PVT Publications

M.A. Al-Marhoun, E.A. Osman, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

Copyright 2002. Society of Petroleum Engineers

This paper was prepared for presentation at the 10^th Abu Dhabi International Petroleum Exhibition and Conferenceheld in Abu Dhabi, UAE, 13-16 October 2002.

Abstract:

Reservoir 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.

Note

A full text version of this paper is available on the Society of Petroleum Engineers web site. Remember to jot down the paper's author(s), title and published date. You will need it after you log-in to find the paper in the SPE archives.

© 2000-2008   Reservoir Technologies.                       www.restec.com     www.restec.biz