Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
Abstract
Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$$$_2$$$$ sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$$$_2$$$$ fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.
- Authors:
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)
- OSTI Identifier:
- 1897291
- Alternate Identifier(s):
- OSTI ID: 1909554
- Report Number(s):
- LA-UR-22-24347
Journal ID: ISSN 2045-2322; 18734; PII: 22832
- Grant/Contract Number:
- 20200575ECR; 89233218CNA000001
- Resource Type:
- Published Article
- Journal Name:
- Scientific Reports
- Additional Journal Information:
- Journal Name: Scientific Reports Journal Volume: 12 Journal Issue: 1; Journal ID: ISSN 2045-2322
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; carbon capture and storage; computational science; hydrology
Citation Formats
Pachalieva, Aleksandra, O’Malley, Daniel, Harp, Dylan Robert, and Viswanathan, Hari. Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management. United Kingdom: N. p., 2022.
Web. doi:10.1038/s41598-022-22832-7.
Pachalieva, Aleksandra, O’Malley, Daniel, Harp, Dylan Robert, & Viswanathan, Hari. Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management. United Kingdom. https://doi.org/10.1038/s41598-022-22832-7
Pachalieva, Aleksandra, O’Malley, Daniel, Harp, Dylan Robert, and Viswanathan, Hari. Fri .
"Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management". United Kingdom. https://doi.org/10.1038/s41598-022-22832-7.
@article{osti_1897291,
title = {Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management},
author = {Pachalieva, Aleksandra and O’Malley, Daniel and Harp, Dylan Robert and Viswanathan, Hari},
abstractNote = {Abstract Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO $$_2$$ 2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO $$_2$$ 2 fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model’s accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400 000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.},
doi = {10.1038/s41598-022-22832-7},
journal = {Scientific Reports},
number = 1,
volume = 12,
place = {United Kingdom},
year = {Fri Nov 04 00:00:00 EDT 2022},
month = {Fri Nov 04 00:00:00 EDT 2022}
}
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