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From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers
Open accessAbstract
Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO 2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO 2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · cited 1×
“, 2015), material science (Butler et al., 2018), drug discovery, social media (Golbeck et al.”
BackgroundAn overview of full-waveform inversion in exploration geophysics
Virieux, J., & Operto, S. (2009) · Geophysics · doi:10.1190/1.3238367 · Cited by 3,750
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Johnes, P. J., & Demonstration Test Catchments Team (2013) · American Geophysical Union eBooks · Cited by 3,126
Fault zone architecture and permeability structure
Caine, J. S., Evans, J. P., & Forster, C. B. (1996) · Geology · doi:10.1130/0091-7613(1996)024<1025:fzaaps>2.3.co;2 · Cited by 2,603
Ellsworth, W. L. (2013) · Science · doi:10.1126/science.1225942 · Cited by 2,436
Machine learning applications in genetics and genomics
Libbrecht, M. W., & Noble, W. S. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3920 · Cited by 2,084
Pratt, R. G. (1999) · Geophysics · doi:10.1190/1.1444597 · Cited by 1,583
Cnudde, V., & Boone, M. N. (2013) · Earth-Science Reviews · doi:10.1016/j.earscirev.2013.04.003 · Cited by 1,572
Metadata
- Authors
- Viswanathan, H. S., Ajo‐Franklin, J., Birkholzer, J. T., Carey, J. W., Guglielmi, Y., Hyman, J. D., Karra, S., Pyrak‐Nolte, L. J., Rajaram, H., Srinivasan, G., & Tartakovsky, D. M.
- Venue
- Reviews of Geophysics
- Publisher
- American Geophysical Union (AGU)
- Published
- 2022-02-22
- Volume
- 60
- Issue
- 1
- ISSN
- 0096-1043 (ISSN-L)
- 8755-1209
- 1944-9208
- OpenAlex
- W4221001009
- Cited by
- 336
- References
- 159
- FWCI
- 16.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Viswanathan, H. S., Ajo‐Franklin, J., Birkholzer, J. T., Carey, J. W., Guglielmi, Y., Hyman, J. D., Karra, S., Pyrak‐Nolte, L. J., Rajaram, H., Srinivasan, G., & Tartakovsky, D. M. (2022). From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers. Reviews of Geophysics. https://doi.org/10.1029/2021rg000744
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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