Exploring Particle Clogging Mechanisms through Deterministic Pore Network Modeling

Abstract This study integrates particle clogging mechanisms into Pore Network Modeling (PNM) to simulate and analyze clogging dynamics in porous media, a critical process affecting permeability and fluid flow. The model employs a deterministic criterion for throat clogging and establishes rules for updating conductance and connectivity within the network. Validated against experimental data, the PNM effectively captures the temporal evolution of clogging and its impact on permeability, demonstrating an exponential decline in permeability consistent with observed trends. Sensitivity analyses reveal the influence of particle size and conductance ratios on clogging progression and flow field alterations. The findings indicate that larger particles accelerate clogging but result in less pronounced permeability reductions, underscoring a complex interplay between clogging rates and permeability loss. Identified limitations include the absence of mechanisms for pre-clogging particle retention and the inability to handle bimodal particle mixtures. Future improvements are proposed, such as incorporating particle attachment dynamics and probabilistic clogging scenarios to enhance model accuracy. With ongoing refinements, this PNM framework offers a robust tool for predicting clogging behavior in porous media, with broad applicability across fields such as groundwater remediation, filtration system design, and geo-energy applications.