Article(id=1281627175199686850, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.0911, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755100800000, receivedDateStr=2025-08-14, revisedDate=1760889600000, revisedDateStr=2025-10-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493330048, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493330048, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493330048, creator=13701087609, updateTime=1783493330048, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=72, endPage=80, ext={EN=ArticleExt(id=1281627177166815428, articleId=1281627175199686850, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=A multi-class intelligent identification model for kick risk, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=
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.
, authors=Shengnan WU
1, 2, Laibin ZHANG
1, 2, Yiming HU
1, 2, 3, Rong CUI
1, 2, Shujie LIU
4, Zhiming YIN
5, authorsList=Shengnan WU, Laibin ZHANG, Yiming HU, Rong CUI, Shujie LIU, Zhiming YIN, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281627177749823687, articleId=1281627175199686850, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=面向溢流风险的多分类智能识别模型, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=
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。
, authors=武胜男
教授1, 2, 张来斌
教授1, 2, 胡一鸣
1, 2, 3, 崔蓉
1, 2, 刘书杰
4, 殷志明
5, authorsList=武胜男
教授, 张来斌
教授, 胡一鸣, 崔蓉, 刘书杰, 殷志明, authorCompany=null, correspAuthors=null, authorNote=
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1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
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1中国石油大学(北京) 安全与海洋工程学院,北京 102249
2应急管理部 油气生产安全与应急技术重点实验室,北京 102249, bio={"content":"
武胜男(1986—),女,山西大同人,博士,教授,主要从事深层、深水油气开采及关键安全装备风险评估与预警、可靠性与测试性维护方面的研究。E-mail: wushengnan@cup.edu.cn。
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武胜男(1986—),女,山西大同人,博士,教授,主要从事深层、深水油气开采及关键安全装备风险评估与预警、可靠性与测试性维护方面的研究。E-mail: wushengnan@cup.edu.cn。
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1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
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1中国石油大学(北京) 安全与海洋工程学院,北京 102249
2应急管理部 油气生产安全与应急技术重点实验室,北京 102249, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1281627178047619272, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, xref=1, ext=[AuthorCompanyExt(id=1281627178056007881, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, companyId=1281627178047619272, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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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, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281627179066835177, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, authorId=1281627178907451620, language=CN, stringName=胡一鸣, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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1中国石油大学(北京) 安全与海洋工程学院,北京 102249
2应急管理部 油气生产安全与应急技术重点实验室,北京 102249
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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, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281627179326882031, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, authorId=1281627179159109867, language=CN, stringName=崔蓉, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
1, 2, address=
1中国石油大学(北京) 安全与海洋工程学院,北京 102249
2应急管理部 油气生产安全与应急技术重点实验室,北京 102249, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1281627178047619272, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, xref=1, ext=[AuthorCompanyExt(id=1281627178056007881, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627175199686850, companyId=1281627178047619272, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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4, address=
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