Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People's Republic of Chin a, by NewsRx editors, research stated, "Asphaltene precipitation is one of the c hallenging flow assurance problems as it can cause permeability impairment and p ipeline blockages by depositing on the surface of well tubing, flowlines, and he at exchangers. The cost of remediating an unexpected asphaltene problem is expen sive and time-consuming wherever offshore or on land." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the China Universit y of Petroleum, "Thus, the provision of asphaltene precipitation is vital and an effective approach is stability screening for monitoring asphaltene precipitati on problems. In this study, asphaltene stability performance in crude oil was ev aluated using six machine learning (ML) techniques, namely decision tree (DT), N aive Bayes (NB), support vector machine (SVM), artificial neural networks (ANN), random forest (RF), and k-nearest neighbor (KNN). A large stability data contai ning 186 crude oil samples of known stability were used to design the classifica tion models for predicting asphaltene stability. The inputs to the models were t he content of saturates, aromatics, resins, and asphaltenes (SARA); and the outp ut was stability. The classification results showed that the best classification model is the KNN classifier, and it has an accuracy of 82%, area u nder the curve (AUC) of 83%, precision of 75%, and f1- score of 83%. Also, three empirical correlations with high accuracy including stability index (SI), stability crossplot (SCP), and asphaltene stabi lity predicting model (ANJIS) were utilized comparatively with the ML models to evaluate asphaltene stability. Results revealed that the KNN classifier has supe rior performance in this work with an accuracy of 80%, a precision of 82%, and an f1-score of 79%."
NETL
NSTL