中文版 | English
Title

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

Author
Corresponding AuthorZhang,Dongxiao
Publication Years
2022-10-01
DOI
Source Title
ISSN
0022-1694
EISSN
1879-2707
Volume613
Abstract
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.
Keywords
URL[Source Record]
Indexed By
SCI ; EI
Language
English
SUSTech Authorship
Corresponding
WOS Research Area
Engineering ; Geology ; Water Resources
WOS Subject
Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS Accession No
WOS:000862505600004
Publisher
EI Accession Number
20223612699912
EI Keywords
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
ENGINEERING
Scopus EID
2-s2.0-85137165561
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401606
DepartmentNational Center for Applied Mathematics, SUSTech Shenzhen
Affiliation
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 AffilicationNational Center for Applied Mathematics, SUSTech Shenzhen
Recommended Citation
GB/T 7714
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.
APA
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.
MLA
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).
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