ARTIFICIAL NEURAL NETWORK MODEL FOR ACCURATE PREDICTION OF PRESSURE DROP IN HORIZONTAL AND NEAR-HORIZONTAL-MULTIPHASE FLOW

ABSTRACT

Accurate prediction of pressure drop for multiphase flow in horizontal and near horizontal pipes is needed for effective design of flow lines and piping networks. The increased application of horizontal wells further signified the need for accurate prediction of pressure drop. Several correlations and mechanistic models have been developed since 1950. In addition to the limitations on the applicability of all existing correlations, they all fails to provide the desired accuracy of pressure drop predictions. The recently developed mechanistic models provided some improvements in pressure drop prediction over the empirical correlations. However, there is still a need to further improve the accuracy of prediction for a more effective and economical design of wells and surface piping networks. This paper presents an Artificial Neural Network (ANN) model for prediction of pressure drop in horizontal and near-horizontal multiphase flow. The model was developed and tested using field data covering a wide range of variables. A total of 225 field data sets were used for training- and 113 sets data for cross-validation of the model. Another 112 sets of data were used to test the prediction accuracy of the model and compare its performance against existing correlations and mechanistic models. The results showed that the present model significantly outperforms all other methods and provides predictions with accuracy that has never been possible. A trend analysis was also conducted and showed that the present model provides the expected effects of the various physical parameters on pressure drop.

ACKNOWLEDGEMENT

The authors acknowledge King Fahd University of Petroleum and Minerals for supporting the present research.