Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

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.