A novel approach to predict oil and water relative permeabilities in porous media

Relative permeability of multiphase flow through porous media plays a vital role in the petroleum industry, especially in enhanced oil recovery (EOR) processes. In this study, models were developed based on CSA-LSSVM algorithm to predict oil-water relative permeability in sandstone and carbonate porous media. Comparing the model to thousands of experimental data resulted in overall squared correlation coefficients (R2) of 0.9866 and 0.9965, and minimum squared error (MSE) of 0.0014 and 0.000143 for Kro and Krw, respectively. In addition, Model predictions were found to accord excellently with experimental data. The results of CSA-LSSVM model were compared with some of the well-known mathematical equations and empirical correlations while predicting the oil-water relative permeability in a heterogeneous carbonate core sample. The models developed in this study were found to excel in performance compared to mathematical equations and empirical correlations in the prediction of both oil and water relative permeability.

Keywords:

Relative Permeability , Multiphase Flow , Porous Media , CSA-LS SVM , Model