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. Introduction 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 determination (estimation) of the initial hydrocarbons in place, the future reservoir performance, optimal exploration and production schemes, and the ultimate hydrocarbon recovery. Crude oil viscosity is an important physical property that controls and influences the flow of oil through porous media and pipes. The viscosity, in general, is defined as the internal resistance of the fluid to flow. The oil viscosity is a strong function of the temperature, pressure, oil gravity, gas gravity and gas solubility. Whenever possible, oil viscosity should be determined by laboratory measurements at reservoir temperature and pressure. The viscosity is usually reported in standard PVT analyses. If such laboratory data are not available, engineers may refer to published correlations, which usually vary in complexity and accuracy depending upon the available data on the crude oil. The viscosity of crude oils is a critical property in predicting oil recovery. Viscosity reduction and thermal expansion are the key properties to increase productivity of heavy oils. Reservoir simulators are routinely used to predict and optimize oil recovery from oil fields. These simulators require as input properties of the reservoir fluids as a function of pressure, temperature and composition. The accuracy of the fluid properties can decisively affect the results of the simulation. Among the required fluid properties are phase densities, phase viscosities, formation volume factors and dissolved gas-oil ratios. The physicochemical properties of the reservoir fluids are a function of the fluids' composition. These compositions can be determined by experimental analysis such as, true boiling point essays and gas chromatography. In many practical cases no compositional information is present. A practical method to predict reservoir fluids' viscosities should be able to calculate viscosity of compositional and black oils. Numerous viscosity-correlation methods have been proposed. None, however, has been used as a standard method in the oil industry. Since the crude oil composition is complex and often undefined, many viscosity estimation methods are geographically dependent. Most correlation methods can be categorized either a black oil or as compositional.