A novel multi-hybrid model for estimating optimal viscosity correlations of Iranian crude oil

Viscosity is defined as one of the principal measure of fluid resistance to shear stress. Efficiently estimating and predicting of oil viscosity in different operating conditions is vital. A new multi-hybrid model is employed to estimate the crude oil viscosity below, at, and above the bubble points using the South Pars data located in Persian Gulf. Five variables consisting oil API gravity, reservoir temperature, solution gas-oil ratio, pressure and saturation pressure as inputs are imposed to the model. A general structure of group method of data handling along with Genetic algorithm, are proposed to obtain efficient polynomial correlations to estimate oil viscosity at the aforementioned points. These correlations also are compared with seven correlations presented in previous studies. Results show that the proposed multi-hybrid model is superior to the other models for estimating the viscosity values of Iranian crude oils.