Hoss Belyadi, Alireza Haghighat

Machine Learning Guide for Oil and Gas Using Python (eBook, ePUB)

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

• Format: ePub

Produktbeschreibung

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.

• 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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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 476
• Erscheinungstermin: 9. April 2021
• Englisch
• ISBN-13: 9780128219300
• Artikelnr.: 68400459

Autorenporträt

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.

Inhaltsangabe

1. Introduction to Machine Learning and Python2. Data Import and Visualization3. Machine Learning Workflows and Types4. Unsupervised Machine Learning: Clustering Algorithms5. Supervised Learning6. Neural Networks7. Model Evaluation8. Fuzzy Logic9. Evolutionary Optimization