Intelligent Prediction of Reservoir Fluid Viscosity

Abstract

Abstract Accurate information on phase behavior and properties of fluids is an essential element in proper management of petroleum reservoirs. These fluid properties which are usually determined by laboratory experiments performed on samples of actual reservoir fluid 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 application of fuzzy logic and neural networks in petroleum engineering. Artificial intelligence techniques such as neural networks, fuzzy logic and genetic algorithms for data analysis and interpretation are an increasingly powerful and reliable tool for making breakthroughs in the science and engineering and it is becoming clear that our industry has realized the immense potential offered by intelligent systems. The introduced model in this paper can predict the reservoir fluid viscosity data with neural networks and fuzzy logic approach. We can use these techniques in order to recognize the pattern between the given data sets where this pattern may not be understood clearly or no precise mathematical relationship exists Prediction of the proposed model has been tested against the measured reservoir fluid viscosity data. Results indicate that the proposed prediction model with recognizing the possible patterns between input and output variables can successfully predict and model reservoir fluid viscosity.