Prediction of Wax Disappearance Temperature by Intelligent Models

It is well-known that reservoir hydrocarbon fluids contain heavy paraffins that may form solid phases of wax at low temperatures. Problems associated with wax formation and deposition are a major concern in production and transportation of hydrocarbon fluids. Thus, testing of wax disappearance temperature (WDT) is essential in high-efficiency development of crude oil. For the sake of reduction of time and improvement of accuracy, four metaheuristic models called gray wolf optimizer-based support vector machine (GWO-SVM), least-squares support vector machine, genetic algorithm-based adaptive network-based fuzzy inference system, and particle swarm optimization-based adaptive network-based fuzzy inference system were used for the prediction of WDT in binary, ternary, and multicomponent systems in the range of 0.1–100 MPa. The input parameters are molar mass and pressure, and the output is the WDT at every point. The comparison between the four models shows that the GWO-SVM gets the best accordance with experimental data sets with the minimum average absolute relative deviation (AARD = 0.7128%), maximum determination coefficient (R2 = 0.9546), and minimum root-mean-squared error (RMSE = 2.4208) in all 272 data points. And outliers detection using the leverage approach to detect the doubt points, where only 6 data points in all 272 data points.