West Virginia University & Intelligent Solutions Inc.
Shahab D. Mohaghegh, a pioneer in the application of Artificial Intelligence and Machine Learning in the petroleum 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 currently the director of WVU-LEADS (WVU 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 220 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 Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to AI and Machine Learning (2011). He has been honored by the U.S. Secretary of Energy for his technical AI-based 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 through 2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).
Petroleum Data Analytics (PDA) is the engineering application of Artificial Intelligence & Machine Learning in petroleum engineering related problem solving and decision-making. PDA will fully control the future of science and technology in the petroleum industry. It is highly important for the new generation of scientists and petroleum professionals to develop a scientific understanding of this technology. Similar to the application this technology in other engineering related disciplines, Petroleum Data Analytics addresses two major issues that determine the success or failure of this technology in our industry: (a) the differences between “engineering” and “non-engineering” problem solving and decision-making, and (b) how AI&ML is differentiated from traditional statistical analysis. Lack of success or mediocre outcomes of AI&ML in our industry has been quite common. To a large degree, this has to do with superficial understanding of this technology by some petroleum engineering domain experts and concentration on marketing schemes rather than science and technology.