Pittsburgh Petroleum Section

Distinguished Lecture: Using Artificial Intelligence and Machine Learning for Analysis

A full understanding of the physics and the mechanics of the storage and transport phenomena and the production operation in shale has remained elusive to a large extent. When our industry’s traditional techniques are used for analysis and modeling of completion and production from shale, they leave much to be desired. Shale Analytics is an important point of competitive differentiation in the upstream oil and gas industry. Operating companies possess significant amounts of important field measurements that can provide much needed insight about completion and production from shale wells. It is hard to imagine that these collected data do not contain the knowledge we need to optimize production and maximize recovery from this most prolific hydrocarbon resource.  While some of the current applications of AI and Machine Learning in our industry is more about business and marketing rather than science and technology, there are scientific versions of this technology that has proven to be quite realistic. Shale Analytics refers to the set of tools and techniques that provides the means for extraction of information and knowledge from the actual field measurements in order to construct and validate predictive models that can serve as a tool for decision-making and optimization. Actual application of Shale Analytics in Marcellus, Utica, Eagle Ford, and Permian Basin will be demonstrated in this presentation.   Shahab D. Mohaghegh, Ph.D. 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 holds B.S., M.S., and Ph.D. degrees in petroleum and natural gas engineering.  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 CO 2 ), 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 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 (2008-2014). He represented the United States in the International Standard Organization (ISO) on Carbon Capture and Storage technical committee (2014-2016).

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