill drilling, whereas overly conservative spacing may lead to inefficient resource development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: CCUS Opportunities and Strategies<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:05 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMaria Lozano, Veronica Montoya\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">2025 CCUS Play Fundamentals: Gigatonne Dreams, Market Realities<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Bain*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Enverus)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This presentation updates the CCUS &quot;Play Fundamentals,&quot; analyzing market shifts amidst headwinds like policy uncertainty and permitting delays versus tailwinds like 45Q tax credit parity. The scope covers the value chain, focusing on Class VI permitting trends and the dominance of ethanol in recent applications. We specifically evaluate the economics of Direct Air Capture (DAC) and Bioenergy with CCS (BECCS) relative to credit stacking. Additionally, the study assesses low-carbon power solutions for data centers, comparing natural gas with CCS against nuclear and geothermal options to determine the most cost-effective, reliable energy sources for the digital transition.Methods\/Procedures\/Process: We utilized a proprietary subsurface innovation model to analyze permitting timelines, contrasting EPA-led Class VI approvals (~3 years) against state primacy jurisdictions like Louisiana (~19 months). Economic modeling was performed for large-scale DAC projects, calculating Net Present Value (NPV) sensitivities based on contracted Carbon Dioxide Removal (CDR) volumes and credit pricing. We also calculated break-even costs for Permian EOR, adjusting for the new $85\/ton 45Q parity. Finally, we modeled the Levelized Cost of Energy (LCOE) for data center power in the Gulf Coast, stacking revenues for Natural Gas with CCS and comparing them to geothermal and nuclear baseloads.Results\/Observations\/Conclusions: Data indicates EPA permitting is slow, while state primacy accelerates approvals to under two years. DAC analysis reveals that projects like Stratos require ~90% contracted CDR volumes to flip from negative NPV to ~$1 billion positive NPV. Crucially, 45Q parity has lowered EOR break-evens to ~$16\/barrel, making it highly competitive with Permian unconventional inventory. For data centers, Natural Gas with CCS ($75\/MWh) economically outperforms Geothermal ($90\/MWh) and Nuclear ($120\/MWh) in the Gulf Coast, providing the most viable low-carbon, reliable power solution despite the cost of necessary CDR offsets to achieve net-zero power.Applications\/Significance\/Novelty: This study highlights a pivot in CCUS drivers from simple sequestration to integrated value chains like EOR and power generation. It provides a novel quantification of the &quot;credit stacking&quot; required to make DAC and BECCS commercially viable, emphasizing the current reliance on hyperscalers like Microsoft for CDR market liquidity. The findings demonstrate that legislative changes have revived EOR as a superior economic option compared to new drilling. Furthermore, the work offers a strategic roadmap for the AI boom, validating gas-plus-CCS as the immediate solution for data center loads, bridging the gap between reliability and decarbonization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integration of CO\u2082-EOR and Carbon Storage for Sustainable Development of Nigeria\u2019s Unconventional Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. G. Mahe*<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. International University of East Africa (IUEA); 2. OPA ENERGY)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study explores the dual benefits of CO2 injection for increasing oil and gas recovery while achieving long-term carbon storage in unconventional reservoirs. The research focuses on Nigeria\u2019s emerging shale and tight formations, aiming to assess how CO2-based enhanced recovery methods can be integrated into national carbon management strategies. The objective is to optimize reservoir performance and contribute to emission reduction goals under sustainable energy transition frameworks.Methods\/Procedures\/Process: A multidisciplinary workflow was developed by combining reservoir engineering principles with CO2-storage performance modeling. Field data from analogous unconventional reservoirs were applied to model CO2 injection, trapping mechanisms, and fluid interactions. Simulation and analytical tools were used to evaluate recovery factors, pressure behavior, and CO2 containment efficiency under varying operational scenarios, providing a framework adaptable to Nigeria\u2019s sedimentary basins.Results\/Observations\/Conclusions: Results indicate that integrating CO2-EOR with carbon storage substantially enhances hydrocarbon recovery while ensuring secure and permanent CO2 containment. Depleted unconventional reservoirs offer significant capacity for long-term storage. The findings demonstrate that Nigeria\u2019s shale and tight formations have both technical and environmental potential for dual-purpose CO2 injection, advancing low-carbon development within the country\u2019s energy sector.Applications\/Significance\/Novelty: This work provides a novel framework for coupling CO2-EOR and carbon storage in unconventional Nigerian reservoirs. It highlights the potential for balancing energy production with carbon management and aligns with national decarbonization goals. The study bridges petroleum engineering and environmental sustainability, offering a scalable model for developing countries seeking to integrate CCUS technologies into hydrocarbon resource utilization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluation of U.S. Underground Natural Gas Storage Systems for CO2-Based Cushion Gas Replacement<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Mirzaei Paiaman*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: U.S. underground natural gas storage (UNGS) facilities exist in depleted oil and gas reservoirs, saline aquifers, and solution-mined salt caverns. While UNGS sites have a long track record of safe and effective storage, their potential for CO2-based applications has not been systematically assessed. This study evaluates the feasibility of substituting cushion gas with lower-cost anthropogenic CO2, a strategy that yields three distinct benefits: (1) CO2 sequestration for climate change mitigation, (2) liberation of existing cushion gas for market use, and (3) creation of additional space for working gas storage due to the greater volume shrinkage of CO2 relative to natural gas at certain pressure and temperature ranges.Methods\/Procedures\/Process: A newly developed, comprehensive nationwide database of 384 U.S. UNGS facilities (encompassing 447 reservoirs, saline aquifers, and caverns) was utilized. Using a volumetric framework, site-level CO2 storage potential, cushion gas liberation, and the additional space for storing working gas were quantified. The outcomes were further organized by geographic region and UNGS facility type.Results\/Observations\/Conclusions: Replacing all cushion gas with CO2 could store up to 0.33 gigatonnes of CO2 while releasing 4.5 Tscf of natural gas. Storage capacities span approximately 2,200 tonnes to about 19 million tonnes, with an average of roughly 0.9 million tonnes. At 362 facilities, CO2 replacement also creates additional space for working gas storage, with an average increase of 30% (up to a maximum of 344%). This translates to gains ranging from about 1 million scf to 68 Bscf per facility, averaging 3.1 Bscf, and yielding a cumulative increase of approximately 1.12 Tscf in working gas storage capacity. These 362 facilities currently contain about 4 Tscf of cushion gas and 4.5 Tscf of working gas, with an estimated CO2 storage capacity of approximately 0.29 gigatonnes.Applications\/Significance\/Novelty: This study presents the first field-scale, nationwide evaluation of CO2-based cushion gas replacement in U.S. UNGS systems, identifying a novel pathway to couple carbon sequestration with enhanced gas storage by freeing cushion gas and increasing working gas storage capacity. By integrating CO2 storage into existing gas storage infrastructure, this approach leverages established assets to deliver both climate and energy-security benefits without requiring new subsurface developments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Topicals\" style=\"border-top: 4px solid #01a3a4;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Topical Luncheon: Evolving Issues in Produced Water Management in Texas<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        12:15 PM &#8211; 1:20 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Susan Nash\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Topicals\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. M. Kingham*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(GSI Environmental Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-6\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Topicals\" style=\"border-top: 4px solid #01a3a4;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Topical Luncheon: From Wells to Workflows: Agentic AI as the Next Operating Partner<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t382\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        12:15 PM &#8211; 1:20 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Denise Benoit\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Topicals\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Sharma*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Enverus)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Best of SPWLA<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:30 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Katerina Yared\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Automating Geological Formation and Marker Propagation Using Self-Supervised Deep Learning: Application to the Powder River Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tV. Simoes*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Laboratory Measurement of Effective Permeability for Movable Oil and Water in Shale<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Peng*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Quantification of Organic Matter and Clays in Organic-Rich Shale Using NMR T1-T2 with Solid-Echo<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Triana Camacho*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Rice University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Geomechanical Integration and Application of AI\/ML<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMehrnoosh Saneifar, Didi Ooi\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Geomechanical Behavior of Oil Shales from Northeastern Brazil<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. A. Soares*<sup>1<\/sup>, F. Ferreira<sup>2<\/sup>, A. G. Sobrinho<sup>1<\/sup>, J. C. Amorim<sup>1<\/sup> and M. S. Dutra<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. UFCG; 2. PETROBRAS)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to perform a geomechanical characterization of oil shales and to establish correlations with the petrophysical, mineralogical, and organic properties of the rock samples, thereby supporting hydrocarbon exploration and production in these types of unconventional reservoirs. The relationships identified between geomechanical and other physical properties may also be applicable to similar rock types in unconventional reservoir settings.Methods\/Procedures\/Process: The shale samples analyzed in this study were obtained from mines located in the Araripe Basin, northeastern Brazil. Macroporosity and grain density were both measured in a gas picnometer. Mineralogical characterization was performed through thin-section petrography, XRD, XRF and SEM-EDS analyses. SEM images were used to investigate microporosity. Organic matter content was estimated from TGA essays. Geomechanical properties were assessed using uniaxial and triaxial compression tests. Correlations among all measured properties were then established. Finally, a nonlinear failure envelope was developed for the Mohr-Coulomb criterion, enabling the prediction of shear failure under a range of subsurface stress conditions.Results\/Observations\/Conclusions: Macroporosity varies from 0.1% to 14.5%, while grain density ranges from 1.58 to 2.29 g\/cc. SEM images show a predominance of micropores. Thin-section analyses reveal a typical lamination pattern. XRD, XRF and EDS indicate the main presence of clay minerals, organic matter, calcite, quartz and muscovite. Organic matter content ranges from 3 to 19 wt%, suggesting these rocks as potential unconventional reservoirs. Compressive strength ranges from 5.2 to 36.9 MPa. Young\u2019s modulus ranges from 1.7 to 8.8 GPa, bulk modulus from 2.2 to 4.0 GPa, and shear modulus from 1.1 to 3.9 GPa. Poisson\u2019s ratio varies between 0.13 and 0.30. Samples with higher porosity and organic content exhibit lower mechanical strength, whereas those with greater carbonate content show increased mechanical competence.Applications\/Significance\/Novelty: The economic exploitation of unconventional oil shale reservoirs requires techniques whose effectiveness depends critically on a detailed understanding of the mechanical properties of rocks. The high organic matter content makes these shales potential targets for unconventional hydrocarbon extraction, while the significant presence of minerals such as carbonates and quartz contributes to their brittleness - an essential condition for fracture initiation. In the absence of these brittle minerals, these rocks would exhibit viscoelastic behavior, making hydraulic fracturing ineffective. The nonlinear failure envelope derived in this study provides a predictive model for shear failure under various stress scenarios, offering a valuable tool for reservoir engineering and development planning.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Machine Learning for Water Saturation Prediction: Beyond Archie\u2019s Model<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Gyimah<sup>1<\/sup>, S. Kelley<sup>1<\/sup>, A. Amosu<sup>2<\/sup>, M. Metwally<sup>3<\/sup>, D. Bui<sup>2<\/sup> and G. Akpabli*<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. New Mexico Bureau of Geology and Minerals Resources; 2. Petroleum Recovery Research Center; 3. New Mexico Institute of Mining and Technology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Traditionally, the Archie\u2019s equation has been utilized to predict water saturation. However, the model\u2019s dependence on empirical parameters (tortuosity factor \u2018a\u2019, cementation exponent \u2018m\u2019, saturation exponent \u2018n\u2019), which are variable in complex and geothermal formations, and assumptions with homogeneous and clean formations makes it limited. In this study, we compare machine learning (ML) models and Archie\u2019s model to make predictions for water saturation. The study aims to moves beyond Archie\u2019s assumption of a clean, homogeneous, clay-free sandstone formation with a single, well-defined porosity. We further aim to provide a solution for scenarios with &quot;missing log&quot; and &quot;limited experimental core data,&quot; which are often the reality in exploration and development.Methods\/Procedures\/Process: The study compares machine learning (ML) models and Archie\u2019s model predictions for water saturation. Experimental water saturation is utilized as a benchmark for validation. Data pre-processing is utilized to remove outliers, redundant data and erroneous data. Firstly, conventional well logs are utilized to estimate water saturation using Archie\u2019s model and the empirical parameters are tuned based on the experimental water saturation to optimize the predicted water saturation. Then, a heat map is generated for the dataset to analyze the feature correlations. Furthermore, clustering of well log data with K-means is performed to understand the inherent relationship within the dataset before ML regression is undertaken.Results\/Observations\/Conclusions: Our results demonstrate that the Feedforward Neural Network (FNN) significantly outperforms traditional Archie\u2019s model, as quantified by correlation coefficient (R2) and mean absolute error (MAE). The (R2 ) is 0.956 suggests a strong correlation between actual and prediction water saturation. FNN effectively captures complex, non-linear relationship between well log data. The use of 5-fold cross validation test further strengthens model reliability confirming that it generalizes well to unseen data. FNN automatically adapts to shale effects and is suited for heterogeneous and shaly reservoirs.Applications\/Significance\/Novelty: While ML has been applied in petrophysics before, a direct and rigorous benchmark against Archie specifically for the complex, high-heterogeneity environment of geothermal reservoirs is a significant contribution. This study highlights the transformative potential of ML in geothermal water saturation prediction, particularly where conventional methods fail due to reservoir complexity, missing log and limited experimental core data. The core novelty is the demonstration that an ML model can bypass the need to define a, m, and n explicitly. The FNN internally derives its own, highly complex, and variable equivalent of these parameters that change dynamically with the input log responses.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Study of At-Bit Images with Geological Answer Products for Optimal Solutions in U.S. Land Operations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Shrivastava*, S. Das, D. Quesada, Z. Zhang, Y. Shen, A. Borbor and C. Vazquez\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Newly developed at-bit imaging solutions acquire high resolution formation features like fractures, textural variations, and laminations in addition to the bedding dips. A series of these runs were made at different well deviations to compare with established logging-while-drilling (LWD) high-resolution images to develop geological answer products and assess the interpretability and interpretation consistency.Methods\/Procedures\/Process: In a test facility in Oklahoma, at-bit imager runs were made through Carboniferous rocks at different inclinations and images were prepared post the drilling operations. At-bit imaging makes use of a recent innovation where two sensors are mounted as trailing stinger element behind the cutters-arrangement on a polycrystalline diamond compact (PDC) bit to scrape the freshly cut rock and acquire the axial forces experienced against the rock. The axial forces are acquired at a very high frequency with associated magnetometer and accelerometer in the sensor, thereby producing azimuthal oriented high-resolution images as the drill-bit rotates and creates the new borehole. These at-the face of the bit images are then compared with established LWD images that are acquired on the side of the borehole wall. Feature comparison consisted of laminations, fractures and textural comparison through same geologic formation.Results\/Observations\/Conclusions: A comparative study is presented and answer products described with same geologic formations of sandstone, shale and limestone being intersected at different well deviations. Bedding dips are consistent, so are the textural variations. The formations are not intensely fractured; therefore, only few fractures could be compared across different measurements. This study presents a unique way of acquiring borehole images, right at the drill-bit to optimize the operational decisions post drilling.Applications\/Significance\/Novelty: This comparative study proves the applicability of at-bit imaging with respect to LWD images for geological interpretation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 10: New Ideas to Maximize Field Value<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRobin Singh, Anil Ramkhelawan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Revitalizing Mature Shale Basins: Evaluating Behind-the-Meter Power Generation, Geothermal Conversion, Energy Storage, and Produced Water Valorization Pathways<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Nash*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(AAPG)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study examines strategies for revitalizing production economics in mature and lower-quality shale basins across the United States. We investigate four primary pathways: behind-the-meter (BTM) electricity generation for data centers, repurposing depleted wells for compressed air and hydrogen energy storage, converting inactive wells to geothermal energy production, and treating produced water for agricultural irrigation and industrial cooling applications. The analysis spans multiple basins including the Barnett, Appalachia (Marcellus\/Utica), Illinois Basin, and mature Permian assets, evaluating how operators transform stranded or marginally economic gas resources into new value streams amid unprecedented electricity demand driven by artificial intelligence infrastructure development.Methods\/Procedures\/Process: We conducted a comprehensive review of operator announcements, pilot project results, regulatory developments, and techno-economic analyses from 2024-2025. Data sources included DOE Wells of Opportunity initiative reports, Penn State CAES efficiency studies, NREL geothermal conversion assessments, EPA produced water treatment guidelines, and industry disclosures from operators including BKV Corp., Diamondback, Chevron, and EQT. Economic metrics evaluated include levelized cost of electricity, drilling cost savings from well repurposing, produced water treatment costs per barrel, and hydrogen storage recovery rates. Basin-specific analyses incorporated infrastructure proximity, thermal gradients, reservoir characteristics, and distance to emerging load centers.Results\/Observations\/Conclusions: BTM gas-fired generation emerged as the dominant revitalization strategy, with major projects announced across Texas (Stargate, 4.5+ GW), Pennsylvania (Homer City, 4.5 GW), and Louisiana. BKV Corp. demonstrates the model in the mature Barnett, leveraging undervalued assets for gigawatt-scale hyperscaler deals. Geothermal conversion of inactive wells reduces levelized costs by 11% and saves approximately $7 million in drilling costs per project (Tuttle, OK). Geothermal-assisted CAES improves storage efficiency by 9.5% utilizing the nation&#039;s 3.9 million depleted wells. Hydrogen storage field trials achieved 84.3% recovery. Produced water treatment costs $0.10-$8\/bbl depending on end-use, with 2025 EPA rule revisions enabling data center cooling and agricultural reuse pathways.Applications\/Significance\/Novelty: This analysis provides operators a decision framework for evaluating alternative monetization pathways in basins previously considered sub-economic. The convergence of AI-driven electricity demand (projected 515-720 TWh by 2030), grid interconnection backlogs exceeding five years, and regulatory enablement creates unprecedented opportunities. Novel applications include lithium extraction from Marcellus produced water (97% recovery demonstrated), integrated CCS with data center power (Google Illinois), and closed-loop geothermal using existing wellbores (Wells2Watts consortium). Results suggest mature basins including the Barnett, Illinois, and Appalachia may experience significant asset revaluation as operators pivot from hydrocarbon extraction toward integrated energy services.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Maximizing Permian Basin Shale Oil Recovery While Lowering the Cost of Production<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Downey*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Shale Ingenuity LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Permian Basin Wolfcamp shale has produced about 6.1 BBO and 29 TCF gas as of July, 2025, from 36,500 wells, with an average cumulative oil and gas recovery of 225,000 barrels of oil, 1 BCF of gas and 1 Million barrels of water. The Midland Basin Spraberry wells have produced an average of 106,000 barrels of oil and 307 MMCF gas as of November, 2024, from 42,543 wells, with a cumulative oil and gas recovery of 4.1 Billion barrels and 12 Trillion cubic feet of gas. Estimated Wolfcamp primary oil recovery is projected at only about 6% of the oil in place, and even less in the Spraberry. As a result, there is a tremendous amount of oil remaining after primary production that may be recovered via novel wells stimulation and enhanced oil recovery (EOR) processes.Methods\/Procedures\/Process: This paper describes the application of a commercial compositional reservoir simulation model to assess two novel, proprietary shale oil EOR processes and a novel hydraulic fracture stimulation method in production history matched wells producing oil and gas from the Wolfcamp B shale and the Lower Spraberry shale, Midland Basin. The paper illustrates how these proprietary shale oil EOR and hydraulic fracture stimulation treatment processes may be implemented to significantly enhance oil and gas recovery, at about half the cost\/barrel of primary production and with lower GHG emissions. In addition, the paper illustrates how these processes may be implemented in the presence of inter-well fractures.Results\/Observations\/Conclusions: The novel shale oil EOR methods, SuperEOR and UltraEOR, use a triplex pump to inject a tuned composition liquid solvent into the shale oil reservoir, and a method to recover the injectant at the surface, for storage and reinjection. The processes are fully integrated with compositional reservoir simulation to optimize the recovery of residual oil and gas during each injection and production cycle and have numerous advantages over cyclic gas injection \u2013 faster and greater oil recovery, lower cost than primary production, less risk of inter-well communication, and lower environmental impact. Solvent compositional tuning is shown to enable optimum oil recovery. These EOR systems are closed loop, don&#039;t need artificial lift, and have overall environmental impact than primary recovery.Applications\/Significance\/Novelty: In 2023, an untuned solvent SuperEOR process in the Eagle Ford shale recovered 52,800 bbls of oil in 9 months, about 54% more oil than its 8-year prior cumulative. Additional Eagle Ford SuperEOR projects are currently in development. Compositional reservoir simulation modeling of these novel processes in the modeled Wolfcamp B shale and Lower Spraberry shale wells, indicates they may increase oil recovery by more than 3X and 5X of forecasted primary production in 10-20 years of operation, respectively, enabling an enormous amount of new oil production at about half the cost per barrel of new wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fracturing Sleeve System Striving for Excellence in Unconventional Resource Plays: Present and Future<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Lolon*, T. Reynolds, M. Hollaway, M. Mikitin and F. Urbano\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Liberty Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Development of new completion systems to improve efficiency and reduce carbon intensity for multistage horizontal well fracturing continues to be an active pursuit. Plug-and-perforate (PnP) using wireline-deployed bottomhole assembly (BHA) has been the preferred method of completing horizontal wells. However, operators still use sliding sleeve (SS) systems in some cases such as stand-alone horizontal wells, extended reach laterals, conformance control, or wells with restrictions due to casing deformation, but these systems continue to fall behind in ever-increasing multi-well pad completions. Current ball drop sleeve systems can be improved due to their limitation in stage counts, higher levels of ball-seat friction, and post-frac milling. Coiled tubing (CT) activated sleeve systems are limited by CT reach and are rate-restricted to prevent erosion. Sleeve-and-dart methods will benefit from continued field trials to mature the technology.Methods\/Procedures\/Process: The objective of this study is to benchmark fracturing sleeve systems specifically typical completion parameters without endorsing a specific system. Proper installation of the sleeve systems and operational differences encountered that affect the stimulation operations are highlighted. This study also presents a stage-level CO2e emissions analysis on ball drop-activated sleeve (BDAS), coiled tubing-activated sleeve (CTAS), and dart-activated sleeve (DTAS) methods following the previous framework by Hollaway, M. and Aune, R. (2025) where the total well emissions were normalized on a per-lateral-foot basis to consistently compare only frac-related emissions across different completion system methodologies.Results\/Observations\/Conclusions: In the Western Canadian Sedimentary and Permian basins, DTAS treatments used higher rates and hydraulic horsepower (HHP) than BDAS and CTAS treatments when well and reservoir parameters such as measured depth (MD), fracture gradient, and treatment tubulars are similar. CTAS treatments were associated with higher stage counts and higher proppant mass and fluid volume pumped per lateral foot than BDAS and DTAS treatments. These factors (HHP, pump hours, stage length, lateral length, etc.) will have an associated emissions implication. This study implies that wireline associated CO2e emissions are less than the estimated amount claimed in the earlier assessment (Watkins, T. et al., 2023). In addition, the CTAS and DTAS methods result in 12% and 38% higher frac-related CO2e emissions per lateral foot (metric tons\/ft) than the BDAS method, in this particular case, due to primarily higher stage counts (CTAS) and increased pump rates and stage pump times (DTAS).Applications\/Significance\/Novelty: This paper provides valuable updates, merits, and drawbacks of the fracturing sleeve completions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: CO\u2082 Huff-and-Puff and Cyclic Gas Injection Mechanisms<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tDeniz Paker, Rohan Vijapurapu\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Enhanced Oil Recovery Effects of Adding Non-Ionic Surfactants During CO\u2082 Huff-and-Puff<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. P. Grindle*<sup>1<\/sup>,<sup>2<\/sup>, D. Tapriyal<sup>1<\/sup>,<sup>3<\/sup>, L. Burrows<sup>1<\/sup> and A. Goodman<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. National Energy Technology Laboratory (NETL); 2. Oak Ridge Institute for Science Education (ORISE); 3. Leidos)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: After primary oil recovery from hydraulic fracturing, there are multiple mechanisms associated with improved oil recovery using CO2 as an injectant, including CO2 diffusion into oil, oil swelling, and CO2-oil interfacial tension reduction. Previously collected data indicates that these mechanisms can be further improved with an addition of a non-ionic surfactant dissolved into the CO2 phase, which impacts the wettability properties of unconventional rocks. In this study, two non-ionic surfactants dissolved in CO2 were tested to evaluate wetting changes and oil recovery from oil-saturated Mancos and Eagle Ford Shale cores at 4000 psig and 80 \u00b0C.Methods\/Procedures\/Process: Non-ionic propoxylated (TDA-9) and propoxylated-ethoxylated (2-EH-PO5-EO10) alcohol surfactants were selected due to their ability to dissolve in CO2 at these temperatures and pressures. Contact angle measurements showed that both surfactants when dissolved in CO2 at 0.1wt % changed the wettability of Eagle Ford shale from oil-wetting to more CO2 wetting \u2013 82o for TDA-9 and 141o for 2-EH-PO5-EO10. Oil recovery was evaluated both by weight measurements and by NMR spectroscopy after four huff-n-puff cycles on oil-saturated outcrop Eagle Ford and Mancos cores.Results\/Observations\/Conclusions: Weight measurements showed that oil recovery with CO2 ranged between 20 and 30%. Oil recovery with surfactants dissolved in CO2 increased oil recovery was 36% with TDA-9 and 28% with 2EH-PO5-EO10 after the first huff-n-puff iteration. Nuclear Magnetic Resonance spectroscopy (NMR) was applied to further examine oil recovery with 2EH-PO5-EO10 dissolved in CO2 during huff-n-puff tests with Mancos cores. NMR spectra showed that oil is removed throughout four different pore regimes within the Mancos core.Applications\/Significance\/Novelty: Improved oil recovery using CO2-soluble nonionic surfactants deserves further attention, as this laboratory study demonstrates a significant increase in oil recovered with only a minimal addition of surfactant (&lt;0.2% w\/w). These surfactants cost roughly $3\/lb at large scale, meaning additions could cost less than $6 per ton of CO2 in chemical costs. This study presents a comparison between two viable CO2-soluble nonionic surfactants, expanding on previous work and exploring the effects of CO2-soluble surfactants on oil extraction from various pore sizes.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Linking Wettability to CO2 Huff-and-Puff Efficiency: Insights from Facies-Controlled Experiments on Tight Reservoir Cores<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. K. Aljishi, B. A. Mohamed*, N. Truong, S. T. Dang and C. Rai\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Oklahoma)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study investigates how rock properties influence CO2 Huff-and-Puff (HnP) recovery in tight cores. Since wettability governs fluid pathways in reservoirs, the work quantifies wetting-phase fractions and examines their impact on oil and brine mobilization during CO2 HnP alongside mineral and pore-structure characteristics. The goal is to determine how wetting state and pore architecture collectively control multi-cycle recovery efficiency under controlled laboratory conditions.Methods\/Procedures\/Process: Tight-core plugs representing silicate-, mixed-, and carbonate-rich facies were tested under seven multi-cycle CO2 HnP runs at 35 \u00b0C and 2,030 psi. Each cycle included fixed pressurization, 24-hour soaking, and controlled production periods. Phase saturations, recovery factors, and uncertainties were measured using NMR and gravimetry. Previous rock characterization and NMR-derived wettability data were correlated with recovery behavior to establish links between mineral composition, wetting state, and fluid mobilization trends.Results\/Observations\/Conclusions: Silicate-rich and mixed-mineral facies exhibited rapid oil cleanup, achieving 90-100% recovery within three to five cycles regardless of initial saturation. Carbonate-dominated plugs plateaued at 50-60% under oil-only conditions but reached near-complete recovery when dual fluids were present. Brine mobilization remained slower across all cases. Pore-geometry metrics showed weak correlations, confirming wettability and mineral-linked interactions as the principal controls on cycle-wise CO2 recovery performance.Applications\/Significance\/Novelty: Results underscore the importance of laboratory-scale experiments for clarifying factors that govern CO2 Huff-and-Puff recovery in tight formations. The findings show that wettability and mineralogy primarily control fluid mobilization and recovery efficiency. This laboratory framework supports more accurate identification of reservoir targets, guiding the design and optimization of CO2-based enhanced recovery strategies in low-permeability systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Chemical-Assisted CO2-Oil Miscibility Pressure Reduction for Diffusion-Driven Processes in Shales<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Liu<sup>1<\/sup>, N. Tai<sup>1<\/sup>, Y. Guo<sup>2<\/sup>, Y. Shi*<sup>1<\/sup> and K. Mohanty<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Geosciences; 2. University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: CO2 huff-n-puff, as a key development strategy for shale reservoirs, has been considered to be a diffusion-dominated process. However, operation design for CO2 EOR is still commonly based on the minimum miscibility pressure (MMP), a parameter traditionally defined for advection-dominated gas flooding in conventional reservoirs. This mismatch may lead to inaccurate prediction of huff-n-puff performance in the field. This study aims to evaluate the applicability of VIT-based miscibility-pressure determination for diffusion-dominated CO\u2082 huff-n-puff processes. The study also evaluated the impact of chemical additives (with different solubility characteristics) on CO2 miscibility pressure and gas-oil interfacial behavior.Methods\/Procedures\/Process: Both crude oil from shale reservoir and simulate oil were tested. Chemical additives with different solubility characteristics were evaluated, including oil-soluble chemicals (alcohols\/nonionic surfactants), water-soluble surfactants (zwitterionic). Miscibility pressure was determined using VIT techniques. A custom-designed HPHT visual cell was used, which enables both pendant drop method (PDM) and capillary rise method (CRM). Chemical screening was then conducted to evaluate the miscibility-pressure reduction potential of selected additives, including alcohols and surfactant-based candidates.Results\/Observations\/Conclusions: For both the CO\u2082\u2013dodecane and CO\u2082\u2013simulated oil systems, PDM and CRM provided comparable miscibility-pressure estimates, confirming the reliability of VIT-based methods for diffusion-driven miscibility evaluation. Compared with PDM, CRM offered clearer visualization of gas\u2013oil interactions and allowed direct observation of the miscibility condition through the disappearance of capillary rise. In CRM data interpretation, linear fitting of the product of capillary height and oil\u2013gas density difference provided a more representative miscibility-pressure estimate than fitting capillary height alone, showing better agreement with visual observations. Among all tested chemical additives, 1-hexanol showed the highest miscibility-pressure reduction efficiency; however, the reduction was only 3.8%, indicating that the overall chemical effect on miscibility pressure was limited.Applications\/Significance\/Novelty: Unlike conventional studies that rely on MMP, this study emphasizes diffusion-dominated miscibility pressure, which is more applicable to shale CO\u2082 huff-n-puff processes. In contrast to most existing CO\u2082 miscibility studies that have focused primarily on oil-soluble chemicals, this work evaluates a broader range of chemical additives. The comparison between PDM and CRM improves the reliability of VIT-based miscibility-pressure evaluation. The chemical-additive results show that the tested additives have limited miscibility-pressure reduction performance, suggesting that chemicals mainly modifying interfacial properties may have limited ability to substantially reduce CO\u2082\u2013oil miscibility pressure.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Rock and Hydraulic Fracture Modeling<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRuiting Wu, Jichao Yin\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Stress Space-Driven Completions: An Integrated Geomechanical Framework<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Gandomkar* and A. Dalir\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ConocoPhillips)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional reservoir (UR) development relies on vertical hydraulic\u2011fracture propagation from horizontal wells in ultra\u2011low\u2011permeability formations. In some UR basins, fracture effectiveness and horizontal well performance correlate more strongly with stress variations than with reservoir properties. This work developed from the hypothesis that reduced fracture effectiveness and well performance in such basins result from stress\u2011shadowing\u2011induced escalation of minimum horizontal stress (Shmin) above vertical stress (Sv), i.e., stress reversal, which promotes horizontal fracture development (Dontsov, 2025). Deconvolution of other ineffective\u2011fracturing mechanisms (e.g., fluid and proppant diversion into pre\u2011existing strike\u2011slip faults) is shown to be critical for isolating the impact of stress reversal.Methods\/Procedures\/Process: Stress shadowing and its potential to induce stress reversal during multi\u2011stage hydraulic fracturing were previously established (Roussel and Sharma, 2011), with the associated stress escalation demonstrated through stage\u2011by\u2011stage instantaneous shut\u2011in pressure (ISIP) analysis (Roussel, 2017). Horizontal fracture propagation has also been inferred in prior work using casing deformation (Uribe-Patino et al., 2024), induced seismicity, microseismic (MS), DAS fiber monitoring, and related diagnostics. More recent studies proposed the delta of ISIP\u2013Sv as a more reliable indicator of effective stimulation than absolute ISIP values and explored its relationship with completion intensity (Schroeder and Aklilu, 2024); however, a clear linkage to well production has not been demonstrated.Results\/Observations\/Conclusions: This work advances the interpretation that horizontal fracture development may be inferred from de\u2011escalating ISIP trends, which have traditionally been attributed to operational effects or measurement noise. ISIP de\u2011escalation is interpreted here as indicative of limited vertical fracture propagation, implying preferential fluid and proppant placement within horizontal fractures. Excluding ineffective stages characterized by ISIP de\u2011escalation, the remaining stage count exhibits a significantly stronger correlation with well production than total stage count. This behavior is reproduced using calibrated coupled flow\u2013geomechanical simulations capable of modeling horizontal fracture propagation under stress\u2011reversal conditions. This study further identifies a strong correlation between variations in stress space (Sv-Shmin) and well performance, suggesting a critical threshold beyond which stress escalation\u2014particularly under tight cluster spacing\u2014promotes horizontal fracture growth and degraded well performance. Sensitivity to this threshold is demonstrated using calibrated coupled flow\u2013geomechanical simulations across a range of completion parameters.Applications\/Significance\/Novelty: Parallel unpublished internal studies indicate that geological structures, such as thrust faults, can further degrade fracture effectiveness by perturbing stress space and modifying rock fabric. Accordingly, this paper presents an integrated framework and workflow that combine stress space, mechanical stratigraphy, and structural geology to optimize completion design and predict well performance across alternative completion strategies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">In-Situ Geomechanical Properties and Stress Characterization Using a High-Precision Pressuremeter in a Deep Well<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. A. Nagel, A. Iuferova, M. Olan and C. Fehr*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Integrity Insitu)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: A novel, high-precision pressuremeter tool was deployed via wireline at a depth of 4,730 ft in a low-porosity limestone formation in Central Oklahoma. The primary objective was to characterize in-situ geomechanical properties and stresses under true reservoir conditions. Pressuremeter technology has been used since the 1950s predominantly in geotechnical applications at shallow depths. In contrast, this new-generation pressuremeter is designed to quantify the in-situ mechanical response and stresses of rock formations relevant to oil and gas, and other systems, at reservoir-scale depths. Particularely in unconventional developments it is aimed to support the modeling and design of hydraulic fracturing by providing stress and mechanical property data along lateral wells.Methods\/Procedures\/Process: The tool incorporates a central core with twelve instrumented arms in contact with a rubber membrane. These arms expand against the borehole wall to measure radial strain with micron-level precision while sustaining pressure increments up to 12,500 psi above borehole hydrostatic conditions. Standard testing procedures include multiple loading and unloading cycles to evaluate elastic properties, with the potential to induce shear or tensile failure around the borehole. No direct stress measurements or laboratory-derived geomechanical data were available locally. Accordingly, published data and petrophysical logs were used to benchmark results from the pressuremeter test.Results\/Observations\/Conclusions: The pressure\u2013strain response was elastic, with no evidence of yielding or shear failure. The max total pressure was 14,700 psi, with a max radial strain of 4%. Seven loading\u2013unloading cycles allowed shear modulus estimate of 2.1 Mpsi. Multiple tensile failure events were detected, including two exhibiting fracture reopening, propagation, and closure. Fracture closure analysis included direct strain measurements at the borehole wall. A consistent closure pressure of 4,135 psi was interpreted, corresponding to a gradient of 0.87 psi\/ft\u2014in the range with regional published stress data. Stress orientation was inferred by linking azimuthal strain anisotropy to the anisotropy of the horizontal stress field, with a SHmax orientation trending East\u2013West, in agreement with regional stress maps. Inversion of SHmax magnitude based on observed strain anisotropy yielded a SHmax value of 1.03 psi\/ftApplications\/Significance\/Novelty: This test demonstrated the capability to acquire high-fidelity geomechanical data in deep subsurface environments, including elastic properties, insitu stress magnitudes without fluid injection, and reliable in-situ stress orientations. Ongoing efforts include the development of FEM and DEM models to investigate induced fracture geometry and the influence of natural fractures and bedding. Future tests will incorporate in-situ creep measurements and will extend deployment to both vertical and horizontal wells in unconventional formations, providing critical data for hydraulic fracturing modeling and design in settings where geomechanical information is typically very sparse.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pioneering Numerical Framework for Simulating Horizontal Hydraulic Fracture Propagation in Shallow Coal Mine Environments<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Cong<sup>1<\/sup>, Y. Feng<sup>1<\/sup>, S. Cheng<sup>1<\/sup>, P. Lin<sup>1<\/sup>, X. Shang<sup>1<\/sup>, K. Zhao<sup>1<\/sup> and C. Liu*<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. CCTEG Coal Mining Research Institute; 2. SimTech LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to develop a numerical modeling framework to simulate horizontal hydraulic fracture propagation in shallow coal mine settings, where a vertical well is fractured just above an active tunnel to intentionally weaken the strength of overburden. The objective of the paper is to quantify multi-stage fracture geometry, assess induced collapse extent, and provide mining engineers with a predictive tool for evaluating controlled-collapse safety strategies in shallow subsurface environments.Methods\/Procedures\/Process: Because shallow coal mines exhibit a reverse-fault stress regime where vertical stress is minimal, a customized modeling approach is required. Well-logging geomechanics data (Young&#039;s modulus, Poisson&#039;s ratio, minimum\/maximum horizontal stress, vertical stress) from the vertical well were incorporated into an in-house 3D fracture simulator. A coordinate-system rotation was applied to transform the reverse-fault setting into a virtual normal-fault regime, enabling accurate representation of horizontal fracture growth. Multi-stage fracturing scenarios were then simulated to quantify fracture geometry and assess their ability to induce controlled collapse above the mine tunnel.Results\/Observations\/Conclusions: Results from the new simulation framework show that horizontal fractures above the mine tunnel average ~300 m in major axis and ~60 m in minor axis. The modeled geometry aligns well with microseismic patterns and high-frequency DAS signals, confirming horizontal growth and lateral reach. Under a fixed 2-hour pumping schedule, injection-rate tests (12\u201320 m3\/min) indicate near-linear fracture enlargement, but a 45 MPa surface-pressure cap restricts operations. An optimal rate of 13.5\u201314 m3\/min maximizes fracture size while staying within safe pressure limits.Applications\/Significance\/Novelty: This work presents the first-ever numerical framework for simulating horizontal hydraulic fracture growths in shallow coal mining environments. The approach enables reliable prediction of fracture geometry and induced-collapse, offering mining engineers a new quantitative tool to enhance tunnel safety, optimize controlled-collapse treatments, and extend unconventional fracturing technologies into mining applications.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Applications Oriented Technologies for Emission Reduction<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJonathan Ortiz, Nadia Mouedden\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Toward Net-Zero Frac Fleets Using Nanoparticle Based Pulsed-Power Plasma Stimulation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. A. Gabry*, S. Nguyen and M. Y. Soliman\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Houston)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper quantifies the carbon footprint of modern frac fleets and evaluates a nanoparticle-based pulsed-power plasma stimulation (PPPS) concept as a pathway toward lower-horsepower, lower-emission completions. We focus on U.S. and Canadian shale plays, where average fleet hydraulic horsepower (HHP), pumped volumes, and annual frac stage counts have risen sharply despite efficiency gains. We assess how displacing a portion of conventional surface HHP with downhole electrified pressure pulses can reduce breakdown pressure, average treating pressure, and CO2 intensity at the pad, well, and stage level.Methods\/Procedures\/Process: We first compile recent industry data on active frac fleets, average fleet HHP, and annual frac stage and well counts to frame the emissions challenge for diesel and electric frac (e-frac) fleets. CO2 intensity is estimated from published fuel-consumption factors (kg CO2\/HHP-hr) for diesel and natural-gas-powered units. We then couple a nanoparticle-based PPPS model\u2014capable of generating dynamic bottomhole pressure pulses approaching 100,000 psi over microsecond timescales, localized near the wellbore\u2014with a commercial hydraulic-fracturing simulator. Parametric cases compare required HHP, treating pressures, proppant placement, screenout tendency, and stage design using the same water-based fluids and rock properties, with and without PPPS assistance.Results\/Observations\/Conclusions: The compiled activity and HHP data show that average fleet size has increased from ~35,000 to 60,000 HHP over the last decade, while annual frac stages remain in the hundreds of thousands. Simulation results show that incorporating PPPS substantially reduces breakdown and average treating pressures for representative shale completions. For a given target fracture geometry, the required surface HHP can be lowered while maintaining or improving fracture height, half-length, and conductivity. This PPPS integration directly cuts fuel use, CO2 per job, screenout risk, and mechanical wear on surface equipment, indicating that PPPS can complement rather than replace conventional hydraulic fracturing by offloading the most energy-intensive rock breakdown to localized downhole pressure pulses.Applications\/Significance\/Novelty: This paper is among the first to link detailed frac-fleet activity and emissions metrics with a physics-based PPPS model embedded in integrated fracture modelling. The work moves beyond generic electrification discussions by quantifying how much hydraulic horsepower can realistically be displaced by downhole pulsed-power events and by translating those reductions into CO2 savings, giving practicing engineers actionable guidance for designing lower-HHP, lower-emission stimulation programs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Turning Flared Gas in the Permian Basin into Reserves: Quantifying Associated Gas Losses and Salado Formation Subsurface Storage Potential<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Ruiz Maraggi*, L. Ko, E. Rodriguez Calzado and L. Moscardelli\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Bureau of Economic Geology, The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In 2024, more than half of the U.S. flared gas originates in the Permian Basin, totaling 190 Bscf per year. At natural gas prices of 2 to 4 USD per MMBtu, this corresponds to roughly 400 to 800 million USD of lost revenue per year and approximately 10 million tons of CO2 emissions per year, with no reserves added. This work presents the first basin-scale, data-driven study that turns this problem into a subsurface storage opportunity by: (a) assessing the natural gas storage capacity of Salado Formation in the Permian Basin, (b) quantifying the uncertainty associated with its storage potential, and (c) mapping the \u201csweet spots\u201d of high-potential, low-uncertainty storage regions to mitigate flaring and enable future reserve additions.Methods\/Procedures\/Process: Salado is a bedded salt formation in the Permian Basin where solution-mining halite with water creates caverns for natural gas storage. First, we compile more than 4,000 well logs with three key properties: top depth, net halite thickness, and halite percentage. Depth relates to gas density, thickness to cavern volume, and halite percentage to salt purity. Second, we build basin-scale Kriging maps. Third, sequential Gaussian simulations generate P10\u2013P50\u2013P90 scenarios and quantify uncertainty. Fourth, we overlay surface infrastructure to identify suitable regions for cavern placement. Fifth, thermodynamic calculations estimate working gas capacity for the Kriging and P10\u2013P50\u2013P90 scenarios. Finally, a high-potential, low-uncertainty metric maps \u201csweet spots\u201d for natural gas storage.Results\/Observations\/Conclusions: Salado Formation total working gas potential in the Permian Basin ranges from 9 to 217 Tscf across all scenarios, with a median working gas storage potential of 47 Tscf. Our uncertainty analysis highlights Lea County (New Mexico), Gaines and Andrews counties (Texas) as high-potential, low-uncertainty areas. These 3 counties account for more than 60% of the natural gas storage potential of the Salado Formation. Our results show that to offset 100% of current Permian flaring, about 95 caverns with 2 Bscf of working gas each would be required; mitigating 50% of flaring would require approximately 47 of such caverns.Applications\/Significance\/Novelty: This work is the first basin-scale, data-driven assessment that directly links Permian Basin flared gas to Salado Formation storage potential. We integrate more than 4,000 wells, Kriging, sequential Gaussian simulation, infrastructure constraints, and thermodynamic calculations to deliver P10\u2013P50\u2013P90 storage maps with explicit uncertainty. By ranking high-potential, low-uncertainty regions and estimating the number of 2 Bscf caverns needed to offset 50\u2013100% of flaring, we provide an actionable screening tool for operators and regulators, supported by a web-based interactive map that allows visual exploration of storage sweet spots and flaring mitigation scenarios.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Oilless Compression to Capture Gas and Eliminate Flaring<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Schmidt*<sup>1<\/sup>, A. Lorenzen<sup>2<\/sup>, T. Mayer<sup>2<\/sup>, B. Wallace<sup>3<\/sup> and J. Hays<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. EERC; 2. Steffes LLC; 3. IDEX Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The first oilless natural gas compression technology is being deployed in North Dakota with support from the U.S. Department of Energy, the North Dakota Oil and Gas Research Program, and oil and gas industry partners. The technology eliminates flaring at the wellsite and is designed to provide cost-effective gas capture and compression of low-pressure (LP) gas. The compressor is designed to be cost effective for gas flows between 10 to 60 MCFD, eliminates maintenance of lubricating oil, achieves full turn-down, and is designed for liquid-rich gas. Units are presently in field testing with over 30 installations planned for completion by mid-2026. The paper will present a description of the technology, the operating history, and gas capture performance from the field tests.Methods\/Procedures\/Process: The process includes oilless natural gas compression and patented methods to capture and utilize LP gas at highest value. Compared to a conventional vapor recovery unit (VRU), the technology does not require lubricating oil which eliminates the need for oil changes and the associated complex recycle controls within a typical VRU that provide gas turn-down capacity and return lubricating oil to the rotating parts. The innovation greatly simplifies the compressor skid and improves the economics. The oilless design can tolerate liquid-rich gas from heater-treaters and storage tanks. The compressor is used to recycle gas within a production facility and comingle recovered gas-liquids with the oil production. The compressor can also capture tank vapors for use as fuel on-site.Results\/Observations\/Conclusions: Results are being obtained from initial field tests. So far, over 4000 hours of operation time have been logged for the first gas capture units. Installations include new multi-well sites and legacy facilities for both unconventional and conventional oil production. Applications include capture and recycle of heater-treater gas, capture of storage tank vapors for use as fuel on-site, and vapor recovery directly to gas sales. Results will include maintenance experience with oilless operation, gas capture efficiency, process control performance, and winterization performance.Applications\/Significance\/Novelty: According to the United States Energy Information Administration (EIA) most gas flaring occurs in Texas, North Dakota, and New Mexico which primarily include the Permian, Bakken, and Eagle Ford unconventional oil plays. While operators have made recent progress to address flaring, many sources of low-rate gas flaring remain, mostly because of poor economics. LP gas from heater-treaters, storage tanks, and on legacy or low-producing oil production sites in aggregate, comprise a significant share of the flared gas volume in the United States. New technology is needed to address costs and enable the economic capture of this resource, which is the objective of the proposed publication.