AI-based (Top-Down) Full Field Reservoir Simulation and Modeling
Disciplines: Data Science and Engineering Analytics | Reservoir
Numerical Reservoir Simulation is a “Bottom-Up” Reservoir Modeling, while AI-based Reservoir Simulation is a “Top-Down” Reservoir Modeling. AI-based Reservoir Simulation is NOT a “Hybrid Model” through incorporation of the realistic, engineering application of Artificial Intelligence & Machine Learning using ONLY field measurements. AI-based Reservoir Simulation is a full-field model that only uses facts and reality and avoids assumptions, interpretations, simplifications, preconceived notions, and biases.
AI-based (Top-Down) Full Field Reservoir Simulation and Modeling follows AI-Ethics and uses “eXplainable AI (XAI)” and generates AI-based Geological Model (Geo-Analytics), Fully Automated History Matching, Blind Validation Forecasting, and avoids using only Space-related reservoir layer characteristics (k*h) for production allocations and uses both space and time to generate “AI-based Production Allocation”. AI-based (Top-Down) Reservoir Modeling provides OpEx and CapEx Optimization. This presentation includes actual case studies.
- Artificial Intelligence & Machine Learning – Theoretical Background
- Differences between Top-Down Modeling (TDM) and Numerical Reservoir Simulation
- EI-Ethics (Why we should not be using Hybrid Models)
- eXplainable Artificial Intelligence
- Development of Top-Down Modeling
- Case Studies of TDM throughout the World
Intermediate to Advanced
This course will play a crucial role for the enthusiasts of engineering application of Artificial Intelligence and Machine Learning technology in Reservoir Simulation and Modeling. It covers the scientific and realities foundation of Artificial Intelligence and Machine Learning and its true application in Reservoir Engineering. If you are interested 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 designed for geo-scientists, reservoir engineers, and managers. Specifically, those involved with geology, reservoir, completion, and production in operating and service companies. In general, those involved in planning, completion, and operation in assets are the main target audience.
Attendees should bring a calculator and their laptop.
1.6 CEUs are awarded for this 2-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.
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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).