A novel approach for modeling and optimization of surfactant/polymer flooding based on Genetic Programming evolutionary algorithm

• GP was used to model RF and NPV as a function of several important variables.
• 10-fold cross validation were employed to check the models overfitting.
• Simultaneous optimization was performed on both models using Design Expert software.
• Importance of all variables on both RF and NPV were investigated.

In this research, Genetic Programming (GP) as a novel method for modeling the Recovery Factor (RF) and the Net Present Value (NPV) in Surfactant–Polymer (SP) flooding is presented. The GP modeling, has the advantage that the created models did not require a fundamental description of the physical processes. The GP created mathematical functions for both outputs as a function of important parameters which involves in the SP flooding based on 202 different data. Moreover, 10-fold cross validation were employed to check the models overfitting. The Normalized Root Mean Squared Error (NRMSE) and the coefficient of determination (R2) of 4.83%, 0.963 for the RF model, and 5.68%, 0.946 for NPV model represented the accuracy of models. The importance and effect of variables on models were investigated, and simultaneous optimization was performed on both models to find the best results in terms of higher RF and NPV. The highest values of 55.03 and 7.3 Million US Dollars (MMUSD) for RF and NPV were achieved as a result of this optimization.