Abstract:

Efficient and accurate water injection flow prediction can help oilfield departments formulate reasonable production plans, reduce the waste of resources, and improve the injection-production rate of the oilfield. RNN( Recurrent Neural Networks) in deep learning is often used for time series prediction, but it is difficult to extract features from historical series and can not highlight the impact of key information. Early information is also easy to lose when the time series is too long. A method of oilfield water injection flow prediction based on dual attention mechanism CNN ( Convolutional Neural Networks)-BiLSTM ( Bi-directional Long Short-Term Memory) is proposed. Taking the historical water injection data of the oilfield as the input, the CNN layer extracts the characteristics of the historical water injection data, and then enters the feature attention mechanism layer. The corresponding weights are given to the features by calculating the weight value. The key features are easier to get large weights, and then have an impact on the prediction results. The BiLSTM layer models the time series of data and introduces the time step attention mechanism. By selecting the key time step and highlighting the hidden state expression of the time step, the early hidden state will not disappear with time,

which can improve the prediction effect of the model for long time series, and finally complete the flow prediction. Taking public datasets and oilfield water injection data from a certain region in southern China as examples, and comparing them with MLP ( Multilayer Perceptron), GRU ( Gate Recurrent Unit), LSTM ( Long Short Term Memory), BiLSTM, CNN, it is proven that this method has higher accuracy in oilfield water injection flow prediction, can help oilfield formulate production plans, reduce resource waste, and improve injection recovery rate, and has certain practical engineering application value.

Key words: flow prediction, convolution neural network( CNN), long and short term memory ( LSTM) neural network, attention mechanism