Forecasting monthly gas field production based on the CNN-LSTM model

Abstract

Accurate prediction of gas field production is an important task for reservoir engineers, which is challenging due to many unknown reservoir parameters. Aiming to have a low-cost, intelligent, and robust method to predict gas and water production for a given gas reservoir, this paper proposes a CNN-LSTM model to predict gas field production based on a gas field in southwest China. The convolutional neural network (CNN) has a feature extraction ability, and the long short-term memory network (LSTM) can learn sequence dependence. By the combination of the two abilities, the CNN-LSTM model can describe the changing trend of gas field production. A new prediction strategy named partly unknown recursive prediction strategy (PURPS) is proposed that some input features are estimated using the predicted gas and water production according to known equations. The results show that the CNN-LSTM model can effectively predict gas field production. A detailed performance comparison was conducted between CNN-LSTM and other models. The comparison shows that the proposed CNN-LSTM model outperforms the existing methods. The monthly gas production average MAPE errors of the three different stages are CNN-LSTM (7.7%), RNN (18%), Random Forest (23.17%), ARIMA (25.3%), DNN (28.3%), Support Vector Machine (28.3%), CNN (41%), and LSTM (46%).