Volume 52 Issue 5
Nov.  2024
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WANG Biao, LI Jun, YANG Hongwei, et al. An early intelligent kick detection method based on variation trend of engineering parameters [J]. Petroleum Drilling Techniques, 2024, 52(5)：145−153. DOI: 10.11911/syztjs.2024093
Citation: WANG Biao, LI Jun, YANG Hongwei, et al. An early intelligent kick detection method based on variation trend of engineering parameters [J]. Petroleum Drilling Techniques, 2024, 52(5)：145−153. DOI: 10.11911/syztjs.2024093
shu

An Early Intelligent Kick Detection Method Based on Variation Trend of Engineering Parameters

  • 1.

    College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing, 102249, China

  • 2.

    China University of Petroleum-Beijing at Karamay, Karamay, Xinjiang, 834000, China

More Information
  • Received Date: May 09, 2024
  • Revised Date: September 09, 2024
  • Accepted Date: October 09, 2024
  • Available Online: October 11, 2024
    Graphical Abstract
    • Abstract

  • Due to the scarcity of kick data samples, variations in different drilling conditions, and differences in the response rules of drilling parameters during different stages after a kick, existing kick detection methods exhibit poor robustness and are difficult to apply throughout the drilling process. By using the random forest (RF) algorithm, an intelligent recognition model for drilling conditions was established. This model combined a physical model to eliminate the impact of drilling conditions on kick characteristic parameters, extracting and integrating the trend values of these parameters to construct an adaptive calculation method for the kick risk index (KRI). In addition, a method for early intelligent kick detection based on the variation trend of engineering parameters was proposed. The proposed method was validated by using kick data from three wells, and the results showed that the accuracy of the intelligent recognition model for drilling conditions was 96.5%. Compared to manual warning methods, the average early warning time is 8.3 min in advance. The research demonstrates that this method is suitable for kick detection throughout the drilling process and for scenarios where some characteristic parameter measurements may fail. It has significant guiding implications for ensuring drilling safety and shortening drilling cycles.

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    • References (24)
  • [1]
    徐进,胡大梁,任茂,等. 川西深井井下复杂情况及故障预防与处理[J]. 石油钻探技术,2010,38(4):22–25.

    XU Jin, HU Daliang, REN Mao, et al. Prevention and treatment of complicated downhole conditions and accidents of deep wells in western Sichuan[J]. Petroleum Drilling Techniques, 2010, 38(4): 22–25.
    [2]
    史亚红,夏宏泉,彭梦,等. 一种准确预测钻井液安全密度窗口的新方法[J]. 断块油气田,2019,26(2):248–252.

    SHI Yahong, XIA Hongquan, PENG Meng, et al. New accurate prediction method of safe window of drilling fluid density[J]. Fault-Block Oil & Gas Field, 2019, 26(2): 248–252.
    [3]
    王建云,杨晓波,王鹏,等. 顺北碳酸盐岩裂缝性气藏安全钻井关键技术[J]. 石油钻探技术,2020,48(3):8–15.

    WANG Jianyun, YANG Xiaobo, WANG Peng, et al. Key technologies for the safe drilling of fractured carbonate gas reservoirs in the Shunbei Oil and Gas Field[J]. Petroleum Drilling Techniques, 2020, 48(3): 8–15.
    [4]
    高永德,董洪铎,胡益涛,等. 深水高温高压井钻井液当量循环密度预测模型及应用[J]. 特种油气藏,2022,29(3):138–143.

    GAO Yongde, DONG Hongduo, HU Yitao, et al. Prediction model of equivalent circulating density of drilling fluid in deep HPHT wells and its application[J]. Special Oil & Gas Reservoirs, 2022, 29(3): 138–143.
    [5]
    陶振宇,樊洪海,刘玉含,等. 基于地层溢流能量的压井方式选择方法[J]. 石油钻采工艺,2023,45(4):424–431.

    TAO Zhenyu, FAN Honghai, LIU Yuhan, et al. The method for well-killing selection considering the formation overflow energy[J]. Oil Drilling & Production Technology, 2023, 45(4): 424–431.
    [6]
    张来斌,谢仁军,殷启帅. 深水油气开采风险评估及安全控制技术进展与发展建议[J]. 石油钻探技术,2023,51(4):55–65.

    ZHANG Laibin, XIE Renjun, YIN Qishuai. Technical progress and development suggestions for risk assessment and safety control of deep-water oil and gas exploitation[J]. Petroleum Drilling Techniques, 2023, 51(4): 55–65.
    [7]
    王志远,梁沛智,陈科杉,等. 深部地层智能压井多解性分析与优化策略[J]. 石油钻探技术,2024,52(2):136–145.

    WANG Zhiyuan, LIANG Peizhi, CHEN Kesha, et al. Multi-solution analysis and optimization strategy for intelligent well killing in deep formation[J]. Petroleum Drilling Techniques, 2024, 52(2): 136–145.
    [8]
    孙甫南. 用关基井随钻综合录井曲线判断溢流信息[J]. 天然气工业,1986,8(4):50–54.

