Speaker
Description
Fractures significantly influence flow and transport in subsurface geological systems. Quantifying and modeling the complex transport behavior remains difficult due to the spatially discrete nature of fractures combined with uncertainty in fracture geometry, intra- and inter-fracture conductivity heterogeneity, and the variation of these properties across rock lithologies and deformation conditions. This study explores the application and optimization of reduced-physics graph-based modeling to characterize solute transport and fracture-matrix interactions in fractured cores. Model results are compared with a suite of core-scale 3D imaging datasets collected with positron emission tomography (PET) under single-phase flow conditions. PET imaging provides high-resolution, temporally resolved observations of radiotracer distributions in fractured granite and dolomite cores. Experimental data were subsequently used to validate a graph-based time domain random-walk (TDRW) particle tracking model that incorporates matrix diffusion, sorption, and first-order reactions. Results demonstrate that the model is capable of accurately representing fracture-matrix interactions and first-order kinetics such as radioactive decay. The approach efficiently captures complex transport phenomena without requiring a high-resolution representation of fracture geometry, highlighting its potential as a computationally effective alternative to conventional simulation methods. This work advances existing graph or pipe-network based approaches for modeling transport in fractured porous media by validating and optimizing these models against unique high-resolution experimental datasets.
| Country | United States |
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