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The phase behavior of fluid is essential for predicting ultimate oil recovery and determining optimal production parameters. The pore size in shale porous media is nanopore, which causes different phase behaviors of fluid in unconventional reservoirs. Nanopores in shale media can be regard as semipermeable membrane to filter heavy components (sieving effect) in shale oil, which leads to the different distributions of fluid components and different phase behaviors. In addition, the phase behavior of fluid in nanopores can be significantly altered by large capillary pressure. In this paper, the phase behavior of fluid in shale reservoirs is investigated by a new two-phase flash algorithm considering sieving effect and capillary pressure. Firstly, membrane efficiency and capillary pressure are introduced to establish a thermodynamic equilibrium model that is solved by Rachford–Rice flash calculation and Newton–Raphson method. The capillary pressures in different pore sizes are calculated by the Young–Laplace equation. Then, the influences of sieving effect and capillary pressure on phase behavior are analyzed. The results indicate that capillary pressure can suppress the bubble point pressure of fluid in nanopores. The distributions of fluid components are different in various parts of shale media. In the unfiltered part, density and viscosity of fluid are higher. Finally, it is found that the membrane efficiency can be improved by CO2 injection. The minimum miscibility pressure for shale oil–CO2 system is also studied. The developed model provides a better understanding of the phase behavior of fluid in shale oil reservoirs. [ABSTRACT FROM AUTHOR]

CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis. [ABSTRACT FROM AUTHOR]