Machine Learning for the Oil and Gas Industry Using Python

A Step-By-Step Breakdown with Data, Algorithms, Codes, and Applications

Author: Haghighat

Machine Learning for the Oil and Gas Industry Using Python

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Description

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.

About the Author

Hoss Belyadi is the founder and CEO of Obsertelligence, LLC, focused on providing artificial intelligence (AI) in-house training and solutions. As an adjunct faculty member at multiple universities, including West Virginia University, Marietta College, and Saint Francis University, Mr. Belyadi taught data analytics, natural gas engineering, enhanced oil recovery, and hydraulic fracture stimulation design. With over 10 years of experience working in various conventional and unconventional reservoirs across the world, he works on diverse machine learning projects and holds short courses across various universities, organizations, and the department of energy (DOE). Mr. Belyadi is the primary author of Hydraulic Fracturing in Unconventional Reservoirs (first and second editions) and is the author of Machine Learning Guide for Oil and Gas Using Python. Hoss earned his BS and MS, both in petroleum and natural gas engineering from West Virginia University. Dr. Alireza Haghighat is a senior technical advisor and instructor for Engineering Solutions at IHS Markit, focusing on reservoir/production engineering and data analytics. Prior to joining IHS, he was a senior reservoir engineer at Eclipse/Montage resources for nearly five years. As a reservoir engineer, he was involved in well performance evaluation with data analytics, rate transient analysis of unconventional assets (Utica and Marcellus), asset development, hydraulic fracture/reservoir simulation, DFIT analysis, and reserve evaluation. He has been an adjunct faculty member at Pennsylvania State University (PSU) for the past 5 years, teaching courses in Petroleum Engineering/Energy, Business and Finance departments. Dr. Haghighat has published several technical papers and book chapters on machine learning applications in smart wells, CO2 sequestration modeling, and production analysis of unconventional reservoirs. He has received his PhD in petroleum and natural gas engineering from West Virginia University and a master’s degree in petroleum engineering from Delft University of Technology.