Using Intelligent Methods and Optimization of the Existing Empirical Correlations for Iranian Dead Oil Viscosity

Abstract

Numerous empirical correlations exist for the estimation of crude oil viscosities. Most of these correlations are not based on the experimental and field data from Iranian geological zone. In this study several well-known empirical correlations including Beal, Beggs, Glasso, Labedi, Schmidt, Alikhan and Naseri were optimized and refitted with the Iranian oil field data. The results showed that the Beal and the Labedi methods were not suitable for estimation of the viscosity of the Iranian crudes, while the Beggs, Glasso and Schmidt methods gave reasonable results. The Naseri’s correlation and their present method proved to be the best classical methods investigated in this study. Two new intelligent methods to predict the viscosity of Iranian crudes have also been introduced. The study also showed that the neural network and SVM give much better results comparing to classical correlations. It is claimed that this study may provide more exact results for the prediction of Iranian oil viscosity.