Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling

We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs–by three orders of magnitude–compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task modeling a simulator as an end-to-end black box...

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Detalles Bibliográficos
Autores principales: Navrátil, Jiří, King, Alan, Rios, Jesus, Kollias, Georgios, Torrado, Ruben, Codas, Andrés
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931866/
https://www.ncbi.nlm.nih.gov/pubmed/33693356
http://dx.doi.org/10.3389/fdata.2019.00033

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931866/
https://www.ncbi.nlm.nih.gov/pubmed/33693356
http://dx.doi.org/10.3389/fdata.2019.00033

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