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A multi-class intelligent identification model for kick risk
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Shengnan WU1, 2, Laibin ZHANG1, 2, Yiming HU1, 2, 3, Rong CUI1, 2, Shujie LIU4, Zhiming YIN5
China Safety Science Journal | 2026, 36(1) : 72 - 80
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China Safety Science Journal | 2026, 36(1): 72-80
• Safety Technology and Engineering •
A multi-class intelligent identification model for kick risk
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Shengnan WU1, 2, Laibin ZHANG1, 2, Yiming HU1, 2, 3, Rong CUI1, 2, Shujie LIU4, Zhiming YIN5
Affiliations
  • 1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
  • 2Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
  • 3CNOOC Safety & Technology Services Co., Ltd., Tianjin 300450, China
  • 4CNOOC Hainan Energy Co., Ltd., Haikou Hainan 570100, China
  • 5CNOOC research Institute Co., Ltd., Beijing 100028, China
Published: 2026-01-28 doi: 10.16265/j.cnki.issn1003-3033.2026.01.0911
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In order to improve the identification accuracy of kick risk during drilling, a multi-category kick risk intelligent identification model was proposed by integrating feature engineering and machine learning techniques. Firstly, a wavelet transform was employed to achieve noise suppression based on field-measured kick data. Secondly, the dynamic variation trends of key parameters were extracted using smooth spline functions, and the abnormal fluctuation behaviors of kick-related characteristic parameters were analyzed. Based on this, a three-level risk classification criterion (low, medium, and high) was established, and kick risks were labeled according to the variation features of drilling data. Then, the sparrow search algorithm (SSA) was introduced to optimize the extreme learning machine (ELM), and a multi-classification kick risk intelligent identification model based on IELM was constructed. Finally, the performance of the model was validated through training, tuning, and testing on the constructed risk dataset. The results show that the IELM model outperforms the original ELM and back-propagation (BP) neural network model in terms of classification accuracy and discrimination stability, and is capable of identifying different levels of kick risks more accurately and efficiently.

overflow risk  /  identification model  /  improved extreme learning machine (IELM)  /  neural network  /  characteristic parameters
Shengnan WU, Laibin ZHANG, Yiming HU, Rong CUI, Shujie LIU, Zhiming YIN. A multi-class intelligent identification model for kick risk[J]. China Safety Science Journal, 2026 , 36 (1) : 72 -80 . DOI: 10.16265/j.cnki.issn1003-3033.2026.01.0911
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Year 2026 volume 36 Issue 1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.01.0911
  • Receive Date：2025-08-14
  • Online Date：2026-07-08
  • Published：2026-01-28
Article Data
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History
  • Received：2025-08-14
  • Revised：2025-10-20
Funding
Affiliations
    1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
    2Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
    3CNOOC Safety & Technology Services Co., Ltd., Tianjin 300450, China
    4CNOOC Hainan Energy Co., Ltd., Haikou Hainan 570100, China
    5CNOOC research Institute Co., Ltd., Beijing 100028, China
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2026.01.0911
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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