A general regression neural network model offers reliable prediction of CO2 minimum miscibility pressure

Osamah A Alomair,Ali A Garrouch

doi:10.1007/s13202-015-0196-4

Abstract

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 CO2.

Highlights

Enhanced oil recovery (EOR) using carbon dioxide injection can increase the oil production of a reservoir to beyond what it is typically achievable from primary recovery
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 remaining data consisted of experimental MMP data collected from the literature

Summary

Introduction

Enhanced oil recovery (EOR) using carbon dioxide injection can increase the oil production of a reservoir to beyond what it is typically achievable from primary recovery. EOR can be achieved using CO2 injection through two processes, miscible and immiscible displacement, which depend on the reservoir pressure, temperature, and crude oil composition (Andrei et al 2010). In the 1950s, when CO2 injection began as an oil recovery method, the immiscible process was emphasized as an alternative recovery scheme for reservoirs where water-based recovery techniques were inefficient (Jarrell et al 2002). CO2 flooding is one of the most widely used methods for medium and light oil recovery in sandstone and carbonate reservoirs (Moritis 2006; Alvarado and Manrique 2010). Throughout the last five decades, extensive laboratory studies, numerical simulations and field applications of CO2 flooding processes have been reported (Burke et al 1990; Grigg and Schecter 1997; Idem and Ibrahim 2002; Moritis 2006; Chukwudeme and Hamouda 2009; Alvarado and Manrique 2010)

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Conclusion