Crude oil characterization and thermodynamic modeling of solids precipitation in crude oils

Solids precipitation has caused many problems in the petroleum industry. One of the ways to mitigate the problems is to have a model that can predict the conditions under which the precipitation occurs and the amount of solid precipitated at different conditions. Several models have been proposed in the past. However, these models are for either wax or asphaltene precipitation. In a real system, both solid types may precipitate. Furthermore, the previously proposed models usually involve the use of fitting parameters to match the calculated results to the experimental values. The incorporation of fitting parameters limits the predictive capability of the models. Crude oil characterization is also important for the modeling. Experimental data are needed for input to the model and for model validation. Hence, the purposes of this study have been to develop a set of experimental techniques for crude oil characterization and to develop a predictive model that can describe the precipitation of both wax and asphaltene as well as the possible co-precipitation at different conditions.; Detailed compositional analysis and solids precipitation data were obtained from a combination of analytical techniques including TBP distillation, solvent extraction, GC methods, HPLC, GPC, RI measurement, FT-IR spectroscopy, Near-IR spectroscopy, and cold-finger solid deposition. A total of 148 pseudo-components were used to characterize each of the three crude oils: crude oils A, B, and C. The detailed characterization proved valuable for the modeling of solids precipitation from crude oils.; The model was developed based on regular solution-molecular thermodynamic theory and on the detailed characterization of crude oils in this study to describe the solid precipitation behavior in crude oil systems. The properties of the pseudo-components were determined using empirical correlations. New correlations were proposed in this work and were used along with those from the literature in the estimation of properties. The proposed model is predictive and has demonstrated capability for predicting both wax and asphaltene precipitation as well as the co-precipitation. No ambiguous fitting parameters were used to force the model to match experimental data. The precipitation predictions from the model are in agreement with experimental results.