Early Gas Kick Warning Based on Temporal Autoencoder

Zhaopeng Zhu, Detao Zhou, Donghan Yang, Xianzhi Song, Mengmeng Zhou (), Chengkai Zhang, Shiming Duan and Lin Zhu
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Zhaopeng Zhu: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Detao Zhou: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Donghan Yang: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Xianzhi Song: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Mengmeng Zhou: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Chengkai Zhang: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Shiming Duan: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Lin Zhu: School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China

Energies, 2023, vol. 16, issue 12, 1-13

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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