Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (K[sub: rw] and K[sub: ro]) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on K[sub: rw] and K[sub: ro] is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of K[sub: rw] and K[sub: ro]. To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K[sub: rw] and K[sub: ro], respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of K[sub: rw] and K[sub: ro] in TEOR