An Insight into the Prediction of Scale Precipitation in Harsh Conditions Using Different Machine Learning Algorithms

Scale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers. In the current work, different intelligent models, including the decision tree, random forest (RF), artificial neural network (ANN), K-nearest neighbors (KNN), convolutional neural network (CNN), support vector machine (SVM), ensemble learning, logistic regression, Naïve Bayes, and adaptive boosting (AdaBoost), are used to estimate scale formation as a function of pH and ionic compositions. Also, a sensitivity analysis is done to determine the most influential parameters on scale formation. The novelty of this work is to compare the performance of 10 different machine learning algorithms at modeling an extremely non-linear relationship between the inputs and the outputs in scale precipitation prediction. After determining the best models, they can be used to determine scale formation by manipulating the concentration of a variable in accordance with the result of the sensitivity analysis. Different classification metrics, including the accuracy, precision, F1-score, and recall, were used to compare the performance of the mentioned models. Results in the testing phase showed that the KNN and ensemble learning were the most accurate tools based on all performance metrics of solving the classification of scale/no-scale problem. As the output had an extremely non-linear behavior in terms of the inputs, an instance-based learning algorithm such as the KNN best suited the classification task in this study. This argumentation was backed by the classification results. Furthermore, the SVM, Naïve Bayes, and logistic regression performance metrics were not satisfactory in the prediction of scale formation. Note that the hyperparameters of the models were found by grid search and random search approaches. Finally, the sensitivity analysis showed that the variations in the concentration of Ca had the highest impact on scale precipitation.