Abstract
To overcome the shortcomings in current study of oil well production prediction, we propose a combined model (CNN-LSTM-SA) with the convolutional neural network (CNN), the long short-term memory (LSTM) neural network and the self-attention mechanism (SA). The CNN-LSTM-SA model consists of five parts: input layer, CNN module, LSTM layer, self-attention layer and output layer. In this model, CNN is used to extract the spatiotemporal features of the input data, LSTM is used to extract the correlation information, and SA is used to capture the internal correlation. Compared with the traditional machine learning methods, such as linear regression (LR), support vector machine (SVM), random forest (RF), XGBoost and back propagation (BP) neural network; and deep learning methods, such as LSTM, LSTM-SA and CNN-LSTM, the CNN-LSTM-SA model can extract the spatial-temporal features that are hidden in oil well production data more comprehensively. It is enable to mine the internal correlation in oil well production data more precisely, thereby improving the accuracy of oil well production prediction. More specifically, among the existing methods, the CNN-LSTM-SA model achieves the best performance in terms of adaptation to the basic trend of oil well production and the prediction of specific values of oil well production.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.