录井工程 ›› 2023, Vol. 34 ›› Issue (4): 9-15.doi: 10.3969/j.issn.1672-9803.2023.04.002

• 研究与探讨 • 上一篇    下一篇

基于极值分析的钻井参数刺峰噪点数据识别研究

宋涛, 陈添, 梁欣怡, 田宇, 刘世杰, 柴晓武   

  1. ①中国石油渤海钻探第一录井公司;
    ②中国石油渤海钻探工程技术处;
    ③中国石油长庆油田分公司第一采油厂
  • 收稿日期:2023-10-11 出版日期:2023-12-25 发布日期:2024-01-04
  • 作者简介:宋涛 工程师,1986年生.2008年毕业于西安培华学院电子信息工程专业,现在中国石油渤海钻探第一录井公司从事信息化工作。通信地址:300280天津市大港油田团结东路渤海钻探第一录井公司。电话:13802015224。E-mail : song _tao@cnpc.com. cn

Study on identification of spike noise data of drilling parameters based on extreme value analysis

SONG Tao, CHEN Tian, LIANG Xinyi, TIAN Yu, LIU Shijie, CHAI Xiaowu   

  1. ①No.1 Mud Logging Company, BHDC, CNPC, Tianjin 300280,China;
    ②Engineering Technology Department,BHDC,CNPC,Tianjin 300457,China;
    ③No.1 Oil Production Plant, PetroChina Changqing Oilfield Company, Yan′an, Shaanxi 716000,China;
  • Received:2023-10-11 Online:2023-12-25 Published:2024-01-04

摘要: 在石油勘探开发钻井施工过程中,工程监测中产生的离群刺峰噪点数据严重影响智能化诊断报警的准确度。为了准确识别噪点数据,提出了一种基于极值分析的钻井参数刺峰噪点数据识别方法,该方法以噪点数据明显偏离趋势线特征为标准依据,以期精准识别并剔除噪点数据,提升工程数据分析准确度。为此,首先介绍了样本数据极值点的筛选算法,再对极值点刺峰噪点识别算法进行了详细论述,并阐述了刺峰噪点附近数据的噪点识别判断算法,进而完成了对样本数据全部刺峰噪点的识别。将该算法应用于实际钻井现场30口井5种钻井参数的噪点数据识别,试验后识别的噪点数据与作业现场的实际情况吻合度达82%以上,经专业技术人员评估后,证实该方法可应用于实际作业现场。

关键词: 钻井参数, 噪点数据, 噪点识别, 极值, 离群点

Abstract: During drilling construction of oil exploration and development process, the outlier spike noise data generated in engineering monitoring seriously affects the accuracy of intelligent diagnosis and alarm. To accurately identify the noise data, a drilling parameter spike noise data identification method based on extreme value analysis is proposed. The method is based on the characteristic of noise data that obviously deviate from the trend line, in order to accurately identify and eliminate noise data and improve the accuracy of engineering data analysis. Therefore, the screening algorithm for extreme points of sample data was first introduced, followed by a detailed discussion on the algorithm for identifying extreme point spike noise.The noise identification and judgment algorithm for the data near the spike noise points was also expounded, thereby completing the identification of all spike noise points in the sample data. The algorithm was applied to the noise data identification of 5 drilling parameters in 30 wells at the actual drilling sites. The noise data identified after the test were consistent with the actual situation at the working sites by more than 82%. After evaluation by professional technicians, it has been confirmed that the method can be applied to actual working sites.

Key words: drilling parameter, noise data, noise identification, extreme value, outlier

中图分类号: 

