Abstract
The subcritical flow behavior of gas condensates through wellhead chokes under different flow conditions are studied by use of an artificial neural network (ANN). The proposed network is trained using the Levenberg-Marquardt back-propagation algorithm and the hyperbolic tangent sigmoid activation function is applied to calculate the output values of the neurons of the hidden layer. The proposed neuromorphic model outperforms the existing empirical correlations both in accuracy and generality. The results of this work are very important in the design of wellhead chokes under a wide range of flow conditions usually encountered during the flow of gas condensates.
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