    SUN Funan. Judging overflow information by composite log curves while drilling in guan key well[J]. Natural Gas Industry, 1986, 8(4): 50–54.
    [9]
    SCHAFER D M, LOEPPKE G E, GLOWKA D A, et al. An evaluation of flowmeters for the detection of kicks and lost circulation during drilling[R]. SPE 23935, 1992.
    [10]
    CAYEUX E, DAIREAUX B. Insights into the physical phenomena that influence automatic gain/loss detection during drilling operations[J]. SPE Drilling & Completion, 2017, 32(1): 13–24.
    [11]
    刘书杰,杨向前,郭华,等. 井控溢流快速判断方法研究[J]. 煤炭技术,2017,36(5):296–298.

    LIU Shujie, YANG Xiangqian, GUO Hua, et al. Research for judgment method of well control overflow[J]. Coal Technology, 2017, 36(5): 296–298.
    [12]
    王健,陈颖,张军阳,等. 钻井液循环系统减缓液面波动装置研发与应用[J]. 石油钻探技术,2017,45(5):48–52.

    WANG Jian, CHEN Ying, ZHANG Junyang, et al. Development and application of a device for alleviating liquid level fluctuation to be used in drilling fluid circulation system[J]. Petroleum Drilling Techniques, 2017, 45(5): 48–52.
    [13]
    屈俊波,陈平,马天寿,等. 精确监测井底溢流的井下微流量装置设计与试验[J]. 石油钻探技术,2012,40(5):106–110.

    QU Junbo, CHEN Ping, MA Tianshou, et al. Design and test of down-hole micro-flow device for monitoring overflow[J]. Petroleum Drilling Techniques, 2012, 40(5): 106–110.
    [14]
    史肖燕,周英操,赵莉萍,等. 基于随机森林的溢漏实时判断方法研究[J]. 钻采工艺,2020,43(1):9–12.

    SHI Xiaoyan, ZHOU Yingcao, ZHAO Liping, et al. Study on how to determine kicks and losses in real time on basis of random forest method[J]. Drilling & Production Technology, 2020, 43(1): 9–12.
    [15]
    孙伟峰,刘凯,张德志,等. 结合钻井工况与Bi-GRU的溢流与井漏监测方法[J]. 石油钻探技术,2023,51(3):37–44.

    SUN Weifeng, LIU Kai, ZHANG Dezhi, et al. A kick and lost circulation monitoring method combining Bi-GRU and drilling conditions[J]. Petroleum Drilling Techniques, 2023, 51(3): 37–44.
    [16]
    DUAN Shiming, SONG Xianzhi, CUI Yi, et al. Intelligent kick warning based on drilling activity classification[J]. Geoenergy Science and Engineering, 2023, 222: 211408. doi: 10.1016/j.geoen.2022.211408
    [17]
    杨红满,卢世浩,徐高军,等. 西北油田智能监测溢流预警系统建立与应用效果评价[J]. 化工设计通讯,2017,43(7):90–92.

    YANG Hongman, LU Shihao, XU Gaojun, et al. Establishment and application of intelligent monitoring overflow warning system in Northwest Oilfield[J]. Chemical Engineering Design Communications, 2017, 43(7): 90–92.
    [18]
    TANG Hewei, ZHANG Shang, ZHANG Feifei, et al. Time series data analysis for automatic flow influx detection during drilling[J]. Journal of Petroleum Science and Engineering, 2019, 172: 1103–1111. doi: 10.1016/j.petrol.2018.09.018
    [19]
    MAO Youli, ZHANG Peng. An automated kick alarm system based on statistical analysis of real-time drilling data[R]. SPE 197275, 2019.
    [20]
    晏琰,段慕白,黄浩. 基于趋势线法的钻井风险预警技术研究[J]. 钻采工艺,2023,46(2):170–174.

    YAN Yan, DUAN Mubai, HUANG Hao. Research on drilling risk early warning technology based on trend line method[J]. Drilling & Production Technology, 2023, 46(2): 170–174.
    [21]
    董师师,黄哲学. 随机森林理论浅析[J]. 集成技术,2013,2(1):1–7.

    DONG Shishi, HUANG Zhexue. A brief theoretical overview of random forests[J]. Journal of Integration Technology, 2013, 2(1): 1–7.
    [22]
    李欣海. 随机森林模型在分类与回归分析中的应用[J]. 应用昆虫学报,2013,50(4):1190–1197.

    LI Xinhai. Using “random forest” for classification and regression[J]. Chinese Journal of Applied Entomology, 2013, 50(4): 1190–1197.
    [23]
    SLEITI A K, TAKALKAR G, EL-NAAS M H, et al. Early gas kick detection in vertical wells via transient multiphase flow modelling: A review[J]. Journal of Natural Gas Science and Engineering, 2020, 80: 103391. doi: 10.1016/j.jngse.2020.103391
    [24]
    田野,蒋东雷,马传华,等. 钻柱偏心旋转对环空摩阻压降影响的数值模拟研究[J]. 石油钻探技术,2022,50(5):42–49.

    TIAN Ye, JIANG Donglei, MA Chuanhua, et al. Numerical simulation of the effects of eccentric rotation of the drill string on annular frictional pressure drop[J]. Petroleum Drilling Techniques, 2022, 50(5): 42–49.
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