A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

A machine learning framework for rapid forecasting and history matching in unconventional reservoirs

Abstract We present a novel workflow for forecasting production in unconventional reservoirs using reduced-order models and machine-learning. Our physics-informed machine-learning workflow addresses the challenges to real-time reservoir management in unconventionals, namely the lack of data (i.e., t...

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Autores principales: Shriram Srinivasan, Daniel O’Malley, Maruti K. Mudunuru, Matthew R. Sweeney, Jeffrey D. Hyman, Satish Karra, Luke Frash, J. William Carey, Michael R. Gross, George D. Guthrie, Timothy Carr, Liwei Li, Hari S. Viswanathan
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/19a3100ba9fc42afae2d745226851b22
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https://doaj.org/article/19a3100ba9fc42afae2d745226851b22

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