page content

SC-09 Reservoir Engineering Applications of Advanced Data Analytics and Machine Learning Algorithms

Sponsored by: SPE

Sunday, 16 June 2024, 8:00 a.m.–5:00 p.m.  |  Houston, Texas

short course image

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 a 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.


  • 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.

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 is transforming the E&P business in the coming years.

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.

Disciplines: Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir

Learning Level: Intermediate to Advanced


Ashwin Venkatraman
View Bio


Early Pricing:
Member $550
Nonmember $750
Student $300
On-site Pricing:
Member $650
Nonmember $850
Student $350
Room Assignment
Attendee Limit:
36 People
Educational Credits:
0.8 CEU


SC-09 Reservoir Engineering Applications of Advanced Data Analytics and Machine Learning Algorithms
George R. Brown Convention Center
1001 Avenida De Las Americas
Houston, Texas 77010
United States
(713) 853-8000

page content

Important notes regarding short courses:

  • Short courses are limited in size and are reserved on a first come, first served basis and must be accompanied by full payment.
  • If you do not plan to attend URTeC, a $35 enrollment fee will be added to the short course fee upon registering. This fee can be applied to a full-conference registration should you change your mind later.
  • A wait list is automatically created when a short course sells out. You will be notified if you are on a wait list and space becomes available.
  • Before purchasing non-refundable airline tickets, confirm the short course will take place as some may be cancelled if undersubscribed.
  • Please register well before 6 May 2024 to help guarantee your spot. Short course cancellations will be considered at this time — no refunds will be accepted for cancellations after this date.
  • Registrations will continue to be processed for short courses that are not cancelled up until they are sold out or closed.

Top Leaderboard


three box footer