Abstract: Measure planning is the main method to increase oil and control water in oilfield. In order to accurately predict the effect of various measures to increase oil production, a measure effect prediction model based on HDCNN(Hybrid Dilated Convolutional Neural Network)-BIGRU-Attention is proposed with monthly oil production and water content as the prediction targets. The model extracts multi-scale global features of production data through HDCNN. Aiming at the characteristics of strong timing and large volatility of measure production data, the BIGRU(Bidirectional Gated Recurrent Unit) is used to fully mine the long-term dependence between data to improve the utilization rate of time series information and the learning effect. The scaled dot- product attention mechanism (Attention) is introduced, and the weight adjustment strategy is used to make the network focus on the feature dimension with large correlation with the prediction target. In order to verify the effectiveness of the proposed model, LSTM(Long Short-Term Memory), CNN(Convolutional Neural Network)- LSTM and LSTM-attention are taken as experimental comparisons. The results show that the proposed model has lower prediction error and better generalization ability. 

Key words: prediction of oilfield measures effect, bidirectional gated recurrent unit(BIGRU), hybrid dilated convolutional neural network(HDCNN), scaled dot-product attention mechanism