面向溢流风险的多分类智能识别模型

doi:10.16265/j.cnki.issn1003-3033.2026.01.0911

摘要/Abstract

摘要：

为提升钻井过程已发溢流风险的识别准确率,融合特征工程与机器学习方法,建立一种多类别溢流风险智能识别模型。首先,基于现场实测溢流数据,采用小波变换实现噪声抑制;其次,结合光滑样条函数提取关键参数的动态变化趋势,据此分析溢流特征参数的异常波动行为,建立低、中、高3类风险等级的判定准则,根据钻井数据的变化特征标记溢流风险;然后,引入麻雀搜索算法(SSA)优化极限学习机(ELM),构建基于改进型ELM(IELM)的多分类溢流风险智能识别模型;最后,所构建的风险数据集进行模型训练、调优与测试,验证模型的识别性能。结果表明:IELM模型在分类精度与判别稳定性方面均优于原始ELM及反向传播(BP)神经网络模型,能够更准确和高效地识别不同等级的溢流风险。

关键词: 溢流风险, 识别模型, 改进极限学习机(IELM), 神经网络, 表征参数

Abstract:

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.

Key words: overflow risk, identification model, improved extreme learning machine (IELM), neural network, characteristic parameters

中图分类号:

X937

武胜男, 张来斌, 胡一鸣, 崔蓉, 刘书杰, 殷志明. 面向溢流风险的多分类智能识别模型[J]. 中国安全科学学报, 2026, 36(1): 72-80.

WU Shengnan, ZHANG Laibin, HU Yiming, CUI Rong, LIU Shujie, YIN Zhiming. A multi-class intelligent identification model for kick risk[J]. China Safety Science Journal, 2026, 36(1): 72-80.

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表征参数	参数异常形式	重要级别
气测值	数据呈现快速上升趋势、数据大于一定阈值、离群异常数据	Ⅰ-核心参数
活动泥浆池体积	数据呈现上升趋势、离群异常数据	Ⅱ-典型参数
钻井液相对出口流量	数据呈现上升趋势、离群异常数据	Ⅲ-重要参数
立管压力	数据呈现下降趋势、离群异常数据	Ⅳ-辅助参数

溢流分级	划分规则	风险标签
低溢流风险	不多于1个非核心参数出现异常	1
中溢流风险	核心参数出现异常	2
中溢流风险	核心参数与典型参数不同时异常时2个参数出现异常	2
高溢流风险	核心参数与典型参数同时出现异常	3
高溢流风险	3个及以上参数出现异常	3

数据序号	时间范围	井筒深度/m	钻头深度/m	工况
1—714	8:54:15—9:53:40	3 995.29~4 002.43	3 995.29~4 002.43	纯钻进
715—868	9:53:45—10:06:30	4 002.43	3 992.93~4 002.43	划眼
869—976	10:06:35—10:15:30	4 002.43~4 003.45	4 002.43~4 003.45	纯钻进
977—1 003	10:15:35—10:17:45	4 003.45	4 002.78~4 003.45	划眼
1 004—2 678	10:17:50—12:37:20	4 003.45~4 023.55	4 003.45~4 023.55	纯钻进
2 679—3 825	12:37:25—14:12:55	4 023.55	3 994.31~4 023.55	划眼(其间溢流)
3 826—5 839	14:13:00—17:00:55	4 023.55	4 020.93~4 021.41	循环(其间溢流)

数据集	准确率	IELM	ELM	BP	SVM
数据训练集A₁	低风险	99.64	81.58	92.57	98.49
	中风险	96.89	92.81	92.67	96.11
	高风险	87.63	0	24.73	89.49
	整体	97.16	79.81	86.44	96.48
数据测试集A₂	低风险	99.01	82.79	92.10	95.03
	中风险	96.03	92.53	92.30	91.32
	高风险	82.5	0	23.75	72.67
	整体	96.01	80.14	85.96	91.10
	F₁分数	93.01	—	71.47	87.03