Study on Overflow Accident Monitoring Based on Synchronous Features of Multiple Well Logging Parameters

CHEN Qing; HUANG Zhiqiang; KONG Xiangwei; HE Xianjie; XU Zhou; AN Guotao

doi:10.21656/1000-0887.450125

Volume 46 Issue 2

Feb. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(2): 241-253

CHEN Qing, HUANG Zhiqiang, KONG Xiangwei, HE Xianjie, XU Zhou, AN Guotao. Study on Overflow Accident Monitoring Based on Synchronous Features of Multiple Well Logging Parameters[J]. Applied Mathematics and Mechanics, 2025, 46(2): 241-253. doi: 10.21656/1000-0887.450125

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Study on Overflow Accident Monitoring Based on Synchronous Features of Multiple Well Logging Parameters

doi: 10.21656/1000-0887.450125

CHEN Qing^{1, 2
,},
HUANG Zhiqiang^{1, 2},
KONG Xiangwei^{1, 2
,
,},
HE Xianjie³,
XU Zhou^{1, 2},
AN Guotao^{1, 2}

1.
Petroleum Engineering College, Yangtze University, Wuhan 430100, P.R.China
2.
National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, P.R.China
3.
Research Institute of Drilling and Production Engineering Technology, CNPC Chuanqing Drilling Engineering Co. , Ltd. , Deyang, Sichuan 618399, P.R.China

Received Date: 2024-05-07
Rev Recd Date: 2024-06-13
Publish Date: 2025-02-01

Abstract

Abstract

Judging overflow accidents based on well logging parameters relies heavily on the experience of on-duty personnel, and the comprehensive well logging parameters collected in-situ have severe noises and unclear parameter change characteristics, resulting in low accuracy of overflow monitoring. A multi-parameter synchronous overflow identification method was obtained through low-pass filtering and locally weighted linear regression to remove the high-frequency signals and low-frequency noises of the in-situ comprehensive well logging parameter curves, and after normalization processing. Combined with the characteristics of the GCN graph matching and the BRNN bidirectional transmission, the GCN-BRNN fusion model was established to improve the accuracy of overflow accident monitoring. The results show that, the local weighted linear regression can make the curve change characteristics more obvious, and the accuracy of the multi-parameter synchronous monitoring after normalization is higher than that of the single-parameter monitoring. With the comprehensive well logging data of a well in western Sichuan as an example, compared with the original model, the combined model has a higher accuracy reaching 85% in overflow identification. The characteristics of the reservoir affect the accuracy of logging parameter collection; the more uniform the reservoir distribution structure is and the more stable the properties are, the higher the accuracy of overflow monitoring will be. After in-situ application in the JT well, the identification accuracy of overflow accidents is ≥89%, and the actual overflow risk is consistent with the model identification results. This method can effectively handle conflicts between multiple sources of information, improve the accuracy of overflow monitoring, and provide guidance for in-situ overflow accident monitoring methods combined with well logging parameters.
- drilling overflow monitoring,
- parameter characteristic,
- filtering denoising,
- locally weighted linear regression,
- neural network

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References(35)

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