Prediction of multiphase flow rate for artificially flowing wells using rigorous artificial neural network technique
This main aim of this study is to generate an intensive artificial neural network model (ANN) based on FORTRAN language to develop a physical equation for oil rate prediction in wells lifted by ESP pumps. The backpropagation algorithm (BP) is selected in this study as a learning algorithm with its s...
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Veröffentlicht in: | Flow measurement and instrumentation 2020-12, Vol.76, p.101835, Article 101835 |
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Sprache: | eng |
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Zusammenfassung: | This main aim of this study is to generate an intensive artificial neural network model (ANN) based on FORTRAN language to develop a physical equation for oil rate prediction in wells lifted by ESP pumps. The backpropagation algorithm (BP) is selected in this study as a learning algorithm with its sigmoid curve based on the comparison performed against scaled conjugate gradient (SCG) and one-step secant (OSS) algorithms.
300 data points are collected from 2 fields in Gulf of Suez Egypt used in the ANN model. The results show that the optimum distribution for the collected data is of 70% and 30% for training and testing processes, respectively. This distribution yields the highest R2 of 0.988 and lowest mean square error of 0.025. Furthermore, based on the statistical analysis presented in this study, it has been found that the optimum number of hidden layers and neuron are one layer and two neuros, respectively.
The newly ANN and correlation can predict the oil rate at the surface with accuracy exceeding 96% and that is extremely efficient. A comparison is conducted between the presented correlation in this study and other published correlations (Gilbert and Ros correlations) based on R2 value and mean square error. The results show that the new correlation has the highest R2 value with the lowest mean square error.
•This paper presents an intensive neural network model ANN to predict oil flow rate for artificially flowing wells.•The back-propagation algorithm is used as learning algorithm.•A new correlation is developed based on the ANN model. |
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ISSN: | 0955-5986 1873-6998 |
DOI: | 10.1016/j.flowmeasinst.2020.101835 |