Machine Learning Applications in Subsurface Energy Resource Management e-bog
1021,49 DKK
(inkl. moms 1276,86 DKK)
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 stat...
E-bog
1021,49 DKK
Forlag
CRC Press
Udgivet
27 december 2022
Længde
360 sider
Genrer
RBGK
Sprog
English
Format
epub
Beskyttelse
LCP
ISBN
9781000823899
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 organizationsProvides an array of case studies illustrating the latest applications of several ML techniquesIncludes a literature review and future outlook for each application domainThis 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.