Petroleum Data Analytics (PDA); Application of Artificial Intelligence & Machine Learning in the Petroleum Industry

Recorded On: 08/19/2020

Petroleum Data Analytics (PDA) is the application of Artificial Intelligence & Machine Learning in petroleum engineering related problem solving and decision-making. PDA will fully control the future of science and technology in the petroleum industry. It is highly important for the new generation of scientists and petroleum professionals to develop a scientific understanding of this technology.

Similar to the application this technology in other engineering related disciplines, Petroleum Data Analytics addresses two major issues that determine the success or failure of this technology in our industry: (a) the differences between “engineering” and “non-engineering” problem solving and decision-making, and (b) how AI&ML is differentiated from traditional statistical analysis. Lack of success or mediocre outcomes of AI&ML in our industry has been quite common. To a large degree, this has to do with superficial understanding of this technology by some petroleum engineering domain experts and concentration on marketing schemes rather than science and technology.

This webinar is categorized under the Data Science and Engineering Analytics discipline.

All content contained within this webinar is copyrighted by Shahab D. Mohaghegh and its use and/or reproduction outside the portal requires express permission from Shahab D. Mohaghegh.

For more information on this topic, please check out the SPE suggested reading links below. Here you will find topic-related books, publications, and papers for purchase in the SPE Online Book Store as well as on OnePetro.

SPE Bookstore:

Data-Driven Reservoir Modeling

OnePetro Papers:

Data-Driven Vs. Traditional Reservoir Numerical Models: A Case Study Comparison of Applicability, Practicality and Performance

Data-Driven Reservoir Management of a Giant Mature Oilfield in the Middle East

Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling

Data Driven Analytics in Powder River Basin, WY

Modeling and History Matching of Hydrocarbon Production from Marcellus Shale Using Data Mining and Pattern Recognition Technologies

Video: Contribution of Artificial Intelligence and Machine Learning in U.S. DOE's Efforts During the Aftermath of Deepwater Horizon

Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

Reservoir Simulation Using Smart Proxy in SACROC Unit - Case Study

Smart Proxy: An Innovative Reservoir Management Tool; Case Study of a Giant Mature Oilfield in the UAE

Production Management Decision Analysis Using AI-Based Proxy Modeling of Reservoir Simulations – A Look-Back Case Study

Artificial Intelligence (AI) Assisted History Matching

Pattern Recognition and Data-Driven Analytics for Fast and Accurate Replication of Complex Numerical Reservoir Models at the Grid Block Level

Application of Well-Base Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study

Shale Descriptive Analytics; Which Parameters are Controlling Production in Shale

Mapping the Natural Fracture Network in Marcellus Shale

Mapping the Natural Fracture Network in Utica Shale Using Artificial Intelligence (AI)

Fact-Based Re-Frac Candidate Selection and Design in Shale - A Case Study in Application of Data Analytics

Shale Analytics: Making Production and Operational Decisions Based on Facts: A Case Study in Marcellus Shale

Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters

Understanding the Impact of Rock Properties and Completion Parameters on Estimated Ultimate Recovery in Shale

Formation vs. Completion: Determining the Main Drivers Behind Production From Shale; A Case Study Using Data-Driven Analytics

Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling

Using Data-Driven Analytics to Assess the Impact of Design Parameters on Production from Shale

Forecasting, Sensitivity and Economic Analysis of Hydrocarbon Production from Shale Plays Using Artificial Intelligence & Data Mining

Fast Track Reservoir Modeling of Shale Formations in the Appalachian Basin. Application to Lower Huron Shale in Eastern Kentucky

Field Development Strategies for Bakken Shale Formation

Shahab Mohaghegh

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 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 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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08/19/2020 at 9:30 AM (EDT)   |  75 minutes
08/19/2020 at 9:30 AM (EDT)   |  75 minutes
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0.15 CEU/1.5 PDH credits  |  Certificate available
0.15 CEU/1.5 PDH credits  |  Certificate available