A soft computing approach for the determination of crude oil viscosity: Light and intermediate crude oil systems

Crude oil viscosity is a key property needed for petroleum engineering analysis such as evaluation of fluid flow in porous media, reservoir performance, reservoir simulation, etc. This property is traditionally measured through expensive and time consuming laboratory measurements. In this communication, about 1500 dead oil viscosity data points of light and intermediate crude oil systems from various geological locations have been collected. Afterward, a soft computing approach, namely least square support vector machine (LSSVM), has been utilized to develop two distinct viscosity models for temperatures below and above 313.15 K. The parameters of these models have been optimized using coupled simulated annealing (CSA) optimization tool. The results of this study indicated that the developed models can predict dead oil viscosity at all temperatures and oil API gravities with enough accuracy. In addition, statistical and graphical error analyses illustrated that the proposed CSA-LSSCM models outperform all of pre-existing models. Besides, the relevancy factor showed that oil API gravity has the greatest effect on dead oil viscosity. Finally, the Leverage approach demonstrated that the proposed models are statistically valid and acceptable, and only 2% of the data points may be regarded as the probable outliers.