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Subsurface Measurement and Control Technologies for Geothermal Efficiency<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSaeid Khorsandi, Tianjia Huang\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Analytical Solution for Heat Recovery in Enhanced Geothermal Systems (EGS) Including Complex Fracture Geometry<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. A. Acuna*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(J Acuna Consulting, LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: EGS is currently regarded as a key technology that taps into unconventional oil and gas knowledge to unleash the vast geothermal energy potential of hot rock found at depth throughout the entire planet. Analytical models for EGS make possible quick estimations of thermal performance. Unfortunately, available solutions are limited to systems with equally spaced fractures. In this paper we derived an analytic solution for systems with unequally spaced fracture systems and complex fracture geometry in general. New parameters and graphic techniques for analysis of production data are demonstrated and simulated examples are presented.Methods\/Procedures\/Process: The analyzed EGS system consists of one horizontal fractured producer with injectors at each side. The rock is impermeable with complex fracture networks consisting of parallel but unequally spaced fractures called fracture swarm models. The solution of many single fracture models with different spacings at each side and variable flow in Laplace domain are superposed according to the fracture swarm model geometry to get the response of the total system. Different performance stages are identified and simplified equations for each stage proposed. Equivalence of different quantities in porous media and heat transfer analysis are shown. Results are illustrated with numerical modeling.Results\/Observations\/Conclusions: Thermal response of an EGS well can be analyzed with diagnostic log-log plots of normalized power and energy versus dimensionless time. There are four stages akin to flow regimes in RTA. The first one is an isothermal stage, the second one is called thermal breakthrough, the third one shows thermal transient behavior, and the fourth stage has closed boundary behavior. Fitting simplified equations for different stages allows parameter estimation. Log-log diagnostic plots illustrate these four stages, but sometimes, if the maximum fracture spacing is small, the second and third stage are absent.Applications\/Significance\/Novelty: This new methodology to analyze EGS wells behavior using diagnostic log-log plots is akin to those used in oil and gas such as PTA and RTA. We propose calling it Thermal Transient Analysis (TTA). The reasons for the different stages are explained. Effects of maximum and minimum fracture spacing, fracture height, well spacing, circulating flow rate, number of fractures, fracture complexity, etc. are shown. This new method provides a fast way to analyze performance and evaluate EGS configurations. Additionally, geothermal technology is presented in a manner that relates to flow in porous media for the benefit of petroleum engineers.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Pressure\u2013Tracer Diagnostics for Engineered Geothermal and Unconventional Reservoirs Using a Dual-Porosity-Dual-Permeability Framework<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Mindygaliyeva* and H. Kazemi\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study develops a unified diagnostic and modeling framework that links Enhanced Geothermal Systems (EGS) with unconventional shale reservoirs. The objective is to characterize fracture connectivity, stimulated volume, and fracture\u2013matrix interactions using coupled pressure transient and tracer transport modeling. The framework supports clean-energy applications by enabling technology transfer from EGS stimulation workflows to unconventional reservoir development and vice versa, repurposing, and gas injection-EOR strategies.Methods\/Procedures\/Process: A dual-porosity-dual-permeability (DPDK) model was used to simulate pressure falloff behavior, tracer transport, and multi-continuum storage. Laboratory core data constrained matrix porosity, permeability, and fracture properties. Analytical solutions and field-scale numerical simulations were applied to estimate effective fracture permeability, aperture, drainage volume, and advective-dispersive tracer return signals. The workflow integrates EGS stimulation diagnostics with unconventional reservoir modeling needs.Results\/Observations\/Conclusions: Results show that transport in both EGS granitoids and unconventional shales is dominated by micro- and macro-fracture networks, with limited matrix diffusion. Effective fracture permeability is several orders of magnitude larger than matrix permeability, controlling stimulation efficiency, water loss, tracer behavior, etc. The model captures near-wellbore mixing, transient storage, and advective dispersion. These findings highlight the shared physics between EGS and stimulated shale petroleum reservoirs.Applications\/Significance\/Novelty: The framework directly supports both geothermal (EGS) development in hot-dry-rocks and enhanced oil recovery (EOR) in unconventional shale reservoirs. The paper offers a physics-based method supporting the stimulated reservoir volume via pressure transient analysis, tracer response analysis, and the role of molecular diffusion in enhancing oil recovery in shale reservoirs. The novelty lies in defining the diffusive nature of heat extraction versus producing more oil from a shale reservoir within a stimulated reservoir environment.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Acoustically Derived Perforation Efficiency and Near Field Conductivity Using High-Resolution Surface Pressure Analysis in Geothermal Applications<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Gabel*, M. Khan and S. Rahimi-Aghdam\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Seismos, Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In Enhanced Geothermal Systems (EGS), connecting wells through multi-stage stimulation is critical to maintain the connectivity, yet extreme downhole conditions challenge conventional downhole diagnostics. This work applies a fully surface-based acoustic diagnostic to quantify near-wellbore injectivity and flow distribution in geothermal completions. The objective is to isolate how much of the total pressure drop is attributable to wellbore friction vs. perforation\/fracture connection into the formation. The scope includes pre- and post-stimulation measurements to track changes in near-wellbore impedance during geothermal injection. By analyzing end-of-stage water-hammer signals, we index near-wellbore fracture conductivity (connectivity), analogous to transmissivity gains observed in recent superhot EGS tests.Methods\/Procedures\/Process: High-resolution surface pressure data recorded during controlled-rate drops (water-hammer events) were analyzed to quantify the pipe, perforation, and near-wellbore friction components. This acoustic friction analysis yields the perforation efficiency for each stage and a fluid distribution uniformity index across clusters. Repeated measurements before and after stimulation captured changes in near-wellbore transmissibility. The surface-derived connectivity index provides a real-time measure of near-wellbore fracture conductance with-out downhole instrumentation.Results\/Observations\/Conclusions: The perforation efficiency and uniformity index were measured across the lateral for multiple completion designs. Different cluster configurations were evaluated to assess their impact on stage-level injection uniformity. The near-field conductivity index varied along the lateral and among cluster designs, indicating that certain sections exhibited stronger near-wellbore conductivity and improved hydraulic connectivity.Applications\/Significance\/Novelty: The workflow offers operators a practical way to quantify injectivity distribution and evaluate stimulation effectiveness without deploying downhole tools or interrupting operations. It complements diagnostics like fiber-optic monitoring and multi-well interference tests by yielding similar insights from surface pressure data alone. The ability to track changes in near-wellbore losses provides early insight into pressure-limited behavior and supports design optimization. Adapting this acoustic methodology from unconventional introduces a new, data-driven diagnostic for geothermal development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: Reservoir and Production Forecasting I<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        1:35 PM &#8211; 2:50 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSebastien Matringe, Rong Lu\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t1:35 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Hybrid Data-Driven and Physics Approach for Dynamic BHP Estimation and Well-Performance Tracking in Coal Seam Gas (CSG) Fields<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tU. Sinha*, H. Zhu, P. S. Chauhan and H. Zalavadia\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Xecta Digital Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Progressive Cavity Pumps (PCPs) are critical for dewatering Coal Seam Gas (CSG) wells, yet estimating BHP at scale is difficult due to gas interference, pump wear, and limited gauges. A scalable approach to estimate OGIP, reservoir pressure and gas\/water forecasts accurately has been a challenge for CSG wells due to dual-porosity behavior and complex cleat-matrix flow. Traditional approaches to combine flowing and static material balance models capture this complex flow physics but carry high uncertainty due to unknown fluid and reservoir properties and dynamics of fluid flow from multiple stacked coal seams. The paper presents hybrid physics and data-driven workflows to overcome these challenges for accurate BHP and reservoir performance estimation that can scale for the entire fieldMethods\/Procedures\/Process: A physics-informed machine learning (PIML) model is proposed that captures BHP in PCP wells by identifying complex relationship between well parameters and BHPs while constraining physics using calibrated torque pump curves to account for fluid property variations downhole. For the reservoir model, a data-driven PI-decline formulation is coupled with a static material balance using Jensen\u2013Smith desorption model and volumetric water material balance. This approach lumps effects of relative permeability shifts, desorption, matrix shrinkage, and flow-regime transitions. Continuous BHP and reservoir performance estimation enables daily PI anomaly detection and opportunity tracking for under-performing wells, as well as well-deliverability forecasting for production optimization.Results\/Observations\/Conclusions: The workflow is applied and tested for over 500 wells in major CSG assets. Case studies show how the PIML approach for BHP calculation lead to very accurate estimates within approximately 25-35 psi range to gauge BHPs. Similarly, the hybrid approach for reservoir model shows accurate history match and forecasts below a median 10% error across the well population. Continuous PI tracking enabled early detection of well-performance anomalies on these wells. PIs are trended with an empirical function, and deviations indicate reservoir or wellbore issues. The BHP model identifies pump degradation trends causing the productivity to decline, attributing remaining issues to inflow to plan for timely restoration jobs. The workflow identified ~100 Mscf\/d in such production opportunities per well.Applications\/Significance\/Novelty: Integrated physics-based and data-driven approach is proposed that provides a scalable, accurate framework for well diagnostics and forecasting, overcoming limitations of conventional CSG modeling. Key contributions - PIML model is developed to predict dynamic BHP behavior and pump diagnostics in PCP wells. Introduces a hybrid workflow combining static material balance with dynamic, data-driven PI modeling to capture complex CSG reservoir behavior. Enables daily forecasts of gas and water production and reservoir pressure under BHP-controlled operations. Detects early well-productivity anomalies, guiding proactive interventions and improving recoverable reserves. Identify production opportunities based on PI anomalies for under-performing wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Novel Model Integrating Physics-Guided Multimodal and Transfer Learning Model for Shale Gas Well EUR Prediction<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Li*<sup>1<\/sup>, J. Fu<sup>1<\/sup>,<sup>2<\/sup>, L. Zhang<sup>1<\/sup>,<sup>2<\/sup>, W. Yan<sup>1<\/sup>,<sup>2<\/sup>, X. Zheng<sup>3<\/sup>, Z. Huang<sup>1<\/sup>, P. Li<sup>1<\/sup> and H. Zhang<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. School of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology; 2. Chongqing Key Laboratory of Green and Efficient Development for Unconventional Oil &amp; Gas; 3. China National Logging Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate prediction of the estimated ultimate recovery (EUR) for hydraulically fractured horizontal wells in shale gas reservoirs is challenging, particularly in early-development blocks with limited production history. Decline-curve analysis and numerical simulation are hindered by data scarcity, leading to unstable matching and unreliable EUR estimates. Moreover, data-driven deep-learning models enable rapid prediction within a single block but exhibit poor inter-block generalization and lack explicit physical constraints. This study proposes a physics-guided, transfer-learning-based multimodal deep-learning model that incorporates production-decline behavior. It integrates fracturing-operation sequences, 3D fracture networks and reservoir properties to improve EUR prediction accuracy.Methods\/Procedures\/Process: A three-stage physics-guided transfer-learning workflow is developed to construct an accurate multimodal deep-learning model for a target shale gas block. First, a decline-constrained Seq2Seq model is trained on data-rich source blocks to extract representative production evolution features. Second, coupled hydraulic-fracturing and reservoir simulations based on engineering rules are performed to generate a multimodal samples dataset. The dataset is then used to train a Transformer-GNN-PointNet++-based surrogate model that is tailored to the target block. Third, a transfer-learning strategy is adopted to migrate the decline-constrained model from the source block to the target block, where only the upper layers are fine-tuned under domain-alignment constraints to adapt to the target block.Results\/Observations\/Conclusions: The results indicate that the proposed framework improves prediction accuracy and robustness across different shale gas forecasting scenarios. For producing wells, the physics-guided dynamic forecasting model reduces MAE by approximately 55-60%. For undeveloped wells, the multimodal EUR surrogate model improves the average R2 by approximately 45.2% compared with the single-modality model using only fracturing operational features. In the small-sample cross-block application, the transfer learning approach reduces prediction errors by approximately 40-60% compared with models trained only on limited target-block data. These results demonstrate that the proposed methods provide an accurate, efficient, and generalizable forecasting framework for shale gas production and EUR evaluation.Applications\/Significance\/Novelty: The new method establishes a systematic workflow for EUR forecasting and hydraulic-fracturing design optimization in undeveloped or production-limited shale gas blocks. First, a high-accuracy EUR prediction framework is developed by integrating physics-guided neural networks, multimodal deep-learning, and cross-block transfer learning into a unified architecture. Second, the model enables widely applicable EUR assessment and provides guidance for hydraulic-fracturing design in newly developed shale gas blocks with limited production history.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Assessing the Impact of Prior Depletion on Future Inventory in the Delaware Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Sathaye*, A. Toure and D. Niederhut\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Novi Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As basin development matures, prior well depletion poses an increasing risk to the economic viability of future shale development. This study evaluates depletion characteristics in the Delaware Basin for both existing producing child wells and future inventory locations for six major operators. We quantify the impact of prior well depletion for each major operator and target formation in the basin, as well as the change in depletion trends over time.Methods\/Procedures\/Process: To quantify prior well depletion, we sum the total fluid volume removed in the surrounding area of a shale well before its first production, and weight the prior removal by distance to the wellbore of interest. This depletion factor is then provided as an input to a machine learning model alongside variables representing interwell spacing, completion intensity, and geological setting. The machine learning model forecasts the monthly production profile up to 2 years. We then model both existing wells and approximately 80,000 remaining undeveloped horizontal well locations. The comparisons in this study will focus on the effect of prior well depletion on 2-year cumulative oil production in the Delaware Basin for both existing child wells and future developments across the Delaware.Results\/Observations\/Conclusions: Our findings focus on the 6 largest operators in the Delaware Basin, and how they have managed their inventory to minimize the depletion of future locations. One operator has consistently operated in the highest rock quality, but faces the largest future depletion problem. Others have managed average rock quality to minimize future depletion, or have been able to overcome poor rock quality with large completions and low costs. Our machine learning outputs show both the prior strategies that operators have used to work around depletion, and where heavily depleted zones will be developed in the near future.Applications\/Significance\/Novelty: These findings demonstrate the importance of prior depletion in assessing the quality of remaining inventory. Some locations that initially contained some of the best rock in the Delaware Basin are now merely average, and operators that have preserved their future inventory from depletion in average rock are now poised to be performance leaders. This approach also highlights the need for complex depletion calculations, along with the causal machine learning models to learn from these calculated values.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"New Technology Showcase\" style=\"border-top: 4px solid #f093fb;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: New Technology Showcase &#8211; The Pilot Bridge: Strategic Onramps to Commercial Adoption<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        2:00 PM &#8211; 2:00 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Nash*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Boundary RSS)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Garcez*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Darcy Partners)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Feng*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Cogzia)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Yamali*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron Technology Ventures)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:00 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Elshahawi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(NoviDigiTech)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"New Technology Showcase\" style=\"border-top: 4px solid #f093fb;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Panel Session: New Technology Showcase &#8211; Foundation First: Building the Data Architecture That Actually Powers AI<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t381\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        2:30 PM &#8211; 3:00 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Padeletti*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Amazon Web Services)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Ben*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy Applied AI Center of Excellence)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Cheng*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Xiao*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Zhejiang Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"New Technology Showcase\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t2:30 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Panelist<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Gunturu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petrabytes Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Best of IOR<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:20 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Hosein Kalaei\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Chemical Optimization To Improve Oil Recovery In Shales For Higher Salinity And Temperature Reservoir Conditions<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Pinnawala*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Using Sized Calcium Carbonate (caco3) Particles In Mature Co2 Eor Projects For Deep Conformance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Al Busafi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Graph-Based Deep-Learning Framework for Accurate Liquid Production Forecasting in Waterflooded Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tU. Sinha*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Xecta Digital Labs)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:20 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Upscaling Diffusivity in CO2 Storage Processes in Deep Saline Aquifers<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Benavides*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Engineering the Next Generation of Geothermal Projects: Models, Methods, and Insights<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tDenise Benoit, Kristina Holan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Scoping Engineering and Economic Study of a Geothermal Project<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. A. Gracian<sup>1<\/sup>,<sup>2<\/sup>, I. R. Diyashev<sup>1<\/sup>, T. A. Blasingame<sup>1<\/sup>, A. Orangi<sup>3<\/sup> and E. Evans*<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Rock Flow Dynamics; 2. Texas A&amp;M University; 3. Murphy Oil Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: We evaluate the technical and economic feasibility of repurposing depleted gas wells completed in the Haynesville Shale for geothermal electricity generation. This concept leverages existing horizontal wells and stimulated hydraulic fracture networks as conduits to circulate injected cold water through the reservoir for heat extraction, thereby eliminating the need for new drilling and completions. We estimate recoverable thermal output, surface conversion efficiency, and project economics under realistic field and grid configurations, and identify key risks and uncertainties that affect thermal longevity, pressure support, and levelized cost. The goal of this work is intended to guide the design of pilot programs and provide portfolio screening for depleted unconventional assets.Methods\/Procedures\/Process: Reservoir simulations which include heat and fluid flow modelling were performed using average Haynesville properties. In our design, each well pad contains four cold water injectior wells and two centrally located producing wells, giving a field configuration of 16 injection wells and 8 producing wells. The simulations were run for 25 years under various water injection rates; while monitoring reservoir temperature, pressure, and well performance. Simple models for thermal-electric power plants were used to estimate single-flash and double-flash power conversion efficiencies. Discounted cash-flow analysis applied CAPEX\/OPEX estimates taken from literature, and lastly, a reuse scenario of existing wells and surface infrastructure substantially reduces project costs.Results\/Observations\/Conclusions: Our reservoir simulations sustained produced fluid temperatures near 300 \u00b0F over 25 years with two injection wells per production well, limiting premature thermal decline while maintaining high circulation rates. Single-flash conversion yields ~10\u201315% efficiency and \u224815\u201320 MW. of power, while double-flash cases reach up to \u224830 MW, depending on water rate and temperature stability. Reuse of existing wells avoids roughly \u2248$312 MM in drilling and completion costs for a 24-well development. For power prices of $0.05\u2013$0.10\/kWh, the resulting net present value is ~$120\u2013$180 MM, with IRR &gt;15%, and payback period of approximately 6\u20138 years. Our results support technical viability, subject to field validation which can only be achieved by field demonstrations.Applications\/Significance\/Novelty: The repurposing of gas wells in high temperature unconventional reservoirs for geothermal electricity generation offers a no new drilling , substantially reducing project costs relative to greenfield development while also offering a lower risk option for extending the life of depleted assets. This study integrates subsurface thermal behavior with simple power plant and economic models to screen end of life assets and guide pilot feasibility. Our injection-production patterns outline a pathway for staged demonstration pilots and integration of depleted oil and gas assets into renewable power portfolios. Such projects can deliver long-life, low-maintenance electricity while contributing to emissions reductions and supporting the transition to a lower-carbon energy system.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Reduced Order Surrogate Model Framework for Thermal Extraction Prediction in Enhanced Geothermal Systems Under Variable Injection Scenarios<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tV. Kesireddy* and D. Voulanas\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Reduced-order modeling solutions are developed for Enhanced Geothermal Systems (EGS) to predict thermal extraction performance under variable injection-rate schedules. The goal is to provide computationally efficient tools for geothermal well design, operational optimization, and rapid reservoir-management assessment. The analysis considers single-fracture finite rock-matrix systems, time-dependent injection patterns.Methods\/Procedures\/Process: Stepwise injection-rate variations are introduced over 25-year intervals and validated against established numerical simulators. Sparse Identification of Nonlinear Dynamics (SINDy) is used as a fast proxy-modeling technique to identify the dominant dynamic behavior of EGS reservoirs. By combining numerical simulation with data-driven model discovery, the resulting reduced-order models provide accurate temperature predictions at significantly lower computational cost.Results\/Observations\/Conclusions: Results show that thermal depletion is the primary limiting mechanism in finite matrix systems and can offset the expected benefits of higher injection rates. Over a 100-year operating period, producer temperatures decline from 200 \u00b0C to approximately 60 \u00b0C.Applications\/Significance\/Novelty: The proposed approach advances EGS thermal modeling by incorporating time-varying injection rates into a reduced-order, data-driven prediction strategy. Its integration of SINDy-based dynamics discovery with numerical simulation offers a practical path for rapid forecasting and long-term operational planning for EGS systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Investigation of Hydraulic Fracture Properties for Geothermal Heat Extraction from Shale Gas Reservoirs Using Embedded Discrete Fracture Modeling<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Mawa*<sup>1<\/sup>, S. Bhattacharya<sup>2<\/sup>, M. Delshad<sup>1<\/sup> and K. Sepehrnoori<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. The University of Texas at Austin; 2. Bureau of Economic Geology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The production of geothermal energy from deep shale gas reservoirs represents an emerging pathway for converting an unconventional system into a hybrid-energy producer. In these low-permeability systems, hydraulic fractures are the major controllers of fluid extraction. However, the fracture designs that may maximize hydrocarbon production do not necessarily maximize thermal performance. Therefore, a systematic assessment of hydraulic fracture attributes is essential for shale-based geothermal research.Methods\/Procedures\/Process: A numerical modeling study into the properties of hydraulic fracture for heat generation was conducted using the Embedded Discrete Fracture Modeling (EDFM) framework, along with flow and heat transport to simulate multi-stage hydraulic fractures within a reservoir. First, a base-case shale gas reservoir model with a two-well configuration was constructed to reproduce representative production and enthalpy that can be withdrawn. Subsequently, a comprehensive sensitivity study was performed on key hydraulic fracture properties such as fracture conductivity, half length, height, spacing, and fracture stages to quantify temperature and enthalpy.Results\/Observations\/Conclusions: Results indicate that fracture spacing and the connectivity between the network architecture dominate long-term performance and improve cumulative heat recoveries. Fracture half-lengths increase the stimulated contact between the matrix and fractures and are important for long-term energy output. Fracture conductivity can greatly enhance injectivity and substantially increase heat production from the reservoir. Increasing the number of fracture stages also improves the overall heat recovery from the reservoir.Applications\/Significance\/Novelty: The proposed framework is intended to support hydraulic fracture design and operational optimization for geothermal in shale development by enabling comparisons among different scenarios based on thermal sustainability. This EDFM-based approach also offers a feasible solution for fracture characterization that can be applied to other Enhanced Geothermal Systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: Reservoir and Production Forecasting II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tVishal Bang, Yuanbo Lin\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Machine Learning Insights Into Lateral Length and Well Productivity: A Framework for Optimizing Unconventional Development<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Riley*, A. Alzahabi and A. Kamel\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(UTPB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study examines the relationship between initial production (IP 180 BOE) and completed feet in unconventional reservoirs using a dataset of 204 wells from the Permian Basin. The objective is to demonstrate, through data analytics and machine learning, that extending completed footage is associated with higher early production performance and can provide a data-based foundation for improving well design.Methods\/Procedures\/Process: Production and completion data covering the first 180 days of well performance were evaluated, incorporating parameters such as completed feet, stage count, cluster design, perforations, proppant mass, fluid volume, and pressure-related variables. Variance analysis was used to understand data distribution, while Pearson correlation screening identified the most influential variables.Predictive models were then built in Python, including linear regression, K-nearest neighbors (KNN) regression, and regression trees. The dataset was divided into training and testing subsets (80\/20), with inputs standardized to training statistics. To complement supervised models, k-means clustering was carried out on completed feet and IP 180 BOE, with the elbow method used to select the optimal number of clusters and silhouette scoring used to evaluate clustering quality.Results\/Observations\/Conclusions: Correlation results indicated that completed feet had the strongest association with initial production (0.76), followed by Number of Stages (0.67), and both proppant mass and fluid volume (0.64). Linear Regression: R\u00b2 = 0.60 KNN Regression: R\u00b2 = 0.66 Regression Tree: R\u00b2 = 0.58 K-Means Clustering: Optimal k = 9, silhouette score = 0.458. Clustering showed progressive gains in IP 180 BOE across higher completed-feet groupings, from approximately 319 BOE near 4,399 ft to approximately 1,414 BOE near 10,000 ft. Overall, the agreement across correlation screening, predictive modeling, and unsupervised learning supports completed footage as a primary driver associated with early production performance and provides a quantitative workflow for lateral length screening and unconventional development planning.Applications\/Significance\/Novelty: Machine learning techniques were used to develop a quantitative framework that connects lateral length to well productivity. Moving beyond traditional decline-curve or empirical approaches, the integration of correlation screening, predictive modeling, and unsupervised learning validates production trends with greater robustness. The findings highlight how engineering expertise, combined with data-driven techniques such as clustering and model evaluation, can guide lateral length optimization and enhance unconventional field development strategies.Interdisciplinarity (Team Presentation\u2019s only): Integrating petroleum engineering with data analytics allowed for a deeper look at how well design impacts production in unconventional reservoirs. The project applies statistical methods and machine learning to completion data from Permian Basin wells, blending engineering insight with computational modeling. By combining these disciplines, the analysis provides a stronger understanding of how design parameters influence well productivity.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Artificial Intelligence-Assisted Production Forecasting in Unconventional Reservoirs: A Review of Machine-Learning Methods, Decline Frameworks, and Data-Quality Requirements<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Bigdeli<sup>1<\/sup>, H. Moubarak*<sup>2<\/sup>, Y. Al-Enezi<sup>3<\/sup> and C. Temizel<sup>2<\/sup>,<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State University of Campinas; 2. Terra Altai; 3. Kuwait Oil Company; 4. Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The paper examines production forecasting applications of artificial intelligence (AI) and machine learning (ML) systems for unconventional reservoirs. The research aims to evaluate ML-based decline methods and hybrid physics\u2013ML workflows and uncertainty-quantification techniques and data-conditioning practices. The review examines time-series models and feature-based learning and ensemble forecasting and AI-enhanced EUR estimation methods, which operate across worldwide shale and tight oil and tight gas assets.Methods\/Procedures\/Process: The review gathered published ML forecasting research from SPE and URTeC and petroleum journals and academic publications. The evaluation process included testing LSTM networks and gradient boosting and random forests and decline-curve ML hybrids and clustering-based type curves and probabilistic ML forecasting. The review evaluated data-quality practices through an assessment of sampling frequency and cleaning workflows and evaluation of feature engineering and normalization techniques. The review assessed previous studies through benchmarking techniques, which enabled the comparison of their research results. The research only used existing literature to generate its findings because no new models or datasets were developed.Results\/Observations\/Conclusions: The review shows that ML models produce superior results than DCA for short-term predictions, but their performance depends on correct data preparation and appropriate feature selection. The combination of physical constraints with ML models produces superior results because it minimizes model overfitting and improves prediction accuracy. Research evidence demonstrates that ML systems demonstrate high sensitivity to data noise and missing information and rate reduction effects and operational changes, including choke management. The combination of type curves with clustering methods produces better group-level consistency but requires strong normalization techniques. Ensemble forecasting methods produce more reliable uncertainty bands than individual model predictions.Applications\/Significance\/Novelty: The review demonstrates that ML forecasting achieves its best results when it works with decline frameworks that use physical models instead of running independently as a black-box system. The paper presents a literature-based ML Forecasting Maturity Ladder which shows workflow progression from data-driven to hybrid-physics approaches. The research investigates how rate interruptions and operational noise affect ML forecasting systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Designing Depletion Features for Parent\u2013Child Modeling: Comparing Time, Distance, and Volume\u2013Distance Formulations in Machine-Learning Workflows<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. Davis*<sup>1<\/sup>, A. Qualls<sup>2<\/sup>, D. Niederhut<sup>1<\/sup> and K. Sathaye<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Novi Labs; 2. Devon Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper evaluates different approaches for capturing the impact of depletion for parent\u2013child modeling in unconventional reservoirs. As reservoirs mature, the number of undepleted inventory locations dwindles. With machine learning, we can predict future volume impacts by leveraging existing data on infill drilling. Using a Devon\u2011operated asset as a test case, we compare time &amp; distance, depletion volume, and depletion volume\u2013distance representations within a common machine\u2011learning framework. The objective of this study is to determine which physical description of prior well depletion creates the most accurate forecasts for undrilled locations.Methods\/Procedures\/Process: We constructed several features to capture depletion impacts. Distance-time based features capture how long parent wells have been online and how far away the parent wells are. We also examine depletion features that incorporate depletion-volume or distance-weighted-depletion. Tree-based models were trained using each of these approaches to depletion. We then evaluated them for accuracy and robustness across various synthetic parent-child configurations to examine depletion sensitivity. These approaches were also compared to Devon operated infill pads for real-world accuracy. Finally, we evaluate model SHAP outputs as an estimate for depletion effects.Results\/Observations\/Conclusions: We found that these approaches perform well in our real-world validation cases, with about 14% median absolute percent error at 360 days. Volume-distance based depletion features stand out in synthetic tests as the most sensitive to parent depletion, showing upwards of 42% child degradation in the presence of prior depletion. Synthetic testing showed very different behavior in these different feature sets despite similar backtest error metrics. SHAP value analysis helps explain this behavior and confirm that the best performing models have feature relationships that match petrophysical expectations.Applications\/Significance\/Novelty: The proposed workflow provides guidance on selecting depletion features that balance physical interpretability with predictive accuracy. Volume-distance depletion features can be implemented directly in production modeling platforms to enable depletion aware parent-child risk assessment for operators. These tools also enable development planning workflows through scenario analysis for infill scheduling. Remaining inventory impacts from prior depletion will require tools that can handle contemporary infill spacing and parent depletion, in addition to impacts from completions and geology.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Flow Dynamics at the Pore Scale in Unconventional Reservoirs<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tYula Tang, Jichao Han\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pore-Scale Evaluation of Condensate Dropout Impact on Gas Permeability<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Alrehaily, A. W. Alsmaeil*, M. Alsaffar and J. Gao\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Upstream Advanced Research Center)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Condensate dropout in gas condensate reservoirs reduces gas permeability and productivity, especially in tight formations where the liquid phase becomes immobile. This study investigates the pore-scale mechanisms of condensate formation and its impact on gas flow under high-pressure, high-temperature (HPHT) conditions. By directly visualizing liquid hydrocarbon dynamics within a micromodel, the work aims to investigate how liquid saturation, wettability, and interfacial effects control permeability and identify the critical condensate saturation threshold that marks gas flow reduction.Methods\/Procedures\/Process: HPHT microfluidic chip was utilized to mimic porous reservoir rock. N-decane was utilized as condensate and air was utilized as the gas phase. High-speed microscopy captured real-time pores blockage, growth, and coalescence. Qualitative image analysis and pressure measurements were integrated to assess phase distribution and permeability reduction as functions of pressure and condensate saturation.Results\/Observations\/Conclusions: N-decane initially appeared as thin films and droplets on pore walls, then coalesced in constricted regions as pressure dropped. The accumulation of immobile N-decane significantly reduced effective gas permeability, with a distinct threshold condensate saturation beyond which gas flow declined sharply. This behavior confirmed the existence of a critical condensate saturation. The results demonstrate that small increases in condensate volume fraction can lead to substantial gas mobility loss, emphasizing the strong coupling between pore-scale phase behavior and macroscopic productivity decline.Applications\/Significance\/Novelty: This study provides the first high-resolution, real-time visualization of condensate dropout and mobility at HPHT conditions, bridging the gap between theoretical models and field-scale observations. The HPHT microfluidic approach offers a powerful diagnostic tool for evaluating condensate banking mechanisms and testing mitigation strategies. Insights gained can guide pressure maintenance, wettability alteration, and surfactant treatments in condensate-rich, tight reservoirs. The findings advance understanding of multiphase flow impairment and inform improved recovery strategies for gas-condensate systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Microfluidic Visualization and Phase-Behavior Simulation Study on Gas Injection Displacement Mechanisms in Shale Oil<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Zhang*, W. Zhang, B. Yuan, J. Gao and R. Xie\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study addresses the low displacement efficiency and unclear mechanisms of heavy component deposition during gas injection in shale oil reservoirs with micro-nano pores. Current research lacks integrated analysis of microscopic displacement processes, phase behavior changes, and deposition patterns, particularly regarding the effects of pressure decline paths and CO2\/CH4 gas mixtures. By coupling microfluidic experiments with phase-behavior simulation, this work aims to systematically reveal the synergistic effects of gas type, pressure decline path, and pore size on displacement front dynamics, oil recovery, and heavy component deposition behavior, providing a theoretical basis for optimizing shale oil gas injection strategies.Methods\/Procedures\/Process: This study integrates microfluidic experiments with phase-behavior simulation. A visual microfluidic chip replicating shale micro-nano pore structures is fabricated. Gas injection experiments under reservoir conditions are conducted using different gas types and pressure-decline paths. High-speed imaging dynamically records gas-oil interface behavior, displacement-front morphology, and heavy-component deposition while quantifying recovery and pressure drop. Concurrently, phase-behavior simulations based on real shale oil composition predict key parameters and component migration trends under identical conditions. By correlating experimental observations with simulation results, the coupling mechanism between microscopic flow dynamics and macroscopic phase behavior is clarified.Results\/Observations\/Conclusions: The study is expected to reveal that: 1) CO2\/CH4 mixtures lower miscibility pressure versus pure CO2, resulting in more uniform displacement with reduced channeling, albeit with potentially lower light-component extraction; 2) Staged pressure decline enhances miscibility through extended equilibrium time, yielding higher recovery and less heavy deposition than constant-rate depletion; 3) Capillary dominance in nanopores retards front advancement and elevates clogging risk at pore throats. Synergistic optimization of gas composition, pressure path, and pore structure is thus critical for efficient injection and damage mitigation, guiding targeted operational designs.Applications\/Significance\/Novelty: This study achieves visual observation of the entire gas injection process for shale oil at the nanoscale, including displacement, phase change and heavy component deposition, with coupling to phase behavior numerical simulation at the mechanism level. It clarifies the intrinsic mechanism by which pressure paths and gas compositions regulate microscopic phase behavior and thereby affect macroscopic recovery efficiency and plugging risk. The conclusions can be applied to guide gas injection development of shale oil reservoirs and have certain value for improving unconventional oil and gas recovery efficiency.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">CO2 Flowback and Oil Mobilization Mechanisms During CO2 Fracturing Flowback in Tight Oil Reservoirs Revealed by MRI and NMR Experiments<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. Li*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: CO2 fracturing is a promising technique for tight oil reservoirs, offering distinct advantages such as eliminating clay hydration damage in water-sensitive formations, enhancing fracture connectivity, and improving oil mobilization through CO2 extraction and pressure maintenance. However, the mechanisms governing CO2 invasion, selective oil mobilization, and fluid redistribution across micropores, mesopores, and macropores\/microfractures during shut\u2011in and flowback remain poorly understood.Methods\/Procedures\/Process: This study employs an online nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) core flooding system to dynamically monitor CO2 fracturing, shut\u2011in, and flowback experiments on microfractured tight sandstone cores. The flowback characteristics and oil mobilization mechanisms at different pore scales were investigated, and the effects of fracturing fluid injection volume, CO2-oil interaction time, and oil composition on oil recovery and CO2 flowback efficiency were systematically evaluated.Results\/Observations\/Conclusions: The results reveal a three-stage dynamic evolution of fluid distribution. Notably, during the soaking stage, a non\u2011monotonic oil redistribution driven by differential mass transfer is observed, with oil saturation in mesopores declined, while that in micropores and macropores\/microfractures concurrently increased, revealing a previously unrecognized bimodal enrichment pattern driven by differential mass transfer. Oil composition simultaneously regulates mass transfer efficiency and displacement front stability, with a moderately complex oil balancing these effects to yield the highest recovery. The fundamental mechanism for enhancing tight oil recovery lies in CO2 molecular diffusion during the soaking stage, which overcomes the constraints imposed by tight\u2011matrix heterogeneity and achieves deep mobilization of oil in micro\u2011 and nano\u2011pores.Applications\/Significance\/Novelty: This work provides novel experimental data on real\u2011time fluid redistribution and evolution of CO2 occurrence states across multiscale pores, substantially advancing the mechanistic understanding and predictive capability for CO2 fracturing, soaking, and flowback in tight oil reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 8: Diagnostics and Integrated Insights<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRobert Archer, Yuguang Chen, Pinaki Ghosh\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Multi-Stage Hydraulic Fracture Modeling with Rate Transient Analysis (RTA) and Time-Lapse Geochemistry (TLG) Utilization<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Shilkova*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents an integrated workflow to quantify well drainage profiles, characterize inter-well communication, and evaluate spacing impacts in three Niobrara horizontal wells. By combining time-lapse geochemistry (TLG), rate transient analysis (RTA), reservoir simulation, and 3D fracture modeling, the project aims to optimize well stacking and spacing to reduce interference and improve estimated ultimate recovery (EUR) and net present value (NPV) for the considered drilling spacing unit (DSU). The workflow strengthens understanding of productive stimulated reservoir volume (SRV), identifies well interference, and supports more effective field-development planning in the Powder River Basin through data-driven spacing and design recommendations.Methods\/Procedures\/Process: An integrated workflow was applied to define drainage behavior,fracture geometry,and development economics.TLG,integrated with petrophysical modeling and RTA, identified contributing intervals and refined interval-specific fracture half-lengths,enabling construction of an SRV geometry and inter-well communication.Fracture geometry characterization was performed,providing a physically constrained model of fracture propagation and well interference that validated the TLG drainage profile.A numerical reservoir simulation was subsequently developed to assess production trends and dynamic interference behavior to quantify pressure connectivity across the DSU. Finally,a validated SRV model was used to evaluate EUR degradation and NPV development optimization under varying well-spacing scenarios.Results\/Observations\/Conclusions: The results demonstrate that TLG, integrated with petrophysical modeling and RTA, revealed stable drainage patterns concentrated within high-quality Niobrara intervals and indicated measurable vertical and lateral overlap between adjacent wells. By comparing standalone, half-bounded, and fully bounded scenarios, the analysis identified the production loss attributable to overlapping DRV and established a spacing configuration that maximizes long-term recovery and project value.Applications\/Significance\/Novelty: This integrated workflow shows that combining geochemical diagnostics with physics-based modeling provides a direct, high-fidelity method to define SRV and quantify well interference in the Powder River Basin. Unlike geometry-based assumptions, this approach uses measured drainage to guide spacing, explain well-to-well performance differences, and reduce uncertainty in EUR forecasts. The framework is broadly applicable across unconventional plays, offering operators a practical methodology to optimize infill design, evaluate stimulation effectiveness, and improve field-development economics.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluating Depletion Influence Using Surveillance Data, RTA, and Multi-Pad Multi-Bench Depletion Modeling<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Khorsandi*, A. Vissotski, X. Liu, Y. Tang, Y. Cai, Y. Chen, K. Ramsaran, Y. Tan, J. Fleck and H. Park\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Shallower bench development may experience depletion influence from parent wells in deeper benches. Here, the influence is evaluated through a workflow leveraging surveillance and production data. Estimating depletion directly from nearby pads alone is challenging because of other factors controlling depletion influence including production history, reservoir properties, well spacing, and completion design, which vary widely between pads. This study presents an integrated workflow that adopts a depletion model for multi-bench developments including multiple pads. A model calibrated to a nearby pads is applied to estimate the potential influence of depletion in future pads.Methods\/Procedures\/Process: The study employs analog pads data and incorporates multidisciplinary data including frac treatment pressure and rates, geochemistry profiling, DFIT (Diagnostic Fracture Injection Test), production rates, and pressure analysis. Numerical RTA (Rate Transient Analysis) is used for estimating EUR, effective fracture geometry, and frac stimulation efficiency. RTA also normalizes production variations from operational factors. A semi-analytical model that combines reservoir and fracture flow physics is used to evaluate pressure depletion. The semi-analytical solution allows for multi-bench, multi-pad depletion simulation in a few seconds. The model is calibrated with observed depletion in an analog pad. The calibrated model is then used to estimate the effect of depletion for future drills.Results\/Observations\/Conclusions: The analysis of extensive surveillance and production data revealed that the deeper child well in the analog pad is near the top edge of the depleted zone. Because of close proximity to the depletion zone, the well experienced depletion influence and had lower EUR compared to another child well landed shallower. Next, the inputs of semi-analytical solution were slightly adjusted to match the estimated size of the depleted zone. The calibrated model is then used to estimate depletion influence for future pad development. The lookback analysis of the new pad after one year of production indicates that the new child wells landed away from the top of depleted zone are not influenced by depletion, further verifiying the reliability of the workflow.Applications\/Significance\/Novelty: This study provides a robust and practical workflow for assessing influence of depletion in unconventional plays, enabling operators to improve pad design and well placement in multi-bench developments. This work is focused on novel application of comprehensive analog data sets to validate the model prediction, transfer the results from the calibrated model to nearby well pads, and ultimately further validate our development decisions.Interdisciplinarity (Team Presentation\u2019s only): The work exemplifies interdisciplinary collaboration, by integrating reservoir engineering, geomechanics, geochemistry, drilling, completions, production performance and surveillance. This holistic approach validates depletion influence assessments through the use of a solid reservoir-geomechanics foundation and a broad spectrum of technical expertise, the combination of which enhances the reliability and applicability of the findings for field development planning.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Quantifying Key Drivers of Well Performance and Economics in the Haynesville Shale<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. He<sup>1<\/sup>, F. Kong<sup>2<\/sup>, Z. Li<sup>2<\/sup>, C. Xu<sup>2<\/sup> and W. Yu*<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Syracuse University; 2. CNPC USA; 3. Simtech)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study analyzes a basin-scale dataset of 9,969 Haynesville shale wells to (1) identify and quantify the dominant drivers of single-well Estimated Ultimate Recovery (EUR) and early-time performance, and (2) evaluate the coupled relationships among completion design, early production behavior, and well-level economics. The goal is to establish a statistically robust framework that links engineering decisions to long-term production and economic outcomes across the play.Methods\/Procedures\/Process: The dataset was standardized using the 14-digit API as a unique identifier, retaining only the most recent completion record per well. Nine predictors were analyzed: four early production metrics (First 3-Month Gas, First 12-Month Gas, Peak Gas, Months to Peak), three completion parameters (Lateral Length, Fluid Intensity, Proppant Intensity), and two economic indicators (NPV under $60\/$3.50 and $60\/$3.00). Univariate OLS regressions quantified each variable\u2019s explanatory power for EUR using R2 and statistical significance. To mitigate multicollinearity, PCA was applied to completion variables, generating a single \u201ccompletion intensity\u201d factor. A multivariate OLS model incorporating this factor, First 3-Month Gas, and NPV ($60\/$3.50) was then used to assess marginal contributions to EUR.Results\/Observations\/Conclusions: Univariate OLS analysis showed that First 12-Month Gas is the strongest single predictor, explaining 63% of EUR variance (R2 = 0.63, p &lt; 0.001). Peak Gas (R2 = 0.49), First 3-Month Gas (R2 = 0.46), and Lateral Length (R2 = 0.46) also exhibited strong correlations, while Months to Peak Production showed negligible predictive value (R2 &lt; 0.01). PCA reduced the three completion design variables into a single component capturing 78% of their total variance. The final multivariate OLS model achieved R2 = 0.83 (Adjusted R2 = 0.82, F(3,4996) = 820.5, p &lt; 0.001) with all predictors statistically significant. A one-standard-deviation increase in completion intensity was associated with an \u22480.15 Bcf increase in EUR, while First 3-Month Gas and NPV contributed \u22480.12 Bcf and \u22480.10 Bcf, respectively.Applications\/Significance\/Novelty: This work provides a rigorous, scalable methodology for optimizing completion designs and forecasting well performance in the Haynesville Shale and similar high-productivity gas plays. The integration of early-time production indicators, engineering parameters, and economic outcomes fills a major gap in empirical, basin-scale predictive modeling. To our knowledge, this is the first study to combine PCA with univariate and multivariate OLS across nearly 10,000 wells to resolve multicollinearity and isolate the marginal effects of completion design, early production, and economics on EUR. The resulting framework is transferable to other shale basins under diverse geological and operational conditions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Improving Returns Through Smarter Development Strategy<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAnup Viswanathan, David Haddad\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">TripleFrac: Scaling Multifrac Hydraulic Fracturing for Operational Efficiency and Cost Reduction<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Rodriguez*, T. Stom and S. Dieffenbaugher\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron U.S.A.