Petroleum Emulsion Stability Predictions: Performance Evaluation of ANN, GEP and RSM Models

Abstract The co-production of immiscible oil and water phases frequently results in the formation of stable emulsions, posing a persistent challenge in the petroleum industry. These emulsions are metastable colloidal systems, typically stabilized by the presence of surface-active agents, amphiphilic polymers, or fine solid particles. Their occurrence can lead to substantial operational and economic drawbacks, making the ability to accurately predict emulsion stability critical for effective mitigation strategies. In this study, synthetic emulsions were systematically prepared based on experimental conditions prescribed by the Stratigraphic Centurion VII design of experiments. Emulsion stability was evaluated by measuring the volume of water separated at 60 °C over a duration of 30 days (or 15 days in selected cases). The experimental design followed the Box-Behnken Design (BBD) framework, and the resulting data were used to develop predictive models using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Gene Expression Programming (GEP). Model performance was assessed through Analysis of Variance (ANOVA), alongside statistical metrics such as the coefficient of determination (R²) and root mean square error (RMSE). A Pareto chart was also employed to quantify the relative importance of input variables on emulsion stability. Comparative analysis between model predictions and experimental results revealed that the ANN model achieved superior accuracy, with an R² of 0.9828 and minimal deviation. In contrast, the RSM and GEP models exhibited lower predictive performance, with R² values of 0.8682 and 0.8723, respectively. Despite its higher accuracy, the ANN model's black-box nature limits its interpretability. Conversely, the RSM and GEP models provide explicit mathematical expressions, making them valuable tools for understanding system behaviour and guiding process optimization.