Capillary trapping in mixed-wet porous media: Implications for subsurface carbon dioxide sequestration

Subsurface sequestration of carbon dioxide (CO₂) is driving efforts to attain carbon neutrality. For the safe and optimal operation of such complex applications, it is imperative to understand the physics of fluids displacement. Direct numerical simulations are used to investigate the flooding of two immiscible fluids having viscosity contrasts in mixed-wet porous media, which are ubiquitous in reservoirs characterized by multifarious mineralogizes and complex physico-chemical histories. Three mixed-wet systems having different wettability ranges and one mono-wet case are considered for investigation. Flooding by low viscosity fluid caused fingering. Though the fingering patterns vary for different wettability distributions, the sample-scale morphological metrics for all cases are closely comparable. The fingering profiles are preserved and later subjected to flooding by high viscosity fluid. Entrapment of the defending phase due to capillarity for different wettability systems are investigated. When the wettability range increases, the trapping efficiency is also seen to increase linearly, suggesting that reservoirs with strong mixed-wet conditions present an attractive option for CO₂ sequestration. Pore-scale fluid displacements reveal that during viscous fingering the fluid-fluid interface initially developed in non-wet zones retract which contribute towards cooperative pore filling in the surrounding wetting zones that influence the characteristic features of invading fluid's flow morphology. Additionally, various possibilities by which the defending phase gets trapped by flow bypassing are explored. Trapping was prominent in zones having an affinity to the defending phase. The average trapped ganglia size increases commensurately with degree of dispersion in wettability. The study also highlights shortcomings of analyzing multiphase flows in mono-wet systems. Insights from this study can be used for improving pore network models and training machine learning algorithms.