Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network

•TgCNN is used for surrogate modeling of 3D subsurface flow problems.•Dynamic pressure estimation can be obtained given stochastic permeability fields.•Uncertainty quantification and inverse modeling tasks are studied.•TgCNN-based surrogate models show improved efficiency with high accuracy. We buil...

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Vydané v:	Journal of hydrology (Amsterdam) Ročník 613; s. 128321
Hlavní autori:	Xu, Rui, Zhang, Dongxiao, Wang, Nanzhe
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2022
Predmet:	Convolutional neural network equations Inverse problem mathematical models permeability Subsurface flow Surrogate modeling Theory-guided machine learning uncertainty Uncertainty quantification Uncertainty quantification Subsurface flow Surrogate modeling Convolutional neural network Theory-guided machine learning Inverse problem
ISSN:	0022-1694, 1879-2707
On-line prístup:	Získať plný text
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