An improved correlation to determine minimum miscibility pressure of CO2-oil system

An accurate and reliable estimation of minimum miscibility pressure (MMP) of CO2-oil system is a critical task for the design and implementation of CO2 miscible displacement process. In this study, an improved CO2-oil MMP correlation was developed to predict the MMP values for both pure and impure CO2 injection cases based on ten influential factors, i.e. reservoir temperature (T-R), molecular weight of C7+ oil components (MWC7+), mole fraction of volatile oil components (x(vol)), mole fraction of C-2-C-4 oil components (x(C2-C4)), mole fraction of C-5-C-6 oil components (x(C5-C6)), and the gas stream mole fractions of CO2 (y(CO2)), H2S (y(H2S)), C-1 (y(C1)), hydrocarbons (y(HC)) and N-2 (y(N2)). The accuracy of the improved correlation was evaluated against experimental data reported in literature concurrently with those estimated by several renowned correlations. It was found that the improved correlation provided higher prediction accuracy and consistency with literature experimental data than other literature correlations. In addition, the predictive capability of the improved correlation was further validated by predicting an experimentally measured CO2-oil MMP data, and it showed an accurate result with the absolute deviation of 4.15%. Besides, the differential analysis of the improved correlation was analyzed to estimate the impact of parameters uncertainty in the original MMP data on the calculated results. Also, sensitivity analysis was performed to analyze the influence of each parameter on MMP qualitatively and quantitatively. The results revealed that the increase of x(C2-C4),x(C5-C6) and y(H2S) lead to the decrease of MMP, while the increase of T-R, MWC7+, x(vol), y(CO2), y(C1), y(HC) and y(N2) tend to increase the MMP. Overall, the relevance of each parameter with MMP follows the order of T-R > x(C5-C6) > MWC7+ > x(vol) > y(H2S) > y(HC) > y(CO2) > y(C1) > y(N2) > x(C2-C4). (C) 2020, Institute of Process Engineering, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.