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Continuum‐scale models for two‐phase flow and transport in porous media are based on the empirical constitutive relations that highly depend on the porous medium heterogeneity at multiple scales including the microscale pore‐size correlation length. The pore‐size correlation length determines the representative elementary volume and controls the immiscible two‐phase invasion pattern and fluids occupancy. The fluids occupancy controls not only the shape of relative permeability curves but also the transport zonation under two‐phase flow conditions, which results in the non‐Fickian transport. This study aims to quantify the signature of the pore‐size correlation length on two‐phase flow and solute transport properties such as the capillary pressure‐ and relative permeability‐saturation, dispersivity, stagnant saturation, and mass transfer rate. Given the capability of pore‐scale models in capturing the pore morphology and detailed physics of flow and transport, a novel graphics processing unit (GPU)‐based pore‐network model has been developed. This GPU‐based model allows us to simulate flow and transport in networks with multimillions pores, equivalent to the centimeter length scale. The impact of the pore‐size correlation length on all aforementioned properties was studied and quantified. Moreover, by classification of the pore space to flowing and stagnant regions, a simple semianalytical relation for the mass transfer between the flowing and stagnant regions was derived, which showed a very good agreement with pore‐network simulation results. Results indicate that the characterization of the topology of the stagnant regions as a function of pore‐size correlation length is essential for a better estimation of the two‐phase flow and solute transport properties.

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Density‐driven mixing resulting from CO 2 injection into aquifers leads to the CO 2 entrapment mechanism of solubility trapping. Crucially, the coupled flow‐geochemistry and effects of geochemistry on density‐driven mixing process for “sandstone rocks” have not been adequately addressed. Often, there are conflicting remarks in the literature as to whether geochemistry promotes or undermines dissolution‐driven convection in sandstone aquifers. Against this backdrop, we simulate density‐driven mixing in sandstone aquifers by developing a 2‐D modified stream function formulation for multicomponent reactive convective‐diffusive CO 2 mixing. Two different cases corresponding to laboratory and field scales are studied to investigate the effect of rock‐fluid interaction on density‐driven mixing and the role of mineralization in carbon storage over the project life time. A complex sandstone mineralogical assemblage is considered, and solid‐phase reactions are assumed to be kinetic to study the length‐ and time‐scale dependency of the geochemistry effects. The study revealed nonuniform impact of rock‐fluid and fluid‐fluid interaction in early‐ and late‐time stages of the process. The results show that for moderate Rayleigh (Ra) numbers, rock‐fluid interactions adversely affect solubility trapping while improving the total carbon captured through mineral trapping. Simulation results in the range of 1,500 <  Ra < 55,000 in the field‐scale model showed more pronounced impact of geochemistry for higher Ra numbers, as geochemistry stimulates the convective instabilities and improves the total sequestered carbon. This study gives new insights into the effect of rock‐fluid interactions on density‐driven mixing and solubility trapping in sandstone aquifers to improve estimation of the carbon storage capacity in deep saline aquifers.

Reaction rates for different minerals are usually measured in ideal conditions in batch experiments, where the impact of pore morphology and hydrodynamics have been fully neglected. Such reaction rates are used at continuum-scale (Darcyscale) models without the impact of pore structure on upscaled reaction rates under flow conditions. Therefore, to address the gap from batch experiments to upscaled reaction rates in continuumscale models, a pore-network model coupled with geochemical modeling has been developed. As a case study, we simulate the geochemical reactions of geothermal energy storage/recovery in sandstone rocks by coupling PhreeqcRM (a geochemistry model) with a pore-network model. The main purpose is to delineate the impact of pore morphology and dynamic conditions on upscaling of reaction rates using the surface-weighted and volume-weighted averaging. The results show that the kaolinite reaction rate in porous media highly depends on both the flow rate and spatial distribution of reactive pores. We evaluate the impact of correlation between the reactive pores and pore size distribution on upscaled reaction rates. Results indicate that if reactive pores do not belong to the main flow path, then upscaling the geochemical reactions based on the continuum-scale or batch experiments would be erroneous. In such a scenario, the discrepancy between volume-averaged and surface-weighted average reaction rates are highlighted. Moreover, increasing the injection flow rate results in lower average concentration of different species in the effluent, while it results in higher reaction rates in porous media. This research provides insights into the complex aspects of flow-based reaction rates versus the batch reaction rates. That has a significant impact on continuum-scale modeling of reactive transport for applications such as geothermal energy and enhanced oil recovery.