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Prediction of crude oil blends compatibility and blend optimization for increasing heavy oil processing

Kumar, Rajeev, Voolapalli, Ravi Kumar, Upadhyayula, Sreedevi
Fuel processing technology 2018 v.177 pp. 309-327
asphaltenes, color, distillation, flocculation, fouling, mixing, models, oils, petroleum, pour point, prediction, regression analysis, sulfur, viscosity
In the present study, prediction of crude oil blends compatibility and blend optimization for increasing heavy oil processing has been attempted. The crude oil blend compatibility (K model) is determined based on the physical parameter ratios of the crude oils. The physical parameter ratios of the crude oil include at least log (Sulphur)/Carbon Residue, API/Sulphur, and Kinematic Viscosity/API. The K model is developed by coefficients obtained by regression analysis between the ratios of physical parameters of known crude oils and composite compatibility measure determined from multiple compatibility test results of the known crude oils. Nine different tests conducted to estimate crude oils blend compatibility viz. colloidal instability index (CII), colloidal stability index (CSI), Stability Index (SI), Stankiewicz plot (SP), qualitative-qualitative analysis (QQA), Stability Cross Plot (SCP), Heithaus parameter (P value), Oil compatibility model (OCM) and Spot tests. 50 different crude oils have been participated in the development and tuning of the model. The compatibility criterion is proposed as if K ≥ 0; blend is compatible and if K < 0 blend is incompatible. Further, sixteen new crude oils and fourteen blends have been used for validation. Eventually, K model is able to predict composite results of all nine different laboratory based compatibility tests. Further, the predicted blend compatibility along with other blending constraints viz. viscosity, pour point, sulphur content, overall distillation yields and crude oil parcels availability have been considered for blend optimization.There is strong relationship of K model with intensity of spot color, desalting performance and fouling behaviour which further verified through experiments. If K is positive; Spot color is darker, desalting is better and fouling is minimum. But if K is negative; there is lighter Spot color with asphaltene flocculation or precipitation, poor desalting and high fouling is observed. K model accurately predicts the blend composition to minimize operational problems while increasing heavy oil processing.