Pore-to-Core Upscaling of Two-Phase Flow in Mixed-Wet Porous Media: Part II-A Dynamic Pore-Network Modeling Approach

We present a new, computationally efficient, and massively parallelized pore-network modeling (PNM) platform, referred to as the loosely-coupled dynamic PNM (LCD-PNM). To the best of our knowledge, this study introduces the first dynamic PNM framework that is capable of performing physics-based pore-scale simulations of two-phase flow processes in large-scale disordered pore networks under a wide range of fluid properties, wettability scenarios, and flow conditions. To validate the LCD-PNM platform, we perform primary drainage and waterflooding simulations under both water-wet and mixed-wet conditions on equivalent pore networks of Berea and Bentheimer sandstone miniature core plugs. We then compare the oil and water relative permeability and oil recovery curves predicted under steady-and unsteady-state simulations against their experimental counterparts. The pore networks have been extracted in a seamless and deterministic manner from micro-CT images of the entire core sample. For comparison, we also present the relative permeability predictions obtained from quasi-static PNM simulations to highlight the improvements we observe in the LCD-PNM results, such as more accurate predictions of oil breakthrough and relative permeability curves during the primary drainage processes. In our analysis, we find the dynamic simulation results to be in close agreement with experimental data. Additionally, we employ the LCD-PNM to investigate the effects of wettability and flow conditions on oil and water relative permeabilities and remaining oil saturation. To this end, we investigate different displacement flow regimes including viscous fingering, capillary fingering, and stable front displacement by adjusting injection flow rate and fluid viscosity ratio. The simulation results provide invaluable insights into the complex interplay between the viscous and capillary forces that controls pore-scale displacements and ultimately influences the macroscopic behavior of two-phase flow processes.