Comprehensive Analysis of Asphaltene Stability Predictors under Different Conditions

This research study aims to examine the performance of seven Saturates, Aromatics, Resins, and Asphaltenes (SARA) based predictors that are commonly used to monitor the asphaltenes precipitation risk in crude oils. Predictors are employed on 45 crude oils whose stability is already known from three different experiences present present in published literature. Crude oils are divided into three conditions, containing 15 oil samples each, on the basis of their stability experiences. Detailed statistical analysis is carried out to analyze the performance of predictors. It is found that predictor performance changes when applied to oil samples of different conditions. Results indicate that Colloidal Instability Index ( CII), Stankiewicz plot (SP), Chamkalani Stability Classifier (CSC), and Modified Jamal plot (M Jamal) are good predictors for unstable samples while Stability Index (SI), and Jamaluddin's Plot (Jamal) predict stable samples better. Colloidal Stability Index (CSI) proves to be the best predictor in terms of average accuracy of three conditions.