关键词: 注水预测, 长短期记忆神经网络, 循环神经网络, 人工智能

Abstract: In order to solve the problem that the commonly used artificial intelligence water-flood prediction fails to consider the correlation of data in time, by selecting an improved LSTM ( Long Short-Term Memory Neural Network) based on RNN (Recurrent Neural Network) the oilfield water injection prediction model is built. The model can take into account the relationship between the historical water injection volume and the influencing factors, and take into account the trend and correlation of water injection volume with time. Taking the water injection prediction of a complex fault-block reservoir in China for example, the LSTM water injection prediction model is established to predict the water injection volume of a single well in a period of time, and compared with the prediction model established by traditional RNN. The experimental results show that the model has more ideal prediction effect and higher prediction accuracy, which can effectively improve the accuracy of oil field water injection prediction.

Key words: water-flood prediction, long short-term memory neural network, recurrent neural network;artificial intelligence