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper introduces TripleFrac\u2014a next-generation multifrac approach designed to stimulate three wells simultaneously from a single pad. The objective is to detail the planning, execution, and scaling of this patented technology to deliver operational efficiency, cost savings, and sustainability benefits in unconventional resource development.Methods\/Procedures\/Process: TripleFrac was developed through a multidisciplinary collaboration. The process enables concurrent pumping into three or more wells from a common pad, requiring rigorous feasibility analysis to address operational complexity, stakeholder alignment, and technical challenges. Design optimization focused on hydraulic horsepower (HHP) utilization, frac fleet configuration, and logistics scalability. Rig-up enhancements included additional frac tanks, improved sand logistics, and pump realignment to support higher throughput. Execution relied on integrated planning and real-time coordination between engineering and field teams to ensure seamless operations.Results\/Observations\/Conclusions: Field trials demonstrated significant cost reductions and cycle time acceleration by approximately four days. TripleFrac also enabled innovative slurry designs that improved proppant placement and reduced job duration. These efficiencies contributed to Chevron\u2019s lower-carbon objectives by reducing total emissions associated with fracturing operations per pad. Successful deployment across multiple assets validates the operational viability and scalability of TripleFrac.Applications\/Significance\/Novelty: TripleFrac represents a significant advancement in hydraulic fracturing, combining engineering innovation with operational excellence. Beyond cost and efficiency gains, the approach aligns with Chevron\u2019s lower carbon goals, reinforcing Chevron\u2019s commitment to delivering value while reducing environmental impacts in unconventional developments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Using Drilling-Derived Mechanical Specific Energy to Map Pore-Pressure Contrasts Across Faulted Eagle Ford Blocks<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Roberts<sup>1<\/sup>, P. Chapman<sup>1<\/sup> and K. Wutherich*<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Devon Energy Corporation; 2. Drill2Frac)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Devon Energy, in collaboration with Drill2Frac, conducted a field study in a heavily faulted area of the Eagle Ford basin to test whether a drilling-derived, rock-centric mechanical specific energy measurement (rock-centric MSE) can serve as a quantitative proxy for pore-pressure variation between fault blocks. The objectives were to determine if MSE normalized to remove drilling artifacts, and derived from horizontal wells, can robustly discriminate lower-pressure versus near-original-pressure compartments despite strong stratigraphic and lateral heterogeneity, which significantly complicates interpretation.Methods\/Procedures\/Process: Mechanical specific energy was computed from drilling parameters in 19 horizontal wells and rigorously normalized for drilling dysfunctions to obtain a drilling-derived strength log, here referred to as rock-centric MSE. Within each fault block, these horizontal strength logs were projected into pseudo-vertical profiles using geosteering interpretations, then combined into weighted average vertical trends. To account for lateral variability, the study area was partitioned into six sectors assumed internally consistent in baseline strength. For each sector, the fault block with the lowest averaged strength response was interpreted as the reference, and inter-block differences in rock-centric MSE were translated into relative pore-pressure values.Results\/Observations\/Conclusions: The analysis identifies systematic increases in rock-centric MSE between fault blocks consistent with expected reservoir pressure patterns. Several blocks exhibit elevated rock-centric MSE, reflecting higher drilling energy in lower-pressure rock, with pore-pressure differences up to ~1,200 psi relative to the least-strength reference blocks in each sector. Vertical projections preserve Eagle Ford stratigraphy while highlighting lateral pressure contrasts that align with mapped fault compartmentalization. These projections also show vertical variation in inferred pore pressure. The resulting pressure maps provide internally consistent, field-scale pressure trends that compare favorably with limited independent pressure control where available.Applications\/Significance\/Novelty: This work demonstrates that appropriately normalized mechanical specific energy from routine drilling can yield a practical, low-cost indicator of pore pressure in structurally complex unconventional reservoirs. The methodology enables construction of pressure-contrast maps from existing data, supporting selection of landing zones, frac staging and intensity, and risk mitigation for wellbore stability and fault reactivation. Novel aspects include the sector-based control on lateral strength trends, systematic vertical projection of horizontal strength logs by fault block, and quantitative linkage of strength contrasts to relative pore pressures using readily available drilling data.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 10: Laboratory Advancement of New Approaches<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSama Morsy, Deepak Devegowda\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">From Simulation to Field Trial: A Full-Cycle Integrated Workflow for Energized-Induced Temporary Plugging Refracturing in Tight Oil Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Zhang*, H. Qu and F. Zhou\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Production from fractured vertical wells in tight-oil reservoirs commonly declines rapidly because of pressure depletion and fracture conductivity deterioration. Conventional refracturing designs based on temporary-plugging and diverting (TPD) agents often assume a static in-situ stress field, thereby overlooking depletion-induced stress redistribution. Such stress evolution can strongly affect fracture re-initiation, reorientation, and diversion. To address this issue, this study proposes a full-cycle integrated workflow for energized-induced temporary plugging refracturing in tight-oil vertical wells.Methods\/Procedures\/Process: The workflow is implemented on a commercial integrated platform that combines a black-oil reservoir simulator, a 3D geomechanical model, and a 3D unconventional fracture model (UFM). It consists of four stages: (1) simulating primary fracture geometry and conductivity; (2) history matching the reservoir model to production data to calibrate key parameters and reproduce depletion; (3) modeling energization as a pressure-rebuilding stage to update the in-situ stress field; and (4) simulating refracturing under the altered stress conditions. Refracturing stimulation effectiveness is quantified using fracture volume, while production performance is evaluated via cumulative oil forecasts. A multi-objective optimization is performed to identify designs that jointly improve stimulation effectiveness and production under operational constraints.Results\/Observations\/Conclusions: Results show that depletion increases near-wellbore horizontal stress contrast by approximately 1.0\u20131.2 MPa, which stabilizes fracture growth along existing dominant fractures and limits diversion. Energized refracturing partially reverses this effect, reducing the stress contrast by 0.5\u20130.8 MPa and creating favorable conditions for refracturing. Critically, applying TPD before energized refracturing promotes greater near-wellbore pressure build-up, more effective stress modification, and stronger stimulation effectiveness, yielding higher forecasted cumulative oil than the reverse sequence.Applications\/Significance\/Novelty: The proposed workflow provides a mechanistic and field-oriented basis for designing energized temporary plugging refracturing. These findings demonstrate that energized refracturing should be designed as a stress-engineering process rather than a diversion-only treatment, and that applying TPD before energization provides a practical strategy for improving refracturing effectiveness in depleted tight-oil vertical wells. The significance lies in its proven ability to enhance stimulation effectiveness and maximize incremental recovery, offering a practical pathway to revitalize aging unconventional wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Stabilizing In-Situ Combustion in Bakken Formation Using Hydroxide Additives: Evidence of Enhanced Combustion Efficiency and Reduced Oxygen Demand<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Hajiyev<sup>1<\/sup>, L. Karabayanova<sup>1<\/sup>, H. Ye<sup>1<\/sup>, I. Akhilgov<sup>1<\/sup>, S. Haque<sup>1<\/sup>, J. Franks<sup>2<\/sup>, T. Benson<sup>2<\/sup>, J. Bauman<sup>3<\/sup>, C. Lane<sup>3<\/sup> and B. Hascakir*<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Harold Vance Department of Petroleum Engineering, Texas A&amp;M University; 2. Hunt Energy Corporation; 3. Hunt Oil Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: In-situ combustion (ISC) efficiency depends strongly on oxygen demand, flame stability, and sustained high-temperature reaction pathways. Unstable combustion causes inefficient hydrocarbon oxidation, excessive oxygen consumption, and incomplete upgrading. This study introduces an alternative ISC strategy that utilizes hydroxide-bearing additives to internally supply oxygen during high-temperature reactions.Methods\/Procedures\/Process: Controlled experiments on Bakken shale were conducted to assess whether Ca(OH)2, NaOH, and Mg(OH)2 additives stabilize combustion, reduce external oxygen requirements, and improve residual heat management when compared to dry and distilled-water-assisted ISC. Seven laboratory-scale combustion experiments were performed using crushed Bakken samples: (1) dry-ISC, (2) wet-ISC with distilled water, (3) wet-ISC with reservoir brine (4) Ca(OH)2-assisted wet-ISC, (5) NaOH-assisted wet-ISC, (6) Mg(OH)2-assisted wet-ISC, and (7) NaOH-assisted wet-ISC with reservoir brine. Each experiment was initiated under identical airflow and ignition conditions. Real-time temperature profiles, combustion-front progression, and oxygen depletion were measured. Produced gas compositions and CO\/CO2 mole ratios were monitored to evaluate oxidation intensity. Stability was quantified using front propagation uniformity, peak temperature sustainment, and fluctuation analysis.Results\/Observations\/Conclusions: Results show that conventional wet ISC improves combustion stability compared to dry ISC, reducing residual O2 from ~7.5% to ~2.5% and increasing CO2 production. However, the introduction of salinity (brine) led to reduced oil recovery (53.8%) compared to distilled water (66%), despite similar combustion temperatures (~390\u2013400\u00b0C). The addition of alkaline agents significantly altered system behavior. NaOH-assisted ISC exhibited the most favorable performance, increasing oil recovery to 88.7% and water recovery to 96.9%, while raising peak combustion temperatures from ~390\u00b0C to ~470\u00b0C. In contrast, Ca(OH)2 and Mg(OH)2 resulted in higher front temperatures (~495\u2013500\u00b0C) but lower oil recoveries (76.5% and 79.9%, respectively), indicating less efficient sweep. Alkaline-assisted experiments showed reduced CO2 production (~9% compared to ~16\u201318% in non-alkaline cases), significantly higher produced-water TDS (up to 80,250 ppm), and elevated residual O2 levels (up to ~8.6%), indicating modified reaction pathways and reduced oxygen consumption. These results suggest that stable combustion can be sustained at lower air injection rates, directly reducing compressor requirements. However, the presence of formation brine suppressed these benefits, with oil recovery decreasing to 46% in the brine + NaOH case despite strong combustion (~490\u00b0C).Applications\/Significance\/Novelty: This study provides the first systematic experimental evidence of the synergy between alkaline flooding and ISC in unconventional reservoirs. The findings demonstrate that alkaline-assisted ISC can enhance combustion efficiency while reducing dependence on high air injection rates, offering a promising pathway toward more energy-efficient and economically viable thermal EOR processes.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Unraveling the Complex Interactions Between Biocides and Friction Reducers to Preserve Functionality<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. R. Dreyer*, D. Garza and P. Kurian\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Finoric LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Cationic biocides and anionic friction reducers (FRs) are widely used in fracturing fluids, yet their chemical interplay remains under-characterized. This study presents a systematic investigation into their interactions, revealing that\u2014contrary to prevailing assumptions\u2014these interactions are just as likely to be beneficial as they are to be detrimental. While adverse effects such as performance impairment of either component are possible under certain conditions, we also observed notable enhancement of FR efficiency under other conditions.Methods\/Procedures\/Process: Standard laboratory methods were used to characterize the performance of both the biocide and the FR, including flow loop analysis, viscosity testing and ATP luminometry. Four different commercial FRs comprising a range of intended applications (e.g., brine-sensitive versus brine-tolerant) and two different biocide blends were systematically varied to understand where there might be interactions. Multiple test fluids, ranging from low salinity freshwater to high salinity brine, were evaluated due to the fact that preliminary screening revealed surprising and difficult-to-predict behavior.Results\/Observations\/Conclusions: Our testing has shown that while there can be interactions between biocides and FRs, the nature of those interactions is complex. In some cases, the intuitive impairment of performance was observed. Surprisingly, however, in other cases we saw a negligible impact, or even enhanced performance (particularly with regards to FR efficacy). Through the course of the work, it became apparent that FR composition, biocide composition, brine composition and even relative product concentration play a role in whether an interaction was observed and what the nature of the interaction might be. Given this complexity, it is not possible for us to draw broad conclusions, but we do show that the observed performance isn\u2019t random; whether positive, negative or neutral, behavior was consistent.Applications\/Significance\/Novelty: It is widely known in the industry that interactions between additives in a fluid are complicated and can have severe impacts on the effectiveness of any given completion. Many of these interactions are poorly understood, however, or are guided by anecdotal evidence or a limited scope of experience. In this paper, we seek to provide clarity on one specific set of additive interactions (biocides and FRs), enabling informed decisions when designing and applying completion fluids. Perhaps most importantly, we show that the laboratory tools we have at our disposal are capable of addressing these questions and it thus becomes incumbent on the end user to do their due diligence in ensuring that good product selections are made for a given treatment scenario.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Innovative Logging Measurements and Interpretation Methods<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        3:25 PM &#8211; 4:40 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSebastian Ramiro-Ramirez, Edgar Ignacio Velez Arteaga\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:25 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Case Study Utilizing Advanced Wireline Formation Testing Technology to Characterize Reservoir Fluids in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Guo*, J. Miller, A. K, T. Longbottom and J. Hinojosa\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ExxonMobil)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Significant challenges exist when utilizing conventional wireline formation testing tools to obtain accurate reservoir pressure and fluid data in Permian shale reservoirs, which typically comprise of stacked siltstones and organic-rich source rocks with high degrees of heterogeneity in rock properties and reservoir quality. The inherent characteristics of shale formations, such as low porosity and limited fluid mobility, often result in suboptimal operational performances and poor-quality fluid samples when deploying formation testing tools equipped with conventional probes of limited flow capacity. These limitations hinder the completion of pressure drawdown and buildup processes within practical timeframes in low-permeability formations.Methods\/Procedures\/Process: Field development strategies for mature core assets necessitate reservoir surveillance data, including reservoir connectivity and depletion assessments, which present additional challenges for dual-packer tools with enhanced flow capacities due to their constraint pressure differential specifications. This paper presents a case study utilizing 3D radial probe technology to conduct pretests, acquire formation fluid samples, and deliver critical reservoir connectivity insights for development planning in shallow Permian Basin reservoirs. This technology demonstrated compliance with demanding operational and performance requirements in mudstones and highly depleted siltstone intervals.Results\/Observations\/Conclusions: A pilot data collection program was designed and successfully executed across multiple appraisal wells. Operational efficiency was optimized through an integrated wireline logging suite comprising quad-combo, nuclear magnetic resonance (NMR), and borehole image logs. During fluid sampling, real-time decision-making, supported by NMR and borehole image interpretation to identify intervals with probable movable fluids, substantially improved the likelihood of obtaining clean samples.Applications\/Significance\/Novelty: Subsequent data analytics were performed by a multidiscipline team of geologists, petrophysicists, and geochemists, leveraging produced water analysis, core-extracted fluids, and historical production data to delineate regional reservoir fluid flow pathways and inter-bench communication patterns. The study demonstrated that, with rigorous job planning, 3D radial probe technology can successfully acquire high-quality fluid samples in tight and high depleted shale reservoirs. Best practices in operational planning, execution, and fluid data interpretation will be discussed.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t3:50 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Wireline Microfracture Test Implementation and Analysis Using the DFIT-FBA Procedure<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Clarkson*<sup>1<\/sup>, S. Haqparast<sup>1<\/sup>, X. Xie<sup>2<\/sup>, K. Newsham<sup>2<\/sup>, B. Gomez<sup>2<\/sup>, S. Naone<sup>2<\/sup>, I. Uzun<sup>2<\/sup>, R. Naveena-Chandran<sup>3<\/sup>, C. Batzer<sup>3<\/sup>, R. Medina<sup>3<\/sup>, E. Lopez<sup>3<\/sup>, G. Hashmi<sup>3<\/sup>, J. Hemsing<sup>3<\/sup>, J. Mata<sup>3<\/sup>, M. Hernandez<sup>3<\/sup>, P. Lowrey<sup>3<\/sup> and S. Torgerson<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Calgary; 2. Occidental; 3. Halliburton)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Diagnostic fracture injection tests (DFITs) are commonly used to derive minimum in-situ stress, reservoir pressure and permeability, as key input to hydraulic fracture and reservoir simulation models, and for caprock integrity evaluation. These tests are normally performed at surface through wells targeting subsurface reservoirs. Wireline-conveyed microfracture testing can similarly be used to obtain reservoir properties for isolated intervals within a vertical well. Recently, a flowback microfracture test was introduced to mimic flowback DFITs. In this study, a flowback microfracture test was designed, implemented, and analyzed using the DFIT-FBA (FBA=flowback analysis) procedure to obtain minimum in-situ stress and reservoir data in a caprock for CO2 storage.Methods\/Procedures\/Process: The wireline-conveyed formation tester used in this work has recently been upgraded to enable the implementation of pump-in\/flowback cycles analogous to the flowback DFIT. Previous work has demonstrated that, after the microfracture has been created during the pump-in cycle, the flowback cycle can be analyzed to obtain rapid estimates of minimum in-situ stress using a Cartesian plot of flowing pressure versus net volume (injection \u2013 flowback volume). In this study, the DFIT-FBA analysis procedure is applied to a flowback microfracture test performed in an isolated interval of a CO2 storage caprock to obtain not only minimum in-situ stress but also reservoir pressure and permeability estimates. Rate-transient analysis (RTA) of flowback data is used to derive the reservoir properties.Results\/Observations\/Conclusions: The microfracture test involved injection of ~0.004 m3 of filtered borehole mud into the caprock interval at an average rate of ~6x10-4 m3\/min to create the microfracture, followed by an approximately constant flowback period (average rate ~5x10-5 m3\/min) for 39 minutes. After smoothing the flowing pressure profile, a Cartesian flowing pressure versus flowback time plot was used to estimate minimum in-situ stress using the Plahn et al. (1997) method. Reservoir pressure was estimated using a log-log plot of the integral of rate-normalized pressure versus material balance time with the method of Zanganeh et al. (2020). Finally, reservoir permeability, obtained using the before-closure straight-line analysis method of Zeinabady et al. (2022), is in good agreement with petrophysical analysis.Applications\/Significance\/Novelty: It is demonstrated herein for the first time that minimum in-situ stress, reservoir pressure and reservoir permeability can be obtained from a microfracture test using the DFIT-FBA analysis procedure. The data was acquired in a total test time (single pump-in\/flowback cycle) of &lt; 1 hour. This testing approach, combined with the ability of the formation tester to isolate small intervals through a straddle packer system, allows rapid characterization of multiple intervals in a caprock\/reservoir system in a day. The approach could revolutionize tight rock characterization for subsurface energy\/storage systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t4:15 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Automated Basin-Wide Mineralogy Estimation Using Deep Learning Integration of Core, Spectroscopy, and Basic Log Data<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tV. Simoes*, C. Sena Santiago and P. Irgens\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Understanding mineralogy estimation and its distribution is essential for formation evaluation, geomechanics, and production planning. XRD core data and spectroscopy logs provide accurate mineral compositions but are expensive and spatially limited. While logs, such as gamma ray, density, neutron, resistivity, sonic, and caliper, are widely available, but require calibration and local geological knowledge to increase accuracy when inferring mineralogy. To bridge this data gap, we propose a deep learning based automated workflow that integrates all data sources to estimate sandstone, clay, carbonate, and anhydrite volumes and quantify uncertainty across the Williston basin, which automates multiple steps commonly performed manually by SME&#039;s for each well.Methods\/Procedures\/Process: The workflow starts with data harmonization for core, spectroscopy, and basic logs, and filtering to remove outliers and ensure data consistency. Wells with advanced measurements are divided into training, validation, and test sets, while the others are used for inference. After preprocessing, we use transfer learning from a pre-trained deep neural network, initially trained to predict and improve the consistency of basic logs across thousands of wells, and adapt it for mineralogy estimation in the Williston basin. The results evaluation triggers a second QC and retraining. Finally, MAE and MSE errors, along with the correlation, are calculated for all minerals in the test set, and the model is applied to the remaining wells in the basin to obtain estimated mineralogy and error analysis.Results\/Observations\/Conclusions: We applied the workflow to the formations: Lodgepole, Bakken, and Three Forks, Birdbear, and Duperow, which include the reservoir and seal in the Williston basin, where we have eleven wells with spectroscopy logs, five with XRD core data, and 391 with basic logs (GR, RHOB, NEUT, CALI, RES, DTC, CALI). The model achieved correlations of 0.8-1.0 for most minerals and zones, and the errors are similar to core-to-spectroscopy differences. Results demonstrate reliable basin-wide mineralogy estimation using limited high-cost data. The trained model can be applied to the entire basin in seconds per well. This scalable approach combines deep learning and uncertainty quantification to enhance formation evaluation and geomechanical characterization in data-sparse environments.Applications\/Significance\/Novelty: The proposed workflow enables field-wide mineralogy estimation in data-sparse environments by integrating core, spectroscopy, and basic well logs through a pre-trained deep learning model. It propagates mineralogical information to hundreds of wells at minimal cost while providing uncertainty estimates. The novelty lies in using transfer learning to build one single model which is a core component in the workflow which handles one or more missing inputs, capture the log interdependencies for all different units, predict multiple mineral components simultaneously, consider physical constraints, and unify heterogeneous data sources using an automated approach, and finally identify common log quality issues and improves the overall data quality, such as depth shift.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"scholarone-tab-content\" id=\"day-2\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: EOR for Shale Plays<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:50 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Doug Valleau\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Zhang*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Bureau of Economic Geology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Alvarez*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. Xie*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oxy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Integrated Petrophysical Interpretation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRishikesh Shetty, Hasan Khan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">From Characterized Sweet Spot to Stage Optimization: An Enhanced Integrated Workflow Applied to Major Shale Plays Across Diverse Geological Settings in the Americas, Case Studies<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Betancourt*<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>, A. M. Quaglia<sup>2<\/sup>, R. D. Panesso<sup>2<\/sup>, J. C. Porras<sup>2<\/sup> and E. Solorzano<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Universidad Central de Venezuela; 2. InterRock; 3. University of Oriente)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study proposes an enhanced methodology for unconventional rock typing through the refinement of the Unconventional Rock Type Index (Known as URT_Index). The URT_Index is a composite metric derived from an integrated analysis of geochemical, mineralogical, petrophysical, and geomechanical data. In this updated approach, a novel geochemistry and geomechanical based analysis is incorporated, enabling a more robust evaluation of hydrocarbon potential and rock behavior. This enhancement allows for a more precise classification of unconventional rock types and improves the predictive capability of the index.Methods\/Procedures\/Process: The study applied the developed methodology in some fields located in North and South America, as Canada, USA, Mexico, Colombia\/Venezuela &amp; Argentina. To develop the Enhanced Workflow, extensive laboratory tests, conventional and special logs, and field measurements were used to characterize the reservoir and to define the appropriate hydraulic fracturing design. The reservoir characterization included lithofacies and pore classification using XRD, SEM, and Thin Sections. The rock physics and geomechanical analysis included stress field analysis, brittleness analysis, Poisson\u2019s Ratio, Young\u2019s Modulus, and acoustic impedance analysis.Results\/Observations\/Conclusions: The results demonstrate that this integrated approach not only identifies prospective zones and usable net thickness but also correlates strongly with production sensitivity performance and reduced costs by 35%. Leveraging integrated analysis of rock properties as TOC 3.6%, Brittleness 55%, Porosity 9.6%, Abs permeability 0.854 mD, and effective permeability 0.159 mD, horizontal well lengths, benchmarked fracturing stage designs, and reservoir-specific geological settings, we achieved effective thickness optimization of up to \u00b120% in several case studies.Applications\/Significance\/Novelty: The workflow to optimize the number of fracture stages in horizontal well sections. This optimization is based on the spatial distribution of the enhanced URT_Index, and Hydraulic Fracture Zone Index values. The proposed methodology is designed to ensure that stimulation efforts are focused on zones with the highest potential for fracture propagation and reservoir connectivity.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Formation Evaluation at a Drilling Qualification Facility Using Core and Downhole Sensors<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Balliet*<sup>1<\/sup>, G. Singer<sup>1<\/sup>, K. Cox<sup>2<\/sup>, D. Gonzalez<sup>1<\/sup>, G. Carpio<sup>1<\/sup>, R. Gales<sup>1<\/sup> and G. Davalos<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Halliburton; 2. Corelab)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Reservoir characterization for qualification of LWD and Wireline sensors is an important step in establishing a \u201cfootprint\u201d or baseline during early sensor testing. A dedicated facility to drill and log wells through a range of different earth formations provides a strong method of early sensor development characterization. Multiple types of core data measurements were used for assessing accuracy of the downhole sensors. Several types of LWD and Wireline measurements were acquired, both commercial and new generation downhole sensors in development. Downhole sensor measurements and analysis were critiqued with respect to the laboratory core results. All core evaluation and downhole sensor results will be presented and described in an integrated fashion.Methods\/Procedures\/Process: Core measurements performed on rotary sidewall cores included XRD, XRF, NMR, Dean Stark, helium porosity, grain density and digital core micro-CT. Mud logs and drilling cutting were also available. Three primary lithologies were penetrated include the Georgetown limestone, Hosston dolomite and Cotton Valley sandstone. Wireline and LWD data included imaging, nuclear, resistivity, elemental spectroscopy, and NMR. Drilling and wireline logging procedures were designed and executed to evaluate both individual sensor responses and integrated analysis in a water-based mud drilling environment. Drilling and logging parameters were controlled and varied by design as wells and evaluation programs evolved.Results\/Observations\/Conclusions: The evaluation focus using both core and logging results demonstrated a more complex mineralogy than originally anticipated. Core and elemental logging measurements were evaluated and the relationship to downhole logging measurements were clarified for NMR and nuclear measurements. Laboratory NMR measurements improved the evaluation using both fully saturated and at irreducible. Dean Stark measurements provided understanding of the presence or absence of native residual oil or if nearby wells drilled with an oil-based mud system contributed to downhole sensor behavior. Digital core evaluation provided insight toward pore structure and connectivity. Downhole sensor performance and accuracy were evaluated and core information used to validate the sensor results for a final assessment.Applications\/Significance\/Novelty: Evaluating new and existing technologies allows characterization of accuracy and precision with a range of drilling and logging conditions. A detailed evaluation plan is executed during the qualifications and during the various stages of the testing plans. Elaborate evaluation provides significantly better downhole sensor development with access to dedicated drilling rig, personnel, and support facilities which are specifically committed to integrated analysis of sensors and core data. A drilling qualification facility capability has been described showing examples of testing procedures and results evaluation. This paper presents a wide range of core measurements in relationship to both currently commercial LWD and Wireline measurements and new generation downhole sensors.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Updated Non-Electrical Solutions for Unlocking Gas Potential In Ultradeep Tight Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tQ. Guo<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>, Z. Li<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>, K. Bie<sup>1<\/sup>,<sup>2<\/sup>,<sup>3<\/sup>, S. Ge<sup>1<\/sup>,<sup>3<\/sup>, L. Cai*<sup>4<\/sup>, X. Gu<sup>4<\/sup> and K. Li<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Department R\uff06D Center for Ultra-Deep Complex Reservoir Exploration and Development, CNPC; 2. Xinjiang Key Laboratory of Ultra-Deep Oil and Gas; 3. PetroChina Tarim Oilfield Company; 4. SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Tarim Basin\uff0cwell known for its high-gas production\uff0cthe reservoirs are mainly located deep at approximately 8000-m vertically with the reservoirs containing tight petrophysical properties. Fluid identification is a key step to locate the sweet spot. Resistivity is the most straight-forward method, however, mixed effects from pore structure, sedimentary dips, far-end fractures, and influence of surrounding rocks etc., hydrocarbon and water cannot be easily identified with the resistivity difference. 2D NMR successfully solved the some of the difficulties, however, the success rate for fluid identification is still low. This paper presents an updated methodology based on nonelectric logs reveals the fluid type in the tight sandstone and carbonate reservoirs in this ultradeep environment.Methods\/Procedures\/Process: The workflow based on wireline logging, including advanced spectroscopy and array dielectric measurements. Advanced spectroscopy provided precise dry weights for elements and minerals, together with total organic carbon (TOC). It also provides sigma and chlorine concentration measurements (DWCL), which are sensitive to saline water and suitable for fluid characterization while the formation is saline. Array dielectrics provides water-filled porosity independent of water salinity in the invaded zone, with multi-frequency conductivity and permittivity measurements, it also provides the estimation of formation salinity, which is an input for the DWCL or Sigma water saturation estimation methods.Thus, a internal verification and integrated workflow was established to identify formationResults\/Observations\/Conclusions: This paper presents case studies from this ultradeep reservoir in China that solved the fluid identification issue when resistivity cannot directly distinguish fluid type. For hydrocarbon zones, Sigma and DWCL derived a low water saturation, and low water porosity seen from dielectric although in different formation types. The interpretation results matched well with the test, which provided a novel solution to finalize the sweet spot interval for the targeted reservoir. The results show the compatibility of this workflow in different formation types and reservoirs.Applications\/Significance\/Novelty: This paper presents a successful and novel integrated workflow that combines multiple wireline measurements for fluid identification in tight carbonate reservoirs when the uncertainty of the resistivity method is high due to multiple factors. Also, the nonelectrical methodology significantly lowers the uncertainty for water saturation estimation. This result has helped to enhance the methodology for fluid identification and earn considerable economic value by escaping the water zones in the further development of the reservoir. This workflow is also promoted to other locations in China containing high-salinity formation water environments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Multiphysics Controls Characterization for CCUS Reliability<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tKatrina Ostrowicki, Graham Bain\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Multi-Decadal Evolution of a CO\u2082-Driven Reaction Interface in Wellbore Cement: Insights from Field-Retrieved Cores<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Ahmed Banu<sup>1<\/sup>, M. Meng<sup>2<\/sup> and S. Abedi*<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas A&amp;M University; 2. Los Alamos National Lab)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Wellbore cement exposed to CO2-rich environments undergoes progressive chemical and mechanical alteration that threatens long-term well integrity. However, field-scale evidence over multi-decadal timescales is limited. This study investigates a naturally formed reaction interface in wellbore cement retrieved after more than 30 years of CO2 exposure. A distinct orange-to-gray transition zone represents a sustained in-situ reaction front, enabling rare insight into degradation mechanisms under realistic downhole conditions and their impact on sealing performance.Methods\/Procedures\/Process: A multi-scale characterization approach was used to quantify chemical and mechanical gradients across the interface. High-speed nanoindentation provided spatially resolved elastic modulus and hardness. SEM-EDS measured elemental redistribution, including Ca depletion and Si enrichment. Micro-CT imaging quantified porosity evolution and mineral reorganization. Together, these datasets enabled reconstruction of the degradation pathway and identification of coupled chemo-mechanical processes shaping long-term cement transformation.Results\/Observations\/Conclusions: Nanoindentation reveals substantial reductions in stiffness and hardness within the reacted zone, defining a pronounced mechanical gradient across the boundary. SEM-EDS shows progressive decalcification and localized carbonate precipitation, while micro-CT indicates porosity redistribution and accompanying phase transitions. These observations demonstrate that the interface forms through dissolution\u2013precipitation reactions governed by ionic transport. The boundary acts as a chemo-mechanical transition layer controlling degradation progression and load-bearing capacity.Applications\/Significance\/Novelty: This study provides one of the rare field-validated datasets on wellbore cement exposed to CO2-rich brine for more than 30 years, using actual downhole cement rather than laboratory-aged analogs. The naturally developed reaction interface offers unprecedented insight into long-term degradation mechanisms that cannot be replicated through accelerated aging. Results highlight interfacial processes as key controls on sealing performance and support improved cement design and risk mitigation for CO2-EOR, long-term abandonment, and well-integrity management.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">NMR Spectroscopy to Quantify Oil Recovery and Image Saturation Profiles During Cyclic CO2 Gas Injection on Carbonate Source Rocks<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Mathur*<sup>1<\/sup>, S. Althaus<sup>2<\/sup>, A. Gupta<sup>3<\/sup>, R. Vaidya<sup>3<\/sup>, R. Mesdour<sup>4<\/sup> and W. Von Gonten, Jr.<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. WDVG Engineering; 2. Aramco Americas; 3. Former Aramco Employees; 4. Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Low primary recovery and residual fluid composition make carbonate source rocks (CSRs) good candidates for carbon dioxide to enhance oil recovery (CO2 EOR). Huff-and-puff CO2 EOR (HnP-CO2-EOR) studies enable evaluation of improved recovery potential. However, monitoring saturations at reservoir conditions is challenging. The objective of this study is to demonstrates application of nuclear magnetic resonance (NMR) spectroscopy to image saturations\/recoveries during HnP-CO2-EOR experiments on CSRs to provide insight into the recovery mechanisms and quantify recovery.Methods\/Procedures\/Process: Three plugs were collected from CSR whole core. One sample was split and loaded with 100-mesh sand to simulate a hydraulic fracture. Two samples were left intact. NMR and micro-CT were used to screen plug quality, heterogeneity, and initial saturations. The samples were saturated by core flooding with live-oil in NMR overburden cells at reservoir conditions. Steady-state permeability to live oil was measured, followed by frac water injection, and then primary depletion. Five HnP-CO2-EOR cycles were conducted with 24-hour soak periods and recoveries measured using NMR T1-T2 scans. Saturations were quantified, and saturation profiles monitored, using NMR at each stage of the HnP-CO2-EOR process while maintaining reservoir conditions.Results\/Observations\/Conclusions: Despite having low permeability, consecutive NMR scans at reservoir pressure and temperature confirmed that all samples achieved complete oil saturation.Steady-state permeability to live crude showed good reservoir quality although injection of frac water caused a significant drop in oil productivity. For intact samples, recovery from primary depletion was typical for unconventional reservoirs (i.e.,~5-10%) and was twice as much for the split sample. Five HnP-CO2-EOR cycles were performed with high-pressure CO2 and varied puff-cycle drawdowns. When HnP-CO2-EOR cycles were consistent, the split sample recovery was higher due to the increased surface area.Applications\/Significance\/Novelty: This study reports NMR-measured oil and water saturations and steady-state live crude permeability of nano-Darcy CSRs at reservoir conditions. Three-dimensional NMR imaging provides insight of fluid distributions, saturation fronts change with each HnP-CO2-EOR cycle, recovery mechanisms and enabled quantification of recovery. Time-lapse Spatial T2 scans gave profiles of oil saturation changes along the sample, while time-lapse NMR Scans provided insight into evolution of spatial distribution of oil in the plugs showing differences in depletion profiles of intact versus split samples. During each stage of the HnP-CO2-EOR test, NMR T1-T2 scans were instrumental in tracking saturations and recoveries at different stages of testing, while maintaining reservoir pressures and temperatures.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Prioritizing Multiphysics (HMTC) Controls on CO\u2082 Injection Behavior Using a Fully Coupled Simulator<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Mura* and M. M. Sharma\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to quantitatively evaluate the contribution of each hydro\u2013mechanical\u2013thermal\u2013chemical (HMTC) mechanism to CO2 injection behavior using the MF3D simulator (Mura et al., 2024). Although HMTC effects are often discussed qualitatively, their relative importance under field-scale injection scenarios remains unclear. We assess the dominant mechanisms controlling wellbore and caprock integrity, injectivity, and fluid\u2013rock interactions to support risk prioritization and scenario screening.Methods\/Procedures\/Process: We used MF3D, a numerical simulator capable of fully coupled hydro\u2013mechanical\u2013thermal\u2013chemical (HMTC) processes, to isolate and quantify multiphysics contributions during CO2 injection. A sequence of controlled simulations was conducted in which thermal, mechanical, hydrological, and chemical modules were activated individually and in combination. Key processes examined include thermal propagation, thermo\u2013poro\u2013elastic stress, and geochemical reactions. The chemical module accounts for ion exchange among aqueous species, mineral dissolution and precipitation, and the resulting porosity and permeability changes. Comparative analysis across toggled-physics cases enabled systematic identification of dominant mechanisms under representative field-scale injection conditions.Results\/Observations\/Conclusions: The simulations show clear differences in how HMTC processes propagate and influence the reservoir. Pressure buildup is the fastest response and drives broad poroelastic stress changes, followed by the salinity front from CO2 dissolution, which can elevate the risk of leakage through existing pathways. CO2 migration and thermal effects advance more slowly, yet cooling-induced thermal stress reduction can still trigger unintended hydraulic fracturing. Geochemical reactions modify porosity and permeability across a wide region but have limited impact on injectivity. The relative importance of these mechanisms depends on formation properties such as Young\u2019s modulus, mineralogy, and brine chemistry, underscoring the need to quantitatively prioritize multiphysics effects in CO2 storage design.Applications\/Significance\/Novelty: This study provides the first systematic numerical decomposition of HMTC mechanisms for CO2 injection using a fully coupled simulator. By quantifying\u2014rather than qualitatively inferring\u2014the relative influence of thermal, mechanical, hydrological, and chemical processes, the results offer practical guidance on which physics must be included for specific injection scenarios. The findings support more efficient simulation workflows and more targeted risk assessments for CO2 storage operations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Foam, Mobility Control, and Conformance Management<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAmit Katiyar, Kaveh Ahmadi\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Impact of Nanobubble Generation Pressure on Enhanced Oil Recovery<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Lawal*<sup>1<\/sup>, O. Oyenowo<sup>1<\/sup>, H. Wang<sup>1<\/sup>, A. Rovirosa<sup>1<\/sup>, W. De Alwis<sup>1<\/sup>, R. Okuno<sup>1<\/sup>, Z. Li<sup>2<\/sup>, N. Zhou<sup>2<\/sup>, L. Zhang<sup>2<\/sup> and M. Lu<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Hildebrand Department of Petroleum and Geosystems Engineering, University of Texas at Austin; 2. CNPC USA Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Aqueous carbon dioxide nanobubble (CO2-NB) fluids have been proposed as enhanced oil recovery (EOR) agents, yet the influence of NB-generation pressure on oil displacement remains uncertain. Recent thermodynamic studies indicate that high-pressure CO2-NB fluids should be viewed not merely as suspensions of small bubbles but as CO2-supersaturated aqueous fluids whose ability to release CO2 for EOR depends on generation pressure relative to the minimum supersaturation pressure (MSP) at the generation temperature. Above the MSP, aqueous supersaturation increases because the CO2-rich bubble phase approaches a liquid-like density, lowering the energetic barrier to nanobubble generation and enabling greater CO2 storage in the aqueous phase. This study evaluates how CO2-NB generation pressure affects tertiary oil recovery in Berea sandstone cores.Methods\/Procedures\/Process: Two CO2-NB generation methods were compared. A commercial low-pressure generator produced \u201cNB-L\u201d at 0.2 MPa and 21\u00b0C, whereas an in-house high-pressure high-temperature generator produced \u201cNB-H\u201d at 7.2 MPa and at either 21\u00b0C or 43\u00b0C. The fluids were characterized using nanoparticle tracking analysis at ambient conditions, dynamic light scattering at high pressure, CO2-content measurements, and thermodynamic modeling. After waterflooding, three tertiary-mode corefloods were conducted in Berea sandstone saturated with dead oil at 7.2 MPa and 43\u00b0C.Results\/Observations\/Conclusions: NB-L contained nanoscale bubbles at ambient conditions, with an average diameter of approximately 140 nm and a number density of 5.68 \u00d7 108 bubbles\/mL. However, after pressurization to 7.2 MPa, no detectable bubbles remained, indicating that the fluid became undersaturated under oil-displacement conditions. Consequently, NB-L produced only 0.3% incremental recovery of original oil in place (OOIP). In contrast, NB-H generated at 7.2 MPa and 21\u00b0C contained 1.64 mol\/kg CO2, approximately 15% above inherent solubility, and exhibited a bimodal distribution with nanoscale and microscale modes. This fluid had a gas-release capacity (GRC) of 0.606 mol\/kg and a released-gas volume (RGV) relative to water volume of 13.0%, yielding an incremental recovery of 23.3% OOIP. NB-H generated isothermally at 43\u00b0C had a lower GRC, 0.155 mol\/kg, and a RGV of 3.3%, resulting in an incremental oil recovery of 3.6% OOIP.Applications\/Significance\/Novelty: These results show that CO2-NB EOR performance is fundamentally governed by GRC, rather than by bubble properties, such as number density and size. Elevated-pressure NB generation above the relevant supersaturation threshold promotes CO2 release during injection, enabling in-situ formation of a CO2-rich phase that can displace oil more efficiently than water or undersaturated carbonated brine.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Diverter Stabilized Foams for Temporary Flow Restriction in Parent\u2013Child Fracture Geometries in Unconventional Shale Reservoirs: A Microfluidic Assessment<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Sivaraman<sup>1<\/sup>, L. Houchin*<sup>2<\/sup>, N. Koster<sup>3<\/sup> and J. Trivedi<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Alberta; 2. Production Improvement Chemicals; 3. Diverter Plus)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Parent-child well communication in unconventional shale reservoirs originates when depleted parent fractures act as low-pressure conduits, diverting fluid and energy away from newly stimulated zones. Particulate diverters are widely used to temporarily restrict flow into these dominant pathways, yet their behavior within nitrogen foam-based carrier fluids at the pore scale is still poorly resolved. This work experimentally evaluates the role of diverting particles in stabilizing foam lamellae and promoting localized, reversible flow restriction in fracture analogues using a high-resolution microfluidic visualization platform.Methods\/Procedures\/Process: Experiments were conducted using a glass-etched micromodel (15 mm \u00d7 97 mm) incorporating 2 \u00b5m fracture channels and 80 nm low-permeability regions. Previously optimized foaming solutions, comprising a mixture of surfactant and nanoparticle (0.5 wt%), with a low leak-off rate and non-damaging characteristics, were injected with nitrogen at 0.2\u20130.4 mL\/min to generate foam films (foam quality ~90%) at varying controlled temperatures and pressures. The degradable diverter particles (mean size ~2.1 \u00b5m) were added at two different concentrations of 0.5 g and 2 g per 100 mL. The bubble size, lamella half-life, pressure differential (\u0394P), and flow redistribution in the matrix near the parent well were monitored using inline pressure transducers and a high-speed visual camera.Results\/Observations\/Conclusions: Without divertors, foam films collapsed rapidly near constricted fracture regions (mean bubble size 78 \u00b1 12 \u00b5m; half-life 8 min; \u0394P ~20 kPa). At 0.5 g particle loading, bubble size reduced to 56 \u00b5m, half-life increased to 18 min, and \u0394P rose to ~40 kPa. At 2 g loading, fine-textured foams (~40 \u00b5m bubbles) with half-lives beyond 30 min were produced, generating a \u0394P of ~75 kPa, indicating temporary foam\u2013particle flow restriction in parent fracture channels. Flow redistribution toward the adjacent (child) channel increased by ~30% and maintained significantly higher pressure in the matrix. Imaging confirmed progressive particle accumulation at lamella surfaces and constricted fractures, forming transient bridging networks supported by foam films.Applications\/Significance\/Novelty: This work provides pore-scale experimental evidence of how diverting particle-stabilized foams can temporarily restrict flow and pressure maintenance behavior in unconventional shale fracture geometries. The findings offer quantitative insight into lamella reinforcement, fracture throat bridging, and reversible plugging dynamics that support the design of particle enhanced diversion fluids for frac hit mitigation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Foam-Based Mobility Control for Cyclic Gas Injection in High-Temperature, High-Salinity Shale Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Singh*, A. Katiyar, P. Akhade, C. Clark, S. Camacho and J. Hutchens\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The Dow Chemical Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Gas containment remains a critical limitation for cyclic gas injection (huff-n-puff) in unconventional liquid-rich shale reservoirs due to the inherently high mobility of injected gas in fracture-dominated systems. This work addresses this challenge by developing and screening foam forming surfactant formulations capable of operating under the combined extremes of high temperature (up to 180 \u00b0C), high salinity (up to 200,000 ppm TDS), and the presence of crude oil. The experimental program focused on reservoir conditions representative of the Bakken, Eagle Ford, and Powder River Basin, encompassing a wide range of brine chemistries, and temperatures.Methods\/Procedures\/Process: A systematic, two-phase screening methodology was employed to evaluate different anionic and amphoteric surfactants and their binary blends. Screening protocols incorporated aqueous phase stability at surface and reservoir conditions, long-term thermal stability, oil tolerant bulk foam generation, and high temperature foam persistence (t\u2088\u2080).Results\/Observations\/Conclusions: Phase I stability screening at 115\u00b0C and 200,000 ppm TDS (Bakken conditions) identified clear formulation trends and enabled down selection of robust blends. Selected formulations remained single phase after extended exposure (two weeks) at 140\u00b0C and 180\u00b0C in Eagle Ford brines, confirming strong thermal and salinity tolerance across multiple reservoir environments. Phase II foam evaluations demonstrated that binary anionic\u2013zwitterionic blends significantly outperformed single component systems, with optimized formulations achieving foam persistence (t\u2088\u2080) exceeding 300 minutes at 115\u00b0C. Synergistic enhancement of foam stability was consistently observed at low to intermediate anionic fractions, highlighting the critical role of blend design in achieving oil tolerant, high temperature foam performance. These results establish quantitative design principles for oil tolerant foam systems and identify candidate formulations suitable for field scale foam assisted cyclic gas injection.Applications\/Significance\/Novelty: The study provides a practical, reservoir relevant methodology for designing and deploying foam mobility control formulations in unconventional shale reservoirs, bridging the gap between laboratory screening and field implementation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: Emerging Geological Evaluations: Integrated Workflows for Prospectivity Assessment and Development Efficiencies II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tDavid Hume, Devon Verellen\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Characterizing the Karst Features of the Rustler Formation and the Ochoan Evaporites in Southeast Eddy County, New Mexico<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Mackay*<sup>1<\/sup>, A. Fernandez<sup>2<\/sup> and A. Lewis<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Fairfield Geotechnologies; 2. Eliis Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to interpret shallow horizons related to Ochoan-aged evaporites and the overlying Rustler formation, and to characterize their associated karst systems in Eddy County, NM. Legacy seismic data over the same area was too sparse to image the Rustler previously. Understanding the geometry and extent of these dissolution features is essential, as the associated velocity variations directly influence depth control and structural positioning of deeper producing formations. Improved definition of these shallow strata supports safer drilling, reduces uncertainty in target depths, and enables optimal well placement in the highly productive Bone Spring and Wolfcamp formations.Methods\/Procedures\/Process: A 2024-2025 high trace density (HTD) seismic survey was processed through Kirchhoff pre-stack depth migration (KPSDM). This included a high-resolution velocity model derived from full-waveform inversion (FWI) up to 17 Hz, for a robust depth image. This dataset was used to construct a Relative Geological Time (RGT) model and generate 150 correlated horizons from surface to the Bone Spring Lime. The combination of HTD seismic data with the RGT workflow, and multi-attribute visualization, provides an efficient approach to resolve complex shallow collapsed features. Karst-related geobodies were extracted using a multi-attribute K-means classification, integrating RGT shape index and seismic amplitude.Results\/Observations\/Conclusions: In the study area, localized karst features occur across several shallow intervals, with major development in the Salado and Castile formations. Areas of intense Salado dissolution cause the Rustler formation to collapse and result in significant post-Permian sedimentary fill. This causes strong near-surface velocity variations resulting in imaging distortions. The RGT horizons constrain the relative geological timing of these collapsed features and enables more accurate geobody delineation. The HTD seismic survey illuminates detailed karst geometry constraining localized fill zone extents and improving resolution at the reservoir level. The combination of HTD seismic data and the RGT workflow enables the interpretation of some karst features at shallow depths previously unseen.Applications\/Significance\/Novelty: Shallow karst features present drilling hazards which need to be avoided. Furthermore, this shallow structural complexity creates a major challenge to properly image the underlying producing reservoir formations. This study demonstrates that the combination of HTD seismic acquisition, FWI velocity modeling, RGT interpretation, and detailed multi-attribute analysis can accurately characterize these features. Additionally, this workflow provides a transferable methodology for understanding near-surface complexity in other karst-affected basins.