The Current Realistic State of Artificial Intelligence and Machine Learning in the Petroleum Industry

Recorded On: 11/12/2021

Interest in the application of Artificial Intelligence and Machine Learning in the Petroleum Industry has been increasing, specifically among the young professionals. Exposing the new generation of petroleum professionals to realistic science and philosophy of Artificial Intelligence along with its engineering application is an important part of the future of Petroleum Engineering.

While the business and marketing characteristics of what claims to be AI and Machine Learning is incredibly active in our industry, unfortunately, the realistic version of AI has been minimally used (if at all) in our industry. Lack of realistic understanding of the science and philosophy of Artificial Intelligence has resulted in the application of “traditional statistics”, “inclusion of mathematical equations for data generation”, and “non-engineering application of machine learning algorithms” in petroleum engineering, while being called an AI-based modeling.

This presentation covers the state of AI and Machine Learning in our industry while explaining what the realistic approach of this technology in our industry should be.

This webinar is categorized under the Reservoir, Completions and Drilling technical disciplines.

All content contained within this webinar is copyrighted by Shahab Mohaghegh and its use and/or reproduction outside the portal requires express permission from Shahab Mohaghegh.

For more information on this topic, please check out the SPE suggested reading links. Click here to find topic-related books, publications, and papers for purchase.

Dr. Shahab Mohaghegh

West Virginia University and Intelligent Solutions Inc.

Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the Exploration and Production industry, is Professor of Petroleum and Natural Gas Engineering at West Virginia University and the president and CEO of Intelligent Solutions, Inc. (ISI). He is the director of WVU-LEADS (Laboratory for Engineering Application of Data Science).

Including more than 30 years of research and development in the petroleum engineering application of Artificial Intelligence and Machine Learning, he has authored three books (Shale Analytics – Data Driven Reservoir Modeling – Application of Data-Driven Analytics for the Geological Storage of CO2), more than 200 technical papers and carried out more than 60 projects for independents, NOCs and IOCs.

He is a SPE Distinguished Lecturer (2007 and 2020) and has been featured four times as the Distinguished Author in SPE’s Journal of Petroleum Technology (JPT 2000 and 2005). He is the founder of SPE’s Technical Section dedicated to AI and machine learning (Petroleum Data-Driven Analytics, 2011). He has been honored by the U.S. Secretary of Energy for his AI-based technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico (2011) and was a member of U.S. Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

SPE Webinars are FREE to members courtesy of the

image

Key:

Complete
Failed
Available
Locked
The Current Realistic State of Artificial Intelligence and Machine Learning in the Petroleum Industry
11/12/2021 at 10:00 AM (EST)   |  90 minutes
11/12/2021 at 10:00 AM (EST)   |  90 minutes
Survey
20 Questions
Certificate
0.15 CEU/1.5 PDH credits  |  Certificate available
0.15 CEU/1.5 PDH credits  |  Certificate available