Predicting bottomhole pressure in vertical multiphase ﬂowing wells using artiﬁcial neural networks
Over the years, accurate prediction of pressure drop has been of vital importance in vertical multiphase ﬂowing oil wells in order to design an effective production string and optimum production strategy selection. Various scientists and researchers have proposed correlations and mechanistic models for this purpose since 1950, most of which are widely used in the industry. But in spite of recent improvements in pressure prediction techniques, most of these models fail to provide the desired accuracy of pressure drop, and further improvement is still needed. This study presents an artiﬁcial neural network (ANN) model for prediction of the bottomhole ﬂowing pressure and consequently the pressure drop in vertical multiphase ﬂowing wells. The model was developed and tested using ﬁeld data covering a wide range of variables. A total of 413 ﬁeld data sets collected from Iran ﬁelds were used to develop the ANN model. These data sets were divided into training, validation and testing sets in the ratio of 4:1:1. The results showed that the research model signiﬁcantly outperforms all existing methods and provides predictions with higher accuracy, approximately 3.5% absolute average percent error and 0.9222 correlation coefﬁcient.