Thermodynamically consistent interfacial curvatures in real pore geometries: Implications for pore-scale modeling of two-phase displacement processes

In conventional Pore-network Modeling (PNM) approaches, fluid flow and transport are solved in a network of pore elements with idealized geometries. Such simplification can lead to inaccurate predictions when the original pore space features complex geometries. To overcome this limitation, this study introduces a novel workflow that integrates four key components: (i) an enhanced pore network extraction (PNE) platform capable of identifying and extracting pore cross-sections from high-resolution micro-computed tomography (micro-CT) images of the pore space, (ii) a computationally-efficient semi-analytical model that can faithfully predict capillary entry pressure and the corresponding fluid configuration of piston-like displacements using real two-dimensional cross-sections of pores, (iii) a PNM approach for two-phase flow modeling that utilizes the capillary entry pressure of pores predicted by the semi-analytical model, and (iv) an Artificial Intelligence (AI)-driven model that paves the way for future advancements in efficiently predicating fluid displacement properties in intricate pore structures. To validate this new workflow, we constructed various pore networks containing real pore and throat cross-sections over a diverse group of sandstone and carbonate rock samples. Subsequently, we simulate capillary pressure curves of Mercury Intrusion Capillary Pressure (MICP) and oil–water primary drainage displacements in Bentheimer and Berea sandstones, respectively, using both the conventional and enhanced PNM approaches. The latter demonstrated improved prediction accuracy compared to conventional methods. Next, primary drainage simulations are conducted for two carbonates, and the resulting capillary pressure curves from both PNM approaches are compared. In addition, we conduct an in-depth analysis of fourteen geometric features of the pore space, identifying key factors of hydraulic radius, circumradius, sphericity, and area, that significantly impact capillary entry pressure of pores. After that, we construct an Artificial Neural Network (ANN) to predict the capillary entry pressure of pores using their critical geometric features. This AI model, trained using data derived from the semi-analytical model, exhibits excellent predictive accuracy (with a R2 of 0.995 for the test data set) in estimating capillary entry pressure of pores. Overall, our newly proposed, integrated workflow represents a significant step forward in the field of digital rock technology (DRT), offering an accurate and efficient method for modeling fluid flow in rock samples with complex pore geometries.