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.