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Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis

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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).

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

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

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

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

This book is targeted at the practicing petroleum engineer or geoscientist 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.

Author(s): Srikanta Mishra
Publisher: CRC Press
Year: 2024

Language: English
Pages: 378
City: Boca Raton

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
Editor
Contributors
SECTION I: Introduction
Chapter 1: Machine Learning Applications in Subsurface Energy Resource Management: State of the Art
Chapter 2: Solving Problems with Data Science
SECTION II: Reservoir Characterization Applications
Chapter 3: Machine Learning-Aided Characterization Using Geophysical Data Modalities
Chapter 4: Machine Learning to Discover, Characterize, and Produce Geothermal Energy
SECTION III: Drilling Operations Applications
Chapter 5: Real-Time Drilling and Completion Analytics: From Cloud Computing to Edge Computing and Their Machine Learning Applications
Chapter 6: Using Machine Learning to Improve Drilling of Unconventional Resources
SECTION IV: Production Data Analysis Applications
Chapter 7: Machine Learning Assisted Production Data Filtering and Decline Curve Analysis in Unconventional Plays
Chapter 8: Hybrid Data-Driven and Physics-Informed Reservoir Modeling for Unconventional Reservoirs
Chapter 9: Role of Analytics in Extracting Data-Driven Models from Reservoir Surveillance
Chapter 10: Machine Learning Assisted Forecasting of Reservoir Performance
SECTION V: Reservoir Modeling Applications
Chapter 11: An Efficient Deep Learning Based Workflow Incorporating a Reduced Physics Model for Drainage Volume Visualization in Unconventional Reservoirs
Chapter 12: Reservoir Modeling Using Fast Predictive Machine Learning Algorithms for Geological Carbon Storage
Chapter 13: Physics-Embedded Machine Learning for Modeling and Optimization of Mature Fields
Chapter 14: Deep Neural Network Surrogate Flow Models for History Matching and Uncertainty Quantification
Chapter 15: Generalizable Field Development Optimization Using Deep Reinforcement Learning with Field Examples
SECTION VI: Predictive Maintenance Applications
Chapter 16: Case Studies Involving Machine Learning for Predictive Maintenance in Oil and Gas Production Operations
Chapter 17: Machine Learning for Multiphase Flow Metering
SECTION VII: Summary and Future Outlook
Chapter 18: Machine Learning Applications in Subsurface Energy Resource Management: Future Prognosis
Index