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Neural Network Application on Selection of the Best Correlation of Multiphase Flow in Pipes
The established correlations of vertical multiphase flow in tubing are usually applicable to systems with similar laboratory and field conditions as were used for developing the correlations. This study concludes that the most suitable correlations for East Kalimantan Fields are Hagedorn and Brown (Hagedorn et al., 1966), Ansari (Ansari et al., 1994), and Duns and Ros (1963). In particular, this study presents an Artificial Neural Network model as a guidancee tool to choose the best vertical multiphase flow correlation. The model was developed from data from 73 fields in East Kalimantan region.
Several Artificial Neural Network models for predicting the best correlation of vertical mult iphase flow were investigated. The models were developed using 60 sets of training data, 6 sets of crossvalidated data, and 13 sets of prediction data.
The models were structured in several forms depending on the number of input variable used which were wellhead pressure, API gravity, water cut, GLR, liquid total production, vertical depth of gas lift mandrel, and gas total production.
The results show that the best model has a 70% prediction accuracy.
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