A CNN-BiGRU liquid production prediction method for electric submersible progressive cavity pumps

To improve the prediction accuracy and consistency of liquid production virtual metering of electric submersible progressive cavity pumps (ESPCPs), a hybrid model (CNN-BiGRU) integrating convolution neural network (CNN) and bidirectional gate recurrent unit (GRU) was proposed, which also incorporated the dual attention mechanism. This production forecast of oil wells is subjected to tubing pressure, casing pressure and pump conditions. First, the dimensionality reduction of data was performed using Pearson’s correlation coefficient and principal component analysis, and the main influential factors were identified. Subsequently, the local connecting and global sharing of the CNN network was used to extract spatial features of liquid production data of oil wells. The extracted features were then fed to the GRU network to extract the temporal features of the data. Finally, the weights were assigned corresponding features via the dual attention mechanism to improve the prediction accuracy of the model. The presented method has been applied to 50000 data samples of liquid production. The root-mean-square error (RMSE) and mean absolute percentage error (MAPE) of the model were 5.24% and 3.17%, respectively. Compared with the GRU and CNN-GRU models, the presented method delivers excellent prediction performance and greatly improves the prediction accuracy, which marks its high value for engineering applications.