Depleted gas reservoirs are viable choices for large-scale CO₂ storage and to displace remaining methane volumes to further increase the storage capacity (EGR). However, deployment of such projects depends on an informed knowledge of the magnitude of mixing of the miscible gases, efficiency in displacing in-situ methane by CO₂, composition of the produced gas, and CO₂ storage capacity. This study focuses on the fundamental analysis of mixing during CO₂-EGR using a numerical approach. We propose to conduct very fine grid compositional simulations to provide insights into the mixing of CO₂ and methane in a gas reservoir at different reservoir and operational conditions. We first analyze a stratified layer model to understand the basic mechanisms of scale-dependency of dispersion and the significance of reservoir heterogeneity on fluid mixing. To consider more realistic reservoir heterogeneity, a two-dimensional stochastic reservoir model is analyzed to estimate dispersivity generated as fluids flow in porous media at different scales. Reservoir heterogeneity is represented by the Dykstra Parsons coefficient (V_DP) and autocorrelation length, and fluid properties are modeled depending on pressure and temperature conditions. Field-scale simulation is also performed to discuss the way dispersion is modeled in reservoir simulation affects simulated gas recovery. Our study shows that the variance of permeability and convective spreading are the primary causes of fluid mixing at any scale. In addition, molecular diffusion is not always negligible in gas mixing even in large-scale heterogeneous reservoirs since gas has much larger diffusivity than liquid. Furthermore, the mechanism of fluid mixing during CO₂-EGR is complex with the interplay between convective spreading, transverse dispersion (including molecular diffusion), and gravity segregation. Although geoscientists often assume numerical dispersion can represent physical dispersion, our study indicates this is an oversimplification and could cause significant errors in calculated gas recovery. Permeability heterogeneity is essential for the dispersion growth process and the final displacement behavior. Reservoir heterogeneity should be modeled with high-resolution grid models to analyze mixing behaviors more accurately.

The study aims to quantitatively assess the risk of hydrate formation within the porous formation and its consequences to injectivity during storage of CO₂ in depleted gas reservoirs considering low temperatures caused by the Joule Thomson (JT) effect and hydrate kinetics. The aim was to understand which mechanisms can mitigate or prevent the formation of hydrates. The key mechanisms we studied included water dry-out, heat exchange with surrounding rock formation, and capillary pressure. A compositional thermal reservoir simulator is used to model the fluid and heat flow of CO₂ through a reservoir initially composed of brine and methane. The simulator can model the formation and dissociation of both methane and CO₂ hydrates using kinetic reactions. This approach has the advantage of computing the amount of hydrate deposited and estimating its effects on the porosity and permeability alteration. Sensitivity analyses are also carried out to investigate the impact of different parameters and mechanisms on the deposition of hydrates and the injectivity of CO₂. Simulation results for a simplified model were verified with results from the literature. The key results of this work are: (1) The Joule-Thomson effect strongly depends on the reservoir permeability and initial pressure and could lead to the formation of hydrates within the porous media even when the injected CO₂ temperature was higher than the hydrate equilibrium temperature, (2) The heat gain from underburden and overburden rock formations could prevent hydrates formed at late time, (3) Permeability reduction increased the formation of hydrates due to an increased JT cooling, and (4) Water dry-out near the wellbore did not prevent hydrate formation. Finally, the role of capillary pressure was quite complex, where it reduced the formation of hydrates in certain cases and increased in other cases. Simulating this process with heat flow and hydrate reactions was also shown to present severe numerical issues. It was critical to select convergence criteria and linear system tolerances to avoid large material balance and numerical errors.

