Machine Learning Applications in Subsurface Energy Resource Management - State of the Art and Future Prognosis

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 techniq