Book Companion
Machine Learning Guide for Oil and Gas Using Python
Edition 1
Welcome to the companion site for Machine Learning Guide for Oil and Gas Using Python, 1st Edition.
Welcome to the companion site for Machine Learning Guide for Oil and Gas Using Python, 1st Edition.
Resources
Downloadable content
Book Chapters
This site contains exercises with synthetically generated databases which are referenced in each of the book’s chapters.
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.
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Chapter 1. Introduction to Machine Learning and Python
Chapter 2. Data Import and Visualization
Chapter 3. Machine Learning Workflows and Types
Chapter 4. Unsupervised Machine Learning: Clustering Algorithms
Chapter 5. Supervised Learning
Chapter 6. Neural Networks
Chapter 7. Model Evaluation
Chapter 8. Fuzzy Logic
Chapter 9. Evolutionary Optimization
About the Authors:
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). 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. He was also an adjunct faculty member at Pennsylvania State University (PSU), 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.
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