Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies-电子书百科大全

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

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

Author(s): Patrick Bangert

Publisher: Gulf Professional Publishing
Publication Date: 1 Mar. 2021
Language: English
Print length: 288 pages
ISBN-10: 0128207140
ISBN-13: 9780128207147

Book Description

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.

Editorial Reviews

Review

Helps readers understand the terminology, technology and skills needed to apply machine learning to oil and gas operations

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.

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