Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications

Author:   Hoss Belyadi (Founder and CEO, Obsertelligence, LLC, PA, USA) ,  Alireza Haghighat (Senior Technical Advisor and Instructor for Engineering Solutions, IHS Markit, TX, USA)
Publisher:   Elsevier Science & Technology
ISBN:  

9780128219294


Pages:   476
Publication Date:   13 April 2021
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications


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Author:   Hoss Belyadi (Founder and CEO, Obsertelligence, LLC, PA, USA) ,  Alireza Haghighat (Senior Technical Advisor and Instructor for Engineering Solutions, IHS Markit, TX, USA)
Publisher:   Elsevier Science & Technology
Imprint:   Gulf Professional Publishing
Weight:   0.770kg
ISBN:  

9780128219294


ISBN 10:   0128219297
Pages:   476
Publication Date:   13 April 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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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Author Information

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.

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