Abstract
Two new genetic algorithm (GA)-based correlations were proposed for more reliable prediction of minimum miscibility pressure (MMP) between reservoir oil and CO 2 or flue gas. Both correlations are particularly useful when experimental data are lacking and also in developing an optimal laboratory program to estimate MMP. The key input parameters in a GA-based CO 2 -oil MMP correlation, in order of their impact, were: reservoir temperature, MW of C 5+ , and volatiles (C 1 and N 2 ) to intermediates (C 2 -C 4 , H 2 S and CO 2 ) ratio. This correlation, which has been successfully validated with published experimental data and compared to common correlations in the literature, offered the best match with the lowest error (5.5%) and standard deviation (7.4%). For a GA-based flue gas-oil MMP correlation, the MMP was regarded as a function of the injected gas solvency into the oil which, in turn, is related to the injected gas critical properties. It has also been successfully validated against published experimental data and compared to several correlations in the literature. It yielded the best match with the lowest average error (4.6%) and standard deviation (6.2%). Moreover, unlike other correlations, it can be used more reliably for gases with high N 2 (up to 20%) and non-CO 2 components (up to 78%), e.g., H;S, N 2 , SO x , O, and C 1 -C 4 .
Published Version
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