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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| Veröffentlicht in: | Journal of hydrology (Amsterdam) Jg. 613; S. 128321 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.10.2022
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| ISSN: | 0022-1694, 1879-2707 |
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| Abstract | •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 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. |
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| AbstractList | •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 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. 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. |
| ArticleNumber | 128321 |
| Author | Wang, Nanzhe Xu, Rui Zhang, Dongxiao |
| Author_xml | – sequence: 1 givenname: Rui surname: Xu fullname: Xu, Rui organization: Department of Mathematics and Theories, Peng Cheng Laboratory, Shenzhen 518055, Guangdong, PR China – sequence: 2 givenname: Dongxiao surname: Zhang fullname: Zhang, Dongxiao email: zhangdx@sustech.edu.cn organization: National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology, Shenzhen 518055, Guangdong, PR China – sequence: 3 givenname: Nanzhe surname: Wang fullname: Wang, Nanzhe organization: College of Engineering, Peking University, Beijing 100871, PR China |
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| Cites_doi | 10.1016/j.jcp.2010.07.005 10.1016/j.jcp.2018.08.036 10.1016/j.jcp.2018.04.018 10.1016/j.jcp.2018.10.045 10.1016/j.jcp.2021.110318 10.1007/s10596-015-9478-7 10.1016/j.advwatres.2021.103941 10.1029/2020JB020549 10.1002/2017WR021884 10.1016/j.jhydrol.2020.124700 10.1029/2020WR027568 10.1016/j.jcp.2003.09.015 10.1109/TKDE.2017.2720168 10.1016/j.cma.2021.114037 10.1038/nature14539 10.1109/ICNN.1995.488968 10.1038/s43588-021-00171-3 10.1016/j.advwatres.2022.104180 10.1016/j.cma.2020.113492 10.1029/2018WR024638 10.1029/2018WR023528 10.1162/EVCO_r_00180 10.1016/j.fuel.2020.117750 10.1016/j.petrol.2021.109545 10.1029/2019WR026082 10.1016/j.jcp.2017.11.039 10.1016/j.jcp.2019.05.024 10.1002/2013WR014630 10.2118/59802-PA 10.1016/j.fuel.2016.05.011 10.1016/j.jngse.2015.07.005 10.1016/j.petrol.2020.107273 10.1016/j.cageo.2012.03.011 10.1016/j.advwatres.2021.104009 |
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| Keywords | Uncertainty quantification Subsurface flow Surrogate modeling Convolutional neural network Theory-guided machine learning Inverse problem |
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| References | LeCun, Bengio, Hinton (b0075) 2015; 521 Wang, N., Chang, H., Zhang, D., 2021d. Deep-learning-based inverse modeling approaches: A subsurface flow example. Journal of Geophysical Research: Solid Earth 126, e2020JB020549. 10.1029/2020JB020549. Mo, Zabaras, Shi, Wu (b0085) 2020; 56 Xu, Prodanović, Landry (b0165) 2020; 56 Gao, Zhang, Yang, Liu (b0030) 2010 Xu, Zhang, Rong, Wang (b0175) 2021; 436 Zhu, Zabaras, Koutsourelakis, Perdikaris (b0205) 2019; 394 Mo, Zabaras, Shi, Wu (b0090) 2019; 55 Goodfellow, Bengio, Courville (b0040) 2016 Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, in: Proceedings of ICNN’95 - International Conference on Neural Networks. Presented at the Proceedings of ICNN’95 - International Conference on Neural Networks, pp. 1942–1948 vol.4. 10.1109/ICNN.1995.488968. Wang, Chang, Zhang (b0130) 2021; 385 Yin, Qu, Zhang, Zhang, Wang (b0180) 2020; 273 Chang, Zhang (b0015) 2015; 19 Raissi, Perdikaris, Karniadakis (b0105) 2019; 378 Chang, Zhang, Lu (b0020) 2010; 229 Emerick, Reynolds (b0025) 2013; 55 Jin, Liu, Durlofsky (b0045) 2020; 192 Zha, Yeh, Illman, Zeng, Zhang, Sun, Shi (b0185) 2018; 54 Aziz, Settari (b0005) 1979 Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer, in: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). Presented at the 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73. 