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

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., the time-f...

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Detalles Bibliográficos
Autores principales: Srinivasan, Shriram, O’Malley, Daniel, Mudunuru, Maruti K., Sweeney, Matthew R., Hyman, Jeffrey D., Karra, Satish, Frash, Luke, Carey, J. William, Gross, Michael R., Guthrie, George D., Carr, Timothy, Li, Liwei, Viswanathan, Hari S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571309/
https://www.ncbi.nlm.nih.gov/pubmed/34741046
http://dx.doi.org/10.1038/s41598-021-01023-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8571309/
https://www.ncbi.nlm.nih.gov/pubmed/34741046
http://dx.doi.org/10.1038/s41598-021-01023-w

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