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£90.52

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

Name: Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis
Brand: CRC Press
Price: 90.52 GBP
Availability: InStock

9781032074528

AI & Machine Learning

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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 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.

Product Specifications

Brand: CRC Press
Model: 9781032074528
Format: Hardcover
ASIN: 1032074523
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 27 December 2022
Listed Since: 26 July 2022

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

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