 | Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis Subjects: Computer Science; Earth Sciences; Engineering & Technology; Power & Energy; Mining Mineral & Petroleum Engineering; Artificial Intelligence; Earth Sciences; Systems & Control Engineering; Machine Learning - Design; Energy & Fuels; Machine Learning; Geochemistry; 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. Dr. Srikanta Mishra is Senior Research Leader and Technical Director for Geo-energy Resource Modeling and Analytics at Battelle Memorial Institute, the world's largest independent contract R&D organization. He is nationally and internationally recognized for his expertise in developing and communicating physics-based and data-driven predictive models for subsurface resource management. Dr. Mishra currently serves as the Technical Lead of the SMART (Science Informed Machine Learning for Accelerating Real-time Decisions for Subsurface applications) initiative, organized by the US Department of Energy and involving multiple national laboratories and universities. He was a recipient of the Society of Petroleum Engineers (SPE) Distinguished Member Award in 2021, and also served as a Global Distinguished Lecturer on Big Data Analytics for SPE during 2018-19 and received the 2022 SPE Data Science and Engineering Analytics Award. |
