LAUSR

Abstract The interfacial tension (IFT) between the injecting gas and host oil is a key parameter that affects the ultimate displacement efficiency and gas injection enhanced oil recovery (EOR) performance. The accurate characterization of the IFT between varying n-alkanes and injecting gases is crucial to obtaining deeper insight into the predominating mechanisms behind the interfacial behaviors of (gas + oil) systems, and in turn ensuring the optimal design of gas injection EOR projects. Laboratory measurement of the IFT usually requires expensive experimental apparatus, time-consuming operation procedure and cumbersome data deduction. This paper proposed the use of a novel supervised learning (SL) method, namely the eXtreme gradient boosting (XGBoost) trees, for the fast estimation of (gas + n-alkane) IFT. A unified estimation model was constructed for varying injecting gas species and n-alkanes based on a large database consisting of a number of 1561 data sets. Results showed that the unified model is capable of accurately reproducing the experimental IFT based on pressure, temperature, n-alkane molecular weight and gas composition. It was also demonstrated that the new model outperforms the multi-layer perceptron (MLP), support vector regression (SVR) and existing correlations in terms of accuracy and robustness. Furthermore, the permutation importance (PI) was applied to quantify the importance of each input feature to the IFT, which concluded that the ranking of features in terms of decreasing importance to the (gas + n-alkane) IFT are: pressure ≫ n-alkane molecular weight > gas composition > temperature.