Temporal convolution network based on attention mechanism for well production prediction

Well production forecasting has a very important guiding significance for oilfield production and management. The traditional BP neural network is difficult to deal with the data with time continuity, and the recurrent neural network (RNN) has some shortcomings such as vanishing gradients and exploding gradients. In order to avoid these problems and further improve the accuracy of neural network models in well production prediction. In this study, a prediction model combining temporal convolutional network (TCN) and attention mechanism is proposed. TCN is able to obtain more sensory fields to solve longer time series with fewer network layers by causally expanding convolution. And the residual connectivity in the model structure enables the model to transfer information across layers to solve the problem of network degradation caused by overly deep networks. The attention mechanism can reinforce the influence of important features on the prediction results to improve the prediction accuracy of the model. To evaluate the effectiveness of the model, the TCN-attention model was used to forecast the oil production of four producing wells, and the prediction results were compared with those of LSTM, TCN, RNN, and ARIMA. The results showed that TCN-Attention outperformed the other models in the prediction of the four wells in all evaluation metrics. The method proposed in this paper has excellent data fitting ability, can provide accurate prediction results, and have a certain application value in oilfield production.