Abstract

Physics of two-phase flows in heterogeneous rocks plays an important role in many applications such as oil and gas migration and geological sequestration of carbon dioxide. Although current pore-scale models are used to compute macroscopic properties required in reservoir simulators, most work is limited to small sample size and homogeneous rocks. There is a need for pore-scale modeling approaches that can accurately represent the 3D complex pore structure and heterogeneity of real media. Pore network modeling simplifies the geometry and flow equations at pore-scale, but can provide characteristic curves in capillary-dominated systems on fairly large samples with huge saving on computational costs compare to direct numerical simulation methods. However, there are limitations for attaining a large representative pore network for heterogeneous cores, namely the technical limits on sample size to discern void space and computational limits on network extraction algorithms. To address these issues, we propose a novel stochastic pore network stitching method in combination with network generation to provide large-enough representative pore network for a core. Our approach proposes to use micro-CT images of various reservoir rock cores in different resolutions to characterize the pore structure. Few signature parts of the core are selected and their corresponding void space and equivalent pore network are extracted. The space between pore networks is filled by using a stochastic network generator that utilizes statistics of all signature networks and a layered stitching method that glues networks based on their average properties. The output is a large network that can be used in any pore network solver. We are focusing on flow properties via quasi-static pore network modeling solver to obtain absolute permeability, relative permeability, and capillary pressure curves. Since the method is stochastic, the workflow should be run on many realizations and final results yield both average and variability of the derived properties. We have tested the developed method on various generated and extracted networks, and we have extended the stitching method to 3D heterogeneous samples.