基于EMD的井下近钻头振动数据分析  

Analysis of vibration data of underground drill near-bit based on EMD

作　　者：张霞[1] 张涛 李玉梅 黄升 ZHANG Xia;ZHANG Tao;LI Yumei;HUANG Sheng(School of Information&Communication Engineering,Beijing