10.1109/ICEC.1998.699146. Zhu, Zabaras (b0200) 2018; 366 Mo, Zhu, Zabaras, Shi, Wu (b0095) 2019; 55 Kadeethum, O’Malley, Fuhg, Choi, Lee, Viswanathan, Bouklas (b0055) 2021 Zhang, Li, Tchelepi (b0190) 2000; 5 Kitanidis, Lee (b0070) 2014; 50 Wang, Zhang, Chang, Li (b0150) 2020; 584 Wang, Chang, Zhang (b0135) 2021; 1–29 Karpatne, Atluri, Faghmous, Steinbach, Banerjee, Ganguly, Shekhar, Samatova, Kumar (b0060) 2017; 29 Wang, Chang, Zhang (b0125) 2021; 373 Li, Zhang, Li (b0080) 2015; 26 Wen, G., Li, Z., Azizzadenesheli, K., Anandkumar, A., Benson, S.M., 2021b. U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow. arXiv:2109.03697 [physics]. Tripathy, Bilionis (b0120) 2018; 375 Bonyadi, Michalewicz (b0010) 2017; 25 Wang, Chang, Zhang, Xue, Chen (b0140) 2022; 208 Song, Yao, Li, Sun, Zhang, Yang, Zhao, Sui (b0115) 2016; 181 Xu, Wang, Zhang (b0170) 2021; 153 Ghanem, Spanos (b0035) 2003 Jo, Son, Hwang, Kim (b0050) Raissi, Karniadakis (b0100) 2018; 357 Wen, Hay, Benson (b0160) 2021; 155 Zhang, Lu (b0195) 2004; 194 Mo (10.1016/j.jhydrol.2022.128321_b0095) 2019; 55 Jin (10.1016/j.jhydrol.2022.128321_b0045) 2020; 192 Yin (10.1016/j.jhydrol.2022.128321_b0180) 2020; 273 Tripathy (10.1016/j.jhydrol.2022.128321_b0120) 2018; 375 Goodfellow (10.1016/j.jhydrol.2022.128321_b0040) 2016 Ghanem (10.1016/j.jhydrol.2022.128321_b0035) 2003 10.1016/j.jhydrol.2022.128321_b0110 Wang (10.1016/j.jhydrol.2022.128321_b0135) 2021; 1–29 10.1016/j.jhydrol.2022.128321_b0155 LeCun (10.1016/j.jhydrol.2022.128321_b0075) 2015; 521 Mo (10.1016/j.jhydrol.2022.128321_b0085) 2020; 56 Wang (10.1016/j.jhydrol.2022.128321_b0125) 2021; 373 Wang (10.1016/j.jhydrol.2022.128321_b0140) 2022; 208 Xu (10.1016/j.jhydrol.2022.128321_b0175) 2021; 436 Zhang (10.1016/j.jhydrol.2022.128321_b0195) 2004; 194 Raissi (10.1016/j.jhydrol.2022.128321_b0100) 2018; 357 Chang (10.1016/j.jhydrol.2022.128321_b0020) 2010; 229 Karpatne (10.1016/j.jhydrol.2022.128321_b0060) 2017; 29 10.1016/j.jhydrol.2022.128321_b0145 Zhang (10.1016/j.jhydrol.2022.128321_b0190) 2000; 5 Bonyadi (10.1016/j.jhydrol.2022.128321_b0010) 2017; 25 Jo (10.1016/j.jhydrol.2022.128321_b0050) Chang (10.1016/j.jhydrol.2022.128321_b0015) 2015; 19 10.1016/j.jhydrol.2022.128321_b0065 Song (10.1016/j.jhydrol.2022.128321_b0115) 2016; 181 Kitanidis (10.1016/j.jhydrol.2022.128321_b0070) 2014; 50 Wen (10.1016/j.jhydrol.2022.128321_b0160) 2021; 155 Kadeethum (10.1016/j.jhydrol.2022.128321_b0055) 2021 Li (10.1016/j.jhydrol.2022.128321_b0080) 2015; 26 Xu (10.1016/j.jhydrol.2022.128321_b0170) 2021; 153 Emerick (10.1016/j.jhydrol.2022.128321_b0025) 2013; 55 Mo (10.1016/j.jhydrol.2022.128321_b0090) 2019; 55 Wang (10.1016/j.jhydrol.2022.128321_b0150) 2020; 584 Zha (10.1016/j.jhydrol.2022.128321_b0185) 2018; 54 Wang (10.1016/j.jhydrol.2022.128321_b0130) 2021; 385 Raissi (10.1016/j.jhydrol.2022.128321_b0105) 2019; 378 Gao (10.1016/j.jhydrol.2022.128321_b0030) 2010 Xu (10.1016/j.jhydrol.2022.128321_b0165) 2020; 56 Zhu (10.1016/j.jhydrol.2022.128321_b0205) 2019; 394 Aziz (10.1016/j.jhydrol.2022.128321_b0005) 1979 Zhu (10.1016/j.jhydrol.2022.128321_b0200) 2018; 366 |
| References_xml | – volume: 194 start-page: 773 year: 2004 end-page: 794 ident: b0195 article-title: An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Loève and polynomial expansions publication-title: J. Comput. Phys. – volume: 1–29 year: 2021 ident: b0135 article-title: Efficient uncertainty quantification and data assimilation via theory-guided convolutional neural network publication-title: SPE J. – start-page: 67 year: 2010 end-page: 71 ident: b0030 article-title: An improved Sobel edge detection publication-title: 2010 3rd International Conference on Computer Science and Information Technology. Presented at the 2010 3rd International Conference on Computer Science and Information Technology – reference: Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer, in: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). Presented at the 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73. 