Abstract
Modern reservoir engineering practices require accurate information on thermodynamic and transport fluid properties together with reservoir rock properties to perform material balance calculations. These calculations lead to the estimation if initial hydrocarbons, the future reservoir performance, optimum production schemes and ultimate hydrocarbon recovery. These fluid properties which are usually determined by laboratory experiments or using empirically derived correlations provide the information required to properly understand the phase behavior, evaluate various production scenarios, optimize reservoir production and IOR schemes, and to maximize ultimate recovery and optimize production economics. One of these properties is the petroleum reservoir fluid viscosity. Crude oil viscosity is an important physical property that controls and influences the flow of oil through porous media and pipes.
This paper introduces a new implementation of the genetic algorithms technology in petroleum engineering. Intelligent techniques such as genetic algorithms for data analysis and interpretation are an increasingly powerful and reliable tool for making breakthroughs in the science and engineering. The introduced model in this paper can predict the reservoir fluid viscosity data with genetic algorithms technique.
Prediction results of the proposed model have been tested against the measured reservoir fluid viscosity data. Results indicate that the proposed prediction model can successfully predict and model reservoir fluid viscosity.
