Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Health, Safety, Environment, and Sustainability | Production and Operations | Projects, Facilities, and Construction | Reservoir
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.
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).
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 applicat…
Disciplines: Data Science and Engineering Analytics | Reservoir
CCS Analytics – AI-based Carbon Capture and StorageEngineering application of Artificial Intelligence & Machine Learning will significantly address Climate Change in the next several decades. The main reason of positive and important contribution of Artificial Intelligence to Climate Change has much …
Disciplines: Data Science and Engineering Analytics | Production and Operations | Reservoir
Data-Driven Reservoir ModelingData-Driven Reservoir Modeling (Reservoir Analytics) is defined as the application of Artificial Intelligence and Machine Learning in fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) is the manifestation of the digita…
Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Production and Operations | Reservoir
Engineering Application of Artificial Intelligence and Machine LearningIt is very important to learn what are the main characteristics and requirements in applying Artificial Intelligence and Machine Learning to solve Engineering related problems. To learn and apply Science and Engineering, Homo Sapiens must take a serious …
Disciplines: Data Science and Engineering Analytics
Artificial Intelligence and Machine Learning is revolutionizing many industries. This technology is becoming an important point of competitive differentiation in the upstream oil and gas industry. Since optimization of production and enhanced recovery i…
Disciplines: Completions | Production and Operations | Reservoir
Shale Analytics: AI-based Production Optimization in ShaleData-driven analytics is becoming an important point of competitive differentiation in the upstream oil and gas industry. When it comes to production from shale, companies are realizing that they possess a vast source of important facts and information i…
Disciplines: Data Science and Engineering Analytics | Reservoir
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 Smar…
Disciplines: Data Science and Engineering Analytics | Reservoir
SPE Petroleum Data Analytics Series - Week one: Subsurface AnalyticsPetroleum Data Analytics is the application of Artificial Intelligence and Machine Learning in the oil and gas industry. Future of our industry will be highly influenced by Petroleum Data Analytics. Engineering-domain experts who become highly skilled AI…
Disciplines: Data Science and Engineering Analytics | Reservoir