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Machine Learning Guide for Oil and Gas Using Python

  • ID: 5180538
  • Book
  • April 2021
  • 300 Pages
  • Elsevier Science and Technology

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 is utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different data challenges.

  • Helps readers understand how open source Python can be utilized in practical oil and gas challenges
  • Covers the most commonly used algorithms for both supervised and unsupervised learning
  • Presents a balanced approach of both theory and practicality while progressing from introductory to advanced analytical techniques
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1. Introduction to Machine Learning and Python 2. Data Import and Visualization 3. Machine Learning Workflows and Types 4. Unsupervised Machine Learning: Clustering Algorithms 5. Supervised Learning 6. Neural Networks 7. Model Evaluation 8. Fuzzy Logic 9. Evolutionary Optimization

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Belyadi, Hoss
Hoss Belyadi is a senior data scientist/engineer at Vine Oil and Gas, LP. Additionally, he is the founder and CEO of Obsertelligence, LLC, focused on providing artificial intelligence (AI) in-house training and solutions. Mr. Belyadi has served as an adjunct faculty member at multiple universities, including West Virginia University, Marietta College, and Saint Francis University. There, he taught data analytics, natural gas engineering, enhanced oil recovery, and hydraulic fracture stimulation design. Mr. Belyadi has over 10 years of experience working in various conventional and unconventional reservoirs across the world. He has worked on various machine learning projects and held short courses across various universities, organizations, and the department of energy (DOE). Mr. Belyadi is the primary author of "Hydraulic Fracturing in Unconventional Reservoirs (1st and 2nd editions) and has authored and coauthored several SPE papers. Hoss earned his BS and MS, both in petroleum and natural gas engineering from West Virginia University.
Haghighat, Alireza
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.
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