10.1109/ICEC.1998.699146. – volume: 378 start-page: 686 year: 2019 end-page: 707 ident: b0105 article-title: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. – volume: 155 year: 2021 ident: b0160 article-title: CCSNet: A deep learning modeling suite for CO2 storage publication-title: Adv. Water Resour. – volume: 55 start-page: 3 year: 2013 end-page: 15 ident: b0025 article-title: Ensemble smoother with multiple data assimilation publication-title: Comput. Geosci. – year: 2016 ident: b0040 article-title: Deep learning – volume: 394 start-page: 56 year: 2019 end-page: 81 ident: b0205 article-title: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data publication-title: J. Comput. Phys. – volume: 19 start-page: 727 year: 2015 end-page: 746 ident: b0015 article-title: Jointly updating the mean size and spatial distribution of facies in reservoir history matching publication-title: Comput. Geosci. – volume: 56 year: 2020 ident: b0085 article-title: Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities publication-title: Water Resour. Res. – year: 2003 ident: b0035 article-title: Stochastic finite elements: A spectral approach – volume: 29 start-page: 2318 year: 2017 end-page: 2331 ident: b0060 article-title: Theory-guided data science: A new paradigm for scientific discovery from data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 181 start-page: 973 year: 2016 end-page: 984 ident: b0115 article-title: Apparent gas permeability in an organic-rich shale reservoir publication-title: Fuel – volume: 366 start-page: 415 year: 2018 end-page: 447 ident: b0200 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. – volume: 208 year: 2022 ident: b0140 article-title: Efficient well placement optimization based on theory-guided convolutional neural network publication-title: J. Petrol. Sci. Eng. – volume: 229 start-page: 8011 year: 2010 end-page: 8030 ident: b0020 article-title: History matching of facies distribution with the EnKF and level set parameterization publication-title: J. Comput. Phys. – volume: 5 start-page: 60 year: 2000 end-page: 70 ident: b0190 article-title: Stochastic formulation for uncertainty analysis of two-phase flow in heterogeneous reservoirs publication-title: SPE J. – ident: b0050 article-title: Deep neural network approach to forward-inverse problems – volume: 385 year: 2021 ident: b0130 article-title: Theory-guided auto-encoder for surrogate construction and inverse modeling publication-title: Comput. Methods Appl. Mech. Eng. – volume: 50 start-page: 5428 year: 2014 end-page: 5443 ident: b0070 article-title: Principal component geostatistical approach for large-dimensional inverse problems publication-title: Water Resour. Res. – volume: 55 start-page: 703 year: 2019 end-page: 728 ident: b0095 article-title: Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media publication-title: Water Resour. Res. – volume: 375 start-page: 565 year: 2018 end-page: 588 ident: b0120 article-title: Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification publication-title: J. Comput. Phys. – volume: 54 start-page: 1616 year: 2018 end-page: 1632 ident: b0185 article-title: A reduced-order successive linear estimator for geostatistical inversion and its application in hydraulic tomography publication-title: Water Resour. Res. