Prediction of Oil PVT Properties Using Neural Networks

Abstract

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 the remaining 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. Introduction Reservoir fluid properties are very important in petroleum engineering computations, such as material balance calculations, well test analysis, reserve estimates, inflow performance calculations and numerical reservoir simulations. Ideally, these properties are determined from laboratory studies on samples collected from the bottom of the wellbore or at the surface. Such experimental data are, however, not always available or very costly to obtain. Then, the solution is to use the empirically derived correlations to predict PVT properties. There are many empirical correlations for predicting PVT properties, most of them were developed using linear or non-linear multiple regression or graphical techniques.Each correlation was developed for a certain range of reservoir fluid characteristics and geographical area with similar fluid compositions and API gravity. Thus, the accuracy of such correlations is critical and it is not often known in advance. Among those PVT properties is the bubble point Oil Formation Volume Factor (Bob), which is defined as the volume of reservoir oil that would be occupied by one stock tank barrel oil plus any dissolved gas at the bubble point pressure and reservoir temperature. Precise prediction of Bob is very important in reservoir and production computations.The objective of this study is to develop a new predictive model for Bob based on Artificial Neural Networks (ANN) using worldwide experimental PVT data. A new algorithm for training feed forward neural networks was used. That algorithm was found to be faster and more stable than other schemes reported in the literature. Database of 803 published data from the Middle East, Malaysia, and Gulf of Mexico fields was used to develop the present model. Of the 803 data points, 402 were used to train the ANN models, 201 to cross-validate the relationships established during the training process and the remaining 200 to test the model to evaluate its accuracy and trend stability. Using the same 200 data points, several empirical correlations were used to predict Bob. The results show that the present model outperforms all the existing models in terms of absolute average percent error, standard deviation, and correlation coefficient.