录井工程 ›› 2023, Vol. 34 ›› Issue (4): 9-15.doi: 10.3969/j.issn.1672-9803.2023.04.002
宋涛①, 陈添②, 梁欣怡③, 田宇③, 刘世杰①, 柴晓武①
SONG Tao①, CHEN Tian②, LIANG Xinyi③, TIAN Yu③, LIU Shijie①, CHAI Xiaowu①
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。
中图分类号:
[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. |
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