Introduction to Machine Learning for Oil and Gas Professionals

Course Description

Machine learning (ML) algorithms are transforming workflows in the oil and gas industry. The democratization and access to advanced computational tools is helping organizations exponentially accelerate their decision making using these powerful tools. This course introduces the basic principles of machine learning approaches, introduces different ML algorithms, provides a framework on how to identify if these AI/ML algorithms are useful for your data and finally lists key focus areas for successful business outcomes with focus on subsurface.

Topics:
Basic concept of AI/ML – what does this try to achieve and what is required?
Introduction to different types of algorithms
Framing the correct AI/ML problem for the data (Classification vs Regression)
Framework to address typical issues – data quality, data sufficiency, testing and final use in production (based on previous successful implementations)
Successful business cases that drive framework and understanding

Learning Level

Introductory – Intermediate

Course Length

1 day

Why Attend

Upon completion of this course, participants are expected to:

Identify areas in their organization where AI/ML can help
Evaluate whether AI/ML can help independently or supplement existing workflows
Frame AI/ML projects for successful business outcomes to include –
- Preliminary evaluation (does my data lend towards AI/ML?),
- Ask the right questions (classification vs regression) to obtain insights
- Accelerate workflows for quicker decision making.
Set expectations what successful AI/ML project outcome looks like
- Formulate right test cases to evaluate results and
- Plan deployment for use at scale

Who Attends

Professionals that are responsible in the following functional areas should attend:

Business Unit Head
Data and Business Analysts
Data Managers
Data Mining
Data Science Analysts
Drilling and Completions
Information Technology
Operations
Production Technology
Project Management
Reservoir Engineering

CEUs

0.8 CEUs are 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.

Instructor

None

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 the 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.

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