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Overview: 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). It is known that many articles and texts prevent the reader from reproducing the results either because the data were not freely available or because the software was inaccessible, or only available for purchase. Therefore, it was my goal to be as hands-on as possible in stating the methods used, enabling the readers to reproduce the results and extend the methodology to their own data. Furthermore, the authors opted to use the Python language, an open-access software, for all stages of the Machine Learning process. Most of the chapters contain links to the data sets, Python notebooks, and software used here to reproduce the analyses in each chapter. I selected Python as the computational engine of this book for several reasons. First, Python is freely available for multiple operating systems. I encourage you to take the time to compare each of your solutions with the results in this book. Performing this comparison may help you become more familiar with a technique you could have used to solve a specific problem more efficiently.
Genre: Non-Fiction > Tech & Devices
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