Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation

Shahab D. Mohaghegh

ISBN 10: 1032151153 / ISBN 13: 9781032151151

Published by Taylor & Francis Ltd, 2024

About this Item

New copy - Usually dispatched within 4 working days. 453.

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Bibliographic Details

• Title: Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation
• Publisher: Taylor & Francis Ltd
• Publication Date: 2024
• Language: English
• ISBN 10: 1032151153
• ISBN 13: 9781032151151
• Binding: Paperback / softback
• Condition: New
• Weight: 453 grams

About this title

Synopsis

Numerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases.

• Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine Learning
• Details application in reservoir simulation and modeling and computational fluid dynamics
• Includes real case studies based on commercially available simulators

Smart Proxy Modeling is ideal for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics.

About the Author

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 (WVU) 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 230 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).