Novel insights into the effect of drilling fluid particle size distribution on filter cake permeability

Understanding fluid-particle interactions is critical in petroleum engineering, particularly for controlling drilling fluid performance and mitigating fluid loss. Numerical methods, such as the coupled computational fluid dynamics discrete element method, enable a detailed investigation of these interactions without relying on extensive experimental testing. Traditional particle-sizing guidelines, including empirical bridging rules, provide only partial insights into the pore-scale mechanisms governing filter cake formation and permeability evolution. In contrast, numerical modelling directly resolves how the particle size distribution and solid concentration influence the filter cake structure and flow behavior. This study employed a coupled numerical simulation framework to examine filter cake formation for drilling fluids containing unimodal and bimodal particle size distributions across a range of solid concentrations. The key descriptors analyzed included the filtration rate, filter cake porosity, permeability, and pore size distribution. The results show that bimodal particle mixtures exhibit a concentration-dependent transition in permeability behavior. At lower solid loadings, bimodal systems maintain substantially higher permeability than unimodal systems because of the persistence of large, connected pore pathways formed by coarse particles. As the solid concentration increases, finer particles progressively infiltrate and occlude these pathways, leading to a marked permeability reduction and convergence toward unimodal behavior. Pore-size distribution analysis revealed that permeability is governed primarily by the connectivity and continuity of large pore throats rather than by bulk porosity. These findings demonstrate that bimodal distributions require sufficient fine content to achieve effective fluid loss control, providing pore-scale numerical guidance for optimizing drilling-fluid particle-size selection strategies.