Neural network model for estimating the PVT properties of Middle East crude oils

The importance of pressure/volume/temperature (PVT) properties, such as the bubblepoint pressure, solution gas-oil ratio, and oil formation volume fac tor, makes their accurate determination necessary for reservoir performance calculations. An enormous amount of PVT data has been collected and correl ated over many years for different types of hydrocarbon systems. Almost all of these correlations were developed with linear or nonlinear multiple reg ression or graphical techniques. Artificial neural networks, once successfu lly trained, offer an alternative way to obtain reliable results for the de termination of crude oil PVT properties. In this study, we present neural-network-based models for the prediction of PVT properties of crude oils from the Middle East. The data on which the n etwork was trained represent the largest data set ever collected to be used in developing PVT models for Middle East crude oils. The neural-network mo del is able to predict the bubblepoint pressure and the oil formation volum e factor as a function of the solution gas-oil ratio, the gas specific grav ity, the oil specific gravity, and the temperature. A detailed comparison b etween the results predicted by the neural-network models and those predict ed by other correlations are presented for these Middle East crude-oil samp les.