Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies - Softcover

Softcover

ISBN 10: 0128207140 ISBN 13: 9780128207147

Publisher: Gulf Professional Publishing, 2021

Synopsis

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

• Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful
• Gain practical understanding of machine learning used in oil and gas operations through contributed case studies
• Learn change management skills that will help gain confidence in pursuing the technology
• Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)

About this title

About the Author

Dr. Patrick Bangert is the Vice President of Artificial Intelligence at Samsung SDS where he leads both the AI software development and AI consulting groups that each provide various offerings to the industry. He is the founder and Board Chair of Algorithmica Technologies, providing real-time process modeling, optimization, and predictive maintenance solutions to the process industry with a focus on chemistry and power generation. His doctorate from UCL specialized in applied mathematics, and his academic positions at NASA’s Jet Propulsion Laboratory and Los Alamos National Laboratory made use of optimization and machine learning for magnetohydrodynamics and particle accelerator experiments. He has published extensively across optimization and machine learning and their relevant applications in the real world.

From the Back Cover

Today’s more complex oil and gas fields rely on quality of data, new software, and upcoming technology, but engineers are trained in the proven workflows and mechanisms of using large data sets in more sophisticated technology, such as machine learning.

Machine Learning and Data Science in the Oil and Gas Industry explains when the critical facets around machine learning specifically tailored to oil and gas cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in approach, the reference provides a chapter devoted to the early career engineer that is just starting in the industry and then builds up a full-scale project, supported by real-world case studies from various industry and academic contributors. Lessons learned and technology drivers are discussed, carving a path for future engineers to apply. Rounding out with a glossary, Machine Learning and Data Science in the Oil and Gas Industry delivers a reference to cut through the hype and help petroleum engineers today understand machine learning and where it will benefit their operations.

Edition details

• Publisher: Gulf Professional Publishing
• Publication date: 2021
• ISBN 10: 0128207140
• ISBN 13: 9780128207147
• Binding: Paperback
• Language: English
• Edition number: 1
• Number of pages: 306
• Editor: Bangert Patrick