Improved Algorithms for Stochastic Pore Network Generation for Porous Materials

Abstract

Pore network modeling is widely applied to investigate transport phenomena in porous media, as this approach allows for efficient and accurate pore-scale simulation. However, the direct extraction of the pore network (PN) from three-dimensional pore structure images can often not be achieved, due to the conflict between the wide pore size range of many porous materials and the limited image size inherent to many imaging techniques. This obstacle is typically overcome by stochastic PN generation, and this paper proposes and assesses improved stochastic algorithms to generate such statistically similar PNs. Four algorithms for geometry generation as well as two algorithms for topology generation are investigated, both qualitatively and quantitatively, for four porous materials with different degrees of complexity. Particularly, with each algorithm, the materials’ unsaturated moisture storage and transport properties are simulated and compared. The results demonstrate that, as the pore structure’s complexity increases, the basic stochastic algorithms available in the literature do not suffice for an accurate and dependable PN generation. The improved geometry and topology generation algorithms put forward in this paper, on the other hand, highly enhance the reliability of the generated PNs, by reducing the deviations for specific moisture contents and permeabilities by 67–98% on average. The improved stochastic algorithms also set the stage for generating PNs of porous materials with (very) wide pore size ranges, and future research can build on these algorithms to generate full-scale PNs using multiple 3D image sets with different resolutions.