Python for Petroleum Data Analytics


Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Health, Safety, Environment, and Sustainability | Production and Operations | Projects, Facilities, and Construction | Reservoir

Course Description

Combining petroleum engineering domain expertise with computer programming using "Python" as the most popular coding language for data science, artificial intelligence and machine learning, this course enables petroleum engineering professionals to build predictive models to solve the most common petroleum engineering problems through data analytics.

Learning Level

Introductory to Intermediate

Course Length

2 Days

Why Attend

Data science has proven to be a very important technology in the upstream oil and gas industry. An overwhelming majority of petroleum engineering professionals and geoscientists are currently interested in learning technologies associated with data science, including artificial intelligence and machine learning. This course provides the foundations of understanding and learning to use these technologies through free and open source computer technology

Who Attends

Petroleum engineering professionals and geoscientists.

CEUs

1.6 CEUs are offered for this course

Additional Resources

This course has a supplemental book located in our SPE Bookstore entitled Data-Driven Reservoir Modeling. Please check out this valuable resource!

Cancellation Policy

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.

Instructor

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).

Other courses by this instructor

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Shale Analytics: AI-based Production Optimization in Shale
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Smart Proxy Modeling – Engineering Application of Artificial Intelligence in Numerical Simulation
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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 Sm...

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