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ABSTRACT
Viscosity is one of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. So it is important to use an accurate method to calculate the oil viscosity at various operating conditions. In the literature, several empirical correlations have been proposed for predicting crude oil viscosity. However these correlations are not able to predict the oil viscosity adequately for a wide range of conditions. In present work, an extensive experimental data of oil viscosities from different samples of Iranian oil reservoirs was applied to develop an Artificial Neural Network (ANN) model to predict and calculate the oil viscosity. Validity and accuracy of these models has been confirmed by comparing the obtained results of these correlations and with experimental data for Iranian oil samples. It was observed that there is acceptable agreement between ANN model results with experimental data.
Keywords: Property, Correlation, Artificial Neural Network, Crude oil
1. INTRODUCTION
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. Oil viscosity is a strong function of many thermodynamic and physical properties such as pressure, temperature, solution gas-oil ratio, bubble point pressure, gas gravity and oil gravity (Abedini and Abedini, 2011, Mosayebi and Abedini, 2011).
Numerous correlations have been proposed to calculate the oil viscosity. These correlations are categorized into two types. The first type which refers to black oil type correlations predict viscosities from available field-measured variables include reservoir temperature, oil API gravity, solution gas- oil ratio, saturation pressure and pressure (Beal, 1946; Chew and Connally, 1959; Beggs and Robinson, 1975; Glaso, 1980; Vasquez and Beggs, 1980; Labedi, 1992; Kartoatmodjo and Schmidt, 1994; Elsharkawy and Alikhan, 1999). The second type which refers to compositional models derives mostly from the principle of corresponding states and its extensions. In these correlations beside previous properties, other properties such as reservoir fluid composition, pour point temperature, molar mass, normal boiling point, critical temperature and acentric factor of components are used (Lohrenz et al., 1964; Little and Kennedy, 1968; Ahrabi et al., 1987; Sutton and Farshad, 1990).
2. EXPERIMENTAL DATA
In this study, PVT experimental data...