Oil well production prediction based on CNN-LSTM model with self-attention mechanism

Shaowei Pan, Bo Yang, Shukai Wang, Zhi Guo, Lin Wang, Jinhua Liu and Siyu Wu

Energy, 2023, vol. 284, issue C

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.

Keywords: Convolutional neural network; Long short-term memory; Self-attention mechanism; Oil well production; Prediction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:284:y:2023:i:c:s0360544223020959

DOI: 10.1016/j.energy.2023.128701

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