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Prediction of Condensate Gas Ratio (CGR) Using an Artificial Neural Network (ANN)

Year: 1390
COI: ICHEC07_574
Language: EnglishView: 726
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m Ebadi - Department of petroleum engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
s Gerami - IOR Research Institute, National Iranian Oil Company, Tehran, Iran
m Vares - Department of petroleum engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran


Added values to project economy from condensate sales and gas deliverability loss due to condensate blockage are the main differences between gas condensate and dry gas reservoirs. Toestimate the added value, one needs to obtain condensate to gas ratio (CGR); however, this needsspecial PVT experimental study and field tests. In the absence of experimental studies during early period of field exploration, techniques which correlate such a parameter would be of interest forengineers. Artificial Neural Network (ANN) is a multi-dimensional correlation including a large number ofparameters, relating input and output data sets. Compared with an empirical correlation, an ANN model can accept more information substantially as input to the model, thereby, improving theaccuracy of the predictions significantly and reducing the ambiguity of the relationship betweeninput and output. Moreover, ANNs are fast-responding systems. Once the model has been trained , predictions on unknown fluids are obtained by direct and rapid calculations, withoutiterative computations or tuning. This paper demonstrates how ANN predicts the CGR of a gas condensate reservoir with minimumand easily accessible parameters. In development stage of the ANN model, a large number of data covering wide range of gas condensate properties and reservoir temperature were collected fromthe literature and National Iranian oil Company (NIOC) data bank. The qualified data set wereused to train the model. The predictive ability of the model was tested using experimental data sets that were not used during the training stage. The results are in good agreement with theexperimentally reported data. The proposed model exhibits sensitivity to several parametersincluding reservoir temperature, gas molecular weight and dew point pressure. The network has the R - square of 0.9881, 0.9837 and 0.9821 for training, validation and test, respectively.


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Ebadi, m and Gerami, s and Vares, m,1390,Prediction of Condensate Gas Ratio (CGR) Using an Artificial Neural Network (ANN),07th International Congress on Chemical Engineering,Kish Island,

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  • McCculloch, W.S. and Pitts, W., A Logical Calculus of Ideas ...
  • Abiodun Oladiipo and et al, Artificial Neural Network Modeling of ...
  • Prediction of Condensate Gas Ratio(CGR), Using an Artificial Neural Netw ...
  • Shahab Mohaghegh, Neural Network: What It Can Do for Petroleum ...
  • Curtis H.Whitson, Effect of C, + Properties On Equation - ...
  • Mohaghegh, S. and et al, Petroleum Reservoir Ch aracterization with ...
  • Mohaghegh, Shahab and et al, Design and Development of An ...
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  • Meisam Karbalaee Akbari and et al, Dewpoint pressure Estimation of ...
  • William D. McCain Jr., Heavy component Control Reservoir Fluid Behavior. ...
  • S.M. _ Fattah, Neural Network Approach Predicts U.S. Natural Gas ...
  • Shahab Mohaghegh , Virtual- Intelligence Applications in Petroleum Engineering: Part ...

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Type of center: Azad University
Paper count: 38,241
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