Modelling Interfacial Tension of Crude Oil-Brine-Anionic Surfactant Using a Genetic Programming Approach

Surface active agents (surfactants) as the most important chemicals to enhance oil recovery (EOR) can reduce interfacial tension between the injected aqueous solution and the oil in a reservoir. They change wettability of the porous media to release and move the remaining oil trapped in the pores and throats towards the well. According to the important roles of the surfactants, it is necessary to predict their performance for EOR process. In this research, two data-based mathematical models were developed to estimate interfacial tension of the oil, salty water and anionic surfactant system using 598 experimental data. To obtain the correlations between the independent variables and the objective function, genetic programing has been applied. Squared correlation coefficient (R2) of the models is 0.946 and 0.9387; moreover, root-mean-square deviation (RMSD) of the models is 3.4439 mN/m and 3.3261 mN/m respectively. Simplicity and acceptable estimation are particular features of the models.