Machine Learning Applications in Subsurface Energy Resource Management

State of the Art and Future Prognosis

Srikanta Mishra

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032074528
• eBook ISBN: 9781000823899
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Computer Science General
• Index: Technology & Engineering

The utilization of machine learning (ML) techniques to understand hidden patterns and build data-driven predictive models from complex multivariate datasets is rapidly increasing in many applied science and engineering disciplines, including geo-energy. Motivated by these developments, Machine Learning Applications in Subsurface Energy Resource Management presents a current snapshot of the state of the art and future outlook for ML applications to manage subsurface energy resources (e.g., oil and gas, geologic carbon sequestration, and geothermal energy).

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• Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance)

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• Offers a variety of perspectives from authors representing operating companies, universities, and research organizations

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• Provides an array of case studies illustrating the latest applications of several ML techniques

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• Includes a literature review and future outlook for each application domain

This book is targeted at practicing petroleum engineers or geoscientists interested in developing a broad understanding of ML applications across several subsurface domains. It is also aimed as a supplementary reading for graduate-level courses and will also appeal to professionals and researchers working with hydrogeology and nuclear waste disposal.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Preface
• Acknowledgments
• Editor
• Contributors
• Section I Introduction
• Section II Reservoir Characterization Applications
• Section III Drilling Operations Applications
• Section IV Production Data Analysis Applications
• Section V Reservoir Modeling Applications
• Section VI Predictive Maintenance Applications
• Section VII Summary and Future Outlook
• Index