Machine Learning Applications in Subsurface Energy Resource Management: State of the Art and Future Prognosis
Srikanta Mishra
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).
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).
درجه (قاطیغوری(:
کال:
2024
خپرندویه اداره:
CRC Press
ژبه:
english
صفحه:
378
ISBN 10:
1032074558
ISBN 13:
9781032074559
فایل:
PDF, 20.85 MB
IPFS:
,
english, 2024
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