A pore-network modeling perspective on the dynamics of residual trapping in geological carbon storage

Abstract

This study presents the application of an efficient and robust Dynamic Pore-Network Modeling (DPNM) framework for accurate prediction of carbon dioxide (CO₂) trapping behavior under the dynamic flow conditions prevalent in subsurface applications. Residual trapping of CO₂ is crucial in the context of geological sequestration, serving as a constitutive relationship that controls relative permeability hysteresis and thereby determining the magnitude of CO₂ trapping within formations over varying timescales. Utilizing our DPNM framework, we study the complexities of residual trapping in miniature-core-sized digital replicate of a sandstone. We systematically conduct series of two-phase flow simulations to examine the relationships between total residual CO₂ amount, trapping efficiency, and initial saturations across a spectrum of capillary numbers and wettability states. Dynamic simulation results lead to a simple power-law scaling equation correlating CO₂ trapping efficiency with initial saturation across various capillary numbers. Further analysis explores the morphological and topological characteristics of fluids during primary drainage and various imbibition displacements within the pore network. This work contributes an essential tool and deepens our understanding of CO₂ trapping dynamics, driving progress towards more effective and safer strategies for carbon capture and storage.