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

Name: Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies
Brand: Gulf Professional Publishing
SKU: 9780128207147
Price: 8405.0 INR
Availability: InStock

by Patrick Bangert

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ISBN13: 9780128207147
Binding: Paperback
Subject: N/A
Publisher: Gulf Professional Publishing
Publisher Imprint: Gulf Professional Publishing
Publication Date: March 8, 2021
Pages: 306
Original Price: USD 145.0
Language: English
Edition: N/A
Item Weight: 391 grams
BISAC Subject(s): Power Resources / Electrical, Artificial Intelligence / General, and Energy

About The Book

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.

About The Author

Bangert, Patrick: - 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.

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