Modeling oil-brine interfacial tension at high pressure and high salinity conditions

Accurate estimation of interfacial tension (IFT) in crude oil/brine system is of great importance for many processes in petroleum and chemical engineering. The current study plays emphasis on introducing the Gradient Boosting Decision Tree (GBDT) and Adaptive Boosting Support Vector Regression (AdaBoost SVR) as novel powerful machine learning tools to determine the IFT of crude oil/brine system. Two sorts of models have been developed using each of these two data-driven methods. The first kind includes six inputs, namely pressure (P), temperature (T) and four parameters describing the proprieties of crude oil (total acid number (TAN) and specific gravity (SG) and brine (NaCl equivalent salinity (S-eq) and pH), while the second kind deals with four inputs (without including pH and TAN). To this end, an extensive databank including 560 experimental points was considered, in which 80% of the points were employed for the training phase and the remaining part was utilized as blind test data. Results revealed that the proposed approaches provide very satisfactory predictions, and the implemented GBDT model with six inputs is the most accurate model of all with an average absolute relative error of 1.01%. Moreover, the outcomes of the GBDT model are better than literature models. Finally, outlier diagnostic using Leverage approach was performed to investigate the applicability domain of the GBDT model and to evaluate the quality of employed data.