Prediction of Oil PVT Properties Using Neural Networks

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir 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 B ob for any change in reservoir temperature, Gas Oil Ratio (GOR), gas gravity and oil gravity. The trends were found to obey the physical laws.

GMDH Multilayered Algorithm in Matlab

“…The statistical parameters used in the present work were: average percent relative error, average absolute percent relative error, minimum and maximum absolute percent error, root mean square error, standard deviation of error, and the correlation coefficient. Those statistical parameters are well known for their capabilities to analyze models' performance, and have been utilized by several authors (see Ayoub [2], Osman et al [11] and El-Sebakhy et al [5]). …”

“…11. Comparison of root mean square errors for the polynomial GMDH model against all investigated models.…”

Application of Artificial Intelligence and Data Mining Techniques for Fast Track Modeling of Geologic Sequestration of Carbon Dioxide – Case Study of SACROC Unit

“…29 Since this time, the applications of ANNs in addressing the conventional problems of the petroleum industry have been widely studied. Some applications of ANNs in petroleum engineering literature include well log interpretation, [30][31][32] well test data analysis, 33-36 reservoir characterization, 37-39 calibration of seismic attributes, 40 seismic pattern recognition, 41 inversion of seismic waveforms, 42 prediction of PVT data, [43][44][45][46] fractures and faults identification, [47][48][49][50] hydrocarbons detection, 50,51 formation damage forecast, 52,53 etc.…”

Artificial Neural Networks Approach for Estimating Filtration Properties of Drilling Fluids

“…A three-layer back propagation neural network was used in all cases due to its success in solving other petroleum engineering problems 11) and its ability to generalize with good accuracy. Consequently, this neural network was developed using three layers.…”