Featured Application: This study emphasizes the importance of comprehensive monitoring, calibration, and optimization of storage strategies in a saline aquifer. It also highlights the need to manage geomechanical risks and uncertainties. By understanding these risks and employing suitable monitoring techniques, the integrity and safety of GCS can be ensured, contributing to the reduction of CO ₂ emissions. Geological Carbon Storage (GCS) involves storing CO ₂ emissions in geological formations, where safe containment is challenged by structural and stratigraphic trapping and caprock integrity. This study investigates flow and geomechanical responses to CO ₂ injection based on a Brazilian offshore reservoir model, highlighting the critical interplay between rock properties, injection rates, pressure changes, and ground displacements. The findings indicate centimeter-scale ground uplift and question the conventional selection of the wellhead as a monitoring site, as it might not be optimal due to the reservoir’s complexity and the nature of the injection process. This study addresses the importance of comprehensive sensitivity analyses on geomechanical properties and injection rates for advancing GCS by improving monitoring strategies and risk management. Furthermore, this study explores the geomechanical effects of modeling flow in the caprock, highlighting the role of pressure dissipation within the caprock. These insights are vital for advancing the design of monitoring strategies, enhancing the predictive accuracy of models, and effectively managing geomechanical risks, thus ensuring the success of GCS initiatives.

Depleted gas reservoirs are attractive sites for Carbon Capture and Storage (CCS) due to their huge storage capacities, proven seal integrity, existing infrastructure and subsurface data availability. However, CO₂ injection into depleted formations can potentially lead to hydrate formation near the wellbore due to Joule-Thomson cooling, which might cause injectivity issues. Some challenges encountered when modeling and simulating this process are the computational time caused by Newton's convergence issues and instability. The objective of this work is to propose a novel approach for hydrate risk assessment during CO₂ injection into depleted gas reservoirs using physics-based Machine Learning (ML) approach. First, the selection of input parameters for the ML models is performed based on sensitivity study results using an analytical solution for different operational and petrophysical values. Then the ML models are tuned and tested using datasets from numerical reservoir simulation results based on a wide range of input parameter values. To the best of our knowledge, this is the first time that an ML approach is used for risk assessment of CO₂ hydrate in its storage in depleted gas reservoirs. The ML models developed in this study presented an efficient performance to predict hydrate-forming events. The deep neural network model performed best with a 95% recall value and 84% precision value. These results show that the ML model can be further utilized for risk assessment in the screening stage, and the combination of screening by ML, followed by detailed analysis with numerical simulation in high-risk cases can be an efficient probing workflow for future CCS projects.

Simulation models for foam enhanced oil recovery are of two types: Those that treat foam texture or bubble size explicitly (population-balance models) and those that treat the effects of foam texture implicitly through a gas mobility-reduction factor. The implicit-Texture models all implicitly assume local equilibrium (LE) between the processes of foam creation and destruction. In published studies most populationbalance models predict rapid attainment of local-equilibrium as well, and some have been recast in LE versions. In this paper we compare population-balance and implicit-Texture (IT) models in two ways. First, we show the equivalence of the two approaches by deriving explicitly the foam texture and foam-coalescencerate function implicit in the IT models, and then show its similarity to that in population-balance models. Second, we compare the models based on their ability to represent a set of N₂ and CO₂ steady-state foam experiments and discuss the corresponding parameters of the different methods. Each of the IT models examined was equivalent to the LE formulation of a population-balance model with a lamella-destruction function that increases abruptly in the vicinity of the limiting capillary pressure Pc∗, as in current population-balance models. The relation between steady-state foam texture and water saturation or capillary pressure implicit in the IT models is essentially the same as that in the populationbalance models. The IT and population-balance models match the experimental data presented equally well. The IT models examined allow for flexibility in making the abruptness of the coalescence rate near Pc an adjustable parameter. Some allow for coarse foam to survive at high capillary pressure, and allow for a range of power-law non-Newtonian behavior in the low-quality regime. Thus the IT models that incorporate an abrupt change in foam properties near a given water saturation can be recast as LE versions of corresponding population-balance models with a lamella-destruction function similar to those in current PB models. The trends in dimensionless foam texture implicit in the IT models is similar to that in the PB models. In other words, both types of model, at least in the LE approximation, equally honor the physics of foam behavior in porous media.