地质出版社有限公司 中国地质科学院勘探技术研究所主办

基于工程参数变化趋势异常诊断的卡钻实时预警方法

胜亚楠. 2024. 基于工程参数变化趋势异常诊断的卡钻实时预警方法. 钻探工程, 51(1): 68-74. doi: 10.12143/j.ztgc.2024.01.009
引用本文: 胜亚楠. 2024. 基于工程参数变化趋势异常诊断的卡钻实时预警方法. 钻探工程, 51(1): 68-74. doi: 10.12143/j.ztgc.2024.01.009
SHENG Yanan. 2024. Real-time early warning of pipe sticking based on abnormal diagnosis of engineering parameter change trend. DRILLING ENGINEERING, 51(1): 68-74. doi: 10.12143/j.ztgc.2024.01.009
Citation: SHENG Yanan. 2024. Real-time early warning of pipe sticking based on abnormal diagnosis of engineering parameter change trend. DRILLING ENGINEERING, 51(1): 68-74. doi: 10.12143/j.ztgc.2024.01.009

基于工程参数变化趋势异常诊断的卡钻实时预警方法

  • 基金项目:

    中石化中原石油工程有限公司项目“井下工程参数采集及随钻传输系统研制”(编号:2023101)、“基于VDX实时数据的井下风险监测及预警系统研制”(编号:2021112);中石化中原石油工程有限公司博士后课题“川南页岩气钻井工程风险评估与预警技术研究”(编号:2020116B)

详细信息
    作者简介: 胜亚楠,男,汉族,1989年生,副研究员,钻井工程专业,博士,主要从事钻井工程风险评价、井身结构优化设计和油气井井下信息控制等方面的研究工作,河南省濮阳市华龙区中原东路462号,shengyanan_upc@163.com。

Real-time early warning of pipe sticking based on abnormal diagnosis of engineering parameter change trend

  • 川南工区是中石化重点页岩气勘探开发工区,该工区地层压力系数高、钻井地质条件苛刻,导致该工区钻井复杂、故障频发,其中卡钻故障最为突出,严重制约了川南页岩气的安全高效开发。现有卡钻识别技术存在监控信息综合利用能力差、风险预警不及时、主观性强等问题。本文通过分析钻井作业过程中卡钻故障的专家知识判断,确定了卡钻风险对应的关键表征参数,并研究了卡钻发生位点的关键表征参数的变化趋势,得到了相应的变化规律;在此基础上建立了基于工程参数变化趋势异常诊断的卡钻实时预警方法。选取WY-XX井为实例进行分析,软件预警结果与实际井下风险相吻合,验证了模型的准确性和可靠性,准确率达83%。
  • 加载中
  • [1]

    蒋希文.钻井事故与复杂问题(第2版)[M].北京:石油工业出版社,2006:72-80.JIANG Xiwen. Drilling Accidents and Complex Problems (Second Edition)[M]. Beijing: Petroleum Industry Press, 2006:72-80.

    [2]

    [2] 李紫璇,张菲菲,祝钰明,等.钻井模型与机器学习耦合的实时卡钻预警技术[J].石油机械,2022,50(4):15-21,93.

    LI Zixuan, ZHANG Feifei, ZHU Yuming, et al. Real time stuck drilling warning technology coupled with drilling model and machine learning[J]. Petroleum Machinery, 2022,50(4):15-21,93.

    [3]

    [3] 刘海龙,李彤,张奇志.基于自适应遗传算法改进的BP神经网络卡钻事故预测[J].现代电子技术,2021,44(15):149-153.

    LIU Hailong, LI Tong, ZHANG Qizhi. BP neural network based on adaptive genetic algorithm improvement for predicting stuck drill accidents[J]. Modern Electronic Technology, 2021,44(15):149-153.

    [4]

    [4] 朱硕,宋先知,李根生,等.钻柱摩阻扭矩智能实时分析与卡钻趋势预测[J].石油钻采工艺,2021,43(4):428-435.

    ZHU Shuo, SONG Xianzhi, LI Gensheng, et al. Intelligent real time analysis of drill string friction and torque and prediction of stuck trend[J]. Petroleum Drilling and Production Technology, 2021,43(4):428-435.

    [5]

    [5] Khakzad N., Khan F., Amyotte P. Quantitative risk analysis of offshore drilling operations: A Bayesian approach[J]. Safety Science, 2013,57(3):108-117.

    [6]

    [6] 苏晓眉,张涛,李玉飞,等.基于K-Means聚类算法的沉砂卡钻预测方法研究[J].钻采工艺,2021,44(03):5-9.

    SU Xiaomei, ZHANG Tao, LI Yufei, et al. Research on the prediction method of sand sticking based on K-Means clustering algorithm[J]. Drilling and Production Technology, 2021,44(3):5-9.

    [7]

    [7] 魏纳,李蜀涛,陈亮,等.AD401-7井定向井卡钻复杂事故的处理及分析[J].探矿工程(岩土钻掘工程),2018,45(4):10-16.

    WEI Na, LI Shutao, CHEN Liang, et al. AD 4017 directional well’s processing and analysis of drilling complex accident[J]. Exploration Engineering (Rock & Soil Drilling and Tunneling), 2018,45(4):10-16.

    [8]

    [8] 翟育峰,赵辉,王鲁朝,等.湘南3000 m科学深钻孔内事故处理及对策[J].钻探工程,2023,50(4):32-40.ZHAI Yufeng, ZHAO Hui, WANG Luzhao, et al. Down-hole incident treatment and prevention for the 3000m scientific deep borehole in southern Hunan[J]. Drilling Engineering, 2023,50(4):32-40..

    [9]

    [9] 朱迪斯,赵洪波,刘恩然,等.长江下游(安徽)地区页岩气钻井工程难点及对策分析[J].钻探工程,2022,49(5):11-21.ZHU Disi, ZHAO Hongbo, LIU Enran, et al. Shale gas drilling difficulties and their solutions in the lower reach of the Yangtze River (Anhui)[J]. Drilling Engineering, 2022,49(5):11-21..

    [10]

    [10] 陈庭根,管志川.钻井工程理论与技术[M].东营:中国石油大学出版社,2006: 51-54.

    CHEN Tinggen, GUAN Zhichuan. Theory and Technology of Drilling Engineering[M]. Dongying: Petroleum University Press, 2000:51-54.

    [11]

    [11] Jahanbakhshi R, Keshavarzi R, Shoorehdeli M A, et al. Intelligent prediction of differential pipe sticking by support vector machine compared with conventional artificial neural networks: An example of Iranian Offshore Oil Fields[J]. SPE Drilling & Completion, 2012,27(4):586-595.

    [12]

    [12] 赵虎.数据采集中的未确知有理数滤波方法[J].自动化仪表,2008(8):12-14.

    ZHAO Hu. Unascertained rational number filtering method in data acquisition[J]. Automatic Instrument, 2008,14(8):12-14.

    [13]

    [13] 柳小桐.BP神经网络输入层归一化[J].机械工程与自动化,2010,11(3):122-126.

    LIU Xiaotong. Input layer normalization of BP neural network [J]. Mechanical Engineering and Automation, 2010,11(3):122-126.

  • 加载中
计量
  • 文章访问数:  860
  • PDF下载数:  609
  • 施引文献:  0
出版历程
收稿日期:  2023-06-16
修回日期:  2023-08-22

目录