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">The Wattenberg Temperature Anomaly, Denver Basin, CO<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. A. Sonnenberg*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: A major temperature anomaly coincides with the giant Wattenberg Field of the Denver Basin. The anomaly covers an area of 1450 square miles. Wattenberg Field was discovered in 1970 and produces oil and gas from nine Cretaceous horizons (Dakota Ss, Muddy Ss, D Ss, Greenhorn, Codell Ss, Niobrara Formation, Hygiene Ss, Terry Ss, and Larimer-Rocky Ridge Ss). Cumulative production for all the reservoirs combined exceeds 24 TCFe gas. Temperature anomalies in basins may be caused in several different ways. The objective of the study is to determine the origin of the Wattenberg temperature anomaly.Methods\/Procedures\/Process: Geothermal gradients were calculated from bottom hole temperatures on well logs. Source rock maturity data (Ro and Tmax) were acquired in the study area from various sources. Geophysical maps (aeromagnetic and gravity) were compiled for the study area.Results\/Observations\/Conclusions: The temperature anomaly in Wattenberg is confirmed by bottom hole temperature measurements on well logs and drill stem tests, vitrinite reflectance data, Tmax source rock data, and oil and gas ratios (various formations) across the field. The temperature anomaly in the Denver Basin is located where the Colorado Mineral Belt (CMB) intersects the basin. The CMB is a northeast trending area of ore deposits defined by Proterozoic shear zones, Laramide-aged plutons and related ore deposits, major gravity low, low-crustal velocities, and high heat flow. High heat flow and hydrothermal mineralization are formed from hot fluids circulating through fractures. The source of the heat is most likely deep intrusive igneous bodies.Applications\/Significance\/Novelty: Geothermal anomalies are key in the exploration of hydrocarbon traps. The anomalous areas may also serve as targets areas for future geothermal development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Core-Based Electrofacies \u2013 Integrating Rock-Log Data for Mixed System Shelf-to-Basin Reservoir Characterization<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Eljalafi*<sup>1<\/sup>, C. Cross<sup>1<\/sup>, J. Bellian<sup>2<\/sup>, D. Katz<sup>1<\/sup>, A. Bonilla-Rodriguez<sup>3<\/sup>, R. Holman<sup>1<\/sup>, D. Hull<sup>1<\/sup>, C. Harman<sup>1<\/sup>, N. Allen<sup>1<\/sup>, B. Bryce<sup>1<\/sup>, B. Ruskin<sup>1<\/sup>, W. Walker<sup>1<\/sup>, S. Mitocky<sup>1<\/sup>, M. Elasmar<sup>1<\/sup>, A. Godoy<sup>4<\/sup>, O. Djordjevic<sup>1<\/sup>, C. Christofferson<sup>1<\/sup> and A. Leavitt<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Ovintiv; 2. Koloma Energy; 3. ExxonMobil; 4. Summit Petroleum LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Facies description constitutes a fundamental pillar of reservoir characterization. Delineating facies distribution in a basin allows for the proper prediction of oil prone rocks; whether unconventional or conventional, or hybrid plays. Whole core allows for direct observational analysis of reservoir characteristics. However, cores are expensive and spatially restricted. The process of translating facies observations into actionable interpretations often presents challenges, due to the inherently qualitative nature of core descriptions, and difficulties in geologic model design. The objective of this study is to explore statistical methods that integrate qualitative core-based facies observations rooted in fundamentals of sedimentology and stratigraphy, and quantitative well-log data.Methods\/Procedures\/Process: This study utilizes two methods to predict electrical facies using core and open hole log data. The first method utilizes unsupervised hierarchical cluster analysis to establish electrofacies predictions using triple combo datasets (gamma-ray [GR], neutron porosity [NPHI], deep resistivity [LLD], and bulk density [RHOB]) without the use of core data. The second method employs categorical decision tree modeling to establish a relationship between three triple combo data sets and their respective core descriptions (~4000\u2019 total). The cores are positioned along slope, toe of slope, and the basin in the basin center. In this study we use EasyCore to record depositional facies of the cores, while the statistical modeling is executed in JUMP.Results\/Observations\/Conclusions: The two log facies models were propagated over ~6000 wells with adequate triple combo logs using an R script. Each model delineated 10 distinct electoral facies. Both models adequately captured the carbonate slope and a siliciclastic basin-center spanning the late Wolfcampian to early Leonardian successions of the Midland basin. Models derived from categorical decision tree relationships are superior to the unsupervised hierarchical cluster analyses. The categorical decision tree model produces electrical facies that are more representative of the depositional facies described from core and likely to be predictive of reservoir behavior. The unsupervised hierarchical cluster analyses models oversimplify the relationships and parse the logs irrespective of depositional system elements.Applications\/Significance\/Novelty: With increasing focus on free cash flow, valuable core data is becoming rarer. By utilizing facies prediction, it is possible to stretch existing core data further. This requires good judgement early in making observations and processing the statistics. Leveraging the accumulated knowledge of geoscience and depositional system with facies allows for superior results to simple kriging of log measurements. This enables geologist to map and identify analogues for new developments, allowing engineers to make precise predictions by limiting type curve regions to relevant wells. Facies also allow the inference of properties that are difficult to measure; e.g. lamination (kv vs kh), and kerogen type, which are valuable inputs for reservoir models in spite of measurement difficulty.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Diagnostics and Monitoring in Hydraulic Fracturing with Geomechanical Models II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tQin Ji, Xinghui Liu\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Well Integrity Challenges and Cement Damage Mechanisms in Hydraulic Fracturing Operations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Ahmed* and K. Sepehrnoori\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Hydraulic-fracturing wells experience extreme net pressures, rapid pressure cycling, and complex stress interactions around perforation clusters that can degrade both the cement sheath and near-wellbore rock. This work develops a physics-based pseudo\u2013finite-element framework to quantify cement and formation damage around perforations as a function of perforation geometry, net pressure history, injected volume, and material properties. The scope targets unconventional multi-stage completions and aims to provide design-relevant integrity metrics for the stimulated interval.Methods\/Procedures\/Process: The wellbore is represented as concentric casing\u2013cement\u2013rock cylinders discretized on a 3D cylindrical grid (\u03b8\u2013z cells) in a pseudo\u2013finite-element fashion. Local stresses around perforation tunnels are initialized from Lam\u00e9\u2013Kirsch solutions and updated under applied net pressure and Biot-effective stresses. In each cell, failure is checked with Mohr\u2013Coulomb criteria using cohesion and friction angles for cement and rock. Optional Paris-law fatigue drives crack extension under repeated pressure cycles, while injected volume per stage controls radial damage growth away from the perforation tunnels. The model outputs 3D damage fields and damaged fractions for each cluster and stage.Results\/Observations\/Conclusions: The simulations produce high-resolution cement and rock damage maps that capture intense damage halos adjacent to perforation tunnels and their evolution with cycling and injected volume. Parametric studies show that higher Biot coefficient and lower-cohesion cements fail earlier, with damage extending farther from the perforation tips. Increasing net pressure, injected volume, and cycle count amplifies radial penetration and the damaged fraction within the target interval. Reducing perforation-cluster spacing strengthens stress interaction and leads to coalescence of damage zones, highlighting specific completion designs that pose elevated integrity risk for the cement sheath and near-wellbore rock.Applications\/Significance\/Novelty: The framework provides a fast, physics-based pseudo\u2013finite-element tool for pre-job screening of hydraulic-fracturing designs from a well-integrity perspective. It allows engineers to compare cement systems, perforation diameters and spacing, stage volumes, and pressure schedules based on their predicted impact on near-perforation cement and rock damage. The novelty lies in coupling classical near-wellbore stress solutions with a cylindrical pseudo\u2013finite-element damage grid, Mohr\u2013Coulomb failure, poroelastic effects, fatigue, and volume-driven radial growth into a single perforation-scale integrity model specifically tailored to hydraulic-fracturing wells.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Effect of Propped Fracture Volume on Pressure Transient Behavior in Fractured Production Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Li*, J. Hu, Y. Ou and M. M. Sharma\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Pressure-transient analysis (PTA) is one of the most widely used diagnostic tools for evaluating hydraulically fractured wells, yet most existing studies rely on the fluid diffusivity equation and overlook key geomechanics processes such as rock deformation, fracture closure and proppant distribution. The objective of this study is to quantify how different proppant-distribution scenarios affect pressure-transient behavior during drawdown tests in hydraulically fractured wells, and to develop a diagnostic correlation that enables us to infer the effective propped fracture volume from standard pressure drawdown measurements.Methods\/Procedures\/Process: A fully implicit, three-dimensional reservoir\u2013fracture\u2013wellbore simulator is employed to jointly solve the governing equations for rock deformation, fluid flow, and proppant transport. The Barton\u2013Bandis model represents fracture closure and contact mechanics in both propped and unpropped fracture sections. Drawdown tests are simulated across multiple proppant configurations (0.3, 0.5, 0.7, and fully propped fractures), three reservoir permeabilities (1, 10, and 100 \u03bcD), and three production rates, enabling systematic investigation of proppant effect for formations with different permeability, and wells producing at different rates.Results\/Observations\/Conclusions: Four flow regimes are observed during drawdown: fracture-storage, transitional, pseudo-steady-state, and linear flow. The transitional regime exhibits the strong sensitivity to proppant distribution. Considering \u201cflow + geomechanics\u201d in the model extends fracture-storage and transitional regimes by nearly one order of magnitude relative to the \u201conly flow\u201d model. The pressure-derivative response closely mirrors the temporal evolution of fracture volume. Higher reservoir permeabilities shift the pressure curves upward and delay the end of transitional flow, while lower production rates produce a counter-intuitive increase in dimensionless pressure drop. A polynomial correlation between fracture volume and the pressure-derivative value at the end of transitional flow yields R\u00b2 values of 0.08, 0.77, and 0.98 at 1, 10, and 100 \u03bcD, respectively, suggesting a possible diagnostic pathway for post-fracture evaluation.Applications\/Significance\/Novelty: This work advances the interpretation of drawdown tests in hydraulically fractured wells by explicitly coupling rock deformation and fracture closure within a unified pressure-transient framework. The novelty lies in mechanistically linking proppant-distribution patterns to flow-regime signatures and demonstrating that the end-of-transition time on a derivative plot encodes quantitative information about the effective propped fracture volume.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">An Alternative Approach for Estimating Reservoir Pressure from the Flowback Period of DFIT-FBA<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Clarkson*<sup>1<\/sup>, S. Haqparast<sup>1<\/sup> and D. Zeinabady<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Calgary; 2. ResFrac Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The diagnostic fracture injection test-flowback analysis (DFIT-FBA) method was introduced and designed to accelerate estimates of minimum in-situ stress, reservoir pressure and permeability compared to a conventional (pump-in\/shut-in) DFIT. This is achieved by performing rate-transient analysis on the flowback portion of DFIT-FBA. Reservoir pressure is identified as the start of the second unit slope on a log-log plot of rate-normalized pressure (RNP) derivative versus material balance time, which corresponds to inflow of fluid from the near-fracture region. However, with field data, picking pore pressure this way may be difficult due to derivative noise. In this work, an alternative and complementary method is suggested based on a Cartesian plot of flowing pressure versus flowback time.Methods\/Procedures\/Process: Historical studies of post-stimulation treatment flowback data of hydraulically fractured wells have suggested that flowing pressure may flatten at the point of reservoir fluid breakthrough to hydraulic fractures, providing a method to estimate reservoir pressure. Extending this concept to DFIT-FBA, reservoir pressure may be identified as a flattening of flowing pressure on a Cartesian plot of flowing pressure versus flowback time after full mechanical closure of the created minifracture has occurred. Specifically, the deviation of flowing pressure from a tangent line drawn through the full mechanical closure point, as generated when using the Plahn et al. (1997) method for minimum in-situ stress, can be used for this purpose. This concept is tested using both simulated and field cases.Results\/Observations\/Conclusions: Simulation of DFIT-FBA using a coupled hydraulic fracturing-reservoir simulator confirms that the new method results in a correct estimate of reservoir pressure. For one case studied, the point of flowing pressure deviation from the tangent line corresponds to maximum inflow from the reservoir and start of the second unit slope on the log-log RNP plot. Ongoing simulation studies are examining the effect of test operational parameters and reservoir properties on these observations. Analysis of a wireline-conveyed microfracture test designed to mimic DFIT-FBA, and an actual DFIT-FBA test, confirm that the new method yields a similar estimate of reservoir pressure as the derivative method, but only if the integral of rate-normalized pressure (IRNP) is used for the latter to reduce noise.Applications\/Significance\/Novelty: A new, alternative approach for estimating reservoir pressure from the flowback period of DFIT-FBA is proposed herein. This is significant given that DFIT-FBA is gaining popularity in the industry, and reservoir pressure is the most uncertain parameter derived from DFIT-FBA. For the cases studied, use of the new method, in combination with the traditional RNP (or IRNP) derivative method, allows for consistent reservoir pressure estimates. Ongoing simulation and field case analysis will help to determine the conditions under which the new method yields confident reservoir pressure estimates. These findings will be of importance to the growing community of operators implementing the DFIT-FBA method.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6:  Subsurface Characterization, Applied Case Studies, and Business Implementation II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tHardik Zalavadia, Lucy Luo\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Well Spacing Optimization in the Delaware Basin Wolfcamp Formations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Alzahabi*, A. Kamel, J. King and L. Gamez\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Texas Permian Basin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Optimizing well spacing in the Delaware Basin has become increasingly critical as operators implement tighter development programs and encounter complex parent\u2013child interactions. This study investigates how well-spacing parameters influence early-time oil production performance using a dataset of 12,191 Wolfcamp wells from Enverus. The primary objective is to quantify the impact of spacing\u2014particularly within parent\u2013child configurations\u2014on productivity and to identify spacing strategies that maximize reservoir drainage while minimizing interference.Methods\/Procedures\/Process: The analysis integrates statistical correlation techniques using early oil productivity normalized per lateral foot as the dependent variable. Key independent variables include lateral spacing, neighbor well density, 2D\/3D distance metrics, completion intensity, lateral length, and stimulated-interval characteristics. Initial scatter-plot diagnostics are applied to identify global trends and potential non-linear behavior. Early screening indicates higher normalized production at shorter spacing intervals (e.g., wells with ~917 ft spacing averaging 51,958 bbl\/1,000 ft in the first 12 months). Wells showing clear interference patterns are further filtered for detailed evaluation to isolate spacing ranges most strongly associated with optimal performance.Results\/Observations\/Conclusions: Preliminary results show that longer laterals do not necessarily translate to higher normalized production, and that parent\u2013child proximity plays a significant role in performance degradation. Certain spacing thresholds correlate with reduced productivity, suggesting possible over-capitalization or fracture-hit effects. Case studies of two operators further illustrate variability: Operator-1: Optimal spacing ~656 ft; AvgOil = 41,530 bbl\/1,000 ft. Operator-2: Optimal spacing ~981 ft; AvgOil = 9,333 bbl\/1,000 ft despite longer laterals. These findings demonstrate strong sensitivity of performance to spacing design and highlight the need for data-driven approaches rather than generic spacing assumptions.Applications\/Significance\/Novelty: This study provides operators with a quantitative framework for evaluating well-spacing decisions in densely developed unconventional reservoirs. The results support improved development planning, reduced reservoir interference, and enhanced economic outcomes in the Delaware Basin. The novelty lies in the integration of large-scale spacing analytics, parent\u2013child interaction filtering, and normalized productivity metrics to identify spacing thresholds that optimize resource recovery.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Tansformers -Based Induced Earthquake Prediction in the Permian Basin and Its Impact on SWD Injection and CO2 Storage Site Evaluation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Sen*<sup>1<\/sup>,<sup>2<\/sup>, B. Dindoruk<sup>1<\/sup> and G. Bozkurt<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas A&amp;M University; 2. ShaleCode Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to develop a Transformer-based framework to predict induced earthquakes in the Permian Basin and to assess their implications for Salt Water Disposal (SWD) injection operations and future CO2 storage site evaluation. The scope includes using induced seismicity and associated parameters\u2014magnitude, depth, energy, and b-value from the TexNet catalog\u2014 along with the SWD injection volumes and oil and gas production data to forecast induced seismicity pattern. The study focuses on identifying operational factors that influence induced seismicity and supporting risk-informed decision-making for both SWD management and future potential CO2 sequestration planning, as the findings here could for the basis for the future at-scale CO2 sequestration projects.Methods\/Procedures\/Process: This study applies a 180-day sliding-window framework to induced seismicity, SWD injection, and oil and gas production data for Transformer-based modeling. Induced seismic data (35495 earthquakes occurred between 01\/01\/2017 and 11\/20\/2025 from the TexNet catalog) are combined with SWD injection volumes (3.2 bb\/day - 5.6 bb\/day) from the RRC of Texas and oil (2.1 - 6.7 m\/d) and gas (6.2 - 28.4 bcf) production rates from the EIA. Extensive feature engineering is performed, including rolling statistics, cumulative sums. All inputs are transformed into sequential tensors. The Transformer model is implemented using TensorFlow in Google Colab with Python, and trained to forecast 30-day induced seismicity. b-values are calculated and used to generate real-time seismic risk maps.Results\/Observations\/Conclusions: The Transformer model accurately forecasted 30-day induced seismicity in the Permian Basin, achieving 98% accuracy on the training set and 97% on the validation set. Predicted seismic events closely matched observed patterns in both time and magnitude. Analysis revealed strong correlations between SWD injection volumes, production rates, and induced seismicity, highlighting critical operational thresholds. Feature-engineered inputs improved model sensitivity to temporal trends. Calculated b-values allowed generation of seismic risk maps, identifying high-risk zones and supporting informed decisions for SWD management and CO2 storage site planning. The results demonstrate that Transformer-based models provide reliable, data-driven tools for induced earthquake forecasting.Applications\/Significance\/Novelty: This study demonstrates the novel application of Transformer-based machine learning to forecast induced seismicity in the Permian Basin. By integrating seismic, injection, and production data with feature-engineered inputs, the model captures complex temporal dependencies and operational effects. Calculated b-values enable the creation of real-time seismic risk maps, supporting proactive SWD management and future potential CO2 storage site planning. The approach offers a data-driven, accurate, and scalable tool for risk assessment, while all results, maps, and predictions are accessible through the PermianBasinAI mobile app as a ready to go deployable technology, facilitating decision-making for operators and regulators.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fault Prediction Over a Densley Fractured, Low Offset Permian Basin Acreage: A 3D Convolutional Neural Network Case Study<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Espinosa* and H. M. Garcia\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Geoteric)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As production in the Permian Basin continues to intensify, subsurface targets are becoming increasingly complex. Oil and gas operators are focused on optimizing well plans to mitigate drilling hazards and avoid unintended flow from faults in the shallow section. To efficiently characterize the dense network of faults and fractures, 3D convolutional neural networks (CNNs) have emerged as a powerful tool. In this case study, three 100 GB 3D seismic volumes, each covering the same area of the Permian Basin, were evaluated using a set of pre-trained 3D CNNs (CNNs 1-3) to assess fault identification performance across varying seismic data quality, specifically comparing raw versus conditioned volumes.Methods\/Procedures\/Process: The most effective input volume was selected for fine-tuning two of the top-performing pre-trained CNNs. This fine-tuning process involved providing the networks with examples of fault geometries using fault sticks interpreted on time slices, enhancing their ability to recognize relevant fault patterns in the seismic data. The CNN output is a fault confidence volume, assigning probability values from 0 to 1 to indicate the network\u2019s confidence in fault presence. To evaluate the quality of these results, the fault confidence volume was co-rendered with frequency decomposition products. As independent processes, these can be used together to validate structural discontinuities, since frequency changes are typically observed across fault blocks when visualized on a timeslice.Results\/Observations\/Conclusions: The 3D CNN outputs were used for automated fault extraction, resulting in the identification of over 1,400 faults across a 40 square mile area. The level of detail and fault density achieved through these models would require substantial time and resources if interpreted manually. In this case, the advantages of using 3D CNNs over traditional variance-based attributes were clear, demonstrating superior performance in both fault detection and efficiency. While the approach does introduce a larger volume of data, especially when multiple networks yield slightly different interpretations and noisy inputs may require additional fine-tuning and slightly increase turnaround times, these challenges are manageable and outweighed by the overall gains in accuracy and productivity.Applications\/Significance\/Novelty: Low offset faulting is characteristic of Permian Basin oil and gas fields with operators struggling to identify these features, often located whilst drilling which imposes increased risk of flows or mischaracterized fault blocks. 3D CNNs accelerate interpretation of large datasets and can also provide new insights even in mature fields for safer drilling operations and increased subsurface understanding. This workflow has applications beyond the Permian Basin, with many basins around the world characterized by low offset or subseismic faulting.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 8: Performance Prediction Methods:\u00a0Physics-Based Models and Data-Driven Forecasting Approaches II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        8:55 AM &#8211; 10:10 AM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAlexsandra Martinez, Frank Male, Xiao Jin\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t8:55 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Relationship Between the Stretched-Exponential Decline Model and Analytical Solutions for Fractal Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. R. Valdes-Perez*<sup>1<\/sup>,<sup>2<\/sup> and T. A. Blasingame<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. KAPPA Engineering; 2. Johns Hopkins University; 3. Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The stretched-exponential (SE) empirical relation: y = a0 exp[b0 tm], which is also known as the Kohlrausch-Williams-Watts function, is widely used across many disciplines. In reservoir engineering, the SE has been extensively applied to predict production rate behavior for wells drilled in shale reservoirs. In this paper, we derive and verify a mathematical relationship between the SE decline-curve model and analytical diffusion models for fractal reservoirs.Methods\/Procedures\/Process: The SE relationship is derived from relaxation theory, where relaxation theory models irreversible, energy-dissipating state transitions analogous to reservoir depletion. Because this phenomenon occurs over a wide range of timescales, the &quot;relaxation time&quot; is expressed as a power law of scalar time. We express the basis of SE in the form of the following relation for transient pressure: dpsD,cr\/dtD = a0 tDv-1. The SE decline-curve model is derived by combining this relation with the standard convolution relation in terms of the relaxation. This connects the empirically observed production behavior to the physics of diffusion in highly heterogeneous reservoirs. Consequently, the SE decline-curve model captures the long-term shale-well decline and offers a physical basis for the SE model parameters.Results\/Observations\/Conclusions: The critical result of this study is the mathematical proof of the SE rate decline relation. And more specifically, that this relation can be expressed in terms of the fractal parameters controlling the flow behavior. The SE has been in use for analyzing rate decline behavior since the mid-2000s, but as an empirical relationship (derived from direct observation of the rate and its time-based derivative functions). This work confirms the theoretical basis of the SE model and demonstrates that the SE rate decline relation provides a practical, physics-based method for analyzing production data, connecting observed well performance to the underlying heterogeneity of the reservoir.Applications\/Significance\/Novelty: By integrating the relaxation theory within the convolution relation, we have established a rigorous, theoretical foundation for the empirical SE decline model, relating it to analytical diffusion models for fractal reservoirs. This framework provides a physics-based explanation of production decline behavior and enables direct estimation of key reservoir parameters from well data.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:20 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Automated Well Performance Prediction in the Delaware Basin: Integrating Diffusive Time of Flight with Machine Learning<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Li*, E. A. Kinzler, S. Esmaili and J. Park\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents an automated workflow for production forecasting from early\u2011time data using Diffusive Time of Flight (DTOF) coupled with machine learning. DTOF tracks pressure\u2011front propagation without assumptions about flow regime or fracture geometry. Applied to more than 100 wells in the Delaware Basin, this approach enables rapid, physics\u2011based assessment of well performance and long\u2011term production forecasting from minimal early-time input data.Methods\/Procedures\/Process: The workflow calculates drainage volume from early production data, then converts it to drainage surface area, w_tau, which reflects effective fracture area and stimulated reservoir properties\u2014the primary drivers of well performance. For each well, only two months of production and PVT data are required to compute initial w_tau. This parameter, combined with geological and completion data, serves as the core feature for machine learning-based production forecasting models. Initial w_tau aligns closely with the Linear Flow Parameter (LFP) derived from RTA but offers a critical advantage: automatic calculation and consistent results across well large populations, eliminating subjective interpretation required for LFP determination.Results\/Observations\/Conclusions: Shapley value analysis of the machine learning models reveals that initial w_tau exhibits the strongest correlation with cumulative oil and total fluid production, demonstrating that two months of production data can reliably predict long-term well performance. Wells with higher initial w_tau consistently demonstrate higher production rates across the Delaware Basin, validating w_tau as a robust metric for evaluating well performance and completion efficiency. The automated workflow successfully analyzed a large set of wells with consistent and objective results. The models demonstrate robust predictive capability, enabling operators to identify high-performing wells early, and improve capital allocation decisions based on quantitative, physics-informed forecasts.Applications\/Significance\/Novelty: Early production data, including bottomhole pressure and liquid rates, is consolidated into a single diagnostic parameter (initial w_tau), which serves as a key driver for predicting future production. The automated workflow enables assessment of more than 100 wells without subjective interpretation, avoiding the manual effort required for LFP calculations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t9:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Some Pitfalls of Mass Flowing Material Balance for Fluid-In-Place Estimation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Hamdi* and C. Clarkson\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Calgary)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Mass Flowing Material Balance (mFMB) is a rate-transient analysis (RTA) technique that can be used to estimate fluid-in-place (FIP) from production rates (multiple phases) and flowing pressure data sourced from multi-fractured horizontal wells (MFHWs). mFMB is a noniterative technique based on density variations and relies on the assumption of production from a single tank. This method is attractive, mainly because it is believed to be independent of relative permeability data. The objective of this study is to utilize numerical simulation and field data to determine whether mFMB is truly independent of the relative permeability curve, and to identify scenarios where mFMB could yield significant errors in fluid-in-place estimates.Methods\/Procedures\/Process: Relative permeability curves, matrix permeability, and degree of undersaturation can significantly impact the performance of MFHWs. Therefore, ranges of these parameters are utilized to construct multiple black-oil numerical simulation models and to generate production data under constant and variable production constraints. A small fracture spacing of approximately 55 ft, and a long simulation time of 1000 days, ensure that boundary-dominated flow is achieved and maintained. Field cases from the Eagle Ford and Montney reservoirs are also studied, with mFMB applied to both measured and model history-matched data. The history-matched models range from simple single-porosity to more complex compositional local dual-porosity, dual-permeability models.Results\/Observations\/Conclusions: Synthetic simulation results indicate that mFMB underpredicts FIP estimation by &gt;45% when using suppressed gas relative permeability curves, common for unconventional reservoirs. Errors also grow as multi-phase flow dominates, but decrease with higher matrix permeability. For field cases, mFMB errors were ~45% for the Eagle Ford and &gt;70% for the compositional Montney case. While mFMB provides poor estimates during the simulation time frame, the error in FIP estimates can fall below 10% once reservoir-wide depletion exceeds 70%, and the well approaches abandonment. Analysis of the results demonstrates that mFMB accuracy is contingent on the validity of the single-tank assumption, which is frequently violated by complex relative permeability and large fracture\u2013matrix permeability contrasts.Applications\/Significance\/Novelty: This study provides a comprehensive, systematic evaluation of mFMB for estimating FIP. By highlighting the main pitfalls of mFMB applied to multi-phase production data from MFHWs, practical guidelines for mFMB use are provided to RTA practitioners.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Special Session\" style=\"border-top: 4px solid #feca57;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Special Session: Agents in the Trenches: Real-World Lessons from Autonomous Subsurface Workflows<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 6\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:45 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tModerator Susan Nash\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Boden*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(i2K Connect)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Nash*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Boundary RSS)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Hermes*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Special Session\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:45 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Speaker<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Gunturu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petrabytes Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-time-sidebar bg-primary rounded\">\n\t\t\t\t\t\t\t<div class=\"text-white text-center pt-lg-4 py-2 fs-14 font-bold position-sticky top-20\">\n\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Surfactant Design, Chemistry, and Transport in Unconventionals<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 1\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tChao-yu Vence Sie, Shunxiang Xia\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Ester-Based Surfactant to Enhance Oil Recovery in Fractured Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Alys*, J. Deligny and P. Struelens\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Oleon)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Hydraulic fracturing is central to the development of tight oil reservoirs, yet optimizing post-stimulation fluid-rock interactions remains critical to improve oil recovery. In this context, a novel ester-based surfactant (S1) was developed to promote spontaneous imbibition and enhance oil recovery, with a focus on Permian basin formations such as the Wolfcamp shale.Methods\/Procedures\/Process: Its performance was evaluated under representative reservoir conditions and benchmarked against a commercial ethoxylated alcohol surfactant. Key performance indicators included chemical stability, brine compatibility, interfacial tension (IFT) reduction, wettability alteration, emulsification tendency, and oil recovery efficiency.Results\/Observations\/Conclusions: At 80\u00b0C, the ester-based surfactant demonstrated excellent thermal and chemical stability, with less than 10 % degradation after two weeks. It showed outstanding compatibility in Wolfcamp brine, forming clear and homogeneous solutions without emulsification. The surfactant reduced IFT to 0.9 mN\/m and achieved strong, repeatable wettability reversal of Wolfcamp rocks from oil-wet or intermediate-wet to water-wet, with a contact angle of 55\u00b0. To assess robustness toward stimulation fluids, the surfactant was also tested in a simple fracturing fluid formulation, where it maintained excellent compatibility and preserved its strong wettability alteration capability. Spontaneous imbibition experiments conducted in brine systems demonstrated a significant enhancement in oil recovery, with a total oil recovery tripled compared to the untreated case and a 41 % performance increase over the commercial benchmark.Applications\/Significance\/Novelty: Overall, the ester-based surfactant combines high thermal stability, compatibility with both brine and fracturing fluid environments, effective IFT reduction and durable wettability alteration, highlighting its potential as a reliable solution to improve oil recovery in hydraulically fractured reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Experimental Investigation of Surfactant- and Ketone-Based Enhanced Oil Recovery in Oil-Wet Shale Systems<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. K. Aboahmed* and K. Mohanty\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional shale reservoirs are characterized by ultra-low permeability, leading to rapid production decline and limited primary recovery (&lt;10% OOIP). Enhancing oil recovery (EOR) in these systems remains a critical challenge as primary depletion leaves a large portion of hydrocarbons unrecovered. Cyclic injection processes such as Huff and Puff (HnP) provide a practical pathway for stimulating additional production. This study investigates the effectiveness of different chemical agents-including surfactants and ketones-for EOR in shale reservoirs, using Mancos outcrop samples. Oil recovery improvements in oil-wet systems was investigated through spontaneous imbibition and HnP tests, while examining the underlying mechanisms governing wettability alteration and fluid distribution.Methods\/Procedures\/Process: Mancos cores of extremely low porosity (&lt;5%) and permeability (&lt;300 nD) were used. They were first saturated and aged with crude oil to establish oil-wet conditions. Spontaneous imbibition and HnP experiments were then conducted using surfactant, ketone, and brine formulations. All experiments adopted the same conditions of 80 \u00b0C and 12.5% salinity. Surfactant phase behavior was conducted to further investigate surfactant partitioning between different phases. Surfactant adsorption measurements on crushed shale samples were conducted using LC-MS. NMR T\u2082 measurements at three stages-dry, saturated, and imbibed-were implemented to capture changes in fluid saturation and distribution during each process.Results\/Observations\/Conclusions: Recovery factors from both imbibition and HnP tests indicate that ketone significantly outperform both surfactant and brine formulations. Surfactants showed limited efficiency compared to the ketone in this complex system of extremely tight samples with oil wet nature. Phase behavior experiment showed no 3rd phase micro emulsion demonstrating no ultra-low IFT values. The surfactant adsorption results were matched perfectly using the Langmuir Adsorption Isotherm giving maximum adsorption of 10 mg\/g and Langmuir constant of 0.05 L\/mg.Applications\/Significance\/Novelty: This work provides a comprehensive experimental framework combining different chemical EOR testing with advanced NMR characterization to quantify recovery mechanisms in shale systems. The comparative evaluation of surfactant and ketone chemistries highlights their potential as next-generation injectants for enhancing oil recovery in ultra-low permeability formations beyond conventional brine. The combined use of NMR T2 and controlled wettability alteration provides new insights into recovery mechanisms, highlighting the potential of ketones in complex systems where surfactant utilization might be less effective.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Micelle Behavior of Surfactants in High-Salinity, High-Hardness Brines, and Its Implications for Transport and Stability in Unconventional Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Herman*<sup>1<\/sup>, H. Au Yong<sup>1<\/sup> and C. Bittner<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. BASF; 2. BASF SE)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Chemical EOR in unconventional reservoirs requires surfactants to remain stable and mobile in highly saline, hardness-rich brines and to traverse fracture networks with minimal retention. Traditional assumptions that smaller micelles correlate with superior performance have not been validated under such conditions. This study investigates how micelle size, restructuring, and stability evolve in representative unconventional brines and evaluates the implications for surfactant transport, adsorption, and interfacial activity in tight-rock environments.Methods\/Procedures\/Process: Surfactants representing anionic, nonionic, and mixed systems (LABSA, alkyl ether sulfates, alcohol ethoxylates, and sulfosuccinate diesters) were formulated in high-TDS, high-hardness synthetic shale brines containing Ca, Mg, and Na2CO3. Dynamic Light Scattering (DLS) quantified micelle size, polydispersity, and time-dependent stability, while spinning drop tensiometry measured IFT against crude oils and surrogate oils of known EACN. The impact of hardness, alkali, and cosurfactants on micelle swelling, aggregation, or precipitation was evaluated. Mobility-related implications were interpreted using pore-scale transport concepts reported in unconventional retention studies.Results\/Observations\/Conclusions: Alcohol ethoxylates kept stable 8\u201310 nm micelles across temperature, while Na2CO3 caused only slight growth. LABSA failed quickly in hard brines, but blends with alcohol ethoxylates stayed dispersed longer, with DLS detecting early instability before visible issues. Micelle size did not predict IFT, but time-stable micelles consistently signaled better tolerance to hardness and alkali. These behaviors directly indicate which surfactant systems are more likely to remain stable, mobile, and effective under unconventional reservoir conditions.Applications\/Significance\/Novelty: This work reframes micelle characterization as a stability and mobility screening tool for unconventional EOR rather than a predictor of IFT performance. By linking micelle restructuring to hardness tolerance, alkali response, and transport-relevant stability, the study provides a mechanistic criterion for formulating surfactant packages suitable for tight-rock environments, helping further inform unconventional EOR formulation methodologies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Development and Screening of Surfactant-Nanoparticle Package for Unconventional Bakken Reservoirs at 150 \u00b0C and ~30%Salinity: Comparison Between Blends of Conventional (Anionic, Nonionic, and Amphoteric), Bio-Based and Bio-Surfactants (Rhamnolipids And Sophorolipids) with Nanoparticles<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Ma<sup>1<\/sup>, J. Trivedi*<sup>1<\/sup> and L. Houchin<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Alberta; 2. Production Improvement Chemicals)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Bakken reservoirs hold significant potential for surfactant EOR due to less than 8% primary recovery and billions of barrels of oil left stranded. However, the development and selection of a surfactant-nano package (SNP) is challenging for complex Bakken reservoirs, either as a completion fluid or fracfluid package, or as an EOR agent due to extremely high salinity for an unconventional reservoir (~30% TDS) with strong presence of di- and tri-valent ions, including Fe, and high temperature ~150 \u00b0C. Various blends of conventional (anionic, nonionic, and amphoteric), bio-based, and bio-surfactants (rhamnolipids and sophorolipids), and nanoparticle blends were examined under Bakken conditions.Methods\/Procedures\/Process: Formulations were screened for brine and thermal stability, as well as phase stability under Bakke conditions for at least 14 days, and corresponding changes in interfacial and surface properties were observed. Wettability alteration and oil recovery were assessed by contact angle and spontaneous imbibition using core plugs. The dosage of SNP was selected based on economic constraints, and the screening was conducted in three stages. In Stage 1, thermal stability and interfacial property measurements were performed at 80 \u00b0C, followed by imbibition at high temperature. In Stage 2, the stability of SNPs in Bakken brine at 150 \u00b0C was studied for 14 days. In Stage 3, SNPs were tested in the presence of a high concentration of Fe in brine at 150 \u00b0C.Results\/Observations\/Conclusions: For tight sandstone cores, biosurfactant nanoblends A (0.5 GPT, IFT = 3.61 mN\/m), B (1 GPT, IFT = 1.10mN\/m), and biosurfactant-anionic blend C (1 GPT, IFT = 0.55 mN\/m) achieved recovery factors (RF) of 41.1, 41.6, and 47.3%. Zwitterionic-anionic nanoblends D (0.5 GPT, IFT = 0.27 mN\/m) and E (1 GPT, IFT = 0.18 mN\/m) yielded 45.2 and 42.5% RF. A microemulsion nanoblend formulation increased RF to 35.2% (1 GPT, IFT 3.73 mN\/m). Notably, nanoparticle-containing blends consistently outperformed surfactant-only counterparts. The biosurfactant nano-blends destabilized rapidly with IFT increasing from 0.211 to 14.047 mN\/m for blend A and from 0.074 to 6.735 mN\/m for blend B. By contrast, the zwitterionic-anionic surfactant nano-blend showed long-term stability without significant loss of IFTApplications\/Significance\/Novelty: The biosurfactant nano-blend remained visually stable for 5 days, but IFT increased from 0.740 to 11.716 mN\/m. The microemulsion-nanoblend formulation maintained stability for 3 days only; however, the IFT remained stable. With the optimal use of citric acid and selected iron dissolver (dosage: 1 GPT to 3 GPT), SNP\u2019s stability was increased up to 14 days. This study provides a detailed assessment and screening of various SNPs, with economic dosage thresholds for high temperature (~150 \u00b0C) and high salinity (~30%), particularly in Bakken reservoirs containing high iron content.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: Data-Driven Prediction of Well Fracturing and Production Performance<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 2\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tUtkarsh Sinha, Yue Li, Laura Santos\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Data Driven Approach for Early Frac Hit Detection in ESP Wells Within Unconventional Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. K. Singh*<sup>1<\/sup>, J. Kaus<sup>2<\/sup> and B. J. Spivey<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ExxonMobil Services &amp; Technology Pvt Ltd; 2. ExxonMobil Upstream Integrated Solutions Company; 3. ExxonMobil Upstream Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Developing unconventional reservoirs include drilling and fracturing of infill wells. In such high well-count systems, fractures can hit nearby offset wells, severely affect their production and often go undetected for a long time. Manually detecting frac hits and mitigating production losses at an early stage is extremely challenging. This paper presents an automated approach to quickly detect frac hits in ESP wells, enabling operations to analyze and identify profitable production recovery strategies at early stages without having to attain well tests. The generated frac hit database also supports strategic planning of well shut-ins during fracturing operations to avoid frac hits.Methods\/Procedures\/Process: The frac hit detection system comprises a time-series machine learning (ML) model along with a knowledge-based expert system on cloud, that runs frequently to detect frac hits on ESP wells by analyzing key ESP parameters like Intake Pressure, Intake Temperature, Motor Current, Drive Frequency, etc. Model training involved innovative feature engineering techniques, strategic sampling and weighting approaches, enabling the model to be capable of detecting frac hits at an early stage very accurately. The detected frac hits are verified by the expert system which queries well GIS and fracturing treatment databases to confirm if the detection from model corresponds to an active hydraulic fracturing on any offset wells.Results\/Observations\/Conclusions: This frac hit detection system has demonstrated state-of-the-art accuracy by identifying frac hits correctly in the Midland and Delaware basins. Apart from detecting the frac hits, the system also analyzes the previous production decline rate and estimates the resultant volume lost due to frac hits along with the expected recovery time. This not only saves a significant amount of time in identifying the cause of production losses but also helps develop mitigation strategies quickly. The results are systematically integrated into other automated workflows for effective surveillance and production optimization. Realized parent-child connectivity also plays a critical role in development strategies and maximizing total production from the reservoir.Applications\/Significance\/Novelty: This paper revolutionizes frac hit detection for ESP wells by eliminating the need to wait for a well test to detect frac hits. In unconventional assets with high well count and infrequent well tests, frac hits often go undetected or get detected very late leading to significant deferred volumes and missed opportunities for production-optimization. Our solution is a proactive step towards embracing digital transformation and achieving data-driven operational efficiency.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Predicting RTA Parameters for Delaware Basin Unconventional Wells Using Supervised Machine Learning<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. M. Kholy, Y. Ben*, Y. Askabe, R. Vaidya and V. Muralidharan\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Analytical Rate Transient Analysis (RTA) provides critical early characterization and enables performance comparison between unconventional wells by estimating the linear flow parameter (LFP), which reflects the fracture contact area and permeability of the stimulated reservoir volume (SRV) and the time to end of linear flow (telf), which is a measure of the originally contacted oil in place. This analysis is valid for single phase flow, which typically requires two to four weeks of rate and pressure data for LFP estimation and up to a year (or even longer) for reliable estimation of telf. This study develops a supervised machine learning-based workflow to predict LFP and telf after one month of production.Methods\/Procedures\/Process: Analytical RTA-derived LFP and telf for more than a thousand Delaware Basin wells were paired with completion, reservoir, and pressure, volume, and temperature (PVT) data from a commercial database. Two Extreme Gradient Boosting (XGBoost) models were trained separately for LFP (463 points) and telf (289 points) after preprocessing the aforementioned datasets. Six scenarios were evaluated to assess predictive performance under varying levels of input data availability. The base case, Scenario 1, used only completion, reservoir, and PVT data. Scenario 2 added one-month cumulative oil, initial oil viscosity, porosity, effective permeability, and stage spacing in both models, with SRV area and LFP included as additional inputs exclusively to the telf model.Results\/Observations\/Conclusions: Scenario 2 yielded improved predictive accuracy with training R\u00b2 values of 0.896 for LFP and 0.875 for telf, and testing R\u00b2 values of 0.807 for LFP and 0.451 for telf. Shapley Additive exPlanations (SHAP) feature-importance analysis indicates that the most important inputs for LFP are effective permeability, lateral length, reservoir thickness, proppant volume, and one-month cumulative oil, while LFP and SRV area are the most important for telf.Applications\/Significance\/Novelty: This data-driven workflow enables rapid and accurate RTA parameter estimation, supporting proactive reservoir management decisions and optimized field development in unconventional plays.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">The Key to Closed-Loop Automation: Real-time Physics-Informed Machine Learning Wellbore Pressure and Friction Decomposition Frameworks<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Wagner*<sup>1<\/sup>,<sup>2<\/sup>, M. Sinkey<sup>1<\/sup>, I. Zaghmoot<sup>3<\/sup>, M. G. Adams<sup>1<\/sup>, A. McMurray<sup>1<\/sup> and G. Grasselli<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Shear Frac Group LLC; 2. University of Toronto; 3. Arrington Oil and Gas LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Reliable and trustworthy predictions of treating pressure require models that represent the governing fluid-flow physics and associated pressure-loss mechanisms while also accounting for the nonlinear variability present in field operations. This paper presents a physics-informed machine learning (PIML) architecture that embeds governing fluid-mechanics equations directly into a deep learning time-series forecasting model. The framework is designed to learn the temporal relationships between operational inputs and treatment pressure, while maintaining physical consistency and interpretability through governing physics equations across a broad range of pumping conditions.Methods\/Procedures\/Process: These governing equations are incorporated into the machine learning architecture through physics-based feature transformations, differentiable physics modules, learnable correction factors, and physics-informed loss constraints. The model predicts intermediate physical parameters, including the Darcy-Weisbach friction factor and an effective perforation area (Cd \u00d7 Aperf), which are then assembled into treating pressure through differentiable implementations of the Darcy-Weisbach and orifice flow equations. Temporal neural networks are employed to process high-frequency sequences of operational parameters to predict short-horizon pressure transients. Model performance was evaluated for predictive accuracy, generalizability across stages and wells, and the interpretability of the resulting pressure components.Results\/Observations\/Conclusions: Initial results indicate the PIML system delivers high accuracy pressure forecasts (119 psi RMSE (R\u00b2 = 0.984) at two-minute forecast horizons and 192 psi RMSE (R\u00b2 = 0.958) at five-minute forecast horizons) while retaining interpretable physical relationships. Importantly, the learned pressure response relationships may reveal meaningful operational phenomena such as evolving perforation friction, perforation erosion, friction reducer effectiveness, cluster efficiency, and early screenout precursors as a warning system. The architecture generalizes effectively across wells and benches, supporting real-time early-warning monitoring and improved optimization of planned completion operations.Applications\/Significance\/Novelty: This physics-informed ML framework improves surface treating pressure prediction with greater interpretability and generalizability than conventional temporal models. By embedding governing fluid-mechanics into the architecture, the system supports intelligent completion design, real-time optimization, and proactive operational decisions during fracturing. Key applications include optimizing cluster efficiency, improving chemical and HHP capital efficiency, providing early screenout prediction, enhancing friction-reducer utilization, and reducing stage execution risk. The result is a path toward more cost-efficient completions, better pressure-related diagnostics, and scalable deployment across pads, wells, and benches.