An accurate estimation of viscosity values is imperative for an optimal production and transport design of hydrocarbon fluids. Based on this requirement, precise and robust empirical correlation models are highly requested. Even though there are numerous correlation models from literature, most models are inadequate to predict an accurate oil viscosity using unbiased data. This study aims to develop new and improved empirical viscosity correlations through available field measurements on the NCS. The performance of the proposed models is then studied through a comparative analysis with published correlations from literature.
New correlation models are developed for dead, gas saturated and undersaturated oils using Particle Swarm Optimization (PSO) and Radial Basis Function Network (RBFN). The first technique is a computational optimization algorithm that aims to improve a function with respect to a specified objective function, while the latter is an artificial neural network model that utilizes radial basis functions as activation functions.
The optimization algorithm is used to re-calculate the coefficients of established viscosity correlation expressions while maintaining their functional form. The results show that the modified correlation models are more in agreement with the test data for all three oil types using the defined parameters from literature, compared to the established empirical correlations and the RBFN. The new correlations provide a mean absolute percentage error of 15.08% and 17.41% and 3.35%, for dead, saturated and undersaturated oil viscosity, respectively. The highly accurate result in the latter correlation is linked to the input variables, as the undersaturated viscosity is a function of saturated viscosity, which is presumed known.
The results of this study make it reasonable to conclude that the proposed correlation methods are more in-line with the measured viscosity on the NCS compared to the discussed correlation models from literature.