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\begin{document}

\title{From fluid flow to coupled processes in fractured rock: recent advances
and new frontiers}



\author[1]{Hari S Viswanathan}%
\author[2]{Jonathan Blair Ajo-Franklin}%
\author[3]{Jens Thomas Birkholzer}%
\author[4]{James William Carey}%
\author[5]{Yves Guglielmi}%
\author[1]{Jeffrey De'Haven Hyman}%
\author[1]{Satish Karra}%
\author[6]{Laura Pyrak-Nolte}%
\author[7]{Harihar Rajaram}%
\author[1]{Gowri Srinivasan}%
\author[8]{Daniel M Tartakovsky}%
\affil[1]{Los Alamos National Laboratory (DOE)}%
\affil[2]{Rice University}%
\affil[3]{Lawrence Berkeley National Laboratory (DOE)}%
\affil[4]{Los Alamos National Laboratory}%
\affil[5]{Lawrence Berkeley National Laboratory}%
\affil[6]{Purdue University}%
\affil[7]{Johns Hopkins University}%
\affil[8]{Stanford University}%


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  \date{May 25, 2024}


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\begin{abstract}
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 CO\textsubscript{2} 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 CO\textsubscript{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 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.%
\end{abstract}%



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