Machine Learning Applications in Subsurface Energy Resource Management

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<P>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, <B>Machine Learning Applications in Subsurface Energy Resource Management</B> 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).</P><P></P><UL><P><LI>Covers ML applications across multiple application domains (reservoir characterization, drilling, production, reservoir modeling, and predictive maintenance)</LI><P></P><P><LI>Offers a variety of perspectives from authors representing operating companies, universities, and research organizations</LI><P></P><P><LI>Provides an array of case studies illustrating the latest applications of several ML techniq