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Hybrid Physics and Machine\u2011Learning Approach to Parent\u2011Child Well Degradation<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Moncayo*, D. Chakravarthy, J. Courtier, D. Galvis, M. Shokry and I. Wang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Ecopetrol Permian)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The objective of this study is to develop a predictive approach for estimating production degradation in child wells resulting from fracture interactions and pressure depletion from parent wells. The workflow combines empirically derived depletion metrics with physics-based reservoir simulations and machine-learning models to address limitations associated with standalone simulation and purely data-driven methods. The specific objectives are to (1) integrate field-based depletion factors with simulation-derived scenarios in a hybrid modeling workflow, (2) quantify child-well production loss across varying spacing, timing, and completion designs, and (3) identify the key geological, geomechanical, and operational drivers of parent-child interference.Methods\/Procedures\/Process: Field production data and matched analog wells are first used to construct a standardized depletion factor that captures child-well performance loss attributable to parent-well depletion. Physics-based reservoir simulations are then applied selectively to represent fracture propagation, stress shadowing, and pressure depletion in development scenarios that are under-represented in the field dataset. These include spacing, timing, and vertically stacked development configurations where reliable non-depleted analogs are unavailable.Results\/Observations\/Conclusions: The combined empirical and simulation-based dataset is used to train a supervised machine-learning model. Input features include inter-well spacing, parent production history, depletion duration, geomechanical properties, and completion design parameters. Model performance is evaluated using blind testing on withheld field data and historical production.Applications\/Significance\/Novelty: The hybrid model predicts cumulative oil degradation with high accuracy while reducing evaluation time by orders of magnitude compared to full-physics simulations. Feature attribution indicates that parent-child spacing and completion design are the dominant controls on depletion-driven degradation, with strongly nonlinear behavior observed at short well spacings. The workflow provides a practical method for screening spacing and development scenarios and supports depletion-aware forecasting and infill planning in mature unconventional developments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Sustainable Water Strategies in Unconventionals<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 3\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSwati Sagar, Qinmin Luo\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Innovative Water Management in UAE Unconventional Resource Development: Advancing Closed-Loop Systems and Cutting-Edge Treatment Technologies<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. A. Al Hassani, F. M. Al Hosani*, F. Silva, M. N. Aftab, M. Ahmad, A. Alharthi, K. H. Al Dhaheri and Y. F. Pangestu\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC Onshore)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The rapid ramp\u2011up of unconventional oil and gas production in the UAE has resulted in the generation of large volumes of flowback and early produced water, creating significant challenges for post\u2011fracturing water management under strict zero\u2011discharge requirements aligned with Environmental, Social, and Governance (ESG) principles. To address these challenges, closed\u2011loop water handling systems were implemented to safely manage, treat, store, and dispose of flowback water without uncontrolled surface discharge.Methods\/Procedures\/Process: This paper presents the post\u2011flowback water management strategy, focusing on closed\u2011loop handling, temporary surface treatment facilities, interim storage in engineered lined pits, and injection into dedicated water disposal wells. A key challenge was the management of high\u2011temperature (&gt;200\u00b0F), sour flowback water with elevated H\u2082S, which required robust treatment solutions prior to reuse or disposal.Results\/Observations\/Conclusions: Field experience from zipper\u2011fracturing operations involving more than 800,000 bbl of injected water is discussed. Surface handling systems were optimized to manage increasing water volumes associated with production growth and to route treated water either for temporary storage or subsurface disposal while preserving system integrity and disposal well injectivity. A hybrid H\u2082S mitigation approach combining nitrogen (N\u2082) sparging and optimized scavenger dosing reduced treated\u2011water H\u2082S concentrations to below 100 ppm, with field results as low as ~30 ppm, while reducing treatment and testing time by approximately 50%.Applications\/Significance\/Novelty: The paper demonstrates how integrated planning between surface handling facilities and the phased drilling of disposal wells enables scalable management of large post\u2011flowback water volumes while supporting sustained oil and gas production under zero\u2011discharge constraints.Interdisciplinarity (Team Presentation\u2019s only): The authors gratefully acknowledge ADNOC\u2019s commitment to sustainability and the advancement of unconventional resource development. Special thanks are extended to ADNOC management, the Diyab project team, stakeholders, partners, and contractors for their invaluable contributions to the successful implementation and continuous improvement of water management strategies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">From Disposal to Resource: Field Deployment of a Produced Water Reuse System for Fracturing in the Middle East<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Anderson<sup>1<\/sup>, E. Lagarrigue*<sup>2<\/sup>, A. Ryan<sup>1<\/sup>, W. Caiza<sup>1<\/sup> and R. Wargo<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Turnwell; 2. ADNOC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As unconventional operations expand in the United Arab Emirates (UAE), improving efficiency has increased the number of wells hydraulically fractured each year. Water availability is the key component of the unconventional resource development. In an arid environment such as in UAE, the water cost can be a roadblock for an unconventional resource development. Once wells are on production, managing large volumes of produced and flowback water becomes an additional operational constraint. To address both challenges, a produced water reuse initiative was deployed for fracturing operations. This case study shows the application of different water treatment and utilization to minimize the water cost in the desert environment.Methods\/Procedures\/Process: A multidisciplinary collaboration was established to evaluate, design, and deploy a produced water treatment and reuse system for fracturing operations. The program involved: Produced Water Characterization: Detailed sampling and analysis of flowback and produced water to assess treatability and compatibility with fracturing fluids. Treatment System Design: Selection of modular treatment technologies for solids removal, filtration, and chemical conditioning to achieve reuse-quality water. Operational Integration: Implementation of a water management workflow enabling treated water to be directly reintegrated into fracturing operations. Monitoring and Verification: Continuous monitoring of fluid quality and system performance to ensure treatment reliability and chemical compatibility.Results\/Observations\/Conclusions: For deployment, a filtration system was deployed to capture the flowback water free of solids and oil content. For water distribution, a set of flexible piping system and booster pumps were deployed to distribute different water sources to fracturing pads. This initiative demonstrated: Operational Impact: Seamless integration of treated water into fracturing operations without adverse impact on fluid chemistry or well performance. Environmental and HSE Benefits: Reduction in water sourcing and disposal requirements. Emission Reduction: Decrease in emissions associated with transportation and logistics. Scalability Potential: The system design allows future expansion.Applications\/Significance\/Novelty: Produced water reuse project in the UAE demonstrates that sustainable water management can be effectively integrated into unconventional fracturing operations without compromising efficiency or performance. The approach provided a practical pathway to reduce freshwater demand, lower environmental footprint, and enhance safety by minimizing trucking exposure. In a remote desert operation, agile and cost effective water management is a key to develop the unconventional resources. The success of this initiative establishes a regional benchmark for water stewardship in desert. . This paper discusses the approach taken to improve the method for reuse of water and optimize completion strategy.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Salt\u2013Tolerant Fracturing Fluid Prepared Directly from High\u2013Salinity Produced Water: Technology and Field Application<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Bai*<sup>1<\/sup>,<sup>2<\/sup>, F. Zhou<sup>1<\/sup>, X. Liu<sup>1<\/sup>, Z. Ding<sup>1<\/sup>, S. Zhang<sup>1<\/sup> and E. Yao<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing); 2. CNPC Engineering Technology R&amp;D Company Limited)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Hydraulic fracturing is highly effective in the Sulige gas reservoir of Changqing Oilfield. However, its continued development is constrained by challenges in water utilization and treatment, particularly the reuse of high-salinity produced water. Produced water in this reservoir is predominantly calcium chloride type, with salinity exceeding 50,000 mg\/L and containing organic contaminants, ferric ions (Fe\u00b3\u207a), and dispersed oil. Such conditions make it unsuitable for the direct preparation of conventional fracturing fluids and require complex pretreatment processes, which increase cost and limit large-scale applicationMethods\/Procedures\/Process: To address this issue, this study develops a salt-tolerant fracturing fluid that can be prepared directly from high-salinity produced water. Rather than attempting to eliminate salinity or compensate through strong crosslinking, the proposed system deliberately utilizes high salinity as a structural regulation factor to promote hydrophobic association and construct a stable and resilient network. This design enables the system to overcome the inability of conventional fracturing fluids to increase viscosity and maintain sand carrying under high-salinity conditions. HS suspension emulsion is identified as the optimal base polymer through systematic screening.Results\/Observations\/Conclusions: At a concentration of 0.80%, the system exhibits good compatibility with produced water, increases viscosity within 0.5 min, and shows negligible settling within 10 min at a 20% sand ratio, indicating excellent viscoelasticity and sand-carrying performance. These results demonstrate that high salinity does not degrade polymer performance as commonly expected but instead enhances intermolecular association and improves structural integrity and sand-carrying stability. Field application further validates the robustness of the system. The sand-carrying fluid is prepared using produced water, flowback fluid, and freshwater at a ratio of (produced water + flowback fluid): freshwater = 1:0-2:1. At an HS concentration of 0.80%, the viscosity exceeds 60 mPa\u00b7s, the pumping pressure ranges from 26.0 to 36.3 MPa, and the maximum sand ratio exceeds 35%. After fracturing, complete gel breaking is achieved, the flowback rate reaches 23.1%, and the trial gas production rate reaches 5-6.0\u00d710\u2074 m\u00b3\/d, demonstrating satisfactory stimulation performance and consistency between laboratory and field results.Applications\/Significance\/Novelty: This technology enables the direct utilization of water resources with salinity exceeding 50,000 mg\/L without complex pretreatment, significantly reducing operational cost and simplifying field processes. More importantly, it establishes a new design concept in which high salinity is utilized as a functional parameter rather than treated as a limitation, providing a scalable and field-validated solution for produced water reuse and offering a new pathway for fracturing fluid design in high-salinity reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 10: Refrac Stimulation<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 4\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tJonathan Ortiz, Reza Safariforoshani\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Physics-Based Refrac Sensitivity and Optimization Using Calibrated Models in the Bakken<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Morsy*<sup>1<\/sup>, C. Abbott<sup>2<\/sup>, M. Almasoodi<sup>3<\/sup>, M. Babazadeh<sup>4<\/sup>, A. Baldwin<sup>3<\/sup>, C. Cipolla<sup>1<\/sup>, A. Gandomkar<sup>4<\/sup>, A. Garbino<sup>1<\/sup>, J. Lassek<sup>5<\/sup>, M. McKimmy<sup>6<\/sup>, M. Paryani<sup>7<\/sup>, R. Safariforoshani<sup>4<\/sup>, A. Tucker<sup>7<\/sup>, J. Zaghloul<sup>2<\/sup> and M. McClure<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ResFrac Corporation; 2. Continental Resources; 3. Devon Energy; 4. ConocoPhillips; 5. Department of Energy; 6. Chevron; 7. APA Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Following successful field calibration and a high-accuracy blind test (URTeC: 4245581), a comprehensive refrac sensitivity and optimization study was conducted using a fully integrated hydraulic fracturing and reservoir simulator. The goals of this study were to identify key drivers of refrac performance, provide guidelines on refrac candidate selection, and deliver direct optimizations of refrac designs for field trials.Methods\/Procedures\/Process: Four Bakken-calibrated models served as the foundation for the study, each representing typical well configurations within the play. Each model included two Middle Bakken refrac wells and two Three Forks offset wells, representing 10-year-old wells. The original completions were characterized by relatively small fluid and proppant volumes, calibrated fracture initiation points, and crosslinked gel. Refrac completion design applied typical industry practices using slickwater, with relative variations in cluster spacing. The sensitivity analysis workflow varied one parameter at a time to quantify its impact on refrac uplift and identify the most impactful parameters. Finally, an optimization workflow was carried out to identify the optimum economic refrac completion designs for field trial.Results\/Observations\/Conclusions: Sensitivity results indicate that original fracture spacing is the dominant factor influencing refrac uplift, especially when fractures are aligned across wells. The normalized pre-refrac oil production and the distance to infill wells are the next most impactful parameters, while fluid and proppant loadings show moderate effects. Optimization shows that wells with longer stage designs deliver comparable production to shorter stages with a notable increase in incremental refrac value. Also, this study found that optimum refrac design depends on field stress profile: areas with contained fractures benefit from bigger refrac jobs, while areas with out-of-zone growth favor reduced refrac fluid loading and higher proppant concentrations.Applications\/Significance\/Novelty: The presented workflow provides a robust framework for physics-based refrac design optimization and economic improvement in unconventional assets. The workflow isolates the impacts of each variable and identifies the most influential parameters affecting refrac uplift. Based on this evaluation, recommended refrac candidates may include wells with a substantial unstimulated portion of the lateral, lower relative production performance compared to expectations, and adequate spacing from nearby infill wells to minimize interference. These criteria help prioritize opportunities where refracturing can deliver the greatest incremental value.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Permian Basin Refrac Candidate Selection and Design with Numerical Modeling<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Garbino*<sup>1<\/sup>, C. Abbott<sup>2<\/sup>, M. Almasoodi<sup>3<\/sup>, D. Armistead<sup>3<\/sup>, M. Babazadeh<sup>4<\/sup>, C. Cipolla<sup>1<\/sup>, G. Gauthier<sup>5<\/sup>, B. Gilmore<sup>5<\/sup>, S. Morsy<sup>1<\/sup>, J. Ndungu<sup>5<\/sup>, M. Paryani<sup>6<\/sup>, C. Ponners<sup>1<\/sup>, A. Tucker<sup>6<\/sup>, J. Zaghloul<sup>2<\/sup> and M. McClure<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ResFrac Corporation; 2. Continental Resources; 3. Devon Energy; 4. ConocoPhillips Company; 5. ExxonMobil Upstream Oil and Gas; 6. APA Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper addresses a simulation workflow carried out to identify the key drivers that impact well re-fracturing performance, improve re-frac candidate selection in the Permian basin, and optimize re-frac design.Methods\/Procedures\/Process: The first step of the workflow involved history-matching numerical models using a fully coupled hydraulic fracturing and reservoir simulator to field observations from two different datasets in the Permian basin. After model calibration, a blind test was conducted to assess the models\u2019 effectiveness in predicting the re-frac uplift (URTeC: 4245581). The magnitude of behind-casing crossflow needed to be adjusted in the models based on the re-frac results. The calibrated models were then used to carry out a sensitivity analysis on key design variables to compare re-frac performance between cases. Finally, an economic optimization analysis was conducted on re-frac proppant loading, water intensity and stage length to improve the re-frac design for each model from an economic standpoint.Results\/Observations\/Conclusions: The sensitivity analysis showed that well spacing, infill well spacing and re-frac completion size are the main variables that impact long-term re-frac uplift. Additionally, well spacing, re-frac completion size, well age and infill well spacing have the strongest impact on the short term. Original cluster spacing was seen to have a strong impact on results at wide spacings (\u2248150ft), and a moderate impact at tighter spacings (&lt;75ft). Guidelines on re-frac candidate selection were put together based on the previous results. The economic optimization showed that the NPV trends vary between datasets depending on the difference in incremental uplifts when transitioning from smaller to bigger jobs. This is mostly driven by the amount of new fracture area created versus fracture re-activation.Applications\/Significance\/Novelty: This paper provides new insights to improve re-frac candidate selection in the Permian basin and identifies the key operational variables that impact re-frac performance. This work also quantifies the expected re-frac uplift under different scenarios to improve re-frac completion design. Additionally, this paper explores the importance of proper behind-casing isolation to prevent strong crossflow between clusters and fracture re-activation that can lead to worse re-frac performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mature Well Re-Fracturing Enabled by Pre-Frac Array Electromagnetic Diagnosis and Well Re-Construction Using Expandable Tubular<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Tong*, R. Wei and H. Liu\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Research Institute of Petroleum Exploration and Development, PetroChina)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Globally, many unconventional wells need to be re-fractured each year to enhance production and ultimate recovery. In China, 8000 mature wells were stimulated annually to economically and effectively develop remaining hydrocarbon. Mature assets commonly suffer from integrity issues such as casing damage and corrosion. Existing tools, such as multi-finger calipers and down-hole cameras, fail to meet the requirements for low-cost, high-precision, quantitative characterization and evaluation. Slim casings and cementing contribute to re-establishing zonal isolation and enabling efficient intervention. However, these technologies are performed in horizontal wells with high operation risks.Methods\/Procedures\/Process: Solid-state array electromagnetic inspection instrument integrates a 32-element annular array of high-sensitive magneto-resistive sensors uniformly distributed around the tool. It features a circumferential resolution of 11.25\u00b0, a radial resolution of 0.5 mm and a wall thickness resolution of 0.2 mm. Through a new analytics model for magnetic induction signals, casing integrity issues can be quantified precisely. Horizontal wellbore reconstruction using full-bore-access expandable tubular deploys a patch with length of 600 m.Results\/Observations\/Conclusions: Operators planned to conduct stimulation in a depleted oil well to enhance EUR and formation energy. After seven-year production, 5.5 in casing faced issues, such as extensive corrosion and thread sealing failure, were identified by EM-based diagnosis. With above quantitative analysis, one wellbore reconstruction using casing patch was performed successfully. The post-patching wellbore achieved a drift diameter of 105 mm and passed a pressure-holding test without any pressure drop.Applications\/Significance\/Novelty: Novel pre-frac array EM diagnosis and analytics is one key method towards wellbore integrity issues. Cost-efficient casing quantitative diagnosis technology combining expandable tubular patching well reconstruction contributes to re-fracturing, energizing and EOR of depleted reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Downhole Pulse-Based Hydraulic Re-Stimulation for Revitalizing Mature Unconventional Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tI. Allahar*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Wellbore Consultants)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Low recovery factors and rapid pressure decline leave most unconventional reservoirs under-stimulated after primary fracturing, limiting EUR and well longevity. This work introduces a downhole pulse-based hydraulic re-stimulation method designed to reactivate near-wellbore fractures, mobilize bypassed rock, and enhance fracture connectivity without high surface horsepower requirements. The objective is to evaluate how controlled low-volume, high-pressure hydraulic pulses generated at reservoir depth can improve fracture initiation, propagation, and re-opening of existing fracture networks in mature shale wells.Methods\/Procedures\/Process: A patented downhole pulse module was analyzed using coupled hydraulic-mechanical models integrating water-hammer physics, steel-wave interactions, and pulse amplification within a constrained wellbore. CFD, FEA, and a 1D wave-propagation simulator were used to quantify pulse rise-time, amplitude, and rectangular pressure behavior. Fracture interactions were modeled using KGD\/PKN formulations, dynamic stress-intensity factors, and pulse-driven compliance changes. Sensitivity studies evaluated pulse sequences, nozzle configurations, and reservoir properties relevant to refracturing.Results\/Observations\/Conclusions: Modeling shows that downhole-generated hydraulic pulses can deliver localized pressure surges of 10\u201320 ksi at perforation clusters with minimal surface pressure. These pulses exhibit high dp\/dt and rectangular hold times that promote crack re-opening and incremental fracture extension. Repeated pulses create a \u201cratcheting\u201d effect, increasing near-wellbore fracture aperture and enhancing connectivity to under-stimulated rock. Simulations indicate potential gains in fracture density, improved proppant redistribution, and greater uniformity of stimulation relative to conventional refrac methods.Applications\/Significance\/Novelty: This technique enables targeted, low-volume re-stimulation of mature unconventional wells without requiring full-scale refracturing operations. By generating high-energy pulses at depth, the method reduces surface horsepower, water use, and cost while improving stimulation efficiency. Beyond shale refracs, the same downhole pulse mechanism is also applicable to geothermal reservoir enhancement, CCS injectivity improvement, and produced-water disposal wells. Its localized hydraulic energy delivery offers a novel mechanical intervention pathway for revitalizing under-performing subsurface assets across multiple energy sectors.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Chevron Special Session: Operational Excellence at Scale<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Room 5\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAlejandro Lerza, Yanli Pei\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">From Surveillance to Strategy: Calibrated Modeling and Sensitivities Unlock Optimum DJ Basin Development<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Xu*, R. Wu, J. Junca-Laplace, J. Dunn and B. Velardo\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Chevron)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unconventional resource development in North America increasingly demands optimization across multi-bench development, well spacing, and completion design. This study leverages advanced subsurface surveillance\u2014including fiber-optic monitoring, geochemistry, and downhole gauges\u2014combined with integrated numerical modeling to deliver actionable insights and strategy for improving asset value in DJ Basin development.Methods\/Procedures\/Process: A comprehensive surveillance program\u2014incorporating a vertical observation well, core analysis, wireline logs, minifrac tests, downhole gauges, fiber-optic measurements, and geochemistry\u2014was used to calibrate hydraulic fracture and production simulation models. These surveillance insights enabled calibration not only of fracture height predictions but also of drained height for wells landed in different target intervals. This robust integration provides high confidence in fracture geometry and drainage volume estimates, supporting more informed decision-making.Results\/Observations\/Conclusions: Sensitivity analyses revealed that lateral landing and fluid loading beyond a threshold have limited impact when proppant loading is constant, while tighter well spacing improves section Estimated Ultimate Recoveries (EURs). Specifically, adjusting completion designs and increasing wells per section in Niobrara formations increased asset value and reserves at competitive returns. Numerical modeling further shows that the angle between fracture plane and wellbore, fracture sequencing, and mechanical stratigraphy strongly influence fracture propagation and proppant distribution, directly affecting resource recovery. These insights translate directly into business objectives by guiding development strategies that improve asset value, enhance returns, and improve capital efficiency.Applications\/Significance\/Novelty: This study presents a case history of an integrated workflow that combines comprehensive field surveillance with calibrated fracture and production modeling for multi-bench development in the DJ Basin. By leveraging advanced diagnostics and targeted sensitivity analyses, the approach improves confidence in fracture geometry and drainage predictions, enabling data-driven strategies that improve recovery and capital efficiency in DJ Basin\u2019s unconventional resource development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Field Validation of Cross-Flow Ceramic Membrane Pilot for Produced Water Reuse in Completion Operations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Cunningham<sup>1<\/sup>, D. G. Leach*<sup>1<\/sup>, G. Padilla<sup>1<\/sup>, M. Hefta<sup>2<\/sup>, M. Bentancur<sup>2<\/sup>, W. Wei<sup>1<\/sup> and T. Barnes<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Chevron Technical Center, a division of Chevron USA, Inc.; 2. Chevron North America E&amp;P Company, a division of Chevron USA, Inc.)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Oil production benefits from effective produced water reuse, both to manage water sources and facilitate cost-effective well completion. With rising water production across many basins, improved options for water treatment, reuse, and disposal that meet industry \u2018Clean Brine\u2019 standards would be valuable. Water quality, including solids, oil, and biological components, can directly affect downhole interactions and production efficiency, and various water management practices can contribute to reduced costs across the lifecycle of a well. This paper will show the process of qualifying water treatment technology, specifically ceramic membranes, with implications for wells and operations. Objectives were to validate performance, understand costs, and increase the technology readiness level (TRL).Methods\/Procedures\/Process: A ceramic membrane treatment pilot was selected to assess performance on metrics including: dissolved inorganics\/organics, oil-in water (OIW), total suspended solids (TSS), total iron, pH, and bacterial kill\/regrowth. The pilot was run for 4 months, with samples regularly taken for physical, chemical, and biological testing to characterize the raw, retentate reject, and final permeate results. A series of 20+ trials 2-3 days each evaluated various chemical pre-treatments, flux, filtration recovery, and water quality outputs. The trials were used to identify optimal chemical dosing for system performance for longer-term run validation.Results\/Observations\/Conclusions: The ceramic membrane treatment relied on water crossflow over nominal membrane pore sizes of 0.06 um. The pilot achieved good water quality, for example with OIW reduction of 99% to &lt;12 mg\/L, iron to &lt;1.0 mg\/L, TSS to &lt; 10 mg\/L, and overall turbidity reduction of 98% to &lt;10 NTU. Furthermore, detailed bacterial analysis validated removal of bacteria from the stream, with final permeate achieving consistently low levels (&lt;104 ME\/mL) and zero regrowth of higher risk acid producing bacteria (APB) or sulfate-reducing bacteria (SRB) to sufficiently protect new wells. Some operational challenges included maintaining optimal performance with variable input water quality such as in high OIW cases, highlighting the need for flow equalization as a pre-treatment step.Applications\/Significance\/Novelty: The trial successfully met defined water quality goals for new well fracturing operations, meeting Clean Brine typical industry specifications, showing the potential of ceramic membrane treatment technology in unconventional oilfields. With water quality having a direct impact on completion chemical compatibility, reservoir damage, and future productivity of fractured wells, the results of this study are critical to realizing water recycling value and efficient operations. Eyeing future deployment, this pilot study demonstrated performance under different flow and treatment scenarios that were directly applicable to developing new unconventional resources at scale.Interdisciplinarity (Team Presentation\u2019s only): This project brought together a multi-disciplinary team to fully trial an upstream technology new to Chevron\u2019s unconventional operations, relying on water engineers and strategists, project and facility engineers, process chemists, and even microbiology scientists. The team designed and executed a full lab and field test plan, mechanical design and installation, and project execution and evaluation for potential future scale up. This presentation will showcase the value of cross-functional learning and partnership to deliver outcomes with impact.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Advances in Core Analysis and Rock-Fluid Interaction II<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station A\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSabyasachi Dash, Sandeep Mukherjee\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Cation Exchange Capacity in Tight Rocks: A Scale-Dependent Property<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Vo*, N. Vuong, C. Rai and S. T. Dang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(The University of Oklahoma)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Water saturation is an important parameter for estimating hydrocarbon reserves. Traditionally, Archie\u2019s equation has been widely used to estimate water saturation but assumes clean formations. However, in shaly formations, clay-bound water affects electrical conductivity which leads to an overestimation of water saturation. The Waxman-Smits-Thomas model modifies Archie\u2019s equation with cation exchange capacity (CEC) to improve saturation estimation in shaly formations. In practice, CEC is commonly estimated from mineralogy; however, this assumes full accessibility of clay surfaces. When clays are coated by other minerals, the indirect CEC is overpredicted. Therefore, wet-chemistry CEC measurement is needed to improve saturation evaluation accuracy in unconventional and clay-rich reservoirs.Methods\/Procedures\/Process: Twelve samples from the Mississippian, Hunton, and Barnett formations were prepared in four states; as-received, ashed, Soxhlet cleaned, and cleaned-then-ashed to investigate the effect of organic matter. The samples were crushed into different sizes to investigate the effect of grain size. Low-temperature plasma ashing and Soxhlet solvent extraction were used to prepare ashed and cleaned samples, respectively. Cleaned samples were then ashed to achieve cleaned-then-ashed state. CEC was determined by using the hexaammine cobalt chloride exchange method. Fourier Transform Infrared Spectroscopy (FTIR) was used for mineralogy, Rock-Eval\u2122 (SRA) was used to quantify organic matter content and maturity, and Scanning Electron Microscopy (SEM) imaging was conducted for mineral distribution.Results\/Observations\/Conclusions: Six out of twelve samples from the Mississippian and Barnett formations show strong agreement between direct (wet-chemistry) and indirect (mineralogy-based) CEC values. In contrast, for the six Hunton samples direct CEC values are notably lower than the indirect values. Moreover, when the Hunton samples are crushed to a smaller grain size (mesh 170) thus exposing more surface area, the measured CEC values increase compared to the larger grain size (mesh 35). This indicates that mineral coating limits ion exchange accessibility, and only after increasing exposed surface area through finer crushing, the measured CEC approach its expected value.Applications\/Significance\/Novelty: CEC is widely estimated from mineralogy in industry workflows; however, our results demonstrate that indirect estimation is only reliable when clay minerals are fully exposed and accessible. When coating occurs, indirect CEC significantly overestimates the true exchangeable surface area, ranging from approximately 200% to 400%. This study highlights the importance of validating mineralogical estimates with wet-chemistry measurements to improve water saturation interpretation, especially in clay-rich reservoir systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Thermal Maturity Controls on Pore-Network Wettability: Opposing Behaviors in Oil and Gas Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Zubair and H. Dehghanpour*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Alberta)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: To investigate the wettability variation in oil and gas wells completed in organic shale source rockMethods\/Procedures\/Process: We collect samples from six oil and gas wells drilled in the Duvernay Formation and conduct co-current spontaneous imbibition experiments using produced brine and oil to evaluate their wettability. The samples are further characterized through tight-rock analysis, Rock-Eval pyrolysis, and X-ray diffraction, and pore-scale visualization of organic matter is carried out using scanning electron microscopy (SEM) to support the interpretation of wettability behavior. Additionally, we integrate wettability and reservoir characterization data from previously published studies to develop a broader dataset and examine wettability variations across oil and gas wellsResults\/Observations\/Conclusions: The results show that WIo increases in oil wells but decreases in gas wells. In oil wells, WIo correlates positively with thermal maturity, as low-maturity wells imbibe more water and less oil, while mature wells imbibe more oil. This trend is supported by positive WIo\u2013Tmax and negative WIo\u2013HI correlations. Gas wells show the opposite pattern: WIo decreases with maturity and correlates negatively with Tmax and positively with HI, likely due to bitumen thinning, also reflected in lower gamma-ray values. Overall, oil-wet samples (WIo &gt; 50%) exhibit higher porosity, permeability, gas saturation, and more organic-matter pores.Applications\/Significance\/Novelty: The pore-network wettability of unconventional organic shales is closely linked to thermal maturity. While previous studies have documented increasing oil wetness with thermal maturity, this study demonstrates a reciprocal trend in overmature organic shales.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluating NMR Sensitivity in Tight Reservoir Cores: Impacts of Calibration Volume, Tool Frequency, and Porosity Regime<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tB. A. Mohamed*<sup>1<\/sup>, M. K. Aljishi<sup>1<\/sup>, N. Truong<sup>1<\/sup>, S. Mamoudou<sup>2<\/sup>, S. T. Dang<sup>1<\/sup> and C. Rai<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Oklahoma; 2. Stratum Reservoir)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study explores how NMR tool frequency and calibration volume affect NMR measurements accuracy across reservoir cores with both high and low porosity. Because NMR response depends on hydrogen content and signal-to-noise ratio (SNR), two tools operating at 2 MHz and 12 MHz were tested under consistent calibration and saturation conditions. The goal is to understand how calibration size, tool frequency, and rock porosity collectively influence absolute error, repeatability, and overall measurement reliability.Methods\/Procedures\/Process: Core plugs ranging from below 6% to 20% porosity were cleaned, dried, and saturated through staged imbibition with 2.5% KCl brine. The samples were then sealed to prevent evaporation and stored under controlled ambient conditions to allow fluid distribution to reach equilibrium. Both NMR tools were calibrated using 5.53 mL and 16.11 mL standards. After each calibration, NMR measurements were conducted three times or until achieving an SNR of 100 or 20-minute cutoff. Gravimetric data, absolute error, SNR, and standard deviation were analyzed to evaluate how frequency and calibration volume influence measurement sensitivity across porosity regimes.Results\/Observations\/Conclusions: For high-porosity samples, NMR measurements at both calibration standards produced absolute error &lt; 5%. Standard deviation remained low; 0.02 cc for the 12 MHz tool and 0.04 cc for the 2 MHz tool. 12MHz tool was able to reach SNR = 100 even at low saturation at both calibration volumes, whereas the 2 MHz tool never reached SNR = 100 within the time limit. For low-porosity samples, low-volume calibration yielded much lower error (3 to 10%) compared to high-volume calibration (10 to 30%) across different frequencies. The 12 MHz tool maintained excellent repeatability (&lt; 0.02 cc), while the 2 MHz tool showed higher variability reaching up to 0.10 cc. SNR again favored the 12 MHz tool, as the 2 MHz system remained below SNR = 20 even at Sw &gt; 80%.Applications\/Significance\/Novelty: The study provides practical guidelines for selecting NMR tools and calibration volumes. For high-porosity samples, either calibration volume yields reliable results, though the 12 MHz tool delivers faster and more stable measurements. In contrast, for low-porosity cores, the 12 MHz tool combined with low-volume calibration significantly enhances accuracy and reduces uncertainty. The analysis highlights the direct influence of tool frequency and calibration volume on NMR sensitivity, offering a framework to improve measurement quality, efficiency, and reliability in tight reservoir characterization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Study on Shale Mechanical Parameters Based on Nanoindentation Experiments: A Case Study from the Chang 7 Shale in the Ordos Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tF. Wang*<sup>1<\/sup>,<sup>2<\/sup>, X. Wang<sup>1<\/sup>,<sup>2<\/sup>, X. Zhang<sup>1<\/sup>,<sup>2<\/sup>, Z. Gu<sup>1<\/sup>,<sup>2<\/sup>, H. Ma<sup>1<\/sup>,<sup>2<\/sup>, H. LV<sup>1<\/sup>,<sup>2<\/sup>, X. LV<sup>1<\/sup>,<sup>2<\/sup> and X. Wang<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Oil &amp; Gas Technology Research Institute, Changqing Oilfield Company; 2. National Engineering Laboratory for Exploration and Development of Low-Permeability Oil &amp; Gas Fields)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The continental shale oil in the Chang 7 Member of the Ordos Basin is a strategic unconventional resource. Its development requires multistage fracturing, where micro-mechanical properties control fracability and sweet spot identification. Conventional core-based methods face challenges including high cost and data scatter, creating a need for alternative approaches. This study applies nanoindentation\u2014established in materials science\u2014to characterize the mechanical properties of argillaceous-laminated Chang 7 shale at nano-to-submicron scale.Methods\/Procedures\/Process: This study utilized cuttings from 11 argillaceous-laminated shale samples obtained from an exploration well in the Chang 7 Member of the Ordos Basin. The experimental workflow comprised: (1) nanoindentation testing to determine micro-scale elastic modulus and hardness; (2) X-ray diffraction (XRD) analysis to quantify mineral composition and content, providing basis for physical phase characterization; (3)statistical deconvolution analysis to determine component-specific mineral content and elastic moduli; (4) upscaling methodology to compute macro-scale elastic modulus and Poisson&#039;s ratio; and (5) elastic parameter-based brittleness index calculation to evaluate the fracability of the Chang 7 argillaceous-laminated shale.Results\/Observations\/Conclusions: Nanoindentation tests revealed significant scatter in load-displacement curves, reflecting substantial mechanical heterogeneity at micro-scale. Statistical analysis of 1,914 measurements from 22 test areas yielded average micro-elastic modulus and hardness of 24.49 GPa and 1.44 GPa, respectively. The data showed normal distribution and strong positive correlation between modulus and hardness, indicating representative bulk properties. Upscaling provided macro-elastic modulus of 20.77 GPa and Poisson&#039;s ratio of 0.19. The resulting brittleness index of 0.49 suggests favorable fracability. Strong linear correlation between clay\/quartz-feldspar components and homogenized model confirms they govern macroscopic stiffness, while carbonate influence remains minimal.Applications\/Significance\/Novelty: Integrating extensive micro-scale data with upscaling methodologies establishes a robust micro-macro mechanical framework for predicting elastic modulus and strength from core to formation scale. This approach directly enables optimized shale volumetric fracturing through mechanics-based brittleness evaluation and analysis of fracture behavior at brittle-ductile interfaces. The methodology provides critical support for identifying engineering sweet spots and designing effective fracture networks, delivering significant practical value for precision stimulation and production enhancement in shale reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Oral\" style=\"border-top: 4px solid #43e97b;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 5: Emerging Geochemical-Based Applications<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"session-item-location mb-1\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"lucide lucide-map-pin-icon lucide-map-pin\"><path d=\"M20 10c0 4.993-5.539 10.193-7.399 11.799a1 1 0 0 1-1.202 0C9.539 20.193 4 14.993 4 10a8 8 0 0 1 16 0\"\/><circle cx=\"12\" cy=\"10\" r=\"3\"\/><\/svg>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tExhibit Hall E, Station B\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t                                                    <div class=\"scholarone-session-time small mb-1\">\n                                                        10:50 AM &#8211; 12:30 PM                                                    <\/div>\n                                                \t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAhmed Alsmaeil, Jennifer Adams, Jagos Radovic\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t10:50 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Proposing Productivity Index for Vaca Muerta Unconventional Reservoir Based in Geochemical Properties<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Betancourt*<sup>1<\/sup>, R. D. Panesso<sup>2<\/sup>, C. Rabe<sup>3<\/sup>, E. Solorzano<sup>4<\/sup>, E. Sardelli<sup>4<\/sup> and E. J. Betancourt<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Universidad Central de Venezuela; 2. InterRock; 3. Baker Hughes; 4. University of Oriente)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Shale Oil reservoirs are extremely complex, requiring an intensive subsurface characterization due to diverse clay mineralogy composition, complex pore system, variable lateral fracture system amount others. All these properties make critical to develop a proper comprise specific geochemical properties to obtain the precise values of productivity index (PI) in the right production interval.Methods\/Procedures\/Process: This study proposes correlations between the TOC, S1, S2, PI, uranium and methane (C1) content to identify intervals and minimum content as a potential economical source for extracting hydrocarbons. These correlations are key to define the appropriated hydraulic fracturing design and identify the best flow intervals because methane is a hydrocarbon that can be converted to hydrogen through pyrolysis or catalytic reforming processes.Results\/Observations\/Conclusions: The results show that the type of kerogen present varies from type II (generally marine) to type II-III (prone to dry gas). TOC content averages 4%, while thermal maturity (Tmax) varies from early mature to peak mature window. The Vitrinite Reflectance (Ro) results shows that hydrocarbon was identified all over the formation ranges between 0.72 to 0.83. It was possible to establish this formation as hydrocarbon generator system for the Upper Jurassic Play. Vaca Muerta Shale went through three maturation windows (oil window, transition between oil and gas windows and gas window) as it dips southApplications\/Significance\/Novelty: The study is showing that the amount of hydrogen ranging from 169 to 244 estimated by using neutron logs and physical correlations. The productivity index from geochemical study was 0.27 in upper section and 0.17 to the lower section of the well. This study also described the proposed geochemical correlations and data required for hydrocarbon resources evaluation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:15 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pressure-Dependent Chemo-Mechanical Pathways in Mancos Shale Exposed to CO2-Rich Brine<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Mahgoub, S. Ahmed Banu and S. Abedi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study examines the pressure-dependent chemo-mechanical alteration of quartz-rich Mancos shale exposed to CO2-rich brine at 100\u00b0C for 28 days. The objective is to determine how two pressure conditions (1000 psi and 1800 psi) govern dissolution\u2013precipitation reactions and drive changes in microscale modulus, hardness, and structural stability. High-speed nanoindentation provides detailed mechanical maps that are integrated with machine-learning clustering, along with coupled interpretation of indentation and EDS datasets for tracking chemo-mechanical alterations. The scope includes determining how elevated pressure accelerates dissolution and mechanical degradation, while lower pressure promotes salt precipitation that locally modifies mechanical behavior.Methods\/Procedures\/Process: The mineralogy of the Mancos shale was first determined by XRD, followed by preparation of samples for controlled CO2\u2013brine reactions. Specimens were submerged in 1M NaCl solution inside a titanium batch reactor at 100\u00b0C for 28 days under two CO2 pressure conditions (1000 and 1800 psi), then depressurized gradually to prevent spalling. Post-reaction cross-sections were cut, ground, and polished to assess alteration depth. Mechanical properties were characterized using high-speed nanoindentation on nine grids of 10,000 indents each. SEM\/EDS imaging provided mineralogical information, and multispectral segmentation with unsupervised clustering (UMAP + K-means) classified mechanical responses and tracked pressure-induced alterations, supported by coupled nanoindentation\u2013EDS interpretation.Results\/Observations\/Conclusions: Nano- and microscale analyses revealed distinct pressure-dependent chemo-mechanical alterations in quartz-rich Mancos shale following CO2\u2013brine exposure. At 1800 psi, extensive dissolution and reprecipitation occurred throughout quartz-rich regions, causing pronounced reductions in stiffness and hardness linked to Si\u2013O\u2013Si bond weakening, with degradation persisting across the full reaction depth. At 1000 psi, salt precipitation concentrated near the reacted edge, particularly within clay-rich zones, producing localized stiffening and a notable increase in clay modulus. Machine-learning clustering, together with coupled nanoindentation\u2013EDS mapping, effectively differentiated these contrasting behaviors, confirming two pressure-driven alteration pathways.Applications\/Significance\/Novelty: The findings provide critical insight into how CO2 pressure governs microscale degradation pathways in shale, directly informing geomechanical risk assessment and long-term CO2 storage and CO2-EOR performance. Distinguishing between dissolution-driven weakening at high pressure and salt-induced stiffening at low pressure highlights the necessity of incorporating pressure-specific alteration mechanisms into reservoir modeling, operational planning, and predictive simulations. The integrated use of high-speed nanoindentation, SEM\/EDS mapping, and unsupervised clustering establishes a novel, high-resolution framework for quantifying chemo-mechanical coupling in heterogeneous shales, ultimately improving assessment of rock integrity under reactive fluid exposure.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t11:40 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Different Kerogens in the Kitchen &#8211; Thermal Maturity Trends of the Mowry Shale Across Southwest Wyoming<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Hudson*<sup>1<\/sup>, B. Taylor<sup>1<\/sup>, B. Greenhalgh<sup>2<\/sup> and A. Toner<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Brigham Young University; 2. Wexpro)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Tmax assessment of hydrocarbon source intervals is a quick, inexpensive way to estimate thermal maturity of organic-rich shales, and has been a critical exploration tool for decades. Simple definition of maturation and expulsion thresholds is not always appropriate, however, given that different types of kerogens degrade at different temperatures. A classic example of this is the Cretaceous Mowry Shale, which commonly has reported Tmax values below the early oil window, even in productive basins such as the Powder River Basin. Detailed source rock kinetics from a series of samples across the Greater Green River Basin helps recalibrate the appropriate definition of the oil window in this exploration area, as well as showing maturity trends across the basin.Methods\/Procedures\/Process: High temperature Rock-Eval pyrolysis was conducted on 250 samples collected from five outcrops, five cores, and two well cuttings as part of the larger study. Of these samples, chemical kinetic analysis was conducted on select samples from the middle Mowry, chosen for their production potential. Soxhlet extraction was used to isolate immature kerogens from samples, which were then run through a series of five open pyrolysis temperature ramps. Results were used to model kerogen activation energy and fraction reacted.Results\/Observations\/Conclusions: Kinetic modeling shows that the activation energy for the Mowry Shale in the Greater Green River Basin is below generic standards for black shales. Activation energies from analyzed samples range from 45-52 kcal\/mol, and samples also show bimodality to the activation energy, suggesting a mix of kerogens is consistent across the basin. While there is some uncertainty as to the geothermal gradient across the basement through time, uncertainty modeling shows that for reasonable thermal histories, the Mowry Shale is well within the oil window and the fraction of kerogen reacted ranges from 30% to complete thermal conversion.Applications\/Significance\/Novelty: Detailed study of the kinetics of the Mowry Shale confirms that it is a viable source rock for both conventional and unconventional exploration within the Greater Green River Basin. Modeled activation energies and temperature thresholds refine our understanding of hydrocarbon generation windows, conversion percentages, and the timing of generation and expulsion specific to the Mowry Shale. This insight is critical for enhancing subsurface prediction and optimizing hydrocarbon production in this emerging play.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Oral\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:05 PM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pyrolysis Tmax Suppression as a Robust Indicator for Rapid Identification of Lacustrine Shale Oil \u201cSweet Spots\u201d<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Li*<sup>1<\/sup>,<sup>2<\/sup>, X. Ma<sup>1<\/sup>,<sup>2<\/sup>, M. Qian<sup>1<\/sup>,<sup>2<\/sup>, E. Wang<sup>1<\/sup>,<sup>2<\/sup>, T. Cao<sup>1<\/sup>,<sup>2<\/sup> and X. Guo<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Efficient Development; 2. Sinopec Petroleum Exploration &amp; Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Earlier research in several Chinese lacustrine shale systems highlights four key categories of indicators for evaluating shale oil \u201csweet spots\u201d, including source rock quality, reservoir properties, hydrocarbon mobility and energy, and engineering fracability. This presentation provides an overview of the main geological controls for Paleozoic, Mesozoic and Paleogene lacustrine shale oil enrichment, discusses the key geological, geochemical, geophysical and engineering indicators used for sweet spots identification, and proposes pyrolysis Tmax suppression as a robust indicator for rapid identification of lacustrine shale oil \u201csweet spots\u201d on the drilling site.Methods\/Procedures\/Process: This study utilizes a large data base containing core descriptions, Rock-Eval pyrolysis, molecular geochemistry, molecular simulation, logging pressure interpretation, and production test data from numerous exploration\/evaluation wells. We systematically analyze the key geological controls on shale oil enrichment. We pay special attention to the geochemical parameters for shale oil generation and retention, geological and wireline log parameters for hydrocarbon storage, geomechanic and formation pressure parameters for shale oil preservation, and production test and geophysical parameters for independent validation. Based on these analyses, we develop a comprehensive parameter suite and procedures for lacustrine shale oil sweet spots identification and classification.Results\/Observations\/Conclusions: Our results demonstrate that lacustrine shale oil enrichment is governed by the synergistic effect of four interconnected geological factors, where high-quality source rocks within a stable sedimentary basin generate hydrocarbons that are primarily retained within or near the source rock, facilitated by favorable reservoir spaces in brittle lithologies and sealed by effective pressure conditions. The integration of these factors creates &quot;sweet spots&quot;, which can be constrained using a number of geological, geochemical, geophysical and engineering indicators. We proposes pyrolysis Tmax suppression as a robust indicator for lacustrine shale oil \u201csweet spots\u201d. This is particularly useful on the drilling site, where timely \u201csweet spots\u201d determination is mandatory.Applications\/Significance\/Novelty: Laminated carbonate rich shale systems are distributed widely in the Paleozoic and Cenozoic sedimentary basins in China, constitute favorable targets for shale oil exploration and production. It is remarkable that pyrolysis Tmax suppression is observed almost universally in these carbonate rich shales, where oil productivity appears to correlate positively with the degree of Tmax suppression, especially for the laminated section bounded by thick top and bottom seals. Once contamination from drilling fluids is ruled out, application of pyrolysis Tmax suppression in shale oil \u201csweet spots\u201d recognition has clear advantages over the pyrolysis S1 and S1\/TOC ratio, as the results are less affected by light hydrocarbon evaporation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<div class=\"scholarone-tab-content\" id=\"day-3\">\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 1: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tMatthew Poole, Alejandro Lerza, Luis Baez, Ali Sloan\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Common Equation of State Based Compositional Simulation to Quantify Uncertainty for Multi-Bench Faulted Reservoirs in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Gang*, C. Chen, H. Tang and H. Behzadi\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The simulation of unconventional reservoirs with complex fractures and faults is indeed a challenging problem, especially when co-developed benches have different fluid compositions. This paper presents a field-wide common equation of state (CEOS) model to describe pressure, volume, and temperature (PVT) properties, using all measured PVT properties and fluid compositions of surface samples. The CEOS aims to provide reasonable PVT property estimations of all mixtures found throughout the field, within all reservoirs, throughout the entire production system. The results indicate that the CEOS is critical in understanding the reservoir pressure, gas-to-oil ratios (GORs), and production rate contributions from different benches with a proper development strategy.Methods\/Procedures\/Process: The study focuses on two horizontal wells in Permian Basin, located within two miles co-developing multiple benches. The comparison of well productivity, bottom hole pressure (BHP) and GOR\/CGR (condensate-to-gas ratio) indicated variations in performance between the wells. An integrated approach, including a consistent reservoir simulation history match workflow using CEOS generated fluid compositions, was applied to these wells to understand what factors could cause the production differences. The CEOS models were adjusted, based on multiple PVT data covering a wide range of the GORs and CGRs. Two different compositions were used to represent the one generic PVT system: one for gas condensate and the other for light oil.Results\/Observations\/Conclusions: The simulation models were upscaled from a complicated fine-scale geomodel, incorporating facies-controlled petrophysical properties using proprietary technique developed in Oxy. All petrophysical and geomechanical input data were based on consistent core and log interpretations. The fracture model was constructed based on the three-dimensional geological model which honors the RevoChem data, including the drainage fracture height based on the geochemical fingerprint data. Other parameters, such as relative permeability curves, stimulated reservoir volumes (SRV)\/fracture geometries, and well spacing configurations, were investigated through a history-matching process and sensitivity analysis.Applications\/Significance\/Novelty: This study properly captured the GORs, pressures, and production rates for multi-bench development in Permian Basin. It revealed differences in production drivers between these two wells, in addition to the fault. The equation of state (EOS) based simulations helped estimate the long-term oil\/condensate production rates with different development spacing, within the Permian Basin. Consequently, this comprehensive integrated approach provides a foundation for higher-quality decision-making.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Enabling Successful Hydraulic Fracturing in the UAE Through an Optimized Post-Fracturing Plug Millout Workflow<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Hamidon*<sup>1<\/sup>, A. Ryan<sup>1<\/sup>, C. Anderson<sup>1<\/sup>, Y. Faizov<sup>2<\/sup>, S. Elhanbouly<sup>2<\/sup>, S. A. Elazab<sup>2<\/sup>, M. El Taher<sup>3<\/sup>, A. M. Alghamdi<sup>1<\/sup>, W. Caiza<sup>1<\/sup> and R. Wargo<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Turnwell; 2. ADNOC; 3. SLB)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Post-fracturing plug millout is critical for evaluating unconventional fracturing performance and shaping field development strategies. The UAE\u2019s unconventional field presents harsher downhole conditions than many global unconventional plays, driven by high pressure, high temperature, and elevated H2S levels. These conditions require a carefully engineered approach as the coiled tubing (CT) and bottomhole assembly (BHA) will be at the limits of their operating envelopes. This paper outlines an optimized, scalable workflow designed to navigate these challenges and reliably execute post-fracturing plug millout operations in the challenging unconventional environment.Methods\/Procedures\/Process: High operating wellhead pressures require CT strings with sufficient wall thickness to expand the operating envelope and enable sufficient pump rates to generate turbulent flow for effective solids transport. Furthermore, sour-service constraints limit material selection due to risks of H2S-induced failure. To reduce CT exposure to the harsh downhole environment, plug selection became a primary planning focus, as rapid plug removal shortens operational time. BHA components were selected to achieve the required pump rate, with lowest pressure drop possible to remain within the operating pressure envelope. The fluid system was engineered to balance low friction pressure with the ability to sustain turbulence for efficient cleanout.Results\/Observations\/Conclusions: Fit-for-basin dissolvable plugs, selected through engineering assessment and field trials, was proven to maintain integrity during fracturing yet fully dissolved ahead of millout. Pumping strategies were optimized to promote turbulence and maintain slight overbalanced condition to reduce H2S exposure. These were enabled by simplified BHA consisting of high-rate motor and axial-pulsing extended reach tools, as well as strategic choke management to control the return rate at surface. Quenched and tempered (Q&amp;T) CT strings with hourglass taper design delivered durability, exceeding 500,000 ft of cumulative running footage despite the harsh conditions. Valve-to-valve operating times were achieved below 30 hours for 30-stage wells, validating the overall workflow effectiveness.Applications\/Significance\/Novelty: Successful execution across more than 20 wells demonstrated the workflow\u2019s repeatability and suitability for the challenging downhole condition. Results confirm that the high-pressure, sour environments can be effectively managed through disciplined engineering and structured planning. The approach establishes a foundation for future development as lateral lengths extend to 10,000 ft and beyond. Proven dissolvable plug performance, combined with an improved understanding of operating parameters, will support further refinement of CT hourglass design, BHA configuration, and surface equipment requirements for the next stage of unconventional field development in the UAE.