A general regression neural network model offers reliable prediction of CO sub(2) minimum miscibility pressure

This study introduces a general regression neural network (GRNN) model consisting of a one-pass learning algorithm with a parallel structure for estimating the minimum miscibility pressure (MMP) of crude oil as a function of crude oil composition and temperature. The GRNN model was trained with 91 samples and was successfully validated with a blind testing data set of 22 samples. The MMP for six of these data samples was experimentally measured at the Petroleum Fluid Research Centre at Kuwait University. The remaining data consisted of experimental MMP data collected from the literature. The GRNN model was used to estimate the MMP from the training data set with an average absolute error of 0.2 %. The GRNN model was used to predict the MMP for the blind test data set with an average absolute error of 3.3 %. The precision of the introduced model and models in the literature was evaluated by comparing the predicted MMP values with the measured MMP values and using training and testing data sets. The GRNN model significantly outperformed the prominent models that have been published in the literature and commonly used for estimating MMP. The use of the GRNN model was reliable over a large range of crude oil compositions, impurities, and temperature conditions. The GRNN model provides a cost-effective alternative for estimating the MMP, which is commonly, measured using experimental displacement procedures that are costly and time consuming. The results provided in this study support the use of artificial neural networks for predicting the MMP of CO sub(2).