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Volume 16
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10.3390/en16124606
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Article

Early Gas Kick Warning Based on Temporal Autoencoder

by
Zhaopeng Zhu
1,
Detao Zhou
1,
Donghan Yang
1,
Xianzhi Song
1,2,
Mengmeng Zhou
1,*,
Chengkai Zhang
1,
Shiming Duan
1 and
Lin Zhu
1
1
School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
2
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China
*
Author to whom correspondence should be addressed.
Energies 2023, 16(12), 4606; https://doi.org/10.3390/en16124606
Submission received: 10 May 2023 / Revised: 2 June 2023 / Accepted: 5 June 2023 / Published: 9 June 2023
(This article belongs to the Section H1: Petroleum Engineering)
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Abstract

The timing of the data is not taken into account by the majority of risk warnings today. However, identifying temporal fluctuations in data, which is a vital method for detecting risk, is neglected by the majority of intelligent gas kick warning models now in use. To accurately and early detect the gas kick risk, a temporal series gas kick detection method based on sequence-to-sequence depth autoencoder is proposed in this paper. A depth autoencoder model based on bidirectional long short-term memory (BiLSTM-AE) network is established to encode and compress input series, and decode and reconstruct the output series. Firstly, the BiLSTM-AE network is trained on normal drilling data based on unsupervised learning. Then, the model is tested by gas kick data, and the mean square error of reconstruction is calculated. The results show that the BiLSTM-AE model is more robust and generalized, and its accuracy is 95%. Experimental preliminary results show that this approach is capable of extracting bidirectional temporal information from risk sequence data, but long short-term memory (LSTM) and autoencoder models based on multilayer perceptron (MLP-AE) are unable to do so. By taking into account the temporal characteristics of the data, this study offers a strategy to integrate prior knowledge and significantly enhances the accuracy and stability of the model.
Keywords: early gas kick warning; temporal autoencoder; BiLSTM-AE; reconstruction error early gas kick warning; temporal autoencoder; BiLSTM-AE; reconstruction error

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MDPI and ACS Style

Zhu, Z.; Zhou, D.; Yang, D.; Song, X.; Zhou, M.; Zhang, C.; Duan, S.; Zhu, L. Early Gas Kick Warning Based on Temporal Autoencoder. Energies 2023, 16, 4606. https://doi.org/10.3390/en16124606

AMA Style

Zhu Z, Zhou D, Yang D, Song X, Zhou M, Zhang C, Duan S, Zhu L. Early Gas Kick Warning Based on Temporal Autoencoder. Energies. 2023; 16(12):4606. https://doi.org/10.3390/en16124606

Chicago/Turabian Style

Zhu, Zhaopeng, Detao Zhou, Donghan Yang, Xianzhi Song, Mengmeng Zhou, Chengkai Zhang, Shiming Duan, and Lin Zhu. 2023. "Early Gas Kick Warning Based on Temporal Autoencoder" Energies 16, no. 12: 4606. https://doi.org/10.3390/en16124606

APA Style

Zhu, Z., Zhou, D., Yang, D., Song, X., Zhou, M., Zhang, C., Duan, S., & Zhu, L. (2023). Early Gas Kick Warning Based on Temporal Autoencoder. Energies, 16(12), 4606. https://doi.org/10.3390/en16124606

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