SC-02 Reservoir Engineering Applications of Advanced Data Analytics and Machine Learning Algorithms (SPE)
Sunday, 8 June 2025, 8:00 a.m.–5:00 p.m. | Houston, Texas
Who Should Attend
Who Should Attend: This course is designed for engineers and managers responsible for planning as well as optimizing existing operations. Specifically, those involved with drilling, reservoir, completions, and production in operating as well as service companies will find the course beneficial. Engineers working in newly founded data science teams in oil and gas companies will especially find inspiration from different case studies. Data science engineers will also find the distinction between models and a framework of integration with existing workflows greatly beneficial.
Course Content
Data driven modeling is becoming a key differentiation to unlock higher recoveries from existing fields as well as identify new opportunities. The availability of data and democratization of these advanced algorithms is changing the landscape of subsurface workflows — helping create as well as improve existing ones. We are in an exciting phase in the industry where access as well as ease of using these advanced tools is transforming decision making in organizations.
In this course, we will start by introducing advanced analytical tools and techniques — machine learning and data mining algorithms used to identify trends and patterns in any given dataset and predict future trends. We will showcase how each of these tools and techniques have been successfully applied to subsurface data — formation evaluation data, well testing data, reservoir data as well as data from surface facilities. We shall also present case studies of how integration of this seemingly disparate data can be done through new workflows that help identify opportunities to increase recovery. Finally, we will draw important distinctions between the more traditionally used forward models (physics-based approach such as reservoir simulation) and these statistics-based models. Using a case study that demonstrates integration of these two approaches, we shall conclude by drawing out a framework for integration of these tools in your existing workflows.
In summary, this course looks at successful application of machine learning and data analytics in E&P industry in the last several years. We will start with fundamentals of data mining algorithms, machine learning algorithms (neural networks, decision tree analysis) and present their successful implementation on subsurface data. The course is devoted to field application of these tools and techniques with focus on production optimization and optimization of water/gas injection operations.
Topics:
- Introduction to advanced analytical tools and techniques that includes data mining and machine learning algorithms along with means to access them easily over open source platforms — Python and Google’s Tensor Flow.
- Application of each of these tools to specific subsurface data and the successful implementation that lead to optimization/decision making.
- Sweet spots/new acreage identification as well as likely optimum frac stages for unconventional production using existing reservoir data as well as public data.
- Application of artificial neural networks for a) predictive maintenance on surface facilities, b) identifying lithology by formation evaluation data, and c) fluid characterization.
- Optimize water and/or gas injection operations in conventional fields by application of these advanced tools on production data collected as part of surveillance.
- Framework to integrate these advanced modeling tools with existing workflows such as reservoir simulation using case study to explain the same.
Why Attend: We have been collecting large amounts of subsurface data in the E&P industry. The easy access to advanced analytical tools and techniques at great computational speeds has democratized data-driven modeling. The use of these tools and techniques presents a great competitive advantage as we seek to increase recovery and be more efficient as an industry. Take this course to understand how to apply these tools and techniques to subsurface data and equip yourself with skills that are transforming the E&P business in the coming years.
Disciplines: Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir
Learning Level: Intermediate to Advanced
Instructor
Dr. Ashwin Venkatraman
Dr. Ashwin Venkatraman is the Founder and CEO of Resermine, a niche award-winning oil and gas technology company (MOST Promising at OTC 2018). He is the recipient of the inaugural SPE International Technical Award in Data Science and Engineering Analytics at SPE ATCE 2021 held in Dubai. The award recognized his contributions to bringing hybrid workflows that combine AI/ML with traditional approaches to accelerate subsurface decision making.
Resermine’s products have been used to optimize mature field injection operations and accelerate field development planning for fields in USA, Germany, Oman, UAE, Egypt, Mexico, India, and Malaysia. Resermine is based in USA (HQ) with technology delivery centers in Kuala Lumpur (ARMC – Advanced Modeling Center), Dubai (UAE), and Muscat (Oman) to support projects in different regions.
Dr. Venkatraman has published over 30 manuscripts and is on the advisory board of SPE’s Data Science and Engineering Analytics Committee. He previously worked with Shell for over 12 years at all their technology centers (India, Netherlands, and Houston). Dr Venkatraman served as faculty in the Petroleum Engineering Department of University of Oklahoma (2019-2020) and held research appointments in Princeton University as well as at Institute of Computational Engineering & Sciences (ICES) at the University of Texas before founding Resermine. Dr. Venkatraman holds BSc and MSc in Chemical Engineering from IIT Bombay (India) and earned his PhD from University of Texas at Austin in Petroleum Engineering.
Fees
Registration Fees: TBD
Attendee Limit: 40
Educational Credits: .8 CEUs/ 8 PDH's will be awarded for attending this 1-day course
Venue
George R. Brown Convention Center
1001 Avenida De Las Americas
Houston,
Texas
77010
United States