Development of Oil Production Forecasting Method based on Deep Learning

Identification of the quick declines of the desirable production fluids and rapid increases of the undesirable fluids are the production problems of the oil wells. The main purpose of this work is to develop a method that can forecast oil production with high accuracy, using Deep neural networks based on the debt data of the wells. In this paper, a hybrid model based on a combination of the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) networks, called CNN-LSTM is proposed for the forecasting of oil production time series. The architecture of the proposed CNN-LSTM model is hierarchical. Here, at first the CNN layer of the model is applied to the current time window, then the relationship between the time windows is predicted by applying the LSTM. The challenges of time series prediction often come from the continuity duration of every state. In order to overcome this problem, we try to predict temporal dependency in the certain time window. This issue is solved by the application of the CNN algorithm. Evaluation efficiency of the proposed model is performed on the QRI dataset. The prediction accuracy of the method is tested by RMSLE loss function and the best results are obtained using our proposed in the testing process.