Pore-Scale Numerical Modeling of CO2-Brine Interactions in Lithuanian Formations Based on Microfluidic Chip Derived Porous Networks

Abstract The safe and long-term storage of Carbon dioxide (CO2) in subsurface formations requires a thorough understanding of the CO2-brine interactions occurring at the pore-scale. This study presents an integrated experimental and numerical modeling approach to understand the behavior of CO2 in homogeneous pore network using microfluidic chip designs. The aim is to provide an understanding of the Lithuanian reservoirs and their feasibility for long-term safe storage of CO2 by simulating pore-scale conditions. The 2D pore network represented by microfluidic chips, specifically designed for pore-scale investigations, served as input models for simulating CO2 flow behavior. The homogeneous network was designed with porosity 50%, and permeability 8.94D. This network was used in pore-scale flow simulations based on a finite volume-based multiphase flow simulator, MRST. The simulations modeled CO2 injection into brine- saturated pore spaces under reservoir-relevant pressure and temperature conditions. At first, the microfluidic experiment was performed in homogeneous pore networks to observe the flow of CO2 in a brine saturated chipset and understand the CO2-brine interactions. Simulations were then performed to replicate the experimental results. Simulation results revealed that homogeneous pore networks exhibited uniform CO2 displacement with minimal trapping. This behavior was strongly controlled by pore geometry, connectivity, and heterogeneity. The findings demonstrate that microscale salt precipitation and pore structure variations significantly influence CO2 mobility, trapping efficiency, and storage capacity. This work provides the novel detailed pore-scale modeling of CO2–brine interactions for Lithuanian reservoirs, addressing a key knowledge gap in Baltic region storage studies. The integrated microfluidic experimental– numerical approach offers valuable insights for improving injectivity prediction, risk assessment, and upscaling strategies for field-scale CO2 storage.