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Prediction of Oil PVT Properties Using Neural Networks

E.A. Osman, SPE, O.A. Abdel-Wahhab, and M.A. Al-Marhoun, King Fahd University of Petroleum and Minerals, Saudi Arabia

This paper was prepared for presentation at the 12^th SPE Middle East Oil Technical Conference & Exhibition held in Bahrain, 7-20 March 2001.

Abstract:

Reservoir fluid properties are very important in reservoir engineering computations such as material balance calculations, well test analysis, reserve estimates and numerical reservoir simulations. Ideally, these properties should be obtained from actual measurements. Quite often, however, these measurements are either not available, or very costly to obtain. In such cases, empirically derived correlations are used to predict the needed properties. All computations, therefore, will depend
on the accuracy of the correlations used for predicting the fluid properties.

This study presents Artificial Neural Networks (ANN) model for predicting the formation volume factor at the bubble point pressure. The model is developed using 803 published data from the Middle East, Malaysia, Colombia, and Gulf of Mexico fields. One-half of the data was used to train the ANN models, one quarter to cross-validate the relationships established during the training process and one quarter to test the models to evaluate their accuracy and trend stability. The results show that the developed model provides better predictions and higher accuracy than the published empirical correlations. The present model provides predictions of the formation volume factor at the bubble point pressure with an absolute average percent error of 1.789%, a standard deviation of 2.2053% and correlation coefficient of 0.988. Trend tests were performed to check the behavior of the predicted values of Bob for any change in reservoir temperature, Gas Oil Ratio (GOR), gas gravity and oil gravity. The trends were found to obey the physical laws.

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