Reservoir Engineering Applications of Advanced Data Analytics and Machine Learning Algorithms

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

Course Description

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 of 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.

Learning Level

Intermediate to Advanced

Course Length

1 Day

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

Who Attends

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.


0.8 CEUs (Continuing Education Units) will be awarded for this 1-day course.

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.



Ashwin Venkatraman is currently Associate Professor of Petroleum and Geological Engineering at University of Oklahoma. He is also the Founder and CEO of Resermine Inc. an oil and gas tech startup that was awarded MOST PROMISING STARTUP at Offshore Technology Conference in Houston 2018. Resermine’s integrated subsurface analytics platform is currently being used to optimize field injection operations in Germany, Egypt, Mexico and India.

He has unique research and development expertise in both industry and academia. He worked with Shell for over 12 years at all their technology centers (India, Netherlands and Houston) in various capacities in operations, field development planning as well research. He also held research appointments in Princeton University as well as at Institute of Computational Engineering & Sciences (ICES) at the University of Texas before founding Resermine. Ashwin has published over 25 manuscripts and is currently on the editorial board of Data Science and Digital Engineering (DSDE) journal – a recently launched publication by SPE. He also on the advisory board of SPE’s Management and Information Committee that seeks to establish data standards to drive innovation. Ashwin holds a BS and MS in Chemical Engineering from IIT Bombay (India) and earned his PhD from University of Texas at Austin in Petroleum Engineering while on a sabbatical from Shell.

04 Nov 2020
Virtual [CDT; UTC -5]

Held in conjunction with SPE Virtual Annual Technical Conference and Exhibition

SPE Members
USD  300

USD  400


16 - 17 Nov 2020
Virtual [GST; UTC +4]

16 Nov 2020 10am-3pm
17 Nov 2020 10am-3pm

SPE Members
USD  315

USD  420