Performance of asphaltene stability predicting models in field environment and development of new stability predicting model (ANJIS)

Abstract Asphaltene Precipitation is a major issue in both upstream and downstream sectors of the Petroleum Industry. This problem could occur at different locations hydrocarbon production system i.e., reservoir, wellbore, flowlines network, separation refining facilities, during transportation process. precipitation begins due to certain factors which include variation crude oil composition, changes pressure temperature, electrokinetic effects. deposition may offer severe technical economic challenges operating Exploration Production companies with respect losses production, facilities damages, costly preventive treatment solutions. Therefore, asphaltene stability monitoring oils necessary for prevention aggravation related deposition. study will discuss performance eleven parameters or models already developed by researchers oils. These Colloidal Instability Index, Stability Chamkalani’s classifier, Jamaluddin’s method, Modified Stankiewicz plot, QQA plots SCP plots. The advantage implementing these that they utilize less input data as compared other conventional modeling techniques. Moreover, also provide quick outcomes than expensive experimental methods like Heithaus parameter, Toluene equivalence, spot test, compatibility model. research evaluate accuracies their implementation on known samples present published literature. drawbacks limitations associated applied be presented discussed detail. found CSI performed best SARA based predicting models. However, considering limitation predictors, new predictor, namely ANJIS (Abdus, Nimra, Javed, Imran & Shaine) model proposed. when used sample conditions show reasonable accuracy. helps companies, sector, determine possible parameter its risk screening