On the use of continuous distribution models for characterization of crude oils

摘要

Crude oil characterization plays a key role in upstream as well downstream operations of petroleum supply chain It is usually carried out by a batch distillation process known as true boiling point (TBP) distillation, which represents a "footprint" of the crude oil composition profile, once its shape depends on the amount and volatility of components in a given crude oil. In the last decades, crude oil characterization methods by continuous distribution models have been proposed, as an option to the classic (discrete) pseudo component approach. The comparative performance of five continuous distribution models - Beta, Gamma, Riazi, Weibull and Weibull extreme - in characterizing the TBP crude oil distillation curve is presented in this work. A large TBP database of different types of Brazilian crude oil is used to identify the optimal characterization parameters of these models by a least-squares statistical criterion. The modeling performance of each continuous distribution model was measured using statistical estimators. The Weibull extreme model presented the most adequate performance in terms of the root mean squared error (RMSE) for all crude oils. In general, the model parameters uncertainties increase with the crude oil API density, despite the reversed behavior shown by Gamma model.