Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network
We build surrogate models for dynamic 3D subsurface single-phase flow problems with multiple vertical producing wells. The surrogate model provides efficient pressure estimation of the entire formation at any timestep given a stochastic permeability field, arbitrary well locations and penetration lengths, and a timestep matrix as inputs. The well production rate or bottom hole pressure can then be determined based on Peaceman's formula. The original surrogate modeling task is transformed into an image-to-image regression problem using a convolutional encoder-decoder neural network architecture. The residual of the governing flow equation in its discretized form is incorporated into the loss function to impose theoretical guidance on the model training process. As a result, the accuracy and generalization ability of the trained surrogate models are significantly improved compared to those of fully data-driven models. They are also shown to possess flexible extrapolation ability to permeability fields with different statistics. The surrogate models are used to conduct uncertainty quantification considering a stochastic permeability field, as well as to infer unknown permeability information based on limited well production data and observation data of formation properties. Results are shown to be in close accordance with those of traditional numerical simulation tools, but computational efficiency is dramatically improved.
|WOS Research Area|
Engineering ; Geology ; Water Resources
Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
|WOS Accession No|
|EI Accession Number|
Computation theory ; Computational efficiency ; Convolution ; Convolutional neural networks ; Decoding ; Machine learning ; Network architecture ; Network coding ; Stochastic models ; Stochastic systems ; Uncertainty analysis
|ESI Classification Code|
Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Control Systems:731.1 ; Probability Theory:922.1 ; Systems Science:961
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||National Center for Applied Mathematics, SUSTech Shenzhen|
1.Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen,Guangdong,518055,China
2.National Center for Applied Mathematics Shenzhen (NCAMS),Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
3.College of Engineering,Peking University,Beijing,100871,China
|Corresponding Author Affilication||National Center for Applied Mathematics, SUSTech Shenzhen|
Xu，Rui,Zhang，Dongxiao,Wang，Nanzhe. Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network[J]. JOURNAL OF HYDROLOGY,2022,613.
Xu，Rui,Zhang，Dongxiao,&Wang，Nanzhe.(2022).Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network.JOURNAL OF HYDROLOGY,613.
Xu，Rui,et al."Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network".JOURNAL OF HYDROLOGY 613(2022).
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.