  • TE132.1
[1] 殷志明,刘书杰,谭扬,等.基于机器学习的深水钻井大数据处理方法研究[J].海洋工程装备与技术,2019,6(增刊1):446-453.
YIN Zhiming, LIU Shujie, TAN Yang, et al.Research on outlier marking method of deepwater drilling big data in machine learning[J]. Ocean Engineering Equipment and Technology,2019,6(S1):446-453.
[2] 梅林,张凤荔,高强.离群点检测技术综述[J].计算机应用研究,2020,37(12):3521-3527.
MEI Lin, ZHANG Fengli, GAO Qiang.Overview of outlier detection technology[J]. Application Research of Computers,2020,37(12):3521-3527.
[3] 王振洲. 离群点检测方法研究及其在机器学习中的应用[D].北京:中国地质大学(北京),2018.
WANG Zhenzhou.Study on the method of outlier detection and its application in machine learning[D]. Beijing: China University of Geosciences(Beijing),2018.
[4] 岳峰,邱保志.噪声数据集上的边界点检测算法[J].计算机工程,2007,33(19):82-84.
YUE Feng, QIU Baozhi.Boundary points detecting algorithm for clusters in noisy dataset[J]. Computer Engineering,2007,33(19):82-84.
[5] 刘帆. 基于深度学习的图像噪声识别与去除技术研究[D].天津:天津工业大学,2019.
LIU Fan.Research on image noise recognition and denoising technology based on deep learning[D]. Tianjin: Tiangong University,2019.
[6] 张玉婷,冯山. 一种基于邻域近似精度的离群点检测方法[J].数据采集与处理,2022,37(5):1018-1025.
ZHANG Yuting, FENG Shan.An outlier point detection method based on neighborhood approximate accuracy[J]. Journal of Data Acquisition & Processing,2022,37(5):1018-1025.
[7] 方小勇,黄华东,陈政,等.一种新的基于Bernstein-Bezier曲线的在线降噪方法[J].湖南环境生物职业技术学院学报,2013,19(2):18-21.
FANG Xiaoyong, HUANG Huadong, CHEN Zheng, et al.An online noise reduction method based on Bernstein-Bezier curve[J]. Journal of Hunan Environment-Biological Polytechnic,2013,19(2):18-21.
[8] 缑鹏飞,宋承云. 基于自适应邻居图的离群点检测方法[J].计算机应用研究,2023,40(11):3309-3314.
GOU Pengfei, SONG Chengyun.Outlier detection method based on adaptive neighbor graphs[J]. Application Research of Computers,2023,40(11):3309-3314.
[9] 刘财辉,刘地金.离群点检测的邻近性方法综述[J].计算机工程与应用,2022,58(21):1-12.
LIU Caihui, LIU Dijin.Overview of proximity methods for outlier detection[J].Computer Engineering and Applications,2022,58(21):1-12.
[10] 刘雷. 面向时序数据的离群点异常检测技术应用研究[D].北京:中央民族大学,2019.
LIU Lei.Application research on outlier anomaly detection technology for time series data[D].Beijing:Minzu University of China,2019.
[11] 张忠平,邓禹,刘伟雄,等. FNOD:基于近邻差波动因子的离群点检测算法[J].高技术通讯,2022,32(7):674-686.
ZHANG Zhongping, DENG Yu, LIU Weixiong, et al.FNOD: Outlier detection algorithm based on fluctuation of nearest neighbor difference factor[J].Chinese High Technology Letters,2022,32(7):674-686.
[12] 李锦妮. 监测数据变化趋势自动提取与分析方法研究[D].西安:西安理工大学,2020.
LI Jinni.Research on automatic extraction and analysis method of monitoring data trend[D]. Xi′an :Xi′an University of Technology,2020.
[13] HAN J W, KAMBER M, PEI J,等.数据挖掘概念与技术[M].范明,孟小峰,译.3版.北京:机械工业出版社,2012.
HAN J W, KAMBER M, PEI J, et al.Data mining concepts and techniques[M]. FAN Ming, MENG Xiaofeng, Trans. 3rd ed. Beijing: China Machine Press,2012.
[1] 张 继 军. 基于随钻环空压力测量的侧钻水平井滑动导向技术优化方法[J]. 录井工程, 2013, 24(03): 26-28.
[2] 刘 剑 马同奇 沈 铁 刘芙蓉. 无线钻井参数监测系统的设计与实现[J]. 录井工程, 2011, 22(01): 52-55.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 韩性礼 孟 辉 王西安 袁宝清 何天清 . 国内录井装备的现状与发展建议[J]. 录井工程, 2012, 23(04): 44 -46 .
[2] 阎治全 张丙生 钱文博 刘 青 赵新颖 刘伟刚 刘海越 . 岩屑数字图像采集分析技术应用研究[J]. 录井工程, 2012, 23(04): 58 -61 .
[3] 王 志 战. 利用核磁共振和离子色谱参数开展随钻压力检测的探讨[J]. 录井工程, 2007, 18(01): 1 -4 .
[4] 邓 平 王丙寅 李玉勤 马 红 . 地化录井技术在永安油田致密砂岩油气层评价中的应用[J]. 录井工程, 2012, 23(04): 17 -21 .
[5] 刘 方. 喇嘛甸油田气层井壁取心资料特征研究[J]. 录井工程, 2010, 21(02): 35 -38 .
[6] 何道勇 滕玉明 李洪文 张杰. 热蒸发烃色谱分析技术在胜利油田油气勘探中的应用[J]. 录井工程, 2007, 18(01): 46 -51 .
[7] 邓 辉 王春辉 陈泽新 支丽菊 . 应用人工神经网络模型识别白云岩类型[J]. 录井工程, 2010, 21(03): 5 -8 .
[8] 王守军 慈兴华 王志战 王晓惠. 核磁共振录井技术在胜利油田的应用[J]. 录井工程, 2007, 18(01): 24 -27,62 .
[9] 王 伟 东. Advantage综合录井仪气体处理系统国产化改造[J]. 录井工程, 2008, 19(01): 66 -69 .
[10] 文爱民 田相斋 董高桢. 现场录井资料数字化处理系统的研究与开发[J]. 录井工程, 2007, 18(01): 39 -42 .