Neural Network Model for Estimating The PVT Properties of Middle East Crude Oils

Abstract

Abstract The importance of PVT properties, such as the bubblepoint pressure, solution gas-oil ratio and oil formation volume factor, makes their accurate determination necessary for reservoir performance calculations. 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 with linear or nonlinear multiple regression or graphical techniques that may not lead to the highest accuracy. Artificial neural networks, on the other hand, once successfully trained, cart be excellent, reliable predictive tools for the determination 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. Several neural-network architectures using back-propagation with momentum for error minimization were investigated to obtain the most accurate PVT correlations. The PVT data on which the network was trained contain 498 experimentally obtained data sets of different crude oil and gas mixtures from the Middle East region. This represents the largest data set ever collected to be used in developing PVT models for Middle East crude oils. The neural-network model is able to predict the bubblepoint pressure and the oil formation volume factor as a function of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the temperature. A detailed comparison between the results predicted by the neural-network models and those predicted by other correlations are presented for these Middle East crude-oil samples. Introduction In absence of experimentally measured PVT properties, two methods are widely used. These methods are: equation of state (EOS) and PVT correlations. The equation of state is based on knowing the detailed compositions of the reservoir fluids. The determination of such quantities is expensive and time consuming. The equation of state involves numerous numerical computations. On the other hand, PVT correlations are based on easily measured field data: reservoir pressure, reservoir temperature, oil and gas specific gravity. In the petroleum process industries, reliable experimental data are always to be preferred over data obtained from correlations. However, very often reliable experimental data are not available, and the advantage of a correlation is that it may be used to predict properties for which very little experimental information is available. The importance of accurate PVT data for material-balance calculations is well understood. It is crucial that all calculations in reservoir performance, in production operations and design, and in formation evaluation can be only as good as the PVT properties used in these calculations. The economics of the process also depends on the accuracy of such properties. The development of correlations for PVT calculations has been the subject of extensive research, resulting in a large volume of publications. Several graphical and mathematical correlations for determining the bubblepoint pressure (Pb) and the oil formation volume factor (Bob) have been proposed during the last five decades. These correlations are essentially based on the assumption that Pb and Bob are strong functions of the solution gas-oil ratio (Rs), the reservoir temperature (T), the gas specific gravity () and the oil specific gravity (), or (1) (2) In 1947, Standing presented graphical correlations for the determination of bubblepoint pressure (Pb) and oil formation volume factor (Bob). In developing these correlations, Standing used 105 experimentally measured data points from 22 different crude-oil and gas mixtures from California oil fields. P. 151^