Kiumars kamalyar - Petroleum University of Technology, Ahvaz, Iran
Morteza Yeganeh - Petroleum University of Technology, Ahvaz, Iran

Knowledge of reservoir fluid properties is very important in various reservoir engineering computations such as material balance calculations, well testing, reserve estimating, and numerical reservoir simulations. Ideally, those data should be obtained experimentally. On some occasions, these data are not either available or reliable; then, empirically derived correlations are used to predict PVT properties. An enormous amount of PVT data has been collected and correlated over many years for different types of hydrocarbon systems. Almost all of these correlations were developed by linear or nonlinear multiple regression or graphical techniques. Artificial neural networks (ANN), once successfully trained, offer an alternative way to obtain reliable results for the determination of crude oil PVT properties.

Artificial Neural Network, Bubble Point Pressure, Empirical Correlation