Python for Petroleum Data Analytics
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.
Introductory to Intermediate
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
Petroleum engineering professionals and geoscientists.
1.6 CEUs are offered for this 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.
Shahab D. Mohaghegh, a pioneer in the application of AI, machine learning and data mining 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 holds BS, MS, and PhD degrees in petroleum and natural gas engineering.
He has authored three books, more than 170 technical papers and carried out more than 60 projects for independents, national and international oil companies. He is an SPE Distinguished Lecturer and has been featured four times as a Distinguished Author in SPE’s Journal of Petroleum Technology. He is the founder of the SPE Petroleum Data-Driven Analytics Technical Section dedicated to AI, machine learning and data mining. He was honored by the U.S. Secretary of Energy for his technical contribution in the aftermath of the Deepwater Horizon (Macondo) incident in the Gulf of Mexico and was a member of the U.S. Secretary of Energy’s Unconventional Resources Technical Advisory Committee in two administrations (2008-2014). He recently represented the United States on the International Standard Organization (ISO) carbon capture and storage technical committee (2014-2016).