LAPSE:2024.1660
Published Article
LAPSE:2024.1660
Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM
Xiaocheng Zhang, Pinghua Dong, Yanlong Yang, Qilong Zhang, Yuan Sun, Xianzhi Song, Zhaopeng Zhu
August 23, 2024
Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement.
Keywords
attention mechanism, data augmentation, LSTM neural network, stuck pipe prediction
Suggested Citation
Zhang X, Dong P, Yang Y, Zhang Q, Sun Y, Song X, Zhu Z. Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM. (2024). LAPSE:2024.1660
Author Affiliations
Zhang X: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 102209, China; CNOOC China Limited, Tianjin Branch, Tianjin 300459, China
Dong P: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 102209, China; CNOOC China Limited, Tianjin Branch, Tianjin 300459, China
Yang Y: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Zhang Q: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 102209, China; CNOOC China Limited, Tianjin Branch, Tianjin 300459, China
Sun Y: China France Bohai Geoservices Co., Ltd., Tianjin 300452, China
Song X: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Zhu Z: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Journal Name
Processes
Volume
12
Issue
7
First Page
1296
Year
2024
Publication Date
2024-06-22
ISSN
2227-9717
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PII: pr12071296, Publication Type: Journal Article
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LAPSE:2024.1660
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https://doi.org/10.3390/pr12071296
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Aug 23, 2024
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Aug 23, 2024
 
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