With the rising global energy demand, shale gas and oil emerge as pivotal resources. Recent innovations utilizing CO₂ as an injectant can effectively enhance shale oil and gas recovery and facilitate CO₂ storage within shale reservoirs. However, low-temperature CO₂ injection may result in the coexistence of three hydrocarbon phases, while the abundant nanopores in shale formations also notably influence the phase behavior of reservoir fluids. To optimize shale oil recovery and CO₂ sequestration in shale formations, it is a prerequisite for precisely capturing the effect of confinement on the phase behavior of reservoir fluids within nanopores during CO₂ injection. In this work, we introduce a novel three-phase vapor-liquid-liquid equilibrium calculation algorithm, which is designed to handle the unique phase behavior challenges presented by CO₂ utilization and storage in shale reservoirs. To improve the robustness and efficiency, the proposed algorithm integrates a trust region-based stability test with a hybrid flash calculation algorithm that combines the Newton-Raphson and trust-region methods. Our thermodynamic model incorporates the capillarity effect and shifts in the critical points due to molecule-wall interactions, which are essential for accurate phase behavior simulation under confinement. Initial validations against experimental bulk phase data show promising results, and further investigations indicate that confinement alters three-phase vapor-liquid-liquid equilibria by suppressing two-phase and three-phase regions and shifting boundaries in the phase diagrams. The proposed algorithm not only advances our understanding of multiphase equilibrium in nanoporous media but also enhances the practicality of CO₂ sequestration and improved oil recovery strategies in shale formations.

Vaporization enthalpy (Δ_vapH) is a fundamental thermodynamic property of ionic liquids (ILs). Accurate prediction of vaporization enthalpy relies on appropriate mathematical models grounded in precise experimental measurements. The quantitative structure-property relationship (QSPR) model, a key semi-empirical approach, could predict physicochemical properties based on the molecular structure of a substance. However, accurately predicting vaporization enthalpy and adequately describing the molecular structure of ILs remain significant challenges for this model. In this study, we used the cavity volume and charge density distribution area at specific intervals, derived from the conductor-like screening model for segment activity coefficient (COSMO-SAC) method, as molecular descriptors. Utilizing the developed descriptors, we constructed an improved QSPR model (Δ_vapH-ANN) to predict the vaporization enthalpy of ILs across a broad temperature range, employing the back-propagation artificial neural network (BP-ANN) algorithm. The dataset for our model consists of 3150 data points for 148 ILs within a temperature range of 298–631.86 K. Overall, the results show that the proposed Δ_vapH-ANN model, which treats ILs as “ion pairs”, can accurately predict the Δ_vapH of ILs across various temperatures.