Machine learning approaches for assessing stability in acid-crude oil emulsions: Application to mitigate formation damage

Abstract

The stability of acid-crude oil emulsion poses manifold issues in the oil industry. Experimentally evaluating this phenomenon may be costly and time-consuming. In contrast, machine learning models have proven effective in predicting and evaluating various phenomena. This research is the first of its kind to assess the stability of acid-crude oil emulsion, employing various classification machine learning models. For this purpose, a data set consisting of 249 experimental data points belonging to 11 different crude oil samples was collected. Three tree-based models, namely decision tree (DT), random forest (RF), and categorical boosting (CatBoost), as well as three artificial neural network models, namely radial basis function (RBF), multi-layer perceptron (MLP) and convolutional neural network (CNN), were developed based on the properties of crude oil, acid, and protective additive. The CatBoost model obtained the highest accuracy with 0.9687, followed closely by the CNN model with 0.9673. In addition, confusion matrix findings showed the superiority of the CatBoost model. Finally, by applying the SHapley Additive exPlanations (SHAP) method to analyze the impact of input parameters, it was found that the crude oil viscosity has the most significant effect on the model's output with the mean absolute SHAP value of 0.88.