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

Description

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. 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