Shahab D. Mohaghegh

Intelligent Solutions, University of West Virginia

Shahab D. Mohaghegh, a pioneer in the application of artificial intelligence, machine learning, and data mining in the exploration and production industry, is the president and chief executive officer of Intelligent Solutions and professor of petroleum and natural gas engineering at West Virginia University. He holds BS, MS, and PhD degrees in petroleum and natural gas engineering.

Mohaghegh has authored three books and more than 170 technical papers and has carried out more than 60 projects for independent, national, and international oil companies. He is an SPE Distinguished Lecturer and has been featured four times as Distinguished Author in the Journal of Petroleum Technology.

Mohaghegh is the founder of Petroleum Data-Driven Analytics, SPE’s Technical Section dedicated to machine learning and data mining. He has been honored by the US Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) disaster in the Gulf of Mexico and was a member of the US Secretary of Energy’s Technical Advisory Committee on Unconventional Resources in two administrations (2008–2014). Mohaghegh represented the United States in the International Standard Organization on Carbon Capture and Storage (2014–2016).

AI/machine learning

Ethics of Artificial Intelligence Plays a Role in Engineering

The ethics of artificial intelligence (AI) has become an important topic in the application of AI and machine learning in the past several years. This second part of a two-part series presents the relevance and use of the ethics of AI in engineering applications. Part 1 explains the evolution and importance of AI ethics.

October 4, 2021 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

The Ethics of AI Evolves With the Technology

The ethics of artificial intelligence (AI) has become an important topic in the application of AI and machine learning in the past several years. This first part of a two-part series explains the evolution and importance of the ethics of AI. The second part will present its relevance and use in engineering applications.

September 24, 2021 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

Explainable Artificial Intelligence Thrives in Petroleum Data Analytics

One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This second part of a two-part series presents the use of XA…

June 30, 2021 • Shahab D. Mohaghegh • Data Science and Digital Engineering
Data & Analytics

In Petroleum Data Analytics, Artificial Intelligence Avoids the Black Box

One of the major characteristics of petroleum data analytics is its incorporation of explainable artificial intelligence (XAI). Predictive models of petroleum data analytics are not represented through unexplainable black-box behavior. Predictive models of petroleum data analytics are reasonably explainable. This first part of a two-part series presents the history of…

June 18, 2021 • Shahab D. Mohaghegh • Data Science and Digital Engineering
Data mining/analysis

New Series Tackles Petroleum Data Analytics From the Subsurface up

SPE is planning a series on petroleum data analytics at its Houston Training Center. The series will kick off with Week One: Subsurface Analytics on 24–28 February and will be led by University of West Virginia Professor of Petroleum and Natural Gas Engineering Shahab Mohaghegh.

January 19, 2020 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

The Difference Between Engineering and Nonengineering Problem-Solving Using AI and ML

Major differences exist between engineering- and nonengineering-related problems. This fact results in major differences between engineering and nonengineering applications of artificial intelligence and machine learning.

December 30, 2019 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

Traditional Statistics vs. Artificial Intelligence and Machine Learning

Just because they both deal with data does not mean they are the same.

November 10, 2019 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

How Does the Use of Artificial Intelligence and Machine Learning Differ for Conventional vs. Unconventional Plays?

What is the effect of the reservoir type on the application of AI and ML in reservoir and production modeling?

October 6, 2019 • Shahab D. Mohaghegh • Data Science and Digital Engineering
AI/machine learning

Modeling Physics With Artificial Intelligence and Machine Learning

Blending smart-proxy models with data-driven models to create hybrid models is not always the best idea for physics- and engineering-related applications.

September 8, 2019 • Shahab D. Mohaghegh • Data Science and Digital Engineering
Mature fields

Young Technology Showcase—Top-Down Modeling: A Shift in Building Full-Field Models for Mature Fields

Data-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.

Locked
June 30, 2016 • Shahab D. Mohaghegh • Journal of Petroleum Technology