Use of genetic algorithm to estimate CO(2)-oil minimum miscibility pressure - a key parameter in design of CO(2) miscible flood

A new genetic algorithm (GA)-based correlation has been developed to estimate the CO2–oil minimum miscibility pressure (MMP)—a key parameter in design of CO2 miscible flood to enhance oil recovery. In the order of their effects and importance, the correlation uses the following key input parameters: reservoir temperature, molecular weight of C5+, and the ratio of volatiles (C1 and N2) to intermediates (C2–C4, H2S, and CO2) and has been validated against experimental data and other commonly used correlations reported in the literature, notably that of Alston et al., Glaso, Yellig and Metcalfe, Cronquist, Lee, and Holm and Josendal early correlation. Based on the Darwinian theory of evolution, it is demonstrated that the GA is particularly suited to problems with non-linearity, variable discontinuity, large search space, and all kinds of objective and constraint functions. It works by exploration and exploitation of the search space (that is, the probability of finding the global optimum increases). Moreover, GA solutions are less likely to be misleading, as the problem does not need to be ”restructured” to fit the solution method. That is, the development of the GA-based correlation does not entail any pre-correlation data manipulation, and as a consequence, all data could be utilized as reported. The GA software developed in this study uses chromosomes coded with real numbers to encode correlation coefficients in an initial random population with 100 chromosomes size. Such encoding technique enhances the GA robustness. For the selection technique, the roulette wheel method has been used. Furthermore, to produce the offspring, one-point crossover and mutation with 100% probability are used. Noteworthy advantage offered by GA-based correlation is that this correlation can be used when there is a lack of experimental data and also to design an optimal laboratory program to estimate MMP. Moreover, the correlation errors could be minimized further through a series of iterative optimisation runs. Our results suggest that a GA-based CO2–oil MMP correlation could be superior to other correlations commonly used. For example, compared to other correlations, the GA-based correlation yielded a better match with data with an average error of 5.86% and standard deviation of 7.96%, followed by Alston et al. correlation, which yielded an average error of 8.21% with a standard deviation of 10.04%. Among the correlations tested and compared against the GA-based correlation, Yellig and Metcalfe correlation yielded an error of 14%.http://www.elsevier.com/wps/find/journaldescription.cws_home/503345/description#descriptio