Introducing a New Method for Predicting PVT Properties of Iranian Crude Oils by Applying Artificial Neural Networks

Abstract In this study artificial neural networks (ANNs) have been applied for the prediction of main pressure, volume, and temperature (PVT) properties, bubble point pressure (Pb), and bubble point oil formation volume factor (Bob) of crude oil samples from different wells of Iranian oil reservoirs. Via a detailed comparison, the great power of ANNs with respect to traditional methods of predicting PVT properties, like Standing, Vasquez and Beggs, and Al-Marhoun, with higher prediction precision up to R2 = 0.990 has been illustrated and the obtained parameters of ANNs for the application of prediction of other crude oil samples has been presented. The applied PVT data set in this study consists of 218 crude oil samples from Iranian reservoirs and for assurance of the applicability of the ANN model the PVT data set has been divided into 2 training (190 samples) and cross validation (28 samples) data sets and obtained ANNs from applying the training data set has been tested on the cross validation data set which has not been seen by the network during the training process. The obtained results for both training and cross validation data sets confirm the great prediction power of ANNs, for both data sets with respect to traditional PVT correlations.