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@article{Silva:2021:10.1016/j.cma.2021.113989,
author = {Silva, VLS and Salinas, P and Jackson, MD and Pain, CC},
doi = {10.1016/j.cma.2021.113989},
journal = {Computer Methods in Applied Mechanics and Engineering},
pages = {1--17},
title = {Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow},
url = {http://dx.doi.org/10.1016/j.cma.2021.113989},
volume = {384},
year = {2021}
}
TY - JOUR
AB - A machine learning approach to accelerate convergence of the nonlinear solver in multiphase flow problems is presented here. The approach dynamically controls an acceleration method based on numerical relaxation. It is demonstrated in a Picard iterative solver but is applicable to other types of nonlinear solvers. The aim of the machine learning acceleration is to reduce the computational cost of the nonlinear solver by adjusting to the complexity/physics of the system. Using dimensionless parameters to train and control the machine learning enables the use of a simple two-dimensional layered reservoir for training, while also exploring a wide range of the parameter space. Hence, the training process is simplified and it does not need to be rerun when the machine learning acceleration is applied to other reservoir models. We show that the method can significantly reduce the number of nonlinear iterations without compromising the simulation results, including models that are considerably more complex than the training case.
AU - Silva,VLS
AU - Salinas,P
AU - Jackson,MD
AU - Pain,CC
DO - 10.1016/j.cma.2021.113989
EP - 17
PY - 2021///
SN - 0045-7825
SP - 1
TI - Machine learning acceleration for nonlinear solvers applied to multiphase porous media flow
T2 - Computer Methods in Applied Mechanics and Engineering
UR - http://dx.doi.org/10.1016/j.cma.2021.113989
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000681089400011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://www.sciencedirect.com/science/article/pii/S0045782521003200?via%3Dihub
UR - http://hdl.handle.net/10044/1/92279
VL - 384
ER -