A novel correlation for modeling interfacial tension in binary mixtures of CH4, CO2, and N2 + normal alkanes systems: Application to gas injection EOR process

The Enhanced Oil Recovery (EOR) has received significant attention in recent years by the cessation of primary production of several reservoirs and also the increasing global demand for energy. Miscible gas flooding is one of the most popular EOR techniques. Interfacial Tension (IFT) data plays an important role in determining Minimum Miscibility Pressure (MMP) as well as estimating the recovery performance of the EOR schemes, and the production potential of fractured reservoirs. CH4, CO2, and N-2 are the gases widely used for gas injection EOR practices. Normal alkanes can represent the real crude oil samples as they are their most abundant constituents. Acquiring knowledge about the IFT between vapor phase and n-alkanes can shed light on the governing mechanisms between the injected gas phase and the real crude oil in EOR projects. Experimental determination of IFT is often associated with the high cost and arduous and timely procedures. The theoretical works need expensive numerical and computational effort. Data-based methods can offer a promising alternative to compensate for the disadvantages of the mentioned methods. An extensive database including 606, 1131, and 469 data samples of IFT between normal n-alkanes and CH4, CO2, and N-2 was utilized in this study to develop predictive models. Proper input variables were analyzed and selected for modeling. The Group Method of Data Handling (GMDH) was used in this study. Separate models were developed for the three vapor phases. The Root Mean Square Errors (RMSE) of 1.24, 1.29, and 1.15 mN/m for respective CH4, CO2, and N-2 systems indicate the acceptable accuracy of the developed model. The models were compared with the models available in the literature. It was revealed that the proposed GMDH models provide superior predictions for a wide range of pressure and temperature. External validation was performed using a very recently published dataset to check for the generality of the proposed models. Finally, a systematic method was proposed to select the best predictive model based on the n-alkane type and operational parameters of pressure and temperature. The findings of this study can be utilized in designing and planning efficient EOR schemes.