Smart Proxy Modeling
Artificial Intelligence and Machine Learning in Numerical Simulation
1st Edition
Published on 27. October 2022
204 pages
978-1-000-75519-0 (ISBN)
Description
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.
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
6 Tables, black and white; 181 Line drawings, color; 1 Line drawings, black and white; 27 Halftones, color; 208 Illustrations, color; 1 Illustrations, black and white
ISBN-13
978-1-000-75519-0 (9781000755190)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Shahab D. Mohaghegh
Smart Proxy Modeling
Artificial Intelligence and Machine Learning in Numerical Simulation
Book
10/2024
1st Edition
CRC Press
€87.20
Shipment within 10-20 days
Shahab D. Mohaghegh
Smart Proxy Modeling
Artificial Intelligence and Machine Learning in Numerical Simulation
Book
10/2022
1st Edition
CRC Press
€159.90
Shipment within 10-20 days
Person
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).
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).
Content
1. Artificial Intelligence and Machine Learning. 2. Numerical simulation and modeling. 3. Proxy modeling. 4. Smart Proxy Modeling for numerical reservoir simulation. 5. Smart Proxy Modeling for computational fluid dynamics (CFD).
