Permeability impairment by hydrodynamic pore bridging: probabilistic pore-network modelling and microfluidic experiments
E001
Abstract
Permeability impairment caused by the migration and retention of suspended particles is a critical issue in numerous industrial and environmental processes. While pore-network models (PNMs) have successfully described clogging by sieving and particle aggregation, they have failed to capture hydrodynamic bridging — a mechanism where particle arches form and block pore throats. This study introduces a novel probabilistic PNM that incorporates a stochastic law for arch formation, accounting for the particle-to-throat size ratio, particle concentration, and pore geometry. The probability law is calibrated using high-fidelity CFD–DEM simulations of single-pore bridging.
Microfluidic experiments in heterogeneous micromodels representative of the rock microstructure are carried out to investigate the effect of particle size and concentration, and flow rate on permeability reduction. The results support the probability law. The proposed probabilistic framework successfully reproduces experimental trends in clogging dynamics and permeability decline, thereby extending the capability of PNMs to capture all three pore-clogging mechanisms.