Smart Proxy Modeling – Engineering Application of Artificial Intelligence in Numerical Simulation
Disciplines: Data Science and Engineering Analytics | Reservoir
Smart Proxy Modeling is the application of Artificial Intelligence and Machine Learning in Numerical Simulation. Smart Proxy Modeling has already been successfully applied to Numerical Reservoir Simulation and Computational Fluid Dynamic. Details of Smart Proxy Modeling development will be shared with the attendees of this SPE Short Course.
Smart Proxy Modeling provides highly accurate results of the Numerical Simulation (a) without modification of the requiring mathematical equations that are used to build the Numerical Simulation, (b) without minimizing the number of cells and number of time-steps of the Numerical Simulation, and (c) without the requirement of a large number of numerical simulation deployment. Smart Proxy Modeling is the most realistic application of Artificial Intelligence and Machine Learning for the development of the proxy models for numerical simulations.
Intermediate - Advanced
This course will play a crucial role for the enthusiasts of this technology to identify the scientific realities associated with the foundation of Artificial Intelligence and Machine Learning and its true application in Petroleum Data Analytics. Want to be knowledgeable with the most up-to-date and accurate AI and Machine Learning technology? This class will get you there!
This course is intended for all petroleum engineering professionals, geoscientists, managers, geoscientists, asset managers, and team leaders.
0.8 CEUs are awarded for this 1-day course.
All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.
Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.
SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.
We reserve the right to substitute course instructors as necessary.
Dr. 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).