Modeling interfacial tension of surfactant-hydrocarbon systems using robust tree-based machine learning algorithms

Ali Rashidi-Khaniabadi; Elham Rashidi-Khaniabadi; Behnam Amiri-Ramsheh; Mohammad-Reza Mohammadi; Abdolhossein Hemmati-Sarapardeh

doi:10.1038/s41598-023-37933-0

Modeling interfacial tension of surfactant-hydrocarbon systems using robust tree-based machine learning algorithms

Sci Rep. 2023 Jul 5;13(1):10836. doi: 10.1038/s41598-023-37933-0.

Authors

Ali Rashidi-Khaniabadi¹, Elham Rashidi-Khaniabadi², Behnam Amiri-Ramsheh³, Mohammad-Reza Mohammadi³, Abdolhossein Hemmati-Sarapardeh^{4

5}

Affiliations

¹ Department of Petroleum Engineering, EOR Research Center, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
² Department of Mathematics, Yazd University, Yazd, Iran.
³ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
⁴ Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.
⁵ State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China. hemmati@uk.ac.ir.

Abstract

Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) operation. Measuring IFT in the laboratory is time-consuming and costly. Since, the accurate estimation of IFT is of paramount significance, modeling with advanced intelligent techniques has been used as a proper alternative in recent years. In this study, the IFT values between surfactants and hydrocarbon were predicted using tree-based machine learning algorithms. Decision tree (DT), extra trees (ET), and gradient boosted regression trees (GBRT) were used to predict this parameter. For this purpose, 390 experimental data collected from previous studies were used to implement intelligent models. Temperature, normal alkane molecular weight, surfactant concentration, hydrophilic-lipophilic balance (HLB), and phase inversion temperature (PIT) were selected as inputs of models and independent variables. Also, the IFT between the surfactant solution and normal alkanes was selected as the output of the models and the dependent variable. Moreover, the implemented models were evaluated using statistical analyses and applied graphical methods. The results showed that DT, ET, and GBRT could predict the data with average absolute relative error values of 4.12%, 3.52%, and 2.71%, respectively. The R-squared of all implementation models is higher than 0.98, and for the best model, GBRT, it is 0.9939. Furthermore, sensitivity analysis using the Pearson approach was utilized to detect correlation coefficients of the input parameters. Based on this technique, the results of sensitivity analysis demonstrated that PIT, surfactant concentration, and HLB had the greatest effect on IFT, respectively. Finally, GBRT was statistically credited by the Leverage approach.