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Transforming Completion Practices: UAE\u2019s First Integrated Deployment in Shilaif Unconventional Oil Reservoir<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tO. A. Alvarado Sosa<sup>1<\/sup>, T. Itibrout<sup>1<\/sup>, K. H. Al Dhaheri<sup>1<\/sup>, M. Al Mazrouei<sup>2<\/sup>, P. Hanna<sup>2<\/sup>, A. Cuessy Vazquez*<sup>2<\/sup>, A. Alam<sup>2<\/sup>, A. Singh<sup>2<\/sup>, B. N. Broca<sup>2<\/sup>, T. Ao<sup>2<\/sup>, E. N. Kaul<sup>2<\/sup>, D. N. Cippitelli<sup>2<\/sup>, M. N. Khan<sup>2<\/sup>, A. N. Alhosani<sup>2<\/sup> and M. Y. Al-Ali<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ADNOC Onshore; 2. ADNOC Drilling)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper presents the first successful execution of fully integrated completion operations in the Shilaif Unconventional Oil Reservoir, located in the south-western region of Abu Dhabi, UAE, spanning the Ghurab, Huwaila, and Falaha fields. The Shilaif Cretaceous formation is a low-permeability, high-pressure oil reservoir composed of tight carbonates containing a mix of in-situ and migrated oil. The project unified hydraulic fracturing, wireline plug-and-perf, and coiled tubing services under a single execution strategy to enhance operational efficiency, minimise non-productive time, and enable scalable, cost-effective development in a geologically complex unconventional reservoir.Methods\/Procedures\/Process: The integrated approach combined split-stream hydraulic fracturing with real-time monitoring, optimised plug-and-perf techniques, and coiled tubing post-frac plug milling operations. Field-tested fracturing strategies and equipment configurations ensured effective proppant transport and placement. Wireline operations were streamlined to improve pump-down efficiency, while coiled tubing operations employed a sequenced milling strategy featuring a high-performance milling BHA and optimised schedules for efficient plug cutting and debris removal. Continuous operational diagnostics enabled dynamic stage-by-stage decision-making, ensuring synchronised performance across all service lines.Results\/Observations\/Conclusions: The integrated execution cut total completion time per well by 56%. Wireline pump-down improved 33% with faster tool deployment and retrieval, achieving a 98% success rate and shorter cycles. Hydraulic fracturing set a UAE record for the most frac stages pumped in a single day through optimised schedules and fluid rheology. Coiled tubing milling removed all frac plugs in record time, reducing valve-to-valve duration by 59% versus offsets, driven by optimised BHA design, fluid hydraulics, and real-time adjustments. These efficiencies enabled a shift from 3,000-ft single-well exploration to 6,000-ft pad-based development of 4\u20135 wells, 2-pad per location, de-risking unconventional assets, accelerating delivery, and maximising reservoir contact.Applications\/Significance\/Novelty: This paper provides a first-of-its-kind field example of integrating hydraulic fracturing, wireline plug-and-perf, and coiled tubing milling operations within a reservoir-centric operational framework in the UAE. It offers new insights into real-time coordination across service lines, strategic deployment of high-performance tools for reliable pump-down operations, and the scalable benefits of multi-well pad development. These findings contribute to the evolving playbook for unconventional field development in the Middle East.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Changing Maturity Paradigms: Derisking Hybrid Tight Gas and Shale Exploration in the Frontier Paraguayan Chaco Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. A. Angulo*, G. Franco and I. De Barros Barreto\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Zeus Energy)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: From an operator perspective, our objective was to convert a frontier basin\u2014 where over 97% of historical wells recorded oil or gas shows\u2014 into a drill-ready unconventional play by de-risking the petroleum system and identifying sweet spots. We defined a single hybrid play concept that combines Devonian naturally fractured tight sands (Los Monos Fm., Huamampampa Fm., Icla Fm.) with Silurian shale gas (Kirusillas Fm.), and show how we used legacy wells and seismic, regional analogs, and invested in adquisition of 2D seismic data and 2 new exploration wells, together with modern petrophysics, geomechanics and geophysics, to explain past failures, evaluate the potential and recommend future subsurface appraisal and pilot designs.Methods\/Procedures\/Process: We prioritized reprocessing legacy 2D seismic and acquiring 1,500 km of new 2D data, integrating &gt;50 historical wells with two recent operator wells over more than 5 million acres. New core in the Kirusillas Fm, together with cuttings, TOC\/Ro and mineralogy from all three source intervals (Los Monos, Icla and Kirusillas), calibrated high-potential sections, supported by advanced spectroscopy-based mineralogy logs to refine landing zones. Image logs, fracture interpretation and geomechanical modelling (brittleness, stress, wellbore stability) were then used to map and rank hybrid tight\/shale corridors, guide well orientation and landing points, and design an efficient, phased appraisal data-acquisition program.Results\/Observations\/Conclusions: Our integrated derisking approach and data acquisition show that Los Monos and Icla source-rock facies define a gas-prone system with interbedded fractured and tight reservoirs, while Kirusillas has high TOC and dry-gas maturity, confirming its dual role as source and shale target. New TOC\/Ro data and basin modelling overturn assumptions of immaturity, demonstrating a broad gas window. Image logs and geomechanics reveal fracturing along transtensional faults and define corridors where Kirusillas and Devonian tight sands coincide under a favorable stress regime. Reinterpreting legacy wells shows most \u201cfailures\u201d reflect overbalanced drilling, limited tests and poor locations rather than lack of charge, allowing corridor-scale leads to be upgraded into drill-ready hybrid prospects.Applications\/Significance\/Novelty: This operator case study turns a little-known corner of the Paraguayan Chaco into a newly defined unconventional basin-scale opportunity. Through systematic derisking we replaced empirical assumptions with targeted data acquisition, core, advanced logs, geochemistry, image logs and geomechanics, integrated in a process-based basin model. This work constrains charge, reservoir quality and natural fracturing in the hybrid tight-sand\/shale system and delivers a screened fairway map, ranked prospect inventory and focused appraisal roadmap. The result is a \u201cplug-in ready\u201d technical basis for new entrants to join the play and a transferable template for unlocking other data-sparse basins.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Drilling Mud Loss Mitigation Through Rapid Depletion Forecasting<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Yin*, J. W. Clark, T. Gang, M. Razavi, J. Han, H. Behzadi, O. Raba, V. Muralidharan, X. Xie and C. M. Sirois\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Mud loss during infill well drilling near depleted parent wells remains a critical challenge in unconventional reservoirs, often leading to nonproductive time, higher costs, and operational complexity. Pressure depletion around parent wells increases the risk of fluid invasion into existing fractures when drilling offset wells. Without prior knowledge of depletion magnitude and spatial extent, operators are forced to either accept losses to meet drilling targets or adjust mud weight on the fly, risking wellbore stability. This study presents a fast, reliable forecasting approach that quantifies depletion severity, evaluates mud loss risk for infill locations, and supports proactive mitigation planning, enabling safe, cost effective, and efficient drilling operations.Methods\/Procedures\/Process: This work presents a workflow to forecast parent depletion zones and quantify mud loss risk before drilling. Parent well production data are history matched using a simplified simulation model with representative geological properties and single cluster spacing. The model are constrained by actual well spacing. Fracture height is matched to diagnostic fracture measurements, while fracture length and conductivity are calibrated to production history. Each run completes in less than five minutes, enabling rapid generation of multiple history matched cases for uncertainty analysis. High side and low side fracture dimensions are extracted from the realizations, compared to planned infill trajectories in gun barrel view, and used to guide drilling risk assessments and mud weight design.Results\/Observations\/Conclusions: The workflow was implemented across multiple drilling spacing units (DSUs) in the Delaware Basin and proved highly effective in reducing depletion forecasting timelines from months to less than a week. It consistently provided accurate depletion estimates that informed casing and mud weight strategies, significantly lowering mud loss incidents. Field teams have integrated the process into standard predrilling practices, supported by an interactive dashboard displaying mud loss risk indicators. The dashboard includes base case depletion models for key benches and a scaling algorithm that adjusts predictions for similar wells using production volume correlations. This allows drillers to access risk forecasts instantly, further accelerating planning and improving operational decision-making.Applications\/Significance\/Novelty: This method provides a fast way for depletion prediction and mud loss risk assessment. Calibrated to production and diagnostic data, it delivers accurate results in minutes and integrates seamlessly into drilling workflows. By enabling proactive casing and mud weight design based on quantified parent depletion, it has demonstrably reduced mud loss incidents and avoided substantial costs across four wells. Dashboard integration allows rapid scaling across pads and benches, making this a novel operational practice that unites reservoir forecasting with drilling risk management to protect well integrity and optimize economics.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Tripling Drilling Efficiency \u2014 Record-Setting ROP Transformation in UAE Onshore Unconventional Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Bayov*<sup>1<\/sup>, G. Arbadov<sup>1<\/sup>, M. Nour<sup>1<\/sup>, M. Anwar<sup>1<\/sup>, A. Alajami<sup>1<\/sup>, E. Elshamisi<sup>1<\/sup>, A. Ruzhnikov<sup>2<\/sup>, N. Akhmetov<sup>1<\/sup>, A. H. Abdelkawy<sup>1<\/sup> and M. Reda<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. ADNOC Onshore; 2. Turnwell)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper presents a step-change transformation in drilling performance within Company\u2019s unconventional development program, where overall drilling efficiency was effectively tripled through a structured optimization approach. In this context, drilling efficiency is defined as delivered footage per rig day, achieved through the combined effect of increased section-average rate of penetration (ROP) and significant reduction in repetitive flat time, rather than a proportional increase in instantaneous on-bottom ROP.Methods\/Procedures\/Process: The transformation was driven by a fully integrated, multidisciplinary workflow combining drilling engineering, geosteering, downhole technologies, and rig-site execution. The approach included systematic redesign of bottomhole assemblies (BHAs), deployment of fit-for-purpose drill bits and rotary steerable systems, application of shock and vibration mitigation tools, optimization of drilling parameters, and continuous real-time performance tracking. Execution was governed by a structured test\u2013learn\u2013standardize cycle, enabling rapid scaling of successful practices across rigs and pads.Results\/Observations\/Conclusions: The campaign delivered substantial and repeatable performance improvements across all hole sections, with ROP increases of 202% in the 17\u00bd-in. section, 153% in the 12\u00bc-in. section, and 162% in the 8\u00bd-in. curve and lateral. Weight-to-weight (W2W) connection times were reduced by more than 80%, significantly improving time-based efficiency and operational consistency. At the campaign scale, more than one million feet were drilled and over 140 wells were completed within one year. Average well delivery time was reduced from approximately 46 days to a minimum of 13.5 days (spud-to-rig release), representing the fastest documented unconventional oil well delivery within the Company\u2019s approved drilling program.Applications\/Significance\/Novelty: In addition to operational performance gains, the reduction in drilling time translated into measurable environmental benefits through decreased fuel consumption and associated CO\u2082 emissions per well. The results demonstrate that a disciplined integration of technology, execution practices, and real-time performance management can deliver scalable and repeatable efficiency gains in complex unconventional carbonate reservoirs.Interdisciplinarity (Team Presentation\u2019s only): The study provides a practical optimization framework applicable to high-volume unconventional developments, highlighting the critical role of execution discipline, rapid learning cycles, and cross-functional integration in achieving sustained, industry-leading drilling performance.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 2: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tScott Birkhead, Yulia Faulkner, Haijing Wang, Parag Bandyopadhyay\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">PVT Correlations for Initial Formation Volume Factor Estimation: A Case Study in Delaware Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Moonesan*<sup>1<\/sup>, S. Tavassoli<sup>1<\/sup> and K. Patel<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Texas; 2. Marathon Oil Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The Lower 3rd Bone Spring and Wolfcamp AXY (L3rd BS&amp;WCAXY) formation, the most frequently drilled unconventional reservoir in the Delaware Basin, produces from both oil and gas regions. This study focuses on developing Pressure-Volume-Tempreature (PVT) correlations tailored to the Lower 3rd Bone Spring and Wolfcamp AXY for both oil and gas wells. Since PVT reports are scare but essential in this region, these correlations are crucial for determining the initial formation volume factor for oil ( ) and gas ( ). Combined with petrophysical analysis serve as fundamental parameters for accurately estimating the hydrocarbons in place within any formation.Methods\/Procedures\/Process: To develop PVT correlations for the L3rd BS &amp; WCAXY, initial GOR values were used to identify zones between oil and gas regions. Existing PVT reports with varying GOR were analyzed, and CMG simulations were run to match saturation pressures with measured data. This produced curves and equations to estimate initial gas formation volume factor (Bgi) for different GCR at given reservoir pressures. The approach improves Bgi prediction accuracy and enhances reservoir evaluation in complex gas-condensate systems of the Delaware Basin.Results\/Observations\/Conclusions: Given the initial reservoir pressure and solution gas-oil ratio for oil wells, or the gas-condensate ratio for gas wells at a specific location in the Delaware Basin, these correlations can be used to estimate the initial formation volume factor for the L3rd BS&amp;WCAXY formations. The correlations indicate that the initial formation volume factor increases with higher initial reservoir pressure or solution gas-oil ratios in oil wells, and decreases with higher initial reservoir pressure for gas-condensate ratios greater than 5,000 Scf\/Bbl in gas wells.Applications\/Significance\/Novelty: Full PVT reports are scarce in the Delaware Basin because operators are not required by TRRC or NMOCD to submit them, making data limited or often unavailable. This study provides three PVT samples from oil wells with different GORs and correlations for gas-condensate ratios in gas wells. These tools allow estimation of initial formation volume factors using reservoir pressure or solution GOR in oil wells, and pressure with GCR in gas wells. With petrophysical data and these correlations, hydrocarbons in place can be estimated for the L3rd BS &amp; WCAXY formations across the Delaware Basin.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 3: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAndrew Keene, Marianne Rauch, Jose Delgado, Liwei Cheng, Tomasz Ochmanski\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Intelligent Fault Recognition Driven by Structural-Style Knowledge and Real Seismic Datasets<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Li, T. Duan*, Q. Ma and H. Wang\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SINOPEC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Current intelligent fault recognition algorithms perform well on simple structural styles with high-quality seismic data, but degrade significantly in low signal-to-noise ratio seismic and for complex strike-slip, thrust, and extensional fault systems. Two fundamental challenges underlie the poor performance: existing synthetic training datasets lack structural-style diversity, and current networks trained predominantly on synthetic data exhibit poor transferability when confronted with real seismic data characterized by varying noise levels and imaging quality. This research aims to develop a robust fault recognition deep-learning framework driven by both geological knowledge and observational real datasets, enabling accurate identification of complex fault systems under challenging subsurface conditions.Methods\/Procedures\/Process: We propose a two-stage training strategy built on a 3D encoder-decoder network with Transformer self-attention integrated at the bottleneck. Stage 1 embeds structural geology knowledge through geologically constrained synthetic sample generation spanning extensional, thrust, and strike-slip fault systems with realistic kinematic parameterization, training the network on synthetic seismic-label pairs to encode the fundamental geometric signatures of each fault style. Stage 2 performs transfer learning on real seismic data together with auto-generated labels derived from Stage 1 model predictions applied to fault-enhanced real seismic, constituting an integrated knowledge learning from both the fault-enhancing filter and the synthetic-domain model directly to raw, noisy real seismic. Stage 2 was trained on real seismic data from four geologically diverse survey areas spanning extensional, compressional, and strike-slip tectonic settings, ensuring cross-setting generalizability of the final model.Results\/Observations\/Conclusions: Applications in the Tarim Basin strike-slip fault zone and the Subei Basin extensional fault zone demonstrate clear improvements over conventional synthetic-only trained approaches, showing consistent recognition quality even in challenging low signal-to-noise ratio seismic data. In the Tarim Basin, the two-stage model successfully delineated the main strike-slip fault corridors and subsidiary structures \u2014 including fault branches in low-coherence zones missed by Stage 1 \u2014 subsequently confirmed by well correlation, validating auto-label quality and the effectiveness of real-world practice learning. In the Subei Basin, the two-stage model maintained reliable detection of both large-throw basin-bounding faults and smaller intra-block faults with substantially fewer spurious artifacts than Stage 1. Results from the Sichuan Basin Tongjiang\u2013Malubei thrust system further confirm that Stage 2 recovers additional short-segment thrust faults at structural boundaries with markedly higher detection completeness relative to the Stage 1 baseline. The model capability improves continuously as additional real-world datasets are incorporated.Applications\/Significance\/Novelty: This work presents two key innovations: (1) systematic integration of structural geology knowledge into geologically constrained synthetic sample generation, addressing structural-style coverage gaps in existing training databases by explicitly encoding the kinematic geometry of extensional, thrust, and strike-slip fault systems calibrated from geological analogs; and (2) a novel two-stage training paradigm that bridges the synthetic-to-real domain gap through automated auto-label generation via fault-enhancement filtering, performing integrated knowledge learning from both the enhancement filter and the Stage 1 model into a robust, real-data-adapted network \u2014 without requiring manual fault picks on real seismic volumes. The methodology advances seismic fault interpretation from purely data-driven to knowledge-informed intelligent prediction, with direct scalability to additional tectonic settings as new survey data are incorporated.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Fracture-Driven Interactions (FDIs) in Unconventional Wells: A Review of Mechanisms, Monitoring Tools, and Mitigation Concepts<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Bigdeli<sup>1<\/sup>, H. Moubarak*<sup>2<\/sup>, Y. Al-Enezi<sup>3<\/sup> and C. Temizel<sup>2<\/sup>,<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State University of Campinas; 2. Terra Altai; 3. Kuwait Oil Company; 4. Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The paper unites global understanding of fracture-driven interactions (FDIs) which happen between neighboring wells during hydraulic fracturing operations. The research examines multiple FDI mechanisms together with diagnostic methods and completion elements and mitigation approaches. The research examines pressure hits and fluid migration and cross-well communication and fracture reactivation and their effects on production and parent-child relationships and infill development.Methods\/Procedures\/Process: The review combines data from various published studies which used microseismic analysis and offset pressure monitoring and tracer-based research and fracture modeling and post-fracture performance evaluation. The review combined documented FDI mechanisms which include depletion-induced reorientation and asymmetric frac growth and natural fracture connectivity and near-wellbore stress distortions. The review assessed monitoring systems, which included fiber-optic sensing and tiltmeters and offset pressure gauges and surface microseismic arrays. The research used existing published data and analytical results because it did not conduct any new simulations or field monitoring activities.Results\/Observations\/Conclusions: The research shows that tight reservoirs experience multiple fracture interactions which result in lower well production and decreased performance of neighboring wells. Research studies show that FDI behavior responds to pad-spacing methods and stimulation power levels and natural fracture patterns and reservoir pressure depletion rates. The research shows offset-pressure measurements together with microseismic data patterns serve as primary indicators to detect FDI pathways. The research identifies multiple FDI management mitigation strategies which global studies have developed. Organizations need to create planning systems which unite monitoring tools with geomechanical expertise to successfully operate FDI operations.Applications\/Significance\/Novelty: The paper develops an FDI classification system which links interaction types to operational factors. The research investigates two underreported mitigation approaches, which involve preloading and adaptive pad sequencing.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Geological Insights into Continental Shale Reservoirs: The Dongyuemiao Member in the Fuxing Area of the Sichuan Basin, China<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tD. Feng*, Q. Wang, Z. Hu, S. Xu and W. Du\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: During the Early Jurassic, the onset of compressional thrusting in the Daba Mountains led to the migration of the depositional and subsidence centers in the Sichuan Basin from western Sichuan to the northeastern part of the basin. In the Fuxing area, the Dongyuemiao Member of the Lower Jurassic Ziliujing Formation developed a lacustrine sedimentary system. The entire Dongyuemiao Member records a complete transgressive-regressive lacustrine cycle. This study focuses on the continental shale of the Dongyuemiao Member, employing multiple analytical and experimental techniques to investigate the characteristics of its pore structure.Methods\/Procedures\/Process: To elucidate the reservoir characteristics and pore-controlling factors of the continental shale in the Dongyuemiao Member, this study employs a comprehensive suite of analytical and experimental techniques, including thin-section observation, argon ion polishing\u2013field emission scanning electron microscopy (FESEM), physical property analysis, and whole-rock X-ray diffraction. The research focuses on characterizing the pore structure of the shale, investigating the key factors governing pore development, and analyzing the diagenetic evolution mechanisms of reservoir pores. The findings aim to provide a scientific basis and practical reference for the future exploration and development of continental shale oil and gas in the Fuxing area and analogous regions in China.Results\/Observations\/Conclusions: The Dongyuemiao Member shale shows high mineral heterogeneity (clay-rich, low carbonate) with dominant massive\/laminated argillaceous and calcareous lithofacies. Reservoir pores are mainly inorganic, some organic, with local microfractures, indicating good reservoir quality. Inorganic pores are mineral\u2013controlled, organic pores depend on organic content\/type, and microfractures form from inorganic\u2013organic heterogeneity. At mid\u2013diagenetic stage B, compaction, cementation, dissolution, and hydrocarbon generation created an integrated organic\u2013inorganic pore\u2013fracture system, providing key storage for shale hydrocarbons.Applications\/Significance\/Novelty: This study is the first to systematically reveal the pore formation mechanisms and diagenetic evolution of continental shale in the Dongyuemiao Member, Sichuan Basin. Multi-scale analyses clarify how inorganic-organic interactions control pore systems. The novelty lies in proposing a ternary &quot;mineral composition\u2013diagenetic process\u2013pore structure&quot; model for continental shale, extending beyond marine shale frameworks. This research offers a new methodological framework for evaluating complex reservoirs, deepens understanding of shale hydrocarbon enrichment, and supports sweet-spot prediction and development optimization in analogous plays.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Preliminary Study on the Main Controlling Factors of Deep Coalbed Methane Enrichment in the Slope Zone of the Ordos Basin in China<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Liu*, B. Shen and S. Zhao\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Since 2021, deep coalbed methane (CBM) exploration and development have centered on the No. 8 Coal of the Carboniferous Taiyuan Formation, yielding remarkable progress in key areas including Daning-Jixian (burail depth: 2000-2500 m), Daniudi (burial depth: 2500-3000 m), and Nalinhe (burial depth: 2800-3300 m) in the Orods basin in China. However, the primary controlling factors governing deep CBM enrichment remain poorly understood. The objective of this study is to clarify the enrichment mechanisms of deep CBM in the slope zone of the Ordos Basin by integrating regional geological context, experimental data, and exploration results, thereby providing valuable insights for the global exploration and development of deep CBM.Methods\/Procedures\/Process: Through the interdisciplinary integration of geology, experimental analysis, logging, geophysics, and other related disciplines, coupled with the integration of macroscale and microscale investigations, the combination of thermal simulation and laboratory experiments, the integration of lost gas simulation and high-temperature high-pressure (HTHP) isothermal adsorption tests, as well as the integration of geological anatomy and big data analytics, this study clarifies the primary controlling factors of deep coalbed methane (CBM) enrichment and establishes a play assessment method for deep CBM.Results\/Observations\/Conclusions: Deep coalbed methane (CBM) enrichment is predominantly governed by five core factors: coal-forming environment, thermal maturity, tectonic uplift magnitude, roof sealing capability, and temperature-pressure conditions. These elements synergistically shape the gas generation, storage, and preservation processes which are critical to deep CBM accumulation. The continuous and effective preservation conditions are the key to deep CBM enrichment. A geology-engineering integrated play assessment method for deep CBM has been established, based on coal-accumulating environments, preservation conditions, and fracability.Applications\/Significance\/Novelty: The deep coalbed methane (CBM) in the marine-continental transitional facies of the Taiyuan Formation was prioritized as the strategic exlporation direction. A risk exploration well, Yangmei 1HF, was demonstrated and deployed in the Daniudi Gas Field. After fracturing stimulation in the No. 8 Coal of the Taiyuan Formation, the well achieved a daily gas production of 104,000 cubic meters, marking a major breakthrough in deep CBM exploration at a burial depth of 2800 meters. This breakthrough confirms that deep CBM reservoirs are characterized by high gas content with free gas accounting for 20%-50%. Additionally, it has added 122.7 billion cubic meters of predicted geological reserves, which holds profound significance for the global exploration and development of deep CBM resources.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 4: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRuiting Wu, Fatick Nath, Qin Ji, Abdul Muqtadir Khan, Jaewoo An, Meng Cao\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Simulation of Hydraulic and Natural Fracture Interactions for Improved Bakken Well Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. Wan*, A. Abes, M. Saeed, C. Wu, L. Jin, C. Dalkhaa and J. Sorensen\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Energy &amp; Environmental Research Center)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Fracture interactions play an important role in stimulation and oil production in unconventional reservoirs with natural fractures (NFs). Understanding and modeling of the interactions between hydraulic fractures (HFs) and NFs can assist operators in designing more-effective well completions to improve oil production at a lower cost. The goal of this study was to achieve better oil production performance in the Bakken through investigating HF and NF interactions. The objectives included 1) simulating both hydraulic fracturing treatment and production in Bakken wells, 2) analyzing HF and NF interactions under different conditions, 3) assessing the impacts of key NF parameters on fracture interaction and the oil production performance of the Bakken well.Methods\/Procedures\/Process: The geomechanical properties and in situ stresses were derived from triaxial tests on core samples from a selected well and supplemented with data from previous Bakken studies. The hydraulic fracturing process was modeled using the displacement discontinuity method based on geomechanical properties, in situ stresses, and the pumping schedule applied in field. The obtained fracture geometry was included in a compositional reservoir simulator via the embedded discrete fracture model (EDFM) method to reproduce production history. A sensitivity analysis was conducted to evaluate how key NF parameters affect HF and NF interactions and the resulting production performance of hydraulically fractured wells.Results\/Observations\/Conclusions: The workflow captured HF propagation, NF activation, and associated flow dynamics. The integrated hydraulic fracturing and reservoir simulation model reproduced the historical liquid, oil, water, and gas production trends for the hydraulically fractured Bakken wells. Sensitivity results indicated that approaching angles (between HFs and NFs) under 30\u00b0 led to opening or offsetting interactions, while angles above 45\u00b0 consistently produced crossing behavior. With all other conditions held constant, increasing the approaching angle (0\u00b0 to 90\u00b0), fracture count (100 to 300), fracture length (50 to 150 ft), and fracture conductivity (0.0004 to 4 mD-ft) increased oil production by approximately 35%, 25%, 24%, and 8%, respectively.Applications\/Significance\/Novelty: This study developed an integrated modeling workflow that couples geomechanics-based HF propagation with EDFM-enhanced reservoir simulation to improve Bakken development. The workflow accurately reproduces the historical oil, water, and gas rates while honoring complex HF and NF geometries and connectivity. Sensitivity analyses quantified how NF orientation, density, length, and conductivity influence fracture activation, stimulated reservoir volume, and flow behavior. These insights support more-effective completion design in the Bakken, and the workflow can be applied to other naturally fractured unconventional reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">An Integrated Physics-Guided Neural Network Framework for Joint Prediction of In-Situ Stress and Pore Pressure<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tP. Lian<sup>1<\/sup>,<sup>2<\/sup>, T. Nguyen*<sup>1<\/sup>, B. Das<sup>1<\/sup>, Z. Fan<sup>1<\/sup> and J. Zhang<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Sinopec Tech Houston LLC; 2. Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate in-situ stress and pore-pressure prediction is crucial for safe drilling, fracture design, and reservoir management in structurally complex basins. Widely used empirical methods, such as the Eaton approach, suffer from sensitivity to lithology and shale content, limited regional transferability, and time-consuming analytical workflows. To address these limitations, we develop a physics-guided, data-driven framework that integrates empirical calculations, field-calibrated corrections, and deep learning to enable multi-well, regionally consistent prediction of stress and pore pressure.Methods\/Procedures\/Process: Eaton-based stress and pore-pressure profiles are first computed from density, slowness, and GR logs. Field measurements\u2014including leak-off tests, breakdown pressure, microseismic inversion, and density calibration points\u2014are then used to construct nonlinear corrections that preserve the theoretical Eaton trends while improving fidelity to the formation\u2019s mechanical behavior. These corrected curves serve as training labels for a DNN built on multidimensional log-derived features. A physics-guided composite loss function, including MSE, gradient-smoothness constraints, and Eaton-trend penalties, ensures numerical accuracy and physical consistency.Results\/Observations\/Conclusions: Corrected stress and pore-pressure profiles match field test points with high accuracy and produce engineering-reliable calibration curves. The trained model demonstrates strong lateral generalization across wells, effectively capturing lithologic variation, shale-content trends, and structural perturbations. The predicted SHmax, Shmin, and pore-pressure profiles reproduce heterogeneity linked to stratigraphy and faulting. Compared with empirical methods, the integrated model delivers markedly improved performance in rapidly varying lithologic intervals and diagenetically altered zones.Applications\/Significance\/Novelty: The workflow enables reliable pre-drill pressure and stress prediction, supports safe well design in structurally complex basins, and improves hydraulic-fracture planning through accurate SHmax, Shmin, and pore-pressure profiles. Its regional consistency benefits multi-well field development, and the physics-guided framework reduces the error amplification commonly encountered in empirical approaches. The methodology is adaptable to unconventional reservoirs, real-time drilling support, and digital-twin geomechanics systems.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Novel Method for Identifying Critical Wellbore Failure Regions Induced by Poroelastic Response<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Han*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Aramco Americas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Wellbore stress and pore pressure responses are inherently time-dependent, making stability a dynamic challenge. This time dependence arises from physical, mechanical, hydraulic, thermal, chemical, or rheological mechanisms, with fluid diffusion\u2013rock deformation interaction often dominating in unconventional formations. Conventional approaches rely on analytical elastic and poroelastic solutions to estimate stresses and pore pressures at selected times, applying failure criteria to assess stability. However, these methods cannot capture early-time failures that occur outside predefined intervals, leaving critical instability moments overlooked.Methods\/Procedures\/Process: We propose a novel procedure to identify critical moments during drilling when the most severe failure is likely to occur. The method computes and visualizes failure regions at these moments, enabling operators to anticipate breakout-related issues such as tight holes or stuck pipe. This approach provides a more comprehensive understanding of wellbore stability beyond conventional time-of-interest analysis.Results\/Observations\/Conclusions: Theoretical and numerical analyses reveal that most engineers evaluate stability at operationally relevant times\u2014minutes, hours, or days\u2014while failures driven by hydro-mechanical interaction often occur within seconds, milliseconds, or even microseconds, depending on formation diffusivity. Although these failures manifest later as spalling, washouts, or stuck pipe, their origin lies in overlooked early-time instability. Recognizing these critical moments is essential for proactive mitigation.Applications\/Significance\/Novelty: This procedure accurately predicts the physical time when the formation reaches its most critical state\u2014a factor ignored by conventional methods. By capturing these early instability events, the approach enhances drilling design, reduces operational risks, and improves wellbore integrity in unconventional formations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Impact of Well Trajectory on Fiber Slippage: A Framework for Selecting the Optimal Observation Well for Single-Use Fiber Deployment<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tX. Song*<sup>1<\/sup>, G. Jin<sup>2<\/sup> and K. Wu<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Texas A&amp;M University; 2. Colorado School of Mines)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Single-use fiber is a cost-effective tool for monitoring hydraulic fracture growth in offset wells, yet the resulting data quality depends strongly on fiber\u2013casing coupling along the monitoring well. One of the key factors governing coupling performance is the initial strain introduced during deployment, which is affected by the well trajectory of the monitoring well. Understanding how wellbore trajectory influences the initial strain profile and selecting a monitoring well that minimizes deployment-induced strain to maximize data quality, is critical for achieving reliable cross-well diagnostic measurements.Methods\/Procedures\/Process: Our methodology integrates theoretical analysis, numerical simulation, and field data to quantify how well trajectory influences fiber slippage. Initial strain profiles induced by fluid drag forces, wellbore inclination, and fiber cable self-weight during deployment were generated using synthetic well trajectories. Sensitivity analyses were performed to assess how horizontal deviation and inclination variability affect initial strain distribution. Fiber slippage was quantified using a forward numerical model incorporating these initial strain profiles during fracture monitoring. The influence of well trajectory on fiber slippage was further validated using four field cases by correlating observed single-use fiber slippage with the initial strain associated with each well trajectory.Results\/Observations\/Conclusions: The sensitivity analyses show that wellbore inclination strongly affects the magnitude and gradient of the initial strain along the fiber. For downward-tilted sections (inclination&lt;90\u00b0), fiber self-weight increases fiber tension, producing larger initial strains, greater slippage and asymmetric signal. Toe-up trajectories (&gt; 90\u00b0) reduce effective drag-induced strain and improve coupling. Across the four field cases, the horizontal well sections show distinct spreads and variability in inclination (STD 0.79\u00b0, 0.95\u00b0, 1.23\u00b0, and 2.10\u00b0). Wells with broader deviation ranges and higher variability show more pronounced fiber slippage. Stage-by-stage strain responses confirm that local inclination changes affect signal patterns, demonstrating a clear correlation between trajectory and slippage.Applications\/Significance\/Novelty: This study provides a comprehensive evaluation of how wellbore trajectory influences the initial strain induced during single-use fiber deployment and the resulting fiber slippage observed during hydraulic fracturing. Through integrated theoretical analysis, numerical modeling, and field data evaluation, a workflow is generated to predict the initial strain profile and single-use fiber slippage based on the well trajectory. The findings offer practical guidance for selecting optimal observation wells for single-use fiber installations, enabling operators to minimize slippage, enhance coupling performance, and improve the reliability of cross-well strain diagnostics.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Closed-Loop Hydraulic Fracturing Optimization Using Real-Time Surface Measurements and Automated Control Systems<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Conaway*<sup>1<\/sup>, C. Parra<sup>2<\/sup>, C. Skinner<sup>2<\/sup>, M. Khan<sup>1<\/sup>, K. Fatheree<sup>1<\/sup> and R. Holland<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Seismos, Inc,; 2. ProFrac)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Surface efficiency in hydraulic fracturing has guided operators as they scale to factory operations, yet true efficiency depends on both surface execution and subsurface cluster performance. Major stimulation costs\u2014diesel and chemicals\u2014are best controlled when hydraulic horsepower delivery aligns with downhole behavior. High-frequency surface-pressure technology and temporal calculations now quantify pipe friction, perforation friction, perforation efficiency, Uniformity Index (UI), and effective hydraulic horsepower (HHP) in real time, enabling pumping controls to integrate with subsurface insights so variability can be managed and designs optimized.Methods\/Procedures\/Process: A preprogrammed series of rapid small rate changes is embedded directly into the pumping schedule. Automated execution through the frac pumps generates controlled water hammer signals captured by high-frequency transducers. Acoustic Friction Analysis (AFA) distinguishes pipe friction from perforation friction throughout treatment, with mandatory measurements during pre-sand and post-sand conditions. Pre-sand rate drop responses are used to estimate initial perforation diameter, which, when combined with an erosion model, yields stage-level perforation efficiency. Pipe friction trends captured during treatment allow real-time assessment of friction reducer performance and chemical package effectiveness. Measured results are incorporated directly into the software controlling the hydraulic fracturing equipment allowing for automated adjustments to the pumping schedule.Results\/Observations\/Conclusions: Field integration revealed measurable divergence between surface-benchmarked equipment performance (e.g., flow loop friction reducer behavior, surface charge testing) and their actual downhole behavior. Real-time measurements highlighted operational variability sources\u2014such as inconsistent friction reducer effectiveness, perforation design sensitivity, charge-to-charge variability, flow velocity dependence, and orientation effects. When operators adjusted HHP targets, chemical schedules, or perforation design in response to subsurface feedback, treatment consistency and stimulation quality improved measurably. By capturing these variations quickly, real-time feedback controls enable automated adjustments to correct the pumping schedule for more efficient hydraulic stimulation. The results demonstrate the value of a closed-loop, measurement-informed pumping workflow.Applications\/Significance\/Novelty: Integrating surface-acoustic measurements with pumping operations provides a scalable framework for quantifying stimulation quality at both surface and subsurface levels. This closes the operational loop by ensuring the appropriate effective hydraulic horsepower is delivered to maximize perforation efficiency and cluster uniformity. Field applications show this workflow enhances cluster-level distribution, improves chemical efficiency, and elevates completion consistency across multi-well pads. These findings establish acoustically derived perforation efficiency and UI as practical, real-time KPIs for treatment optimization and development-plan quality control.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Indirect Hydraulic Fracturing in Coalbed Methane and Coal Mine Methane Applications: Not All Parent-Child Well Interactions Have Negative Consequences!<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Johnson Jr.*<sup>1<\/sup>,<sup>2<\/sup> and H. Ramanandraibe<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Novus Fuels; 2. The University of Queensland)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Surface-to-Inseam (SIS) wells have been used in Eastern Hemisphere, Coalbed Methane (CBM) developments and Coal Mine Methane (CMM) pre-drainage or emission reduction applications. However, as wells encounter low-permeability, highly compartmentalised coals, increasing the Stimulated Reservoir Volumes (SRVs) are essential to achieve effective commercial recoveries. Indirect Hydraulic Fracturing (IHF) is proposed as a key enabler for existing CBM and CMM applications in low-permeability coals. This study is based on studies and modelling of initiation mechanics and field-proven workflows for implementation. This study integrates DFIT-calibrated modelling and mine-back verification to demonstrate IHF as a practical solution for compressive stress states or areas of overlapping mining tenures.Methods\/Procedures\/Process: Vertical coal stimulations in low-permeability seams often face near-wellbore pressure loss, fracture complexity, and limited effectiveness. Micro-proppants improve treatments in pressure-dependent environments, but far-field compartmentalization still limits SIS recovery. To address this, five Indirect Hydraulic Fracturing (IHF) wells\u20142 vertical and 3 horizontal\u2014were drilled and fracs initiated below target seams. Workflows included 1D stress profiling, reservoir modelling, and 3D fracture designs to optimize stage spacing and treatment schedules. Horizontal wells used 40\u201350 m sleeve spacing, mini-fracs for leak-off calibration, and strict QC to prevent over-flushing. Diagnostics such as microseismic, SIS pressure monitoring, and tracers confirmed interconnectivity and SRV expansion.Results\/Observations\/Conclusions: Across 55 frac stages (53 in three horizontal wells and 2 in vertical wells), no screen-outs occurred, with only one failed stage which was over-flushed. Treating pressures showed negligible near-wellbore effects except at proppant loads &gt;6 lb\/gal. Mine-backs of two vertical wells revealed simplified fracture geometries and no roof complexities, contrasting with extensive documented cases of extensive fracture complexities in the literature. Horizontal wells achieved strong interconnectivity and boosted recovery in offset SIS and vertical wells. Modelling confirms strategic IHF placement within SIS frameworks improves long-term recovery and economics, with sensitivity guiding optimal stage count and SRV expansion. Modelling shows the process is suitable for intial or infill applications.Applications\/Significance\/Novelty: This paper unifies Indirect Hydraulic Fracturing (IHF) research, field observations, and mine-back case studies into practical, field-proven workflows for low-permeability CBM or CMM stimulations. It demonstrates how DFIT-calibrated modelling, multi-stage horizontal designs, and diagnostics such as microseismic and tracers improve understandings of the Stimulated Reservoir Volume (SRV) and recovery in Coalbed Methane (CBM) and Coal Mine Methane (CMM) projects. Applications address Eastern Hemisphere challenges\u2014high stress, compartmentalization, and mining overlap\u2014while enabling co-application with Surface-to-Inseam (SIS) wells, integration of micro-proppants, and optimized staging to enhance economics, safety, and emission reductions. IHF is suitable for intial or infill applications.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 5: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tCraig Barrie, Wei Wang, Jennifer Adams, Jason Jweda\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Explaining Poor Production in an Eagle Ford Lateral Using a Hydrocarbon Drainage Index<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Schrynemeeckers*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Amplified Geochemical Imaging LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The productivity of organic shales is controlled by two core elements: reservoir quality (RQ) and completion quality (CQ). RQ includes effective porosity, organic content, permeability, fluid saturation, net pay thickness, hydrocarbons in place, and thermal maturity. Although individual RQ parameters can be evaluated through various techniques, few methods provide an integrated assessment of most of them. A more comprehensive solution is required, one that directly measures hydrocarbons rather than relying on proxies, is not constrained to a narrow hydrocarbon range, and functions reliably in both water-based muds (WBMs) and oil-based muds (OBMs).Methods\/Procedures\/Process: Downhole Geochemical Logging (DGL) thermally desorbs hydrocarbons from drill cuttings for GC\/MS analysis, enabling direct measurement of C2\u2013C20 hydrocarbons in both WBM and OBM systems. Hydrocarbon depth profiles can identify an initial target for a lateral. However, this depth may not necessarily be an optimal landing point, because ultimately production depends on the volume of hydrocarbons drained above and below that depth. The Hydrocarbon Drainage Index (HDI), calculated from DGL data, applies a running average spanning a 100-ft interval above and below each sampling point. This allows HDI to model moveable hydrocarbons in vertical sections and compare fracture drainage scenarios. The averaging window can be adjusted to match specific frac designs (e.g., 150 ft above and 75 ft below).Results\/Observations\/Conclusions: In this Eagle Ford case study, the client planned to land a lateral in the Buda Formation based on a strong log response at 5,730 ft, interpreting the peak as a natural fracture. Other operators in Dimmit County had successfully targeted fractures in the Buda, reinforcing this belief. However, the calculated HDI at 5,730 ft in the Buda was only 36,483 ng. By comparison, the Eagle Ford interval exhibited an HDI of 64,832 ng, 78% higher. The low HDI in the Buda resulted from low hydrocarbon richness above and below the single spike at 5,730 ft. In contrast, the Eagle Ford interval showed strong hydrocarbon response across the full 200-ft drainage window. As predicted and explained by the HDI analysis, the well delivered disappointing production due to limited hydrocarbon richness in the Buda.Applications\/Significance\/Novelty: This study shows that DGL delivers a direct and highly sensitive measurement of hydrocarbons, providing clear advantages over conventional well logs and traditional geochemical techniques. In this project, DGL data distinguished the hydrocarbon signatures of the San Miguel, Austin Chalk, and Eagle Ford formations, and also identified sealing intervals above the Anacacho and above the Eagle Ford formations. Most importantly, DGL uniquely enables calculation of the HDI, allowing operators to pinpoint the most productive landing zones for laterals by integrating hydrocarbon richness with enhanced porosity.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Carbonate Replacement of Siliceous Shale in the Dalong Formation of the Sichuan Basin and Its Impact on the Development of Shale Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tL. Lu*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study delves into the complex nanometer-scale reservoir spaces of organic matter-rich siliceous shale in the Dalong Formation, located in the Sichuan Basin, China. Our primary goal is to meticulously characterize the mineralogical composition, micro-morphological features, and pore distribution within these shales. Emphasis is placed on understanding why there is a significant abundance of caicite in organic matter-rich siliceous shale. This research aims to elucidate the carbonate genetic mechanisms and their impact on shale reservoir development in the Dalong Formation.Methods\/Procedures\/Process: We employ a combination of advanced analytical techniques: X-ray Diffraction (XRD) for mineral composition analysis, Environmental Scanning Electron Microscopy (ESEM), and Field Emission Scanning Electron Microscopy (FESEM) with Energy Disperse Spectroscopy (EDS) for microstructural examination, electron probe (EPMA) for element microanalysis and Isotope Ratio Mass Spectrometer (IRMS) for carbon and oxygen isotope analysis of Carbonates.Results\/Observations\/Conclusions: SEM imaging shows typical (partial or complete) replacement pseudomorph structures, confirmed a diagenetic replacement origin by EDS analysis. Calcite replacements exist in two forms: well-crystallized, euhedral single crystals, and (2) poorly crystalline to anhedral aggregates. These calcite grains are distributed irregularly within the siliceous matrix as scattered spots or interconnected patches. EPMA analysis revealed high silicon and anomalous strontium concentrations of calcite grains, attributed to pore fluid-rock interactions during early diagenesis stage. This process led to the filling and destruction of primary inorganic pores, reducing the in-situ storage space available for later liquid hydrocarbon retention and thereby affecting the quantity of secondary organic pores.Applications\/Significance\/Novelty: Our findings significantly contribute to the understanding of shale gas reservoirs of the Dalong Formation. The intricate relationship between calcite content and porosity is a novel discovery, shedding light on how these elements influence the reservoir quality. This research offers a new perspective on evaluating shale gas reservoirs, emphasizing the importance of carbonate cement components in determining reservoir properties. These insights are invaluable for future exploration endeavors in unconventional shale gas reservoirs, potentially leading to more efficient and effective resource utilization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 6: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tSebastien Matringe, Utkarsh Sinha, Wendong Wang, Judy Zhu, Deep Joshi, Nanzhe Wang, Ping Puyang\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Intelligent Machine Learning System for Automated Oil Well Performance Optimization and Economic Evaluation Using Synthetic Dataset<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Agyei*<sup>1<\/sup>, S. Agosu<sup>2<\/sup>, I. Owusu<sup>2<\/sup>, E. Gyimah<sup>3<\/sup>, K. Boateng<sup>1<\/sup>, N. Yeboah<sup>1<\/sup> and G. Akpabli<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Department of Petroleum and Natural Gas Engineering, New Mexico Institute of Mining and Technology; 2. Department of Petroleum Engineering, University of Wyoming; 3. New Mexico Bureau of Geology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study develops a machine learning\u2013based framework for oil well performance prediction, optimization, and economic evaluation. The objective is to capture complex interactions among reservoir properties, fluid behavior, and operational parameters to enable scalable performance optimization and quantify field-level economic impact.Methods\/Procedures\/Process: A physics-informed synthetic dataset representing 5,000 wells was generated using petroleum engineering principles to simulate realistic reservoir and operational conditions. Feature engineering incorporated key physical relationships, including pressure drawdown and reservoir quality indicators. Five machine learning models, Linear Regression, Random Forest, Gradient Boosting, XGBoost, and Support Vector Regression, were trained using cross-validation and hyperparameter tuning. SHAP analysis was applied for model interpretability. A model-driven optimization framework was implemented by varying choke size and bottomhole pressure within physical constraints, followed by economic evaluation using a conservative revenue-scaling approach.Results\/Observations\/Conclusions: Ensemble models, particularly Gradient Boosting and XGBoost, achieved the highest predictive performance (R^2\u22480.72), demonstrating strong generalization and the ability to capture nonlinear relationships. Feature interpretation identified productivity factor, water cut, and pressure drawdown as the dominant controls on well performance. Optimization results showed that approximately 75% of wells improved, with an average performance increase of ~29%. Economic analysis indicates a total annual incremental revenue of approximately $1.95 billion, corresponding to an average gain of ~$390,000 per well.Applications\/Significance\/Novelty: The proposed framework provides a unified and scalable workflow for well performance prediction, optimization, and economic evaluation. Its novelty lies in integrating physics-informed data generation, interpretable machine learning, and operational optimization within a single system. The approach enables rapid identification of optimization opportunities and offers significant potential for maximizing production and economic returns across large well portfolios.