CO2 injection processes are among the effective methods for enhanced oil recovery. A key parameter in the design of CO2 injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from CO2 injection is highly dependent on the MMP. From an experimental point of view, slim tube displacements, and rising bubble apparatus (RBA) tests routinely determine the MMP. Because such experiments are very expensive and time-consuming, searching for fast and robust mathematical determination of CO2-oil MMP is usually requested. It is well recognized that CO2-oil MMP depends upon the purity of CO2, oil composition, and reservoir temperature. This paper presents a new model for predicting the impure and pure CO2-oil MMP and the effects of impurities on MMP. The alternating conditional expectation (ACE) algorithm was used to estimate the optimal transformation that maximizes the correlation between the transformed dependent variable (CO2-oil MMP) and the sum of the transformed independent variables. These independent variables are reservoir temperature (TR), oil compositions (mole percentage of volatile components (C1 and N2), mole percentage of intermediate components (C2-C4, H2S and CO2), and molecular weight of C5+ (MWC5+)), and non-CO2 components (mole percentage of N2, C1, C2-C4, and H2S) in the injected CO2. The validity of this new model was successfully approved by comparing the model results to the pure and impure experimental slim-tube CO2-oil MMP and the calculated results for the common pure and impure CO2-oil MMP correlations. The new model yielded the accurate prediction of the experimental slim-tube CO2-oil MMP with the lowest average relative and average absolute error among all tested impure and pure CO2-oil MMP correlations. In addition, the new model could be used for predicting the impure CO2-oil MMP at higher fractions of non-CO2 components

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