Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis
Srikanta Mishra, 9781032074528, 9781003207009, 978-1-032-07452-8, 978-1-032-07455-9, 978-1-003-20700-9, 978-1032074559, 9781032074559, 1032074523, 1032074558, B0BNCTDSD4
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.