Experimental measurement and modeling of saturated reservoir oil viscosity

Abstract

A novel mathematical-based approach is proposed to develop reliable models for prediction of saturated crude oil viscosity in a wide range of PVT properties. A new soft computing approach, namely least square support vector machine modeling optimized with coupled simulated annealing optimization technique, is proposed. Six models have been developed to predict saturated oil viscosity, which are designed in such a way that could predict saturated oil viscosity with every available PVT parameter. The constructed models are evaluated by carrying out extensive ex- perimental saturated crude oil viscosity data from Iranian oil reservoirs, which were measured using a "Rolling Ball viscometer." To evaluate the performance and accuracy of these models, statistical and graphical error analyses were used simultaneously. The obtained results demonstrated that the proposed models are more robust, reliable and efficient than existing techniques for prediction of saturated crude oil viscosity.