Estimating the isothermal compressibility coefficient of undersaturated Middle East crudes using neural networks

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 are 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 crudes from the region of the Middle East. It is the largest data set ever collected in the Middle East to be used in developing a model to estimate the isothermal compressibility coefficients. An additional set of 35 PVT data points was used to test the effectiveness of the neural network to accurately predict outputs for data not used during the training process. The neural network model is able to predict the isothermal compressibility coefficient as a function of the solution gas/oil ratio, the gas specific gravity, the oil specific gravity, the reservoir temperature, and the reservoir pressure. A detailed comparison between the results predicted by this model and those predicted by others are presented for these Middle East crude oil samples.