prediction of bubble point pressure using new hybrid computationail intelligence models

Determining bpp is one of the critical parameters for developing oil and gas reservoirs, and has this parameter requires a lot of time and money.. as a result, this study aims to develop a new predictive model for bpp that uses some available input variables such as solution oil ratio (rs), gas specific gravity (γg), api gravity (api). in this study, two innovatively combined hybrid algorithms, dwknn-gsa and dwknn-ica, are developed to predict bpp. the study outcomes show the models developed are capable of predicting bpp with promising performance, where the best result was achieved for dwknn-ica (rmse = 0.90276 psi and r^2 = 1.000 for the test dataset). moreover, the performance comparison of the developed hybrid models with some previously developed models revealed that the dwknn-ica outperforms the former empirical models concerning perdition accuracy. in addition to presenting new techniques in the present study, the effect of each of the input parameters on bpp was evaluated using spearman's correlation coefficient, where the api and rs have the lowest and the highest impact on the bpp