Application of grid partitioning based fuzzy inference system as a novel predictor to estimate dynamic viscosity of n-alkane

Abstract

The oil recovery and rate of production are highly dependent on viscosity of reservoir fluid so this term becomes one of the attractive parameters in petroleum engineering. The viscosity of fluid is highly function of composition, temperature, and pressure so in this article, Grid partitioning based Fuzzy inference system approach is utilized as novel predictor to estimate dynamic viscosity of different normal alkanes in the wide range of operational conditions. In order to comparison of model output with actual data, an experimental dataset related to dynamic viscosity of n-alkanes is gathered. The graphical and statistical comparisons between model outputs and experimental data show the high quality performance of predicting algorithm. The coefficients of determination (R²) of training and testing phases are 0.9985 and 0.9980, respectively. The mentioned statistical indexes represent the great accuracy of model in prediction of dynamic viscosity.

Keywords:

• alkane
• dynamic viscosity
• grid partitioning based fuzzy inference system
• operational condition
• predicting algorithm