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Physics-Informed Data Driven Solution to Fractional Diffusion Equations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Liang*<sup>1<\/sup>, Q. Sun<sup>1<\/sup>, M. Zhang<sup>2<\/sup> and X. Li<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Geosciences; 2. China University of Petroleum; 3. The Research Institute of Petroleum Exploration and Development, CNPC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study develops a physics-informed solution for nonlinear fractional diffusion equations that describe fluid flow toward hydraulic fractures in heterogeneous and geologically complex reservoirs. Four physics-informed neural network (PINN) architectures are evaluated to compare their ability to generalize and represent fractional diffusion behavior, including fully connected (PIFC), Kolmogorov\u2013Arnold (PIKAN), Fourier neural operator (PIFNO), and Laplace neural operator (PILNO). The objective is to identify PINN architectures that can efficiently and reliably solve fractional diffusion equations. The prediction accuracy, data requirements, and computational cost are examined through extensive training and blind-test experiments.Methods\/Procedures\/Process: The training dataset is generated using a semi-analytical method that provides dimensionless solutions to the fractional diffusion equation. Different temporal\u2013spatial discretization schemes are used to create datasets of varying sizes and assess data-volume effects on PINN performance. The loss functions combine residual terms from the neural operators with physical constraints imposed by the initial and boundary conditions. The networks learn the time\u2013space pressure evolution of the fractional diffusion process. Performance is evaluated by computational cost, accuracy against the semi-analytical solution, and the data required to reach an acceptable error level.Results\/Observations\/Conclusions: Results show that the choice of PINN architecture strongly influences the robustness of the physics-informed solver, particularly in training cost and prediction accuracy. Several evaluation metrics are used to quantify physical consistency and solution fidelity. Training and testing indicate that with limited data (fewer than 10,000 samples), PILNO provides the best global prediction performance, exhibiting the smallest global and early-time pressure mismatches relative to the semi-analytical solution. When more data are available (over 10,000 samples), PIKAN achieves a better balance between accuracy and physical consistency. Also, PIKAN produces solutions with improved monotonicity and non-negativity compared with the other neural operators.Applications\/Significance\/Novelty: Fractional diffusion equations are usually solved with semi-analytical or numerical methods, but the fractional derivatives make these solutions computationally expensive. PINN-based approaches are also limited because they rely on fixed initial and boundary conditions, so changes in well operations, reservoir geometry, or rock properties can make a trained model unusable. These challenges show the need for physics-informed network models suited to fractional diffusion equations. To the best of my knowledge, this study proposes a physics-informed solution in the first time to the fractional diffusion equation and identifies the most effective neural operator architecture based on data availability.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Harnessing Automation and AI in Directional Drilling: Key Insights From Arrow Exploration&#039;s Horizontal Campaign in Colombia&#039;s Llanos Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Perdomo*<sup>1<\/sup>, A. Neumann<sup>2<\/sup>, A. Nino<sup>2<\/sup>, O. Diaz<sup>3<\/sup>, H. Rueda<sup>3<\/sup>, C. Guerrero<sup>1<\/sup>, O. Vargas<sup>1<\/sup> and H. Borja<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Halliburton; 2. Arrow Exploration; 3. Drill + Solutions)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper reviews the performance of Arrow Exploration&#039;s first horizontal drilling campaign, which involved six horizontal wells with a total drilled footage of 78,643 feet. The scope includes evaluating the effectiveness of advanced directional drilling technologies, the integration of automation and AI tools, and the impact of high-performance mud motors, downhole technologies and drilling platforms on operational efficiency, drilling accuracy, and wellbore quality in the Llanos Basin, Colombia.Methods\/Procedures\/Process: This paper employs a comprehensive performance analysis that integrates field data from six horizontal wells with operational procedures and time-saving practices. Key elements include: 1. Implementation of automation tools, such as intelligent rotary steerable systems (RSS) and autonomous drilling platforms (ADPs). 2. Use of mud motor power sections above the RSS to enhance efficiency. 3. Application of predictive analytics combined with human expertise for recommendations. 4. Utilization of high-performance mud motors and ToolFlex technology for optimizing drilling consistency. 5. Real-time geosteering using deep azimuthal resistivity multilayer mapping to adjust trajectories and maximize target zone exposure.Results\/Observations\/Conclusions: Record horizontal drilling lengths were achieved, including a well with a measured depth of 14,065 feet and a 4,605-foot horizontal section. Automation enabled over 80% directional drilling, improving consistency across six horizontal trajectories. High-performance mud motors maintained strong torque and durability, reaching a total of 29,645 feet drilled at 427.99 feet per hour. Intelligent RSS with ToolFlex enhanced dogleg severity control to above 4.5\u00b0 per 100 feet, maintaining directional control between 50-60%. Continuous survey measurements cut pump-off delays by up to 5.29 hours. Deep azimuthal resistivity mapping and inversion techniques allowed precise trajectory adjustments, achieving 94% navigation effectiveness. These innovations reduced drilling time from 27 to 17 days.Applications\/Significance\/Novelty: This paper provides novel insights by showcasing the successful integration of human-AI collaboration in directional drilling automation and real-time geosteering. It details the benefits of combining intelligent RSS with autonomous drilling platforms and high-performance mud motors, linking technological advances to measurable performance improvements. The use of deep azimuthal resistivity multilayer mapping to adjust trajectories through fault zones enhances geosteering accuracy, offering actionable knowledge to improve drilling technology efficiency in the oil and gas industry.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Completion Optimization on Data-Driven Production Forecasts Improves Tight Oil Well Performance<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tZ. Ren*, F. Male and L. F. Ayala\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Penn State University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Data-driven production forecasting in unconventional reservoirs is frequently treated as an isolated technical endeavor, often failing to translate predictive accuracy into actionable field development strategies. We propose a workflow that combines forecasting with economic optimization to provide a tool for making better completion decisions. Then, we test the workflow in the Bakken.Methods\/Procedures\/Process: This study presents an integrated three-stage framework\u2014comprising feature engineering, surrogate modeling, and optimization, applied to historical data from the Bakken. To address the limitations of spatial representations of wells&#039; locations, we develop a spatial-temporal feature engineering method that captures inter-well interference and parent-child relationships without relying on raw coordinates. We utilize Extreme Gradient Boosting (XGBoost) as a multi-horizon surrogate to predict cumulative production across 3- to 24-month intervals. This surrogate is coupled with a constraint-based genetic algorithm to optimize completion designs for either maximum production or Net Present Value (NPV), respecting operational constraints.Results\/Observations\/Conclusions: The XGBoost model consistently performs well, achieving an R-squared above 0.9 for each time horizon on the training set and above 0.6 for the 12-month to 24-month time horizons on the testing set. The largest consistent impacts on production performance are pressure gradient and cumulative oil production of the parent well. Applying this framework to testing wells demonstrates significant potential, achieving an average production increase of roughly 66,000 barrels and an NPV uplift of $2.47 million per test sampled well.Applications\/Significance\/Novelty: These results validate that integrating predictive modeling as a system component within a constrained optimization workflow yields superior, economically viable completion strategies. Following the optimizer provides a 3.5-to-1 marginal return on additional completion costs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Data-Driven Framework for Evaluating and Predicting Frac-Hit Critical Injection Volumes in Deep Shale Gas Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Deng*<sup>1<\/sup>, W. Wang<sup>1<\/sup>, Z. Cheng<sup>1<\/sup>, W. Yu<sup>2<\/sup>,<sup>3<\/sup>, W. Yan<sup>1<\/sup> and Y. Su<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China); 2. Shale Gas Research Institute, PetroChina Southwest Oil &amp; Gasfield Company; 3. Sichuan Key Laboratory of Shale Gas Evaluation and Exploitation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As unconventional reservoirs globally advance toward dense well spacing and massive hydraulic fracturing, frac-hits (interwell fracture interference) pose a critical challenge. Severe fluid invasion induces water blockage and substantial estimated ultimate recovery (EUR) losses. While post-event monitoring detects interference, quantitative pre-fracturing methods to predict the precise fluid volumes triggering different frac-hit severities remain lacking. This study establishes a data-driven, interpretable machine-learning framework to predict and mechanically decode these critical injection volumes, guiding proactive parameter optimization.Methods\/Procedures\/Process: A comprehensive field dataset comprising hundreds of well stages from a deep shale gas reservoir was compiled, integrating geological and engineering parameters with offset-well pressure monitoring. Dynamic time-volume matching was applied to extract the mild critical volume (MCV) and severe critical volume (SCV) thresholds directly from pressure-response inflection points. Independent Extreme Gradient Boosting (XGBoost) regression models were developed to predict MCV and SCV. To overcome the algorithmic \u201cblack box,\u201d the SHAP (SHapley Additive exPlanations) framework was integrated to quantify the marginal contributions and nonlinear effects of all input features.Results\/Observations\/Conclusions: The XGBoost models demonstrated robust predictive accuracy for both MCV and SCV (R2 &gt; 0.8). SHAP analysis revealed that the natural fracture approach angle is the paramount controlling factor. Mechanistically, frac-hit initiation (MCV) is dominantly controlled by pre-existing geological connectivity, whereas escalation into severe interference (SCV) relies increasingly on continuous operational intensity, such as injection rate. Furthermore, critical volumes exhibit strong dependency on structural architecture: complex fracture networks disperse fluid to raise safe injection thresholds, while dominant planar systems significantly reduce them. Spatially, critical volumes reach their minimums near structural faults, mathematically mirroring the power-law decay of fracture density within fault damage zones.Applications\/Significance\/Novelty: This methodology provides a quantitative tool to shift frac-hit evaluation from reactive monitoring to pre-fracturing threshold prediction. By coupling machine learning with physical interpretability, the framework offers transparent guidelines for field engineers.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Big-Data Hydraulic Fracture Modeling: A Scalable Workflow for Hydraulic Fracture Characterization Across Hundreds of Shale Gas Wells<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Leines* and J. Rodgerson\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(SimTech LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper aims to develop a scalable EDFM-AI framework to calibrate effective hydraulic fracture geometries across couple hundreds of horizontal shale gas wells. The objectives include establishing the industry\u2019s first fracture-related big data foundation, integrating geological and engineering parameters, and enabling a machine learning model to rapidly quantify field-wise hydraulic fracture networks, enhancing reservoir characterization and optimization in unconventional oil and gas reservoirs.Methods\/Procedures\/Process: The workflow employs a three-level methodology. First, EDFM-coupled ESMDA (EDFM-AI) calibrates fracture geometries for selected shale gas pads, addressing high-dimensional uncertainties and fracture effectiveness ratios. Second, an XGBoost machine learning model is established to correlate stage-wise geological and completion parameters with the calibrated effective fracture geometries, enabling field-scale fracture network prediction. Third, a multi-node GPU cluster accelerates multi-million-grid reservoir simulations, reducing computation time significantly. The pre-trained XGBoost model predicts fracture geometries for uncalibrated wells, with ESMDA further fine-tuning the uncertainties associated with the prediction results, across hundreds of wells.Results\/Observations\/Conclusions: Application to five shale gas pads (18 wells) shows effective fracture half-length and height ratios of 36\u201360% and 37\u201373%, indicating significant closure. XGBoost identifies that half-length dominates in layers 1\u20132, while height dominates in layers 3\u20134. The XGBoost proxy model achieves robust accuracy (6% training RMSE, 12% validation RMSE), enabling field-scale extrapolation to 600+ wells. The pretrained XGBoost model is deployed to generate more than 100 thousand hydraulic fractures, and the history matching with EsMDA achieves more than 80% bottomhole pressure calibration accuracy. Predicted 20-year gas EUR confidence interval (0.07\u20130.15 billion cubic meters\/well) aligns with the operator\u2019s RTA analysis, demonstrating workflow\u2019s reliability.Applications\/Significance\/Novelty: This paper introduces the industry\u2019s first fracture-related big data foundation, leveraging EDFM-AI with ESMDA and XGBoost to calibrate and predict field-wide hydraulic fracture geometries across hundreds of shale gas wells. By quantifying effective fracture effectiveness and linking geological and completion parameters to field-scale fracture networks, it offers a novel approach to quickly build big-scale hydraulic fracture model. This revolutionary workflow enhances predictive accuracy and optimization, significantly advancing unconventional reservoir managements.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Development and Field Implementation of a Real-Time Hydraulic Fracturing Simulation and Optimization Platform<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Huang<sup>1<\/sup> and W. Yu*<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Petrochina Southwest Oil &amp; Gas Field Company; 2. SimTech)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Hydraulic fracturing in deep unconventional reservoirs faces challenges such as sand blockages, fracture hits, and casing deformation, with limited real-time diagnostics for operational decision-making. This work presents the Testing and Fracturing Operation Center (TFOC) platform, a cloud-native system that integrates real-time hydraulic fracture simulation, AI-driven automatic calibration, and advanced visualization. The objective is to enhance operational awareness, improve fracture placement consistency, and support predictive control in complex reservoirs, using Sichuan Basin field applications as proof of concept.Methods\/Procedures\/Process: The TFOC platform integrates geomechanical models, natural fracture networks, pumping curves, and microseismic data into a unified real-time environment. A high-fidelity hydraulic fracture simulator provides minute-level fracture propagation, while an AI calibration engine automatically matches simulated and observed pressures by adjusting perforation efficiency, friction parameters, and effective diameter. Model loading is automated using RESCUE and FAB formats, and all outputs are visualized in 2D\/3D to support stage-by-stage interpretation and proactive operational control.Results\/Observations\/Conclusions: Field deployment in the Sichuan Basin demonstrated successful integration of high-resolution geomechanical models (&gt;8 million grid cells), real-time pumping and microseismic data, and continuous fracture updates. The platform characterized fracture geometry, conductivity, stress shadowing, and hydraulic\u2013natural fracture interactions with improved accuracy. AI-driven pressure calibration reduced parameter uncertainty and delivered rapid stage-level diagnostics. Results show improved operational awareness, enhanced fracture containment, and more consistent treatment execution across multiple stages and wells.Applications\/Significance\/Novelty: The TFOC platform provides a scalable framework for intelligent fracturing operations, moving from reactive adjustments to predictive, data-driven control. Its novelty lies in combining real-time simulation, automated calibration, and integrated visualization into a single cloud-based system. The approach supports early detection of downhole risks, improves resource utilization, and enhances fracture placement without relying solely on manual expertise. The system aligns with the industry\u2019s digital-transformation goals and provides a path toward safer, more efficient, and sustainable unconventional reservoir development.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Meta-Learning Framework for Assessing Key Drivers of Shale Oil Production with Few-Shot Samples Across Multiple Basins in China<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Ni* and T. Ye\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Shale oil exploration and development are rapidly advancing in China. However, identifying the dominant factors controlling production remains challenging. Data-driven approaches provide efficient analysis on high-dimensional data, but regular supervised learning requires large and balanced datasets. In addition, the trained models often fail to generalize across basins that have a highly heterogeneous geological background and limited well counts. Moreover, geological measurements and EUR estimates vary across basins due to inconsistent tools, workflows, and modeling practices. To address these challenges, this study introduces a meta-learning framework that enhances prediction robustness and reliably extracts controlling factors in data-scarce, multi-basin settings.Methods\/Procedures\/Process: In this study, the Model-Agnostic Meta-Learning (MAML) algorithm is applied to quantify the influences of geological and engineering properties on shale oil production from the global and basin-specific perspectives. MAML specializes in learning with small sample sizes and large data variability through a nested learning structure. A multi-layer perceptron (MLP) is used as the base model for regression. The outer loop of the algorithm learns a shared initialization that captures the general patterns, while the inner loop adapts model parameters for each basin. After meta-training, the meta-learned model weights provide global interpretability of the controlling factors, and the basin-level fine-tuning reveals local characteristics.Results\/Observations\/Conclusions: The data of 78 shale oil wells from 8 different sags across China are collected. Compared to traditional supervised learning, the meta-training framework establishes a more stable outcome and less overfitting in high-dimensional feature spaces with low well count. The global initialization weights learned from the outer loop revealed that effective porosity and pressure coefficient are the primary drivers of shale oil productivity. From the local perspective, task-level adaptations reveal sag-specific patterns. The weights of geological and engineering parameters vary across basins, affected by the difference in storage capacity, geological structure, maturity, flow capacity, and fracturability.Applications\/Significance\/Novelty: The proposed meta-learning framework provides a practical path for analyzing production drivers in data-limited shale oil plays. The controlling factors of shale oil productivity can be quickly identified, and the global pattern supports the transfer of insights from mature plays. The analysis results comply well with qualitative professional interpretations, while delivering quantitative weights and technical thresholds. Sweet spot identification, template construction, and decision-making on development planning can be facilitated. By bridging multi-basin knowledge with local interpretation, MAML offers an applicable and novel analytical tool for emerging unconventional resources where datasets are small, heterogeneous, and high-dimensional.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 7: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRussell Giesbrecht, Darren McDuff, Shunxiang Xia, Hosein Kalaei, Chao-yu Vence Sie, Deniz Paker, Sarkis Kakadjian\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">When to Inject? Molecular Insights into Optimal Pressure Timing for Surfactant EOR in Organic-Rich Shale<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tW. Zhao and H. Nasrabadi*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Texas A&amp;M University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The objective of this study is to systematically investigate how reservoir pressure at the time of injection governs surfactant performance, hydrocarbon mobilization, interfacial behavior, and compositional recovery trends in organic-rich shale kerogen nanopores. Although surfactant huff-n-puff has demonstrated potential for enhanced oil recovery in shale reservoirs, the optimal pressure timing relative to reservoir depletion remains poorly understood. Using molecular dynamics simulations, this work provides molecular-scale mechanistic insight into the pressure-dependent effectiveness of nonionic and cationic surfactants in mobilizing both light and heavy hydrocarbon components under shale-relevant nanoscale confinement.Methods\/Procedures\/Process: Molecular dynamics simulations were conducted to investigate surfactant-assisted hydrocarbon recovery from a 5-nm kerogen slit nanopore representative of organic-rich shale. The pore was equilibrated with hydrocarbons at 30 MPa, and two surfactants were evaluated: the cationic C16TAB and nonionic C12E6. Surfactant injection was simulated at 30, 20, 15, 10, and 5 MPa, representing progressive reservoir depletion stages. These simulations systematically evaluated how pressure at the time of injection controls hydrocarbon mobilization and surfactant performance under shale-relevant nanoscale confinement.Results\/Observations\/Conclusions: The simulations reveal a strong pressure dependence of surfactant-assisted hydrocarbon recovery from kerogen nanopores. Higher injection pressures significantly enhanced mobilization of both light and heavy hydrocarbons through improved surfactant penetration, stronger interfacial activity, and disruption of oil\u2013kerogen interactions. As pressure declined, surfactant performance deteriorated, with reduced heavy oil displacement and increased hydrocarbon retention. The cationic C16TAB showed stronger kerogen adsorption and greater heavy hydrocarbon recovery at high pressure, while the nonionic C12E6 exhibited more uniform pore distribution and consistent light hydrocarbon mobilization. Overall, early-time surfactant injection is favored for maximizing oil recovery in organic-rich shale.Applications\/Significance\/Novelty: This study provides molecular-scale guidance for optimizing surfactant huff-n-puff timing in organic-rich shale, showing that early-time injection at higher pressures maximizes light and heavy hydrocarbon recovery. The results establish a mechanistic basis for pressure-aware chemical EOR design and improved field decision-making. The novelty lies in the systematic, pressure-resolved molecular evaluation of surfactant performance under shale-relevant nanoscale confinement, directly linking reservoir depletion to effectiveness. Distinct behaviors of cationic and nonionic surfactants further enable more targeted surfactant selection for unconventional reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Critical Review of Confined-Fluid Phase Behavior in Unconventional Reservoirs: Insights, Challenges, and Future Directions<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tN. Qiasi* and X. Li\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Kansas)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Nanoconfined phase behavior is the governing principle in shale and tight reservoirs, representing a major source of global oil and gas production. However, the complex properties of phase behavior of fluids confined within these reservoirs remain inconclusive. Conflicting predictions and limited experimental accessibility have prevented consensus in the literature. This study offers an in-depth overview of the current state of knowledge, synthesizing both experimental and theoretical works. It critically evaluates their respective advantages and limitations and concludes by highlighting the existing research gaps in the field. The study also outlines a roadmap for future research to develop physics-based models essential for reservoir engineering applications.Methods\/Procedures\/Process: The reviewed 24 experimental and 49 theoretical works integrate adsorption-based measurements, calorimetry, optical\/microscopy measurements, modified equation of state, density function theory, molecular dynamics, and Monte Carlo simulation. The dataset consists of various pore sizes, and pore geometries, e.g. silt, cylindrical, spherical, disordered mesopores. Critical assessment helps to analyze and compare the trend in saturation pressure suppression, shifts in critical properties, and deviation from bulk behaviors. The methodological uncertainties and assumptions embedded in each experimental and modeling framework are given special attention, suggesting optimum procedure to capture confined phase behavior.Results\/Observations\/Conclusions: This review confirms the effect of confinement becomes significant below 10 nm, reaching extremely under 5nm pores. Universal observation is the suppression of key transition points including bubble\/dew point pressure, critical property, minimum miscibility pressure, and interfacial tension. Studies consistently reveal the lowered bubble point pressure (for CO2 and light alkanes) and condensation pressure with a deviation exceeding 24% in smallest pores. This suppression exhibits a clear inverse relationship with pore size. Furthermore, confinement in nanopores below 20nm alters molecular behavior, increasing fluid density and viscosity. However, bubble point temperature shifts remain contentious, reported from negligible to a 15K increase, highlighting a critical discrepancy.Applications\/Significance\/Novelty: This review\u2019s novelty lies in its critical synthesis, which clearly maps the discrepancies between different research streams and identifies the precise requirements for a next-generation model. Although many studies report the shifts in bubble point, dew point, and critical temperature under confinement, complete P\u2013V\u2013T phase diagrams remain rare due to experimental limitations and the lack of a universal framework linking critical property changes to pore size, geometry, and fluid-wall interactions. It outlines future research directions toward developing consistent, physics-based tools for predicting reserves, optimizing strategies, reservoir forecasting, and enhanced oil recovery design in unconventional reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">When Lower Fracture Conductivity Wins: A Paradox Explained<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Behmanesh*<sup>1<\/sup>, K. Patel<sup>2<\/sup>, R. Vaidya<sup>3<\/sup>, S. Esmaili<sup>3<\/sup>, M. Carlsen<sup>1<\/sup>, M. Majzoub Dahouk<sup>1<\/sup> and C. H. Whitson<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Whitson; 2. CMG Ltd.; 3. Occidental Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper highlights a production paradox in which low fracture conductivity in Multi-Fractured Horizontal Wells (MFHWs) \u2014and, similarly, fracture compaction\u2014can lead to counterintuitive cumulative oil production trends, i.e. an increase compared with cases exhibiting higher conductivity or no compaction. These effects are particularly evident after the flowing bottomhole pressure decreases below the saturation pressure.Methods\/Procedures\/Process: Studies show that aggressive early drawdown accelerates fracture-conductivity damage and reduces Expected Ultimate Recovery (EUR). Less recognized is that, even under similar drawdown strategies, differences in fracture conductivity can lead to distinct pressure-depletion behaviors, dictating how fast matrix pressure around the fracture drops below saturation pressure and how oil mobility evolves during production. Numerical simulation cases were performed to assess how fracture conductivity and fracture\/matrix permeability modulus affect oil EUR across various fluid systems. Sensitivity analyses spanning wide ranges of conductivity values, permeability-modulus magnitudes, and relative-permeability characteristics quantified their influence on production trends and recovery.Results\/Observations\/Conclusions: The results indicate that in an undersaturated oil reservoir, both oil and gas EUR decline as matrix and fracture permeability modulus increase, reflecting reduced transmissibility. Once flowing pressure falls below saturation pressure, however, a reversal emerges: lower fracture conductivity (or larger permeability modulus) yields higher oil EUR but lower gas EUR. Reduced conductivity keeps near-fracture matrix pressure above flowing pressure, suppressing gas liberation, preserving higher oil mobility, ultimately enhancing long-term oil recovery. In this saturated-flow regime, fracture properties and in-situ PVT behavior primarily influence production by controlling phase mobility within the matrix. Matrix and fracture relative permeabilities characteristics exert only secondary effects.Applications\/Significance\/Novelty: Understanding how fracture properties alter matrix pressure depletion, and oil mobility evolution provides a robust framework for optimizing production strategy. These findings demonstrate that drawdown strategy cannot be decoupled from fracture properties; instead, they must be jointly considered to reliably forecast EUR. Sensitivity analyses across these parameters are therefore essential to understand how the interplay between matrix pressure depletion (controlled by fracture properties) and in-situ phase behavior governs oil recovery. These insights provide operators with a reliable basis for setting early-time choke schedules and pressure drawdown.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Effect of Variable-Scale Pore Systems on Gas Injection Enhanced Oil Recovery (EOR) in Tight Oil Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Kwakye-Tannor* and D. K. Reichhardt\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Montana Technological University)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work provides a deeper understanding of how the dual-scale pore systems present in tight oil reservoirs affect gas injection EOR. The study uses the dual-scale porosity reservoir model with scale-dependent fluid model developed and history matched by Reichhardt and Hoffman (2024). Their work established the impact nanoscale pores have on production mechanisms in tight oil reservoirs. The objectives of this work are to assess the influence of variable-scale pore systems on gas injection EOR in tight reservoirs, identify which pore system predominantly contributes to oil production for different injection gases and determine the best injection gas and strategy to implement on a field scale while assessing the CO2 sequestration potential of the nanopore system after cyclic gas injection.Methods\/Procedures\/Process: This study uses numerical simulation through commercially available reservoir modelling and simulation software, Petrel and Eclipse. The model has a dual-scale porosity (macro and nano) with a scale dependent fluid model. It is history matched to production data from a field scale study in the Eagle Ford. Tracers are used to track and quantify fluid flow between pore networks and from pore network to surface. This allows for the contribution from each pore network to the total production to be assessed while tracking the injection gas throughout the different pore networks. Four different injection scenarios are simulated and the results analyzed. The injection gases include; CO2, methane, propane and produced gas.Results\/Observations\/Conclusions: Preliminary results from the gas injection scenarios show that each gas can be tracked and quantified in both pore systems. For all injection gas scenarios, early post-injection production is dominated by the macro pore system oil, after the macro pores have been fairly depleted, we see a rise in the nano pore oil production. The propane gas case yielded the highest oil production followed by the produced gas, CO2 gas and lastly methane gas. These observations show that, slightly heavier hydrocarbon compositions (propane) in an injection stream significantly improve recovery in tight oil reservoirs due to lower miscibility pressures and further validates the impact of small pores on oil production in tight oil reservoirs.Applications\/Significance\/Novelty: This work provides a means of understanding the role variable-scale pores play in EOR performance and gas sequestration in tight oil reservoirs through a reservoir model that includes nanoscale pore confinement effects and tracer monitoring. By quantifying how macro and nanopores differently contribute to hydrocarbon recovery and potential sequestration, the study delivers new insight for optimizing gas selection, injection strategy, and Carbon capture utilization and storage (CCUS) design in ultra-tight formations. The results highlight unconventional reservoirs as viable dual-purpose EOR and CO\u2082 sequestration targets, leveraging existing infrastructure to reduce cost and gas emissions.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Smart Gas Lift Optimization: Solving the Constrained Compression Challenge in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Stanko*<sup>1<\/sup>, A. Dhanani<sup>1<\/sup>, M. Insan Kamil<sup>1<\/sup>, G. Helfrick<sup>1<\/sup>, F. Munthe<sup>1<\/sup>, K. Lucas<sup>2<\/sup> and J. Pratt<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Whitson; 2. Devon)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This paper presents an automated workflow for routinely optimizing gas lift injection rates in fields with constrained compression. The objective is to maximize production while honoring well-level constraints such as minimum gas lift to prevent liquid loading and maximum injection to avoid erosion. By integrating daily production data, nodal analysis, and dynamically updated gas lift performance curves (GLPCs), the workflow enables rapid, reliable, and scalable optimization across large unconventional assets.Methods\/Procedures\/Process: Daily production data is imported through API calls and used to update flowing material balance models, which provide the current reservoir pressure. This pressure is integrated with PVT, to compute updated IPR\u2013VLP intersections. These intersections are then used to generate GLPCs for each well, with operating limits automatically identified for both minimum and maximum injection. The GLPCs and system constraints are subsequently fed to an optimization engine that searches for lift-gas allocations ensuring wells operate at equal GLPC slope while fully utilizing available compressed gas. The optimized set points are finally transferred to the field SCADA system for implementation.Results\/Observations\/Conclusions: Field deployment across over 100 gas lift systems in the Permian basin demonstrates that automated optimization enables oil production gains while providing a platform for systematic compressor fleet optimization. The results reinforce the importance of routinely updating gas lift rates to reflect changing flowing and reservoir pressures, underscoring the method\u2019s robustness, scalability, and potential to materially improve field-wide production efficiency.Applications\/Significance\/Novelty: The workflow solves a longstanding challenge in gas-lifted unconventional wells: optimally distributing limited compressed gas under depletion. Although the underlying nodal analysis principles date back decades, this work demonstrates a modern, fully automated, cloud-enabled implementation. Its scalability, real-time performance, and proven field impact make it a significant advancement for operators seeking reliable, routine gas lift optimization across large assets.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Review of Gas-Injection EOR in Shale Reservoirs: Mechanisms, Published Pilot Outcomes, and Technical Challenges<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tH. Moubarak*<sup>1<\/sup>, A. Bigdeli<sup>2<\/sup>, Y. Al-Enezi<sup>3<\/sup> and C. Temizel<sup>1<\/sup>,<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Terra Altai; 2. State University of Campinas; 3. Kuwait Oil Company; 4. Saudi Aramco)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The research assesses worldwide EOR programs that use gas injection techniques to enhance shale oil production. The research evaluates published mechanisms and pilot-project results and technical obstacles and reservoir characteristics which affect performance. The review examines huff-and-puff gas injection and cyclic gas recovery and lean-gas injection and CO2-based EOR mechanisms which work in unconventional reservoirs.Methods\/Procedures\/Process: The review combines data from URTeC\/SPE publications and DOE-funded pilot reports and academic research and field-based case studies. The review combines various research studies which investigate gas diffusion and miscibility effects and pressure buildup and near-wellbore transport restrictions and adsorption behavior and compositional gradient effects. The review shows simulation results and pilot test observations which prove recovery improvements but also show operational problems that occur during gas injection EOR operations. The review only presents existing research findings without performing any new EOR experiments or compositional calculations or simulations.Results\/Observations\/Conclusions: Research studies show that gas-injection EOR in shale formations leads to increased oil recovery but the results strongly depend on the characteristics of the reservoir. Research studies demonstrate that gas diffusion serves as the main recovery mechanism for shale reservoirs but pressure maintenance and miscibility effects become significant when conditions allow them to occur. The main barriers to gas-injection EOR implementation occur because fracture conductivity decreases during cycling operations and tight formations restrict injectivity and gas breakthrough happens early and operations become complicated. The reported pilot test results show inconsistent results because they depend on fracture patterns and pressure evolution and reservoir heterogeneity.Applications\/Significance\/Novelty: The section presents common findings from the literature which demonstrate that diffusion and pressure buildup and compositional effects play essential roles in gas-injection EOR. The success of gas-injection EOR depends on three main factors which include reservoir heterogeneity and fracture quality and pressure history. The literature shows that feature importance analysis reveals lateral length and proppant concentration and fluid volume as top parameters which affect results but their impact patterns differ between different basins. Research indicates that higher completion intensity results in worse recovery performance until scientists establish particular intensity limits. The ML-assisted completion optimization produces effective results when researchers verify their datasets.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mechanism of CO2 Miscible Water-Alternating-Gas Flooding and Optimization of Injection Parameters in Tight Oil Reservoirs<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Sun*<sup>1<\/sup>,<sup>2<\/sup>, L. Li<sup>1<\/sup>,<sup>2<\/sup>, X. Wang<sup>3<\/sup>,<sup>4<\/sup>, Q. Liu<sup>1<\/sup>,<sup>2<\/sup>, X. Bian<sup>1<\/sup>,<sup>2<\/sup>, Z. Chen<sup>1<\/sup>,<sup>2<\/sup> and Y. Chen<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. State Key Laboratory of Deep Oil and Gas, China University of Petroleum; 2. School of Petroleum Engineering, China University of Petroleum; 3. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum; 4. Institute of Unconventional Oil and Gas Science and Technology of China University of Petroleum)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The development of tight oil reservoirs (0.1\u20131 mD) faces significant challenges, including difficult fluid injection and severe gas channeling, which render conventional water flooding ineffective. Meanwhile, the micro-scale mechanisms and injection-production strategies for CO2 miscible water-alternating-gas (WAG) flooding in such reservoirs remain poorly understood. This study aims to reveal the pore-scale displacement mechanisms of CO2 miscible WAG flooding in tight oil reservoirs and optimize the injection parameters, with the dual objectives of enhancing oil recovery and achieving efficient CO2 sequestration.Methods\/Procedures\/Process: The study first determined the MMP through slim-tube experiments. Subsequently, a series of core flooding experiments were conducted under reservoir conditions. Innovatively, NMR technology was employed to non-destructively and quantitatively reveal the microscopic displacement mechanisms of crude oil in pores of different sizes. Based on this, a core-scale numerical simulation model was calibrated with the experimental data and used to systematically compare and optimize the slug size and injection sequence for WAG injection. Incorporating XGBoost algorithm and Bayesian optimization, an integrated methodology from the experimental revelation of microscopic mechanisms to the simulation-based prediction of injection performance and the intelligent optimization of operational parameters.Results\/Observations\/Conclusions: The MMP for the target reservoir was experimentally determined to be 20.6 MPa, confirming the feasibility of miscible flooding. Core flooding results demonstrated that WAG injection achieved an oil recovery factor of 55%\u201370%. The synergistic mechanism is that the water slugs effectively suppress gas channeling, while the CO2 slugs significantly enhance microscopic displacement efficiency. Under constant total injected volume, oil recovery exhibits a positive correlation with the number of injection slugs. Moreover, the GAW injection sequence outperformed the WAG, improving the recovery factor by nearly 5%. Parameter sensitivity analysis based on a multiphase, multicomponent numerical simulation model, identified injection sequence as the dominant factor, with an 8-slug GAW being optimal.Applications\/Significance\/Novelty: This study elucidates the pore-scale displacement mechanisms and gas-water synergistic effects of CO2 miscible flooding in tight oil reservoirs. The proposed optimized injection strategy is projected to enhance field oil recovery by 15\u201325% while mitigating gas channeling risks and improving CO2 storage security. The findings provide critical data and technical support for pilot testing and subsequent commercial-scale implementation of CO2 flooding in analogous reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Development and Evaluation of a Novel Bio-Based Displacement Agent Compatible with Produced Water for Enhanced Oil Recovery in the Ordos Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Zhang*<sup>1<\/sup>, X. Bai<sup>1<\/sup>, X. Bu<sup>1<\/sup>, N. Zhou<sup>2<\/sup>, J. Bai<sup>1<\/sup>, T. Zhou<sup>1<\/sup>, Q. Dong<sup>1<\/sup> and Z. Li<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Oil and Gas Technology Research Institute, PetroChina Changqing Oilfield Company; 2. CNPC USA Corporation)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aimed to develop and evaluate a novel bio-surfactant-based displacement agent (BioS-CQS) that can be directly formulated with produced water from the Ordos Basin oilfields, eliminating the need for extensive pre-treatment. The goal was to enhance oil recovery while addressing challenges related to high salinity, suspended solids, and oil emulsification in produced water, ultimately supporting sustainable water management in petroleum operations.Methods\/Procedures\/Process: Produced water samples were collected from nine well groups and two treatment stations in the Changqing Oilfield. Detailed analyses were performed, including ion content, suspended solids composition, microbial content, and oil concentration. A bio-based surfactant, derived from surfactin, was chemically modified to improve interfacial activity, salt tolerance, and suspension stability. The synthesized agent (BioS-CQS) was evaluated through various performance tests, including interfacial tension, contact angle, capillary absorption, and core flooding, all conducted under simulated reservoir conditions.Results\/Observations\/Conclusions: The BioS-CQS agent demonstrated exceptional compatibility with high-salinity produced water (e.g., Husi Central Station: TDS greater than 60,000 mg\/L, divalent ions greater than 6,000 mg\/L). Key findings include ultra-low interfacial tension (0.004 to 0.009 mN\/m) and reduced contact angle (34\u00b0 to 44\u00b0), capillary absorption height greater than or equal to 26 mm (0.35 mm capillary), imbibition efficiency greater than or equal to 24% (0.3% concentration, 50\u00b0C), and injection pressure reduction of up to 42.8% compared to clean water. Additionally, the agent exhibited enhanced suspension stability and inhibition of sulfate-reducing bacteria (SRB), reducing risks of corrosion and formation damage.Applications\/Significance\/Novelty: This research aligns with sustainable water management goals by enabling the direct reuse of produced water for Enhanced Oil Recovery (EOR), reducing freshwater consumption, and minimizing environmental impact. The bio-based surfactant offers a green alternative to conventional chemicals, with potential scalability and cost-effectiveness. Future work will focus on pilot-scale validation and optimization for field application.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Numerical Simulation Study on the Mechanisms of Enhanced Oil Recovery by Nanobubble Water Flooding<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Yao*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: As oilfield development enters its late stages, conventional water-flooding techniques encounter significant limitations, leaving a substantial amount of residual oil trapped in reservoirs. Consequently, Enhanced Oil Recovery (EOR) technologies have become critical. In recent years, nanobubble water flooding has emerged as a novel EOR method, attracting considerable attention. While numerous experiments have demonstrated the ability of nanobubble water to regulate oil-water interface properties, the underlying mechanisms governing this process remain unclear. This study employs microscopic numerical simulations to investigate these mechanisms.Methods\/Procedures\/Process: Two-dimensional simulation models were developed using COMSOL Multiphysics. Nanobubbles were modeled as stable, discrete gas particles using the Particle Tracing Module, while fluid flow was simulated by solving the Navier-Stokes equations via the Finite Element Method (FEM). The study first investigated fluid flow and particle transport within a single pore tube. Subsequently, a model of nanobubble water flooding in porous media was constructed. This model incorporated the effects of interfacial tension and compared the performance of nanobubble flooding against conventional water flooding.Results\/Observations\/Conclusions: Results from the single-tube simulation indicate that Brownian forces significantly influence nanobubble flow in porous media. Brownian motion facilitates nanobubble adsorption onto rock surfaces and diffusion toward the oil-water interface. Diffusion induced by Brownian motion is a non-negligible factor in nanobubble transport. Then, results from the numerical simulation of the flooding process indicate that nanobubbles can penetrate the oil-water interface to interact with residual oil, facilitated by the reduction of interfacial tension and the driving effect of Brownian forces. Also, nanobubbles tend to adsorb onto rock surfaces. This adsorption mechanism contributes to reducing interfacial tension, altering wettability, and improving mobility control, expanding the swept volume.Applications\/Significance\/Novelty: This study clarifies the micro-scale transport mechanisms of nanobubble water systems in porous media through numerical simulation. The findings provide a theoretical basis for understanding the microscopic flow behavior of such dispersed systems in confined channels and complex porous structures, offering valuable insights for optimizing EOR strategies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 8: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAnnie Shen, Junjie Yang, Susan Howes, Yuguang Chen, Gizem Yildirim\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Mitigating Parent\u2013Child Interference in the Haynesville Basin Through Fracture Geometry Control<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Do*<sup>1<\/sup>, S. Horigan<sup>2<\/sup>, C. Widell<sup>2<\/sup> and P. Valora<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. InDZone Consulting LLC; 2. Sponte Operating)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Parent\u2013child well interference has emerged as a critical limitation to infill development in mature unconventional reservoirs, particularly in the Haynesville Basin. Historical performance indicates child well production degradation of 35\u201340% relative to parent wells due to frac-driven interference (FDI). This paper presents a field case study evaluating a frac geometry control (FGC) approach to manage fracture propagation during child well stimulation. The objective is to demonstrate how improving fracture uniformity in the mid- and far-field through engineered fracture control can reduce parent\u2013child interference, improve child well productivity, and support sustainable infill development and enhanced recovery.Methods\/Procedures\/Process: A multidisciplinary workflow integrating production history matching and pressure transient analysis was used to establish baseline parent\u2013child interference behavior. Completion designs were optimized using frac geometry control principles aimed at limiting fracture extension toward parent wells while maintaining effective reservoir stimulation. Child wells completed with FGC-influenced designs were evaluated using chemical tracer diagnostics and compared against historical offsets developed under conventional completion practices. Post-completion metrics\u2014including tracer response in offset wells and parent well pressure behavior during child well stimulation\u2014were analyzed to assess interference mitigation effectiveness and production response.Results\/Observations\/Conclusions: Field results show a substantial reduction in parent\u2013child interference following implementation of frac geometry control strategies. Whereas historical child wells exhibited production degradation of 35\u201340%, FGC-treated child wells achieved production performance within approximately 5% of parent wells. Parent wells also experienced a sustained production uplift following child well completions. Chemical tracer diagnostics indicated no detectable communication between parent and child wells in traced stages. These observations confirm that proactive fracture geometry management can preserve stimulation effectiveness and significantly improve infill well outcomes in densely developed unconventional reservoirs.Applications\/Significance\/Novelty: This study demonstrates frac geometry control as a practical and scalable approach for managing FDI and improving recovery potential in mature unconventional developments. The significance lies in shifting interference mitigation from reactive diagnostics to proactive fracture design. Novelty is demonstrated through the application of engineered fracture geometry concepts to systematically reduce parent\u2013child interaction while maintaining economic well performance. The workflow is transferable to unconventional plays facing similar infill development challenges and production degradation risks.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Understanding Well Performance of Unconventional Extended Laterals in Eagle Ford, TX<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tJ. Zhu*, X. Li and W. Liu\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(CNOOC International)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to evaluate how increasing lateral length affects production performance in the Eagle Ford Shale by examining normalized 3-month oil rates (IP90) and EUR across four key type curve areas representing major geologic and fluid windows. The objective is to quantify degradation trends associated with extended laterals, develop lateral-length scaling factors for each area, and apply fitted correlations to estimate EUR for PUD locations with longer laterals, including those exceeding 15,000 to 17,500 feet.Methods\/Procedures\/Process: Four Eagle Ford areas were selected as analogs to represent distinct geologic and fluid regimes. Production data were normalized by lateral length, and IP90 values were regressed against lateral length to derive degradation trends. The fitted functions were then used to estimate normalized IP90 and EUR at longer lateral lengths, assuming degradation factors remain consistent within each TCA. Normalized IP90 is an effective indicator for early-time comparison and EUR prediction. Regression-based degradation factors provide a practical method for estimating long-lateral PUD performance and support more consistent type curve and development planning.Results\/Observations\/Conclusions: All type curve areas of interests show declining normalized IP90 and EUR with increasing lateral length. Estimated EUR degradation from 12,500 ft to 15,000 ft ranges from roughly 4% to over 16%, depending on the TCA. These differences reflect geologic variability captured by the TCA framework. The fitted trends allow reliable EUR estimation for PUDs with lateral lengths up to 17,500 ft where no direct data exist. Extended laterals in the Eagle Ford increase total well recovery but yield diminishing production per foot. Degradation magnitude varies across type curve areas due to differences in reservoir quality, fluid type, thickness, and completion practices.Applications\/Significance\/Novelty: This work introduces a TCA-based regional framework for quantifying lateral-length degradation in the Eagle Ford using normalized IP90 and fitted production-length relationships. Unlike studies relying on basin-wide averaging, this approach isolates reservoir-specific behavior across distinct geologic and fluid regimes. The method enables reliable EUR estimation for long-reach PUD wells and offers a consistent workflow for integrating lateral-length scaling into Eagle Ford development and economic evaluations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">A Few-Shot AI Framework for Reliable Early-Time Production Forecasting in the Permian Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Katterbauer* and A. W. Alsmaeil\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Saudi Arabian Oil Co)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate early-time production forecasting is critical for optimizing field development in unconventional reservoirs, yet traditional decline-curve and data-driven models typically require long production histories. This study aims to develop and evaluate an AI-driven few-shot learning framework capable of generating reliable production predictions using only the first few weeks of well data. Leveraging a large multi-operator dataset from the Permian Basin, the objective is to determine whether few-shot adaptation can overcome data scarcity and heterogeneity to improve early-life forecast accuracy.Methods\/Procedures\/Process: We propose a meta-learning architecture that learns generalized production dynamics from a broad corpus of historical wells and rapidly adapts to new wells with minimal early-time observations. The framework integrates a sequence-to-sequence neural model with model-agnostic meta-learning (MAML) to capture both basin-level trends and localized well behaviors. Input features include early-time rate and pressure trends, completion parameters, and geologic descriptors. Performance is benchmarked against standard decline-curve analysis, recurrent neural networks trained per-well, and global machine-learning models. Cross-operator synthetic data validation within the Permian Basin is used to assess robustness.Results\/Observations\/Conclusions: The few-shot learning system consistently outperforms baseline models, reducing median forecast error over a 12-month horizon when only the first 2\u20136 weeks of data are available. The model shows resilience to operator-specific practices and geologic variability, with improved stability in wells exhibiting atypical ramp-up behavior. Sensitivity analysis indicates that the meta-learned priors enable rapid convergence toward well-specific decline patterns even under severe data sparsity. Uncertainty quantification further demonstrates narrower predictive intervals relative to conventional methods without sacrificing calibration.Applications\/Significance\/Novelty: This work presents one of the first applications of few-shot meta-learning to early-time production forecasting in unconventional reservoirs. The approach uniquely leverages cross-operator knowledge transfer to compensate for limited well histories, enabling accurate prediction at a stage where traditional physics- or data-driven models are unreliable. The study demonstrates that meta-learned priors can effectively bridge the gap between global models and per-well calibration, offering a new pathway for rapid, data-efficient production evaluation in shale resource plays.