Estimating the Isothermal Compressibility Coefficient of Undersaturated Middle East Crudes Using Neural Networks

Abstract

The isothermal compressibility coefficients are required in several reservoir engineering applications such as transient fluid flow problems and also in the determination of physical properties of crude oils. Over the years, several correlations to estimate PVT properties have been reported in the literature for different types of hydrocarbon systems. All of these correlations were developed using conventional regression or graphical techniques that may not lead to the highest accuracy. On the other hand, the use of neural networks to develop such correlations can be excellent and reliable for the prediction of these properties. This paper presents an artificial neural network model to predict the isothermal compressibility coefficient of undersaturated crude oils of the Middle East region. The back-propagation algorithm with momentum for error minimization was used in this study. The data set, on which the network was trained, contain 520 experimentally obtained PVT data sets, representing 102 different ...