From fluid flow to coupled processes in fractured rock: recent advances and new frontiers

Abstract

Some of the greatest challenges currently facing humanity have roots in the Earth and Energy Sciences. Policymakers rely on scientific research to answer questions related to the transition to green renewable energy, mitigate the climate crisis, and ensure global stability with reliable energy and water resources. A common thread in

A Geo‐Structurally Based Correction Factor for Apparent Dissolution Rates in Fractured Media

“…Our model setup was designed to isolate the effect of network structure on geochemical behavior. Recent studies of single phase steady flow through fracture networks indicate that as the geological heterogeneity increases, for example, distribution of fracture lengths; network density; fracture intensity; or pre‐existing internal aperture variability, flow channelization becomes more pronounced (Doolaeghe et al., 2020; Hyman, 2020; Hyman et al., 2021; Hyman & Jiménez‐Martínez, 2018; Kang et al., 2020; Maillot et al., 2016; Sweeney & Hyman, 2020; Tsang & Neretnieks, 1998; Viswanathan et al., 2022) Thus, in turn, one would expect a clearer distinction of primary and secondary networks, which is the foundation of the proposed correction factor. Nonetheless, how interactions within the hierarchy of length scales in fracture networks influences the apparent dissolution rate and applicability of the correction factor warrants detailed investigation.…”

“…Fractures are the principal conduits for fluid flow through otherwise low permeability rock in much of Earth's subsurface (Bonnet et al., 2001; Deng & Spycher, 2019; National Research Council, 1996; The National Academies of Sciences, Engineering, and Medicine, 2021; Viswanathan et al., 2022). Hydrology within fractured rocks is complex, with variable Péclet conditions reflecting the relative importance of fluid advection and diffusion on the movement of solutes.…”

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

“…An overwhelming amount of research is focused on using machine learning (ML) to improve the efficiency and accuracy of subsurface energy-related fluid-flow applications 13 . Machine learning has been used to create reduced order models for geologic CO sequestration 14 – 24 , CO enhanced oil recovery 25 – 28 , geothermal energy 25 , 29 – 32 , geothermal energy 33 – 37 , and oil and gas extraction 38 – 42 as summarized in Table 1 .…”

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

“…An overwhelming amount of research is focused on using machine learning (ML) to improve the efficiency and accuracy of subsurface energy-related fluid-flow applications 13 . Machine learning has been used to create reduced order models for geologic CO 2 sequestration [14][15][16][17] , CO 2 enhanced oil recovery, geothermal energy [18][19][20][21] , geothermal energy [22][23][24][25] , and oil and gas extraction 26,27 .…”