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Physics-Informed Neural Networks for Enhanced EUR Prediction in Multi-Stage Fractured Horizontal Wells: Addressing Global Reserve Uncertainty<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tO. T. Omokhoa*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Nigeria Nsukka)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Accurate estimation of ultimate recovery remains a critical challenge in unconventional resource development, affecting investment decisions, reserve booking, and field development planning globally. Traditional decline curve analysis and analytical models inadequately capture the complex interactions between geomechanics, fluid dynamics, and completion design governing production in ultra-low permeability reservoirs. This uncertainty drives systematic reserve misestimation, impacting substantial capital allocation decisions. This research develops a physics-informed neural network approach integrating fundamental reservoir engineering principles with deep learning to improve EUR predictions in multi-stage fractured horizontal wells.Methods\/Procedures\/Process: The physics-informed neural network architecture embeds conservation laws, constitutive relationships, and boundary conditions directly into the loss function, ensuring predictions honor physical constraints while learning from production data patterns. Training and validation utilized comprehensive datasets from horizontal wells in the Permian, Eagle Ford, and Montney formations, incorporating completion parameters, geologic properties, production histories spanning multiple years, and microseismic data. The model architecture balances data-driven learning with physics-based constraints through multi-objective optimization. Feature engineering captures completion design metrics, geological heterogeneity indicators, and operational parameters.Results\/Observations\/Conclusions: The physics-informed approach demonstrated superior performance compared to conventional decline curve analysis and purely data-driven models when validated against wells with extended production histories. Prediction errors were substantially lower across all production phases, particularly in the critical transition from transient to boundary-dominated flow. The model identified a significant proportion of wells where traditional methods substantially over-predicted EUR, preventing potential reserve overbooking. Completion design impact quantification revealed that cluster spacing optimization could meaningfully improve EUR in specific geological settings. The physics-informed constraints improved model generalization to new field areas compared to standard neural networks.Applications\/Significance\/Novelty: This methodology addresses urgent industry needs for reliable reserve estimation amid increased scrutiny from investors, regulators, and stakeholders demanding transparency in resource reporting. The framework provides operators with robust, defensible EUR predictions that reduce reserve uncertainty and support informed capital allocation. The novelty lies in embedding physical laws within neural network architectures, combining the flexibility of machine learning with the interpretability and physical consistency of traditional reservoir engineering approaches. This hybrid methodology represents a paradigm shift from purely empirical or physics-based methods, enabling improved predictions while maintaining theoretical rigor and practical applicability across diverse unconventional plays.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">First-Ever Cloud and GPU-Based Reservoir Simulation Framework for Real-Time Post-Frac Production Prediction<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tC. Chang<sup>1<\/sup>, C. Liu*<sup>2<\/sup> and W. Yu<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. PetroChina Southwest Oil &amp; Gas Field Company; 2. SimTech LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This work presents an industry-first, cloud-enabled, GPU-accelerated reservoir simulation framework for ultra-fast post-fracture production forecasting. The workflow seamlessly ingests fracture geometries generated either locally or from cloud-based hydraulic fracture propagation simulators and performs real-time long-term production predictions, enabling rapid completion design optimization and supporting field-scale development planning.Methods\/Procedures\/Process: The proposed framework integrates several coordinated modules, including a cloud-based geology importer, local and cloud fracture-propagation model loaders, a simplified fracture generator (for design experiment), a GPU simulation controller, and a real-time visualization engine. Wetted fracture geometries from hydraulic fracture simulations serve as the initial input; however, because effective fractures are typically smaller, a geometric cutoff module applies independent empirical reductions to half-length and height to avoid over-predicted forecast. The GPU simulator then executes rapid, long-term forecasts while the real-time visualizer concurrently streams production curves, enabling engineers to instantly assess outcomes and refine post-frac decisions.Results\/Observations\/Conclusions: Application of this framework shows strong potential for fast operational decision-making. It completes a 20-year production forecast in under 10 minutes, enabling near-real-time post-fracture evaluation (traditional workflows require hours). Benchmarking shows the cloud platform cuts user interaction by nearly tenfold, reducing manual work and allowing engineers to focus on interpretation. Field testing on a shale gas block confirms high scalability, evaluating up to 50 wells per day with EURs of 0.06-0.13 billion cubic meters, and enabling rapid comparison of completion designs to identify the highest-EUR options.Applications\/Significance\/Novelty: This work introduces a first-of-its-kind cloud\u2013GPU simulation ecosystem that bridges fracture modeling and immediate production forecasting. The framework enables operators to evaluate designs within minutes, rapidly screen development scenarios, and scale analysis to dozens of wells per day. Its real-time performance and drastically reduced user input represent a major step toward automated, data-driven, and physics based unconventional evaluation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12 col-lg-4\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 9: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tRosa Aguilar, Pouria Mousavi, Denise Benoit, Scott Singleton, Maria Lozano, Katrina Ostrowicki, Abouzar Mirzaei Paiaman, I Wayan Rakananda Saputra, Nadia Mouedden, Deepak Devegowda\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Application of CO2-Responsive Gel for Carbon Sequestration in Shale Oil Reservoirs: Advancing CCUS Strategies for GHG Emission Mitigation in the Ordos Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Zhang*<sup>1<\/sup>, L. Mu<sup>2<\/sup>, M. Lu<sup>3<\/sup>, L. Han<sup>1<\/sup>, J. Yu<sup>1<\/sup>, F. Xu<sup>1<\/sup>, Z. Zheng<sup>1<\/sup>, B. Kang<sup>1<\/sup> and J. Miao<sup>4<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Oil and Gas Technology Research Institute, PetroChina Changqing Oilfield Company; 2. PetroChina Changqing Oilfield Company; 3. CNPC USA Corporation; 4. Beijing Karst Science &amp; Technology LTD)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Gas channeling through high-permeability fractures significantly hampers CO2 storage during CO2 flooding in low-permeability shale oil reservoirs. This challenge is addressed with the development of a novel CO2-responsive gel system that combines carbon sequestration with enhanced oil recovery, offering a dual solution for mitigating greenhouse gas (GHG) emissions. The gel, synthesized from acrylamide, pregelatinized starch, a CO2-activated initiator\/cross-linker, and a carbon-fixing agent, features low initial viscosity (approximately 10 mPas), enabling deep fracture penetration, adjustable gelation, and CO2-triggered solidification that effectively traps CO2, contributing to long-term carbon storage.Methods\/Procedures\/Process: Laboratory tests conducted under extreme reservoir conditions (70\u00b0C, 50,000 mg\/L TDS) showed exceptional performance, achieving 97.89% blockage efficiency in 0.1 mm fracture cores, maintaining over 80% viscosity after 60 days of CO2 exposure, and fixing CO2 at a rate of 0.17 g CO2 per gram of gel at a 3% agent concentration. A field pilot at Well Jiang 5 (Dec 2023) involved the injection of 100 m3 of gel, resulting in substantial improvements in conformance and the sequestration of 120 tons of CO2 in the Ordos Basin.Results\/Observations\/Conclusions: The results demonstrate significant benefits in both enhanced oil recovery (EOR) and carbon sequestration: a reduction in water cut from 80% to 45%, a 2.0-ton increase in daily oil production, and permanent CO2 mineralization within thief zones. The gel&#039;s stability and irreversible solidification ensure long-term CO2 entrapment and contribute to carbon neutrality goals.Applications\/Significance\/Novelty: This technology presents a breakthrough in CCUS-EOR for shale oil, transforming high-permeability zones into permanent carbon sinks, effectively reducing GHG emissions while enhancing oil recovery. It is particularly effective for fractured, heterogeneous shale reservoirs like those in the Ordos Basin, aligning with global carbon emission reduction strategies.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Multiscale Characterization of Deep Paleozoic Unconventional Formations for CO\u2082 Storage in the Delaware Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Khan*, F. J. Angel, M. Myers and L. A. Hathon\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(University of Houston)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Deep reservoirs in the Permian Basin exhibit complex heterogeneity, extensive diagenetic overprinting, and significant depth-dependent mechanical variability. These factors directly control the injectivity, containment, and long-term integrity of CO2 storage projects. This study presents new experimental and microstructural insights from a comprehensive laboratory testing program conducted on cores from representative deep wells in the Lower Paleozoic storage interval of the Delaware Basin. This work provides a multiscale assessment of pore structure, mineral heterogeneity, and evolution of elastic properties. The objective is to integrate mechanical and petrophysical observations to support reservoir-quality evaluation and improve understanding of storage behavior in deep formations.Methods\/Procedures\/Process: A suite of laboratory measurements and multiscale characterization was conducted on cores from Lower Paleozoic formations, utilizing multistage triaxial testing, ultrasonic measurements under stress, Boyle&#039;s Law porosimetry, X-ray diffraction (XRD), thin-section petrography, micro-CT imaging, and high-resolution ROI scans. Bulk properties established baseline characteristics, while mechanical evaluation captured stress-dependent velocity behavior and elastic property evolution. Microstructural observations were obtained from thin-section mosaics and CT-derived pore geometry, with ROI scans resolving microporosity. XRD quantified mineral composition and its variability. The workflow provides a framework to link mineralogy, pore structure, and elastic response across scales.Results\/Observations\/Conclusions: The results demonstrate that Paleozoic dolostones possess a mechanically competent framework with limited stress sensitivity; a complex pore system in which microporosity contributes measurably to total storage capacity; and mineralogical heterogeneity at plug scale that must be incorporated into reservoir and geomechanical models. The integrated analysis captures coupled microstructural and mechanical controls that govern storage performance. The combined results define a robust multiscale characterization workflow for deep storage systems in Delaware Basin. When incorporated into field data, seismic interpretations, and regional stratigraphy, these findings improve the prediction of injectivity, plume evolution, and long-term containment in complex CO2 storage projects.Applications\/Significance\/Novelty: This multiscale laboratory characterization provides critical inputs for advancing CO2 storage design in deep Paleozoic formations of the Delaware Basin. The integrated mechanical and petrophysical constraints improve representation of carbonate heterogeneity in static and dynamic models, reducing uncertainty in injectivity forecasts. Microporosity mapping and mineral variability at plug scale enhance upscaling of pore-network properties for reservoir simulation. The results strengthen geomechanical models by defining elastic responses consistent with in-situ stress conditions. Collectively, these findings support more reliable evaluation of containment performance and long-term storage security in mechanically competent but texturally complex intervals.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Multiscale Characterization of Tight Oil Shale: Insights into Mineralogical, Organic, and Pore Properties with Implications for Energy Storage and Adsorption Behavior<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Alanazi* and H. Hoteit\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(King Abdullah University for Science and Technology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Tight oil shales are complex geological formations with unique mineralogical, organic, and pore properties that play a critical role in hydrocarbon production and emerging energy storage applications. This study presents a comprehensive multiscale characterization of tight oil shale, focusing on its mineralogical composition, organic matter content, and pore architecture.Methods\/Procedures\/Process: Advanced analytical techniques, including RockEval pyrolysis, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FE-SEM), atomic force microscopy (AFM), and N2\/CO2 physisorption, were employed on commercial Jordanian oil shale samples to determine kerogen type, total organic carbon (TOC) variability (13\u201318%), mineral composition (carbonate-rich with variable quartz and clays), functional groups, microstructure, and pore-size distribution.Results\/Observations\/Conclusions: The findings reveal that the mineral composition and TOC content significantly influence the physical and chemical properties of the shale. Higher TOC samples exhibited more hydrophobic behavior under high-pressure\/high-temperature (HPHT) conditions, as demonstrated by wettability and interfacial property measurements for H2, CH4, and their mixtures. These results highlight the importance of organic richness in governing fluid-shale interactions, critical for both hydrocarbon recovery and potential energy storage applications. Gas adsorption dynamics were also investigated, with CO2 showing the highest adsorption affinity, followed by CH4 and H2. Adsorption capacity was strongly influenced by TOC and microporosity, suggesting that tight oil shales have potential for energy storage.Applications\/Significance\/Novelty: This study underscores the importance of understanding the interplay between mineralogy, organic matter, and pore structure in tight oil shales. While the primary focus is on hydrocarbon production, the findings also provide valuable insights into the suitability of these formations for emerging energy storage applications, including hydrogen and CO2 storage. The results emphasize the need for further research to explore the full potential of tight oil shales in the context of energy transition and decarbonization efforts.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pad Power: A Clean Energy Drilling System for High Efficiency Unconventional Development<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. H. Abdelkawy*, A. Alajami, R. Bayov, M. Anwar and E. Elshamisi\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ADNOC Onshore)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: The global energy industry is transitioning toward delivery models where operational efficiency must be achieved alongside measurable environmental responsibility. In arid, high\u2011density unconventional developments, operators must reduce carbon intensity, freshwater consumption, waste, and surface disturbance while maintaining competitive well costs and delivery schedules. Against this backdrop, this paper demonstrates how batch\/pad drilling can be executed as a clean\u2011energy\u2013aligned well construction system for unconventional resource development, using field data to quantify environmental and performance outcomes.Methods\/Procedures\/Process: The approach applies multi\u2011well pads with a batch sequence (surface \u2192 intermediate \u2192 lateral) to minimize rig moves, duplicated infrastructure, and repetitive logistics. The \u201cpad power\u201d system integrates (1) pad\/batch operations to eliminate major relocations, (2) closed\u2011loop mud recirculation with centralized solids control and real\u2011time rheology monitoring to enable well\u2011to\u2011well fluid reuse, (3) energy\u2011optimized generator loading to stabilize power demand and reduce inefficient start\/stop events, and (4) digital planning and real\u2011time surveillance, including machine\u2011learning\u2013based anomaly detection, to standardize execution and reduce drilling dysfunctions.Results\/Observations\/Conclusions: Field results from a Middle East unconventional campaign confirm material sustainability and efficiency gains. Fuel consumption was reduced by ~35\u201340% with proportional CO\u2082 reductions\u2014the highest\u2011impact environmental benefit\u2014driven primarily by fewer rig moves, steadier generator loading, and shorter well durations. Closed\u2011loop fluid management delivered 30\u201350% lower freshwater usage, while waste volumes decreased by ~25% through reduced fluid disposal, fewer trucking operations, and optimized solids handling. Operationally, rig relocation time was reduced from days to hours; wells were delivered 12\u201318% faster with lower non\u2011productive time, and total cost per well decreased by ~10\u201315% while maintaining strong operational reliability through real\u2011time surveillance.Applications\/Significance\/Novelty: These outcomes show that sustainable drilling for high\u2011density unconventional development is achievable by integrating batch\/pad drilling, closed\u2011loop fluids, digital surveillance, and energy management into one unified operating system. The resulting framework is scalable and particularly applicable to arid regions and decarbonization\u2011driven programs, enabling operators to meet sustainability targets (emissions, water, waste, and footprint) without compromising drilling performance or economics.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Evaluating Greenhouse Gas Emission Reductions through Non-Condensable Gas Co-Injection in SAGD Operations Using Modified Well Configurations<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Zhang*<sup>1<\/sup>, X. Li<sup>1<\/sup>, C. Xu<sup>2<\/sup>, J. Yu<sup>1<\/sup>, P. Duan<sup>1<\/sup>, C. Wang<sup>1<\/sup>, J. Liu<sup>1<\/sup> and W. Yu<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Oil and Gas Technology Research Institute, PetroChina Changing Oilfield Company; 2. CNPC USA Corporation; 3. Beijing Karst Science &amp; Technology LTD)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to assess the potential for greenhouse gas (GHG) emission reductions in Steam-Assisted Gravity Drainage (SAGD) operations by co-injecting non-condensable gas (NCG), specifically methane, with steam. The project evaluates whether modified well configurations, replacing horizontal injectors with vertical wells, can enhance reservoir performance and reduce the steam-to-oil ratio (SOR), thereby lowering the carbon footprint of bitumen recovery.Methods\/Procedures\/Process: A comprehensive reservoir simulation model was developed for Nexen\u2019s Long Lake Pad 11 using CMG STARS. The model incorporated historical production data, geological characteristics, and fluid properties. Sensitivity analyses were conducted to evaluate the impact of NCG injection (at 0.25 to 1.5 mol%) on SOR, energy loss, and cumulative oil production. Economic and environmental analyses were performed to quantify operational expenditures, carbon tax implications, and overall GHG emissions.Results\/Observations\/Conclusions: Co-injection of NCG reduced the steam-to-oil ratio by 23%, primarily by minimizing heat loss to the overburden. This reduction in steam usage directly translates to lower natural gas consumption and decreased GHG emissions. However, oil production decreased by 12% due to the insulating effect of NCG limiting steam chamber expansion. The modified well configuration demonstrated improved SOR performance and cumulative oil recovery compared to traditional horizontal injectors, though the economic viability was challenged under low oil prices and current carbon tax regulations.Applications\/Significance\/Novelty: The study highlights the trade-off between emission reductions and production performance. While NCG co-injection offers a viable pathway to reduce the carbon intensity of SAGD operations, its implementation requires careful optimization of NCG concentration and well spacing. Further research is recommended to explore hybrid injection strategies and the use of alternative non-condensable gases to improve both economic and environmental outcomes.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Subsurface Risk Assessment for Gas Storage: Coupled Effects of Mineralogy, TOC, Wettability, and Gas Diffusivity on Shale Containment Efficiency<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tA. Alanazi* and H. Hoteit\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(King Abdullah University for Science and Technology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Underground hydrogen storage (UHS) is expected to play a major role in future low-carbon energy systems by providing large-scale, long-duration capacity for balancing renewable power. However, operational success depends on maintaining secure containment and minimizing hydrogen migration through caprock and reservoir interfaces. The inherent physical traits of hydrogen\u2014high diffusivity, low molecular weight, and weak intermolecular forces\u2014can compromise storage security if formation selection is not based on the actual governing rock\u2013fluid interactions. This study develops a subsurface risk-assessment methodology that couples mineralogical composition, organic content, wettability behavior, and pore-scale diffusion properties to identify safe and stable hydrogen storage formations.Methods\/Procedures\/Process: Shale and carbonate samples were characterized using XRD, FTIR, FE-SEM, AFM, nitrogen adsorption, and RockEval pyrolysis to quantify mineralogy, surface chemistry, pore networks, and TOC. High-pressure, high-temperature wettability experiments with H2, CH4, and mixed gases measured contact-angle evolution and interfacial-tension behavior under storage-relevant conditions. Hydrogen adsorption\u2013desorption tests, supported by kinetic modeling, provided diffusion coefficients that describe H2 transport through micro- to nanoporous domains, enabling assessment of trapping and withdrawal performance in underground storage formations.Results\/Observations\/Conclusions: Results show a strong link between TOC and hydrogen wettability: high-TOC shales display advancing contact angles &gt;95\u00b0, lower capillary entry pressures, and greater adsorption capacity that promotes micropore trapping. These factors enhance long-term containment but require careful withdrawal strategies to limit trapped-gas losses. Carbonate-rich rocks respond more strongly to pressure and gas purity, shifting toward hydrogen-wet conditions and increasing leakage risk at shallow depths or under aggressive cycling. Hydrogen diffusion coefficients are 1\u20132 orders of magnitude higher than CO2, underscoring the need to assess sealing efficiency and micropore connectivity when screening caprocks.Applications\/Significance\/Novelty: A risk-classification framework was developed that integrates petrophysical properties, geochemical indicators of hydrophobicity, pressure-dependent wettability, and hydrogen transport behavior. It enables ranking storage formations by sealing reliability and identifying intervals that may require engineered reinforcement, such as nanofluid sealing or optimized cushion-gas strategies. This laboratory-validated approach reduces uncertainty in UHS site selection, modeling, and containment assurance while improving predictions of hydrogen retention and supporting safe deployment of future hydrogen storage projects.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Pore-Throat Gradient Controlled Two-Phase Flow in Porous Media: Investigation of Wettability-Geometry Coupling for Subsurface Engineering<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Zhou*<sup>1<\/sup>,<sup>2<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. University of Manchester; 2. Soft Matter Sciences and Engineering, ESPCI Paris, PSL University, CNRS, Sorbonne Universit\u00e9)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Immiscible two-phase flow in porous media plays a crucial role in CO2 sequestration, hydrogen storage, and subsurface energy recovery. Variations in pore-throat geometry perpendicular to the flow direction create capillary barriers and preferential pathways that fundamentally shape displacement. Wettability further manipulates interface dynamics and trapping mechanisms. This study investigates how pore-throat size gradients, together with wettability conditions, jointly control immiscible flow dynamics and capillary-driven displacement relevant to secure subsurface storage.Methods\/Procedures\/Process: Two-dimensional porous structures with systematically varied throat-size gradients were generated using an in-house script. Gradient magnitude, sign (narrowing vs. widening), and spatial distribution were controlled. OpenFOAM simulations using the VOF method were performed to model water\u2013gas displacement. Contact angles varied to represent water-wet, gas-wet, and intermediate-wet systems. From the simulations, interfacial evolution, displacement pressure, residual saturation, and trapping configurations were extracted to quantify gradient-wettability interactions.Results\/Observations\/Conclusions: Simulations reveal that transverse pore-throat gradients markedly alter displacement behavior. Narrowing-down gradients increase entry pressure, induce early interfacial pinning, and enhance gas or brine trapping, while widening-down gradients promote channeling and reduce sweep efficiency. Wettability strongly shifts these trends: water-wet systems favor piston-like motion, whereas gas-wet conditions amplify fingering and disconnected phase clusters. The combined geometric-wetting effects determine residual saturation, capillary pressure response, and interface stability, offering mechanistic insights into pore-scale trapping during CO2\/H2 injection.Applications\/Significance\/Novelty: This work provides the systematic pore-scale assessment of transverse pore-throat size gradients coupled with wettability variations using numerical simulation. The results isolate geometric gradients as critical yet underexplored control on capillary-dominated flow regimes. The findings improve fundamental understanding of displacement efficiency, capillary trapping, and interfacial stability, providing essential input for designing safer and more effective CO2 sequestration and hydrogen storage strategies in heterogeneous formations.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Determination of Geothermal Gradient in North Cambay Basin, Gujarat, India<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Saikia*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(ONGC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Geothermal energy flow can be estimated more confidently if value of temperature gradient and thermal conductivity of the material is known. This study aims to determine a geothermal gradient in the northern part of Cambay Basin, Gujarat, India. Previously, Gupta (1981) determined heat flow rate along with geothermal gradient in Cambay Basin but it was based on only 7 wells upto a depth of 1,200 m. This study takes into account static bottom-hole temperature (SBHT) data from 1862 measurements from 731 unique wells belonging to 23 different fields of North Cambay Basin. Time range of data is from May,2013 to January,2025. Shallowest depth is 120m and deepest is 2614m. Lowest temperature measured is 360C and maximum is 1420C.Methods\/Procedures\/Process: Static Bottom hole temperatures were measured with electronic gauges fitted with piezo-resistive pressure and temperature sensors which are highly accurate and precise. The gauges were lowered and pulled out in wells using steel wire. Data recorded were recovered by using the application provided by the gauge manufacturer after pulling out of the gauge.Results\/Observations\/Conclusions: Bottom-hole temperatures were plotted against true vertical depths (in metre) of the wells. A robust linear relation was observed with slope 0.0469 0C\/m with co-efficient of determination 0.87. The linear relation can be stated as SBHT(in 0C) =[0.0469 x TVD(in metres)] + 16.942. Thus, it can be concluded that SBHT varies with depth linearly with a gradient ~ 47 0C\/km.Applications\/Significance\/Novelty: This study may help in future in determining geothermal energy prospect of Northern Cambay Basin. Also, it will help in estimating SBHT if depth of a well is known from the linear relation obtained: SBHT(in 0C) =[0.0469 x TVD(in metres)] + 16.942. Further, the study will help in estimating heat flow in the area.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Machine Learning\u2013Driven Optimization of Infill Well Spacing for Geothermal Coproduction in Mature Hydrocarbon Fields Using Synthetic Dataset<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tE. Agyei*<sup>1<\/sup>, S. Agosu<sup>2<\/sup>, G. Akpabli<sup>1<\/sup>, I. Owusu<sup>2<\/sup>, N. Yeboah<sup>1<\/sup>, E. Gyimah<sup>3<\/sup>, A. Ayensigna<sup>1<\/sup> and D. Essel<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Department of Petroleum and Natural Gas Engineering, New Mexico Institute of Mining and Technology; 2. Department of Petroleum Engineering, University of Wyoming; 3. New Mexico Bureau of Geology)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents a machine learning\u2013driven framework for optimizing infill well spacing in mature hydrocarbon fields with integrated geothermal coproduction, focusing on improving hydrocarbon recovery, geothermal energy extraction, and overall economic performance.Methods\/Procedures\/Process: A physics-informed synthetic 3D reservoir model representing a mature field was developed using realistic distributions of porosity, permeability, pressure, temperature, and fluid saturation, with existing wells arranged in a five-spot pattern. Candidate infill locations were generated, and a comprehensive dataset was constructed incorporating reservoir properties, spatial interference metrics, completion design variables, economic factors, and geothermal indicators. A Random Forest regression model, optimized using cross-validation and hyperparameter tuning, was trained to predict optimal well spacing. Three placement strategies\u2014random, fixed-spacing, and machine learning\u2013optimized\u2014were evaluated over a 50-year period using a dynamic interference model.Results\/Observations\/Conclusions: The machine learning\u2013optimized strategy achieved the highest cumulative oil production (~6.0 MMbbl), geothermal revenue (~$136k), and total revenue (~$394 MM), while also exhibiting the highest interference efficiency. Fixed-spacing placement showed reduced efficiency due to excessive well interference, and random placement produced the lowest performance. The results demonstrate that adaptive, data-driven spacing improves both technical and economic outcomes.Applications\/Significance\/Novelty: The proposed workflow integrates physics-informed modeling, machine learning optimization, and dynamic interference effects to provide a practical tool for sustainable redevelopment of mature hydrocarbon assets, enabling simultaneous optimization of hydrocarbon and geothermal energy production.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:00 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Feasibility of Downhole Water Sink-assisted CO\u2082 Sequestration: A Scalable Data-Driven Framework<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Abdulwahhab<sup>1<\/sup>, W. J. Al-Mudhafar*<sup>2<\/sup>, G. Abdul-Majeed<sup>1<\/sup> and K. Sepehrnoori<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Department of Petroleum Engineering, College of Engineering, University of Baghdad; 2. Basrah Oil Company; 3. Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents an integrated workflow to optimize CO\u2082 sequestration by combining Downhole Water Sink (DWS) technology with advanced machine learning (ML) and multi-objective optimization. Two reservoir scenarios were evaluated in the South Rumaila Oil Field: a depleted hydrocarbon reservoir and a fully water-saturated aquifer.Methods\/Procedures\/Process: A 3D compositional reservoir simulation was performed to capture the dynamic behavior of CO\u2082 injection and storage. Radial Basis Function Neural Network (RBF-NN) proxy models were developed to replicate simulation outputs with high accuracy, enabling rapid optimization using Multi-Objective Particle Swarm Optimization (MO-PSO). Two optimization scenarios were conducted: (1) well location optimization, and (2) joint optimization of CO\u2082 injection and DWS extraction flow rates. Proxy model performance was evaluated using R\u00b2, RMSE, and MAE.Results\/Observations\/Conclusions: The RBF-NN proxy model achieved high predictive accuracy, with R\u00b2 = 0.982 for well location optimization and R\u00b2 = 0.983 for flow rate optimization, and low prediction errors (RMSE = 0.66\u20130.70, MAE = 0.54\u20130.60). In the depleted reservoir well location optimization scenario, total trapped CO\u2082 increased by 21.2% (from 3.3 to 4.0 million tons), and the Total Trapping Index (TTI) improved from 57.17% to 69.65%. In the flow rate optimization scenario, the RBF-NN model trapped 15.92 million tons of CO\u2082 with a leakage index of 3.38%, outperforming PSO-based solutions by 488,000\u2013657,000 tons. The inclusion of DWS extraction increased trapped CO\u2082 by 586,159 tons compared to a no-DWS case, while reducing average reservoir pressure by approximately 1,100 psi (from ~6,150 psi to ~5,050 psi), thereby mitigating geomechanical risks such as caprock fracturing and fault reactivation.Applications\/Significance\/Novelty: This is the first documented application of DWS technology for CO\u2082 storage optimization in a real-field case, coupled with RBF-NN proxy modeling and MO-PSO. The results demonstrate that ML-based proxy modeling, when combined with active pressure management via DWS, provides a scalable, computationally efficient, and highly accurate framework for optimizing CO\u2082 sequestration in both onshore and offshore depleted reservoirs.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t\t\t<div class=\"scholarone-time-group\">\n\t\t\t\t\t    \t\t\t\t\t\t<div class=\"scholarone-program-items row g-3\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"col-12\">\n\t\t\t\t\t\t\t\t<div class=\"scholarone-session-item card h-100\" data-session-type=\"Alternate\" style=\"border-top: 4px solid #667eea;\">\n\t\t\t\t\t\t\t\t\t<div class=\"card-body\">\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-header border-bottom\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-info pb-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t<h3 class=\"scholarone-session-title h6 mb-2 fw-bold\">Theme 10: Alternates<\/h3>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-session-host small\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSession Chairs:\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/strong>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tBaosheng Liang, Sama Morsy, Reza Safariforoshani, Xiao Jin, Giselle Garcia Ferrer\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t<!-- Abstracts within this session -->\n\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstracts-list pt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Real-Time Pressure Pattern Recognition for Predicting and Mitigating Permian Basin Frac Hits<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tK. Katterbauer* and A. W. Alsmaeil\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Saudi Arabian Oil Company)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Unintended fracture-driven interactions (\u201cfrac hits\u201d) remain a significant challenge in unconventional field development, particularly in high-density, multi-well Permian Basin pads. These events can reduce parent-well productivity, induce operational non-conformance, and impair reservoir pressure management. This study presents an artificial intelligence\u2013driven frac-interference detection and mitigation system designed to identify precursor patterns in real-time pressure signals and forecast imminent frac hits. The objective is to evaluate whether data-driven temporal\u2013spatial pattern recognition can improve operational awareness and reduce exposure to negative frac-interaction impacts.Methods\/Procedures\/Process: A curated multi-pad dataset from the Permian Basin was compiled, including high-resolution surface pressure, bottomhole pressure, microseismic interpretations, and operational context logs. The proposed system integrates: Signal preprocessing using wavelet-based denoising and normalization. Pattern recognition through a hybrid architecture combining convolutional neural networks (CNNs) for feature extraction and gated recurrent units (GRUs) for sequence-level temporal forecasting. Event labeling guided by expert-verified frac-hit timestamps to train supervised classifiers. Forecasting and interpretability via attention mechanisms. The system was evaluated using cross-pad validation to ensure generalizability across variable geological and completion designs.Results\/Observations\/Conclusions: The AI model achieved high predictive accuracy, detecting a majority of verified frac-interaction events prior to observable operational impact. False-positive rates remained fairly low after ensemble calibration. Feature-attribution analysis revealed that early-stage pressure micro-oscillations, rate-coupled pressure derivative signatures, and lateral-offset-specific spatial clusters were the most recurrent precursors. Operators reported improved situational awareness, enabling rapid mitigation responses such as stage-level rate modulation, temporary shut-ins, and modified zipper-frac sequencing. The system demonstrated robustness across diverse benches and completion styles within the Permian Basin.Applications\/Significance\/Novelty: This work introduces one of the first integrated, physics-informed AI frameworks for frac-hit detection that operates in real time and explicitly links interpretable pressure-signal patterns with actionable forecasts. Unlike traditional threshold-based monitoring, the system captures multi-scale, nonlinear signatures that precede interference events, offering both early-warning capability and transparent explanation of triggering conditions. The approach advances data-driven frac-interference management and provides a foundation for adaptive completion optimization in complex unconventional developments.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Effective Remediation of Polymer Gel Damage in Shale Reservoirs: A Case Study from the Woodford Shale<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tG. Djioto*, S. Smith, B. Conway, S. Bailey and Z. Daniel\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Flex-Chem)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study aims to assess the effectiveness of chemical remediation solutions using metal-complexing agents to repair polymer-gel damage in oil and gas wells, particularly in clay-rich formations. The scope includes testing the solution&#039;s performance in the Woodford Shale and examining its potential use in other shale basins.Methods\/Procedures\/Process: Our approach involved thorough laboratory diagnostics to determine the best chemical remediation solution, followed by field testing across multiple wells and operators. We relied on data from over 1,500 well treatments in the Anadarko Basin, with the majority of these completed in the Woodford Shale, to support our analyses and assess the effectiveness of the remediation.Results\/Observations\/Conclusions: The study found that the chemical remediation solution using metal complexing agents was effective in repairing polymer gel damage in the Woodford Shale. The approach was validated in the field across multiple wells and operators, and its effectiveness was confirmed by laboratory diagnostics and field data. Our findings indicate that polymer damage can occur in all clay-rich formations and that appropriately-designed remediation solutions can be applied to other shale basins with similar mineralogy.Applications\/Significance\/Novelty: This study offers new insights into the causes and effects of polymer-gel damage in shale reservoirs and provides a practical solution to reduce its impact. The findings and remediation approach can help engineers by offering a practical way to restore well productivity and improve well performance in shale reservoirs with similar traits. Successfully applying this remediation across multiple shale basins, including the Marcellus, Wolfcamp, Eagle Ford, Haynesville, and Meramec, shows its potential for wider use and value for operators.Interdisciplinarity (Team Presentation\u2019s only): Reservoir &amp; Production Engineers\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Optimizing Drawdown Strategies Using Streamlined Workflow: Insights from Midland and Delaware Basins<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tY. Gonzalez*, J. Courtier, E. Moncayo, I. Wang, A. Jacquet, C. Garces, M. Shokry and H. Sun\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Ecopetrol Permian)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: 1. Investigate the dynamic interplay between reservoir drawdown strategies and well Performance 2. Provide case studies and results from the impletementation of the Drawdown Operating Envelope in the Midland and Delaware Basins 3.Augment Analytical Frameworks: By integrating empirical and actual field data with subsurface analytics models 4. Guide Strategic Reservoir Management in Overpressure FormationsMethods\/Procedures\/Process: (Gonzalez et al., 2024) presented the dynamic interplay between drawdown strategies and well performance emphasizing the integration of traditional methods like Rate Transient Analysis (RTA) and advanced machine learning techniques.The study enabled us to derive a solution implemented at the field (the Drawdown Operating Envelope), where pressure decline limits are proposed to prevent damage to the hydraulic fractures caused by excessive effective net stress over the proppant pack. By analyzing comprehensive datasets from the Midland and Delaware Basins, this study identifies optimal drawdown parameters that balance performance and economic viability.Results\/Observations\/Conclusions: Machine learning models provide insights into the impacts of various operational strategies on well productivity, particularly focusing on the consequences of aggressive drawdown and long-term performance.This approach not only augments existing analytical frameworks but also offers a refined perspective on maximizing reservoir performance while minimizing detrimental impacts. The findings highlight the potential of merging empirical data with predictive analytics to guide Production Operations decisions resulting in strategic reservoir management.Applications\/Significance\/Novelty: Drawdown Strategies in Permian Basin. Integrating : Historical data, RTA Analysis and Subsurface Analytics. The Drawdown Operating Envelope Implementation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Analyzing Stage Zone Placement Impact on Fracture Pressure and Frac Success: A Montney Case Study<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tR. Gravel*\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(GeoLOGIC Systems LTD)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Maximizing recovery in the Montney often relies on staggered entry strategies to access thick, layered intervals, while drilling complexity can result in zones landing higher or lower than planned. This study will evaluate how stage zone placement\u2014top, middle, or bottom\u2014within the same interval impacts frac initiation and placement success. We will examine geological variability across the interval (e.g., clay content, lithology) and quantify correlations between fracture placement success and interval position. The goal is to determine the magnitude of these impacts to guide well planning, prioritize drilling targets, and optimize stage design for wells landed outside ideal zones.Methods\/Procedures\/Process: The study will analyze Montney wells in a defined area, define the map formation top and bottom, and classify each individual stage by vertical position (top, middle, bottom). We will correlate each zone\u2019s frac parameters such as breakdown pressure, ISIP, frac rate, and sand placement to determine a measure of frac placement success. Planned work also includes assessing rock properties for each section, as defined by determining the magnitude of change over the interval. Operational challenges will be documented to evaluate how the interval influences frac performance.Results\/Observations\/Conclusions: Expected outcomes include: 1) identifying correlations between interval placement and frac success, and 2) understanding how rock properties and placement are correlated. We anticipate that we will observe design adjustments based on interval-specific characteristics. These findings will provide insights into the relative importance of drilling precision and staying in zone, optimizing the frac design and improving predictability for wells targeting thick, layered intervals.Applications\/Significance\/Novelty: This work will support optimized well design in the Montney and similar plays by anticipating fracture placement challenges. Understanding these impacts enables tailored designs for specific intervals and informs drilling strategies to maximize fracture success ultimately production. The outcome is reduced risk, improved efficiency, and better stage-level adjustments when wells land outside preferred zones. Learnings from this study may also apply to other actively developing plays, such as the Duvernay, where coordinated well placement and fracture design continue to evolve, ultimately enhancing recovery and drainage.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Integrated Drilling-Derived Rock Strength and Real-Time Fracture Diagnostics for Completion Optimization in the Powder River Basin<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tT. Szilagyi<sup>1<\/sup>, M. Sinkey*<sup>1<\/sup>, K. Wutherich<sup>2<\/sup>, B. Bundy<sup>3<\/sup>, S. Van Delinder<sup>3<\/sup>, T. Hewett<sup>3<\/sup> and J. Kegel<sup>3<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. Shear Frac Group LLC; 2. Drill2Frac, LLC; 3. Ballard Petroleum Holdings LLC)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: This study presents an integrated workflow combining drilling-derived rock properties with real-time, physics-informed surface-pressure diagnostics to optimize completions across multiple benches in the Powder River Basin. Objectives include identifying rock-driven variability and the presence of localized depleted fractures, improving stage design, mitigating fracture interactions with parent wells, and evaluating how near-wellbore rock properties, seismic flags, and time-series fracture measurements correlate to treatment efficiency and execution quality.Methods\/Procedures\/Process: Drilling data were processed into a continuous rock-strength log to identify geomechanical properties, with additional analysis performed to identify areas of localized depletion. These properties informed stage length, perforation placement, and flagged areas of potential operational risk. During stimulation, second-by-second surface-pressure analytics quantified fracture intensity, frequency, effectiveness, and interaction signatures. Machine-learning models evaluated sensitivity to rate, proppant type, and fluid rheology, while an adaptive guidance workflow supported rate and schedule adjustments to minimize FDIs and improve stage performance in real time.Results\/Observations\/Conclusions: Drilling-derived rock strength trends aligned strongly with fracture behavior: higher rock strength zones consistently exhibited reduced fracture intensity, while lower rock strength corresponded to higher effectiveness and more efficient fracture creation. Real-time diagnostics identified challenging stages early through low fracture frequency and small-derivative interaction signals. Integrating near-wellbore rock properties with live measurements revealed correlations between geologic variability, treatment sensitivity, and offset well interaction, indicating that combined diagnostic workflows support more informed operational decisions.Applications\/Significance\/Novelty: Drilling-derived rock strength trends aligned strongly with fracture behavior: higher rock strength zones consistently exhibited reduced fracture intensity, while lower rock strength corresponded to higher effectiveness and more efficient fracture creation. Real-time diagnostics identified challenging stages early through low fracture frequency and small-derivative interaction signals. Integrating near-wellbore rock properties with live measurements revealed correlations between geologic variability, treatment sensitivity, and offset well interaction, indicating that combined diagnostic workflows support more informed operational decisions.Interdisciplinarity (Team Presentation\u2019s only): This project integrated expertise across drilling engineering, geomechanics, subsurface characterization, and completions. Drilling data specialists derived high-resolution rock strength measurements to characterize variability along the lateral, while completion and data-science teams translated second-by-second pressure signals into fracture behavior metrics. Collaboration with the operator unified geological context, operational constraints, and diagnostic interpretation, enabling a shared workflow from pre-frac planning through execution and post-job evaluation.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Experimental Study of CO2 Huff-n-Puff for Enhanced Oil Recovery in Shale Reservoirs: Mechanistic Insights from Fractures and the Viability of CO2 Energized Huff-n-Puff<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tM. Yang*, S. Fang, Y. He and C. Dai\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(Sinopec Petroleum Exploration and Production Research Institute)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Shale reservoirs have played a ever-increasing critical role in energy supply. And horizontal drilling coupled with multi-stage hydraulic fracturing make the economic development of shale reservoirs possible. Due to the ultra-low matrix permeability, the primary recovery of shale reservoirs is usually less than 10%. Therefore, it is imperative but sometimes challenging to unlock the underground oil. CO2 huff-n-puff (HnP) technique holds great potential for enhanced oil recovery (EOR) in shale reservoirs while simultaneously contributing to CO2 geological storage. The objectives of this experimental study are to investigate the effects of fracture on the recovery performance and to evaluate the feasibility of CO2 energized huff-n-puff (E-HnP).Methods\/Procedures\/Process: Two full-diameter core samples (fractured and fracture-free) retrieved from the Jianghan Basin were utilized. After cleaning and drying, the core samples were saturated with recombined live oil. Then, core flooding experiments were conducted using a two-step strategy at reservoir conditions (75 \u03bfC, 20 MPa): depletion and HnP (3 cycles). For Energized HnP process, the depleted fractured core was rapidly repressurized to the fracture gradient to replenish energy, after which standard HnP cycles were performed. Nuclear Magnetic Resonance (NMR) tests was employed to characterize the oil sweep efficiency in different pores. Furthermore, Gas Chromatography (GC) analysis was conducted on the effluent samples from each cycle to reveal the selective mobilization of hydrocarbon components.Results\/Observations\/Conclusions: CO2 HnP with the fractured core yielded higher oil recovery (24.39%) compared to the fracture-free core (8.65%). NMR results indicated that oil primarily came from pores larger than 0.1 \u03bcm. However, fractures induced noticeable negative convection. Compositional analysis confirmed oil produced in later cycles had a higher fraction of heavy components. Crucially, the E-HnP process achieved the highest overall recovery of 30.27%, showing a marked increase in incremental oil for each cycle. This superior performance is primarily ascribed to the energization, which extended existing fractures and generated micro-fractures, thus expanding the sweep volume. Furthermore, the resulting high pressure significantly promoted CO2 dissolution into the crude oil, enhancing mass transfer.Applications\/Significance\/Novelty: This study explored the impact of fractures on oil mobilization behavior in CO2 HnP processes and verified the oil-increasing potential of E-HnP in shale reservoirs. Fractures play a pivotal role in CO2 EOR by reducing seepage resistance and enlarging the oil drainage area. Nevertheless, fracture closure presents a major limitation, causing a dramatic decline in oil production during later HnP cycles. Fortunately, E-HnP is a promising and efficient technique for CO2-EOR in fractured shale reservoirs, capable of reactivating closed fractures and generating new micro-fractures. These laboratory findings advance our mechanistic understanding of CO2-EOR processes and could provide actionable guidance for future field pilot testing and optimization.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-item mb-2 session-items\" data-session-type=\"Alternate\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-item mt-0\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-start-time\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t12:01 AM\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"abstract-title 1\">Experimental Evaluation of Potential Reservoir Damage Mechanisms in Different Target Intervals of a Continental Shale Oil Play<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"abstract-authors\" data-content=\"4503373\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tS. Pu*<sup>1<\/sup>, X. Yu<sup>1<\/sup>, J. Wang<sup>1<\/sup>, Y. Tian<sup>1<\/sup>, L. Li<sup>2<\/sup>, B. Li<sup>1<\/sup>, H. Li<sup>1<\/sup> and F. Zhou<sup>1<\/sup>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"text-gray-500\">(1. China University of Petroleum (Beijing); 2. Research Institute of Petroleum Exploration and Development)<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"d-flex gap-2 mt-2\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<div class=\"scholarone-abstract-toggle\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<button class=\"scholarone-toggle-btn btn btn-sm btn-primary\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-toggle=\"popover\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-trigger=\"hover focus\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-placement=\"auto\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-html=\"true\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata-bs-content=\"Objectives\/Scope: Continental shale-oil reservoirs commonly experience low flowback efficiency, rapid production decline, and post-fracture conductivity loss after hydraulic fracturing. These problems are associated with coupled interactions among fracturing fluids, reservoir rocks, and in-situ fluids, but the relative importance of different damage mechanisms remains insufficiently constrained. This study evaluates potential reservoir damage mechanisms in two vertically stacked target intervals from a continental shale-oil-bearing system in China, with emphasis on nanopore capillary retention, water-sensitive swelling, emulsification damage, and stress-sensitive compaction.Methods\/Procedures\/Process: Representative shale, sandstone, and limestone cores were collected from the two target intervals. Nuclear magnetic resonance (NMR)-based nanopore imbibition analysis and capillary-pressure calculations were used to identify potential aqueous-phase retention in nanopore-throat systems. Capillary suction time (CST) tests were conducted to evaluate water-sensitive swelling. Fracturing-fluid\u2013crude-oil emulsification experiments were performed to assess fluid compatibility and emulsion tendency. Stress-sensitive permeability measurements were used to quantify permeability damage under changing effective stress.Results\/Observations\/Conclusions: The results show that both target intervals have relatively high clay contents but low absolute contents of strongly swelling minerals, and CST tests indicate weak overall water sensitivity. NMR and capillary-pressure analysis show that pore-throat radii smaller than 20 nm are the main domain for aqueous-phase retention. The corresponding pore-volume proportion is approximately 13.71% in Interval A shale and 7.11% in Interval B shale, indicating greater susceptibility to nanopore capillary retention in Interval A. Stress-sensitive permeability tests show that shale samples from both intervals exhibit very strong stress sensitivity, with permeability damage rates generally exceeding 96%. Emulsification experiments indicate low emulsification tendency for both tested drag reducer systems. Overall, the relative importance of the potential damage mechanisms is ranked as stress-sensitive compaction &gt; nanopore capillary retention &gt;&gt; water-sensitive swelling \u2248 emulsification damage.Applications\/Significance\/Novelty: These findings provide experimental support for fracturing-fluid optimization, controlled flowback, pressure-managed production, and interval-specific reservoir protection in continental unconventional reservoirs. The results also indicate that damage-control strategies should be differentiated by interval: reducing nanopore aqueous-phase retention is more important for Interval A, whereas pressure management and stress-sensitive damage control are more critical for Interval B.\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttabindex=\"0\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\taria-label=\"View abstract\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"scholarone-toggle-text\">View Abstract<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/button>\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div><!-- .scholarone-program-items -->\n\t\t\t\t\t<\/div><!-- .scholarone-time-group -->\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\n\t<!-- Loading indicator -->\n\t<div class=\"scholarone-loading text-center py-5\" style=\"display: none;\">\n\t\t<div class=\"scholarone-spinner spinner-border text-primary\" role=\"status\">\n\t\t\t<span class=\"visually-hidden\">Loading&#8230;<\/span>\n\t\t<\/div>\n\t\t<p class=\"mt-3\">Loading&#8230;<\/p>\n\t<\/div>\n\n\t<!-- No results message -->\n\t<div class=\"scholarone-no-results alert alert-warning text-center mt-3\" style=\"display: none;\">\n\t\t<p class=\"mb-0\">No results found. 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