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- W2331880329 abstract "Condensate-to-gas ratio (CGR) plays an important role in sales potential assessment of both gas and liquid, design of required surface processing facilities, reservoir characterization, and modeling of gas condensate reservoirs. Field work and laboratory determination of CGR is both time consuming and resource intensive. Developing a rapid and inexpensive technique to accurately estimate CGR is of great interest. An intelligent model is proposed in this paper based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) technique. The PSO-ANN model was evaluated using experimental data and some PVT data available in the literature. The model predictions were compared with field data, experimental data, and the CGR obtained from an empirical correlation. A good agreement was observed between the predicted CGR values and the experimental and field data. Results of this study indicate that mixture molecular weight among input parameters selected for PSO-ANN has the greatest impact on CGR value, and the PSO-ANN is superior over conventional neural networks and empirical correlations. The developed model has the ability to predict the CGR with high precision in a wide range of thermodynamic conditions. The proposed model can serve as a reliable tool for quick and inexpensive but effective assessment of CGR in the absence of adequate experimental or field data." @default.
- W2331880329 created "2016-06-24" @default.
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- W2331880329 creator A5053388446 @default.
- W2331880329 creator A5079138302 @default.
- W2331880329 date "2012-05-25" @default.
- W2331880329 modified "2023-10-10" @default.
- W2331880329 title "Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization" @default.
- W2331880329 cites W1968006981 @default.
- W2331880329 cites W1975339955 @default.
- W2331880329 cites W1978515768 @default.
- W2331880329 cites W1979017773 @default.
- W2331880329 cites W1991343959 @default.
- W2331880329 cites W1995341919 @default.
- W2331880329 cites W1995574558 @default.
- W2331880329 cites W1998646954 @default.
- W2331880329 cites W1999360645 @default.
- W2331880329 cites W2001333807 @default.
- W2331880329 cites W2001929396 @default.
- W2331880329 cites W2004867557 @default.
- W2331880329 cites W2007901546 @default.
- W2331880329 cites W2009869638 @default.
- W2331880329 cites W2009964934 @default.
- W2331880329 cites W2012730688 @default.
- W2331880329 cites W2014136924 @default.
- W2331880329 cites W2016721411 @default.
- W2331880329 cites W2022983882 @default.
- W2331880329 cites W2025560667 @default.
- W2331880329 cites W2027197837 @default.
- W2331880329 cites W2028236234 @default.
- W2331880329 cites W2030600248 @default.
- W2331880329 cites W2032926161 @default.
- W2331880329 cites W2033156265 @default.
- W2331880329 cites W2035483863 @default.
- W2331880329 cites W2035747749 @default.
- W2331880329 cites W2036324132 @default.
- W2331880329 cites W2037029572 @default.
- W2331880329 cites W2037681149 @default.
- W2331880329 cites W2039893952 @default.
- W2331880329 cites W2043359988 @default.
- W2331880329 cites W2049379290 @default.
- W2331880329 cites W2051472316 @default.
- W2331880329 cites W2054046860 @default.
- W2331880329 cites W2057596542 @default.
- W2331880329 cites W2060208815 @default.
- W2331880329 cites W2062078464 @default.
- W2331880329 cites W2064185770 @default.
- W2331880329 cites W2065109614 @default.
- W2331880329 cites W2070217403 @default.
- W2331880329 cites W2072798310 @default.
- W2331880329 cites W2073489392 @default.
- W2331880329 cites W2074469210 @default.
- W2331880329 cites W2074835980 @default.
- W2331880329 cites W2085153947 @default.
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- W2331880329 cites W2094980533 @default.
- W2331880329 cites W2116316292 @default.
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- W2331880329 cites W2156720968 @default.
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- W2331880329 cites W4232269135 @default.
- W2331880329 cites W4232654523 @default.
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- W2331880329 doi "https://doi.org/10.1021/ef300443j" @default.
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