Abstract
This paper evaluates the ability of machine learning (ML) algorithms to capture and reproduce complex multiphase behavior in surfactant–oil–water systems. The main objective of the paper is to evaluate the ability of machine learning algorithms to capture complex phase behavior of a surfactant–oil–water system in a controlled environment of known data generated via physical models. We evaluated several machine learning algorithms including decision trees, support vector machines (SVMs), k-nearest neighbors, and boosted trees. Moreover, the study integrates a novel graphical equation-of-state model with ML-generated compositional spaces to test ML’s effectiveness in predicting phase transitions and compares its performance to experimental data and a validated physical model. Our results demonstrate that the cubic SVM has the highest accuracy in capturing key behaviors, such as the shrinking of two-phase regions as salinity deviates from optimal conditions, and performs well even in near-extrapolated scenarios. Additionally, the graphical equation-of-state model aligns closely with both experimental data and the physical model, providing a robust framework for analyzing multiphase behavior. We do not suggest that machine learning models should replace traditional physical models, but rather should complement physical models by extending predictive capabilities, especially when experimental data are limited. This hybrid approach offers a promising method for investigating complex multiphase phenomena in surfactant systems.