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: b0075 article-title: Deep learning publication-title: Nature – volume: 273 year: 2020 ident: b0180 article-title: Three-dimensional pore-scale study of methane gas mass diffusion in shale with spatially heterogeneous and anisotropic features publication-title: Fuel – reference: Wang, N., Chang, H., Zhang, D., 2021d. Deep-learning-based inverse modeling approaches: A subsurface flow example. Journal of Geophysical Research: Solid Earth 126, e2020JB020549. 10.1029/2020JB020549. – volume: 55 start-page: 3856 year: 2019 end-page: 3881 ident: b0090 article-title: Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification publication-title: Water Resour. Res. – volume: 373 year: 2021 ident: b0125 article-title: Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network publication-title: Comput. Methods Appl. Mech. Eng. – volume: 153 year: 2021 ident: b0170 article-title: Solution of diffusivity equations with local sources/sinks and surrogate modeling using weak form theory-guided neural network publication-title: Adv. Water Resour. – volume: 192 year: 2020 ident: b0045 article-title: Deep-learning-based surrogate model for reservoir simulation with time-varying well controls publication-title: J. Petrol. Sci. Eng. – start-page: 819 year: 2021 end-page: 829 ident: b0055 article-title: A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks publication-title: Nat. Comput. Sci. – volume: 56 year: 2020 ident: b0165 article-title: Pore-scale study of water adsorption and subsequent methane transport in clay in the presence of wettability heterogeneity publication-title: Water Resour. Res. – volume: 357 start-page: 125 year: 2018 end-page: 141 ident: b0100 article-title: Hidden physics models: Machine learning of nonlinear partial differential equations publication-title: J. Comput. Phys. – reference: Wen, G., Li, Z., Azizzadenesheli, K., Anandkumar, A., Benson, S.M., 2021b. U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow. arXiv:2109.03697 [physics]. – year: 1979 ident: b0005 article-title: Petroleum reservoir simulation – volume: 584 year: 2020 ident: b0150 article-title: Deep learning of subsurface flow via theory-guided neural network publication-title: J. Hydrol. – volume: 25 start-page: 1 year: 2017 end-page: 54 ident: b0010 article-title: Particle swarm optimization for single objective continuous space problems: A review publication-title: Evol. Comput. – reference: Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, in: Proceedings of ICNN’95 - International Conference on Neural Networks. Presented at the Proceedings of ICNN’95 - International Conference on Neural Networks, pp. 1942–1948 vol.4. 10.1109/ICNN.1995.488968. – volume: 436 year: 2021 ident: b0175 article-title: Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow publication-title: J. Comput. Phys. – volume: 26 start-page: 652 year: 2015 end-page: 669 ident: b0080 article-title: A multi-continuum multiple flow mechanism simulator for unconventional oil and gas recovery publication-title: J. Nat. Gas Sci. Eng. – volume: 229 start-page: 8011 year: 2010 ident: 10.1016/j.jhydrol.2022.128321_b0020 article-title: History matching of facies distribution with the EnKF and level set parameterization publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2010.07.005 – volume: 375 start-page: 565 year: 2018 ident: 10.1016/j.jhydrol.2022.128321_b0120 article-title: Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.08.036 – volume: 366 start-page: 415 year: 2018 ident: 10.1016/j.jhydrol.2022.128321_b0200 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.04.018 – volume: 378 start-page: 686 year: 2019 ident: 10.1016/j.jhydrol.2022.128321_b0105 article-title: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.10.045 – year: 1979 ident: 10.1016/j.jhydrol.2022.128321_b0005 – volume: 436 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0175 article-title: Weak form theory-guided neural network (TgNN-wf) for deep learning of subsurface