ISBN: 9781032074528 - machine learning applications in subsurface energy resource management: state of the art and future prognosis (6 Ergebnisse)

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Zustand: New. In English.

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Hardcover. Zustand: Brand New. 384 pages. 9.00x6.00x1.02 inches. In Stock.

Buch. Zustand: Neu. Neuware - 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 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.…