Speaker
Description
Digital rock serves as a vital tool for pore-scale flow simulation in geo-energy, carbon sequestration, and hydrogen storage studies. Under subsurface conditions, rocks undergo deformation, and pore structures evolve due to changes in temperature and stress. Existing digital rock reconstruction methods—including physical experiments, stochastic modeling, and machine learning—typically do not account for the coupled effects of high temperature and stress. To address this limitation, this paper introduces a process-based method that integrates the discrete element method (DEM) with thermo-mechanical coupling. First, computed tomography (CT) images are segmented using a watershed algorithm, and a contour database is built via spherical harmonic analysis. A clump template library is subsequently developed in PFC3D. Following this, a DEM model is generated based on target porosity and particle size distribution, with accuracy verified through two-point correlation and linear path functions. After calibrating interparticle micromechanical and thermal properties, various temperature and stress boundary conditions are applied to simulate digital rocks under different thermo-mechanical states. The geometric and topological characteristics of these digital rocks are then examined, along with computations of permeability and relative permeability. Using Bentheim sandstone as a case study, digital rocks under multiple temperature-stress scenarios are constructed. Results indicate that elevated temperature and stress reduce pore and throat radii, elongate throats, weaken connectivity, decrease porosity and permeability, and enhance water-wetting behavior. This work offers a theoretical foundation for more accurate pore-scale flow simulations of geo-energy fluids, CO₂, and H₂.
| Country | China |
|---|---|
| Green Housing & Porous Media Focused Abstracts | This abstract is related to Green Housing |
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