single- and two-phase flow publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2021.110318 – volume: 19 start-page: 727 year: 2015 ident: 10.1016/j.jhydrol.2022.128321_b0015 article-title: Jointly updating the mean size and spatial distribution of facies in reservoir history matching publication-title: Comput. Geosci. doi: 10.1007/s10596-015-9478-7 – volume: 153 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0170 article-title: Solution of diffusivity equations with local sources/sinks and surrogate modeling using weak form theory-guided neural network publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2021.103941 – ident: 10.1016/j.jhydrol.2022.128321_b0050 – volume: 1–29 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0135 article-title: Efficient uncertainty quantification and data assimilation via theory-guided convolutional neural network publication-title: SPE J. – ident: 10.1016/j.jhydrol.2022.128321_b0145 doi: 10.1029/2020JB020549 – volume: 54 start-page: 1616 year: 2018 ident: 10.1016/j.jhydrol.2022.128321_b0185 article-title: A reduced-order successive linear estimator for geostatistical inversion and its application in hydraulic tomography publication-title: Water Resour. Res. doi: 10.1002/2017WR021884 – volume: 584 year: 2020 ident: 10.1016/j.jhydrol.2022.128321_b0150 article-title: Deep learning of subsurface flow via theory-guided neural network publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2020.124700 – volume: 56 year: 2020 ident: 10.1016/j.jhydrol.2022.128321_b0165 article-title: Pore-scale study of water adsorption and subsequent methane transport in clay in the presence of wettability heterogeneity publication-title: Water Resour. Res. doi: 10.1029/2020WR027568 – volume: 194 start-page: 773 year: 2004 ident: 10.1016/j.jhydrol.2022.128321_b0195 article-title: An efficient, high-order perturbation approach for flow in random porous media via Karhunen-Loève and polynomial expansions publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2003.09.015 – volume: 29 start-page: 2318 year: 2017 ident: 10.1016/j.jhydrol.2022.128321_b0060 article-title: Theory-guided data science: A new paradigm for scientific discovery from data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2017.2720168 – volume: 385 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0130 article-title: Theory-guided auto-encoder for surrogate construction and inverse modeling publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.114037 – year: 2003 ident: 10.1016/j.jhydrol.2022.128321_b0035 – volume: 521 start-page: 436 year: 2015 ident: 10.1016/j.jhydrol.2022.128321_b0075 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: 10.1016/j.jhydrol.2022.128321_b0065 doi: 10.1109/ICNN.1995.488968 – start-page: 819 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0055 article-title: A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks publication-title: Nat. Comput. Sci. doi: 10.1038/s43588-021-00171-3 – ident: 10.1016/j.jhydrol.2022.128321_b0155 doi: 10.1016/j.advwatres.2022.104180 – year: 2016 ident: 10.1016/j.jhydrol.2022.128321_b0040 – volume: 373 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0125 article-title: Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2020.113492 – volume: 55 start-page: 3856 year: 2019 ident: 10.1016/j.jhydrol.2022.128321_b0090 article-title: Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification publication-title: Water Resour. Res. doi: 10.1029/2018WR024638 – volume: 55 start-page: 703 year: 2019 ident: 10.1016/j.jhydrol.2022.128321_b0095 article-title: Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media publication-title: Water Resour. Res. doi: 10.1029/2018WR023528 – volume: 25 start-page: 1 year: 2017 ident: 10.1016/j.jhydrol.2022.128321_b0010 article-title: Particle swarm optimization for single objective continuous space problems: A review publication-title: Evol. Comput. doi: 10.1162/EVCO_r_00180 – volume: 273 year: 2020 ident: 10.1016/j.jhydrol.2022.128321_b0180 article-title: Three-dimensional pore-scale study of methane gas mass diffusion in shale with spatially heterogeneous and anisotropic features publication-title: Fuel doi: 10.1016/j.fuel.2020.117750 – ident: 10.1016/j.jhydrol.2022.128321_b0110 – volume: 208 year: 2022 ident: 10.1016/j.jhydrol.2022.128321_b0140 article-title: Efficient well placement optimization based on theory-guided convolutional neural network publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2021.109545 – volume: 56 year: 2020 ident: 10.1016/j.jhydrol.2022.128321_b0085 article-title: Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities publication-title: Water Resour. Res. doi: 10.1029/2019WR026082 – volume: 357 start-page: 125 year: 2018 ident: 10.1016/j.jhydrol.2022.128321_b0100 article-title: Hidden physics models: Machine learning of nonlinear partial differential equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2017.11.039 – volume: 394 start-page: 56 year: 2019 ident: 10.1016/j.jhydrol.2022.128321_b0205 article-title: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2019.05.024 – volume: 50 start-page: 5428 year: 2014 ident: 10.1016/j.jhydrol.2022.128321_b0070 article-title: Principal component geostatistical approach for large-dimensional inverse problems publication-title: Water Resour. Res. doi: 10.1002/2013WR014630 – volume: 5 start-page: 60 year: 2000 ident: 10.1016/j.jhydrol.2022.128321_b0190 article-title: Stochastic formulation for uncertainty analysis of two-phase flow in heterogeneous reservoirs publication-title: SPE J. doi: 10.2118/59802-PA – volume: 181 start-page: 973 year: 2016 ident: 10.1016/j.jhydrol.2022.128321_b0115 article-title: Apparent gas permeability in an organic-rich shale reservoir publication-title: Fuel doi: 10.1016/j.fuel.2016.05.011 – volume: 26 start-page: 652 year: 2015 ident: 10.1016/j.jhydrol.2022.128321_b0080 article-title: A multi-continuum multiple flow mechanism simulator for unconventional oil and gas recovery publication-title: J. Nat. Gas Sci. Eng. doi: 10.1016/j.jngse.2015.07.005 – start-page: 67 year: 2010 ident: 10.1016/j.jhydrol.2022.128321_b0030 article-title: An improved Sobel edge detection – volume: 192 year: 2020 ident: 10.1016/j.jhydrol.2022.128321_b0045 article-title: Deep-learning-based surrogate model for reservoir simulation with time-varying well controls publication-title: J. Petrol. Sci. Eng. doi: 10.1016/j.petrol.2020.107273 – volume: 55 start-page: 3 year: 2013 ident: 10.1016/j.jhydrol.2022.128321_b0025 article-title: Ensemble smoother with multiple data assimilation publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2012.03.011 – volume: 155 year: 2021 ident: 10.1016/j.jhydrol.2022.128321_b0160 article-title: CCSNet: A deep learning modeling suite for CO2 storage publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2021.104009 |
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| Snippet | •TgCNN is used for surrogate modeling of 3D subsurface flow problems.•Dynamic pressure estimation can be obtained given stochastic permeability... We build surrogate models for dynamic 3D subsurface single-phase flow problems with multiple vertical producing wells. The surrogate model provides efficient... |
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| SubjectTerms | Convolutional neural network equations Inverse problem mathematical models permeability Subsurface flow Surrogate modeling Theory-guided machine learning uncertainty Uncertainty quantification |
| Title | Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network |
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