Quantitative prediction 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 flow, transport and mechanical deformation behavior. They play a central role in CO2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require prediction of fracture-dependent quantities of interest such as CO2 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 that cover 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 measures 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 to accelerate physics-based models by orders of magnitude to enable uncertainty quantification and near real-time analysis.

Injection-induced seismicity (IIS) typically occurs when pressure diffuses from a sedimentary target formation down into fractured and faulted, low-permeability, critically-stressed basement rock. Previous studies of IIS have used basin-scale models of pressure diffusion that rely on an equivalent porous medium (EPM) approach to assign hydraulic diffusivity and a triggering pressure (TP) criteria for seismic initiation. We show that these models employed unrealistically-large values of hydraulic diffusivity, usually by neglecting the compressibility of the fractures in the specific storage coefficient, to result in pressure diffusion to seismogenic depths (≥2 km into the basement). The EPM-TP approach does not explicitly represent the mechanical and hydrologic behavior of fractures and faults, and it fails to explain why relatively few disposal wells are associated with IIS. We develop a parallelized, partially-coupled, hydro-mechanical, discrete fracture network and matrix model (DFNM) model with thousands of fractures and the capability to calculate Mohr-Coulomb (MC) failure to indicate seismicity and alter hydraulic diffusivity. In consistent comparisons, DFNM-MC simulations allow for deeper, more heterogeneous pressure diffusion than EPM-TP simulations, and they do not need to employ unrealistic diffusivity values to result in pressure diffusion to seismogenic depths. A sensitivity analysis shows that small deviations in fault orientation (≤2 degrees from optimal) and fracture network density outside an intermediate range can drastically decrease the likelihood of IIS, potentially explaining why only a small fraction of disposal wells are associated with IIS. The EPM-TP approach is unsuitable to investigate IIS, but the DFNM-MC approach offers a promising, nuanced approach for further study.