Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractReservoir fluid properties data are very important in reservoir engineering computations such as material balance calculations, well testing, reserve estimates, and numerical reservoir simulations. Ideally, those data should be obtained experimentally. On some occasions, these data are not either available or reliable; then, empirically derived correlations are used to predict PVT properties. However, the success of such correlations in prediction depends mainly on the range of data at which they were originally developed. These correlations were developed using linear, non-linear, multiple regression or graphical techniques.Recently, researchers utilized artificial neural networks (ANN) to develop more accurate PVT correlations. ANNs are biologically inspired non-algorithmic, non-digital, massively parallel distributive and adaptive information processing systems. They resemble the brain in acquiring knowledge through learning process, and storing knowledge in interneuron connection strengths.The present study presents new models developed to predict the bubble point pressure and, the formation volume factor at the bubble point pressure.The models were developed using 283 data sets collected from Saudi reservoirs. These data were divided into three groups: the first was used to train the ANN models, the second was used to crossvalidate the relationships established during the training process and, the last was used to test the models to evaluate their accuracy and trend stability. Trend tests were performed to ensure that the developed model would follow the physical laws. Results show that the developed models outperform the published correlations in terms of absolute average percent relative error, and standard deviation.

PVT correlations for Indian crude using artificial neural networks

“…Based on their conclusion, they proposed a generalized regression neural network model for determining the viscosity of Kuwaiti crude. AlMarhoun and Osman (2002) have presented correlations for bubble point pressure and saturated OFVF using MLP trained by backpropagation with early stopping for Saudi Arabian crudes. They reported an average absolute relative error of 5.89% for bubble point pressure and 0.511% for saturated OFVF.…”

Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines

“…Nowadays, soft computing approaches are helpful robust tools which play a significant role in analyzing and unraveling challenging pitfalls in various scopes of science and engineering (for instance [35][36][37][38][39][40][41][42][43] in comparison to many published empirical and analytical solutions [44][45][46].…”

Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio

“…These data are basically extracted from PVT laboratory reports of bottom-hole samples. According to Al-Marhoun and Osman (Al-Marhoun and Osman, 2002) local models are superior to universal models in terms of accuracy and reliability. Therefore in order to build models for confident prediction of solution gas oil ratio for Iranian southwest oil fields, in this study 157 local unpublished PVT data sets from the region were used.…”