Journal of Petroleum Science and Technology

Journal of Petroleum Science and Technology

Interfacial Tension in Asphaltenic Crude Oil – Brine Systems: Robust Predictive Tools Based on Intelligent Approaches

Document Type : Research Paper

Authors
Mohammad Amin Moradkhani
Seyedeh Hosna Talebian
Department of Chemical and Petroleum Engineering, Faculty of Engineering, Ilam University, Ilam, Iran
10.22078/jpst.2024.5406.1934
Abstract
Accurate interfacial tension (IFT) determination between crude oil and brines is crucial in enhanced oil recovery (EOR) processes. However, the available IFT models only apply to systems containing pure hydrocarbons and saline waters. The current study aims to design comprehensive predictive tools for the IFT between asphaltenic crude oils and various brines. Hence, 339 relevant experimental data covering an extensive range of operating conditions were gathered from the literature, and the most effective input variables were determined through Spearman’s rank coefficient. Then, the experimental data were utilized to train the smart soft-computing approaches, i.e., radial basis function (RBF), multilayer perceptron (MLP), and Gaussian process regression (GPR). Although all novel predictive tools presented excellent results, the one designed based on the GPR method was recognized as the most reliable model with an average absolute relative error (AARE) of 0.67% and an R2 value of 99.63% in the testing stage. Additionally, it estimated most of the IFT data with relative errors below 0.10%. On the other hand, the validity of the gathered databank was confirmed through the leverage method. The influences of pressure, temperature, salinity, and structural characteristics of salts on the IFT were discussed in detail, and the proposed models favorably described the physical trends. Eventually, a sensitivity analysis was carried out based on the GPR model to clarify the order of significance of factors in controlling the crude oil-brine IFT.
Keywords
Interfacial Tension
Asphaltenic Crude Oil
Brine
Intelligent Approaches
Modeling
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