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Machine learning and data s...

Machine learning and data science in the oil and gas industry [best practices, tools, and case studies

Descripción del editor: "Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value" (Elsevier)

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monografia Rebiun28500447 https://catalogo.rebiun.org/rebiun/record/Rebiun28500447 cr 210422s2021 ne o 000 0 eng d 9780128209141 BUS rda Bangert, Patrick autor Machine learning and data science in the oil and gas industry Recurso electrónico] best practices, tools, and case studies Patrick Bangert Servicio en línea Bad Nauheim, germany Elsevier 2021 Bad Nauheim, germany Bad Nauheim, germany Elsevier 1 recurso en línea 1 recurso en línea Texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Descripción del editor: "Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value" (Elsevier) Aprendizaje automático Sciencedirect (Servicio en línea)

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monografia Rebiun28500447 https://catalogo.rebiun.org/rebiun/record/Rebiun28500447 cr 210422s2021 ne o 000 0 eng d 9780128209141 BUS rda Bangert, Patrick autor Machine learning and data science in the oil and gas industry Recurso electrónico] best practices, tools, and case studies Patrick Bangert Servicio en línea Bad Nauheim, germany Elsevier 2021 Bad Nauheim, germany Bad Nauheim, germany Elsevier 1 recurso en línea 1 recurso en línea Texto txt rdacontent ordenador c rdamedia recurso en línea cr rdacarrier Descripción del editor: "Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value" (Elsevier) Aprendizaje automático Sciencedirect (Servicio en línea)