Kuankuan Wu

Force chains, which transmit and distribute loads through networks of interparticle contacts, largely determine the mechanical response of granular materials. However, automatically identifying force chains and quantifying their evolution remain difficult, especially for three-dimensional granular systems under fluid action. In this work, a coupled computational fluid dynamics and discrete element method (CFD-DEM) is combined with community detection to extract and characterize the force chain network of a granular packing during particle migration. The inter-particle contact forces are encoded as a weighted network, and an improved Louvain algorithm based on modularity optimization is employed to partition the force network into sub-communities. The evolution of the community structure is quantified in terms of community size, average contact force, centrality and coordination number at different fluid pressures and dynamic viscosities. The results show that strong force chains assemble into compact and highly branched communities located mainly in the central region of the packing, and that increasing fluid pressure promotes the recombination of strong and weak chains into more branched structures, enhancing the stability of the granular system under high pressure. The community size distributions display a linear decay in the complementary cumulative distribution function without a characteristic scale, and the coordination number distributions of different communities respond to pressure in opposite ways. Higher fluid viscosity retards particle migration and favors more compact and stable community structures. The proposed framework provides a quantitative link between the mesoscale organization of force chains and the macroscopic stability of granular materials under fluid action. Beyond the specific packing studied here, the framework relies only on the interparticle contact forces and can therefore be transferred directly to any numerical or experimental dataset in which such forces are accessible; it thus offers a globally applicable tool for diagnosing fines migration, permeability impairment and sand production in deeply buried reservoirs, as well as internal erosion in dams, filters and embankments and the handling of particulate beds in the chemical and pharmaceutical industries.

Shale oil, despite its substantial reserves and significant development potential, presents a great extraction challenge. The existence of micro- and nanopores within shale formations renders conventional recovery methods largely ineffective, resulting in a limited recovery factor. With stronger adsorption, CO₂ enables it to enter the micro–nanopores, enhancing oil recovery. Current research has primarily focused on studying the flow mechanisms of CO₂ and oil within the pores and the mechanisms of enhanced oil recovery. Research on the mechanism of enhanced oil recovery in pore tips and blind pores is limited. In our study, in order to investigate the mechanism of enhanced oil recovery, variable-diameter blind pores at the nanoscale are constructed. The influences of displacement times, system temperature, injection pressure, and the pore size combination on CO₂-enhanced oil recovery are systematically investigated using molecular dynamics simulations. The simulation results show that the mechanism of CO₂-enhanced oil recovery in variable-diameter pores is different from that in the traditional single-pore-size structure. In the variable-diameter pores, CO₂ molecules preferentially enter the narrow region in the model. The oil in the wide region (the larger-diameter sections of the blind pores, as opposed to the narrower throats) is mobilized and recovered after the CO₂ blockage has been generated. In addition, elevated system temperature improves intermolecular thermal motion and interactions. With the system temperature increasing, the CO₂ molecules can enter the nanopore more readily, ultimately enhancing oil recovery. Higher injection pressure serves as a powerful driving force for CO₂, allowing more CO₂ molecules to enter the nanopore. However, the ultimate recovery will decrease if the injection pressure continues to increase after 20 MPa. This study provides support and recommendations for the investigation of the mechanisms of EOR using CO₂ in nanoscale variable-diameter blind pores.

Clay minerals are significant constituents of shale reservoirs, yet the confinement-induced phase behavior of hydrocarbons within clay nanopores has received comparatively less attention than in carbonaceous materials. In this study, Grand Canonical Monte Carlo (GCMC) simulations were performed to investigate the confinement-induced shifts in the critical properties of hydrocarbons within illite slit pores ranging from 2 to 20 nm. The simulation results indicate apparent reductions in both critical temperature (T_c) and critical pressure (P_c) under confined conditions. The relative shift in critical properties is more pronounced for n-pentane than for methane, which is attributed to the larger molecular size and complex chain-like structure of n-pentane compared to methane. The simulation results also show that the extent of these shifts varies among different pore geometries and materials due to the differences in fluid–solid interactions and specific surface areas. Illite exerts a weaker confinement effect than graphite, while cylindrical pores intensify the shift compared to slit pores. As the pore size increases from 2 to 20 nm, the nanoconfinement-induced shifts in the critical properties of alkanes in illite slit pores progressively diminish, and the critical properties approach their corresponding bulk values at a pore size of 20 nm. Finally, new correlations are proposed to quantify the critical property shifts in illite slit nanopores. Overall, this study provides a quantitative characterization of confinement-induced critical-property shifts for the investigated hydrocarbons in illite slit nanopores.