Assessment and Optimization of Low Salinity Waterflooding via Pore Network Modeling in a Sandstone Reservoir

Low-salinity waterflooding (LSWF) represents a cost-effective enhanced oil recovery (EOR) strategy for mature sandstone reservoirs. However, its success strongly depends on pore-scale transport and wettability mechanisms that conventional reservoir simulators cannot accurately capture. This study implements a pore-network modeling (PNM) framework to evaluate and optimize LSWF performance in sandstone systems. A representative pore network was calibrated to match core-scale petrophysical properties—porosity, permeability, and pore-throat size distributions. The LSWF process was simulated using a coupled advective–diffusive salinity transport model integrated with salinity-dependent wettability alteration, expressed through variations in contact angle and interfacial tension. From the multiphase invasion and flow simulations, macroscopic constitutive relationships were derived, including capillary pressure, relative permeability, and fractional flow curves for different injection salinities. Sensitivity analyses indicate that wettability alteration induced by salinity reduction is the dominant mechanism enhancing oil recovery, as reflected in measurable shifts in the relative permeability endpoints and capillary pressure curves. The model predicts an optimal injection salinity window between 2000 and 4500 ppm, yielding up to 7.2% incremental oil recovery, while extremely low salinities produce non-monotonic trends due to competing interfacial tension effects. Overall, the proposed PNM workflow demonstrates a robust approach for (i) translating pore-scale phenomena into reservoir-scale constitutive laws, (ii) identifying salinity ranges for pilot testing, and (iii) reducing uncertainty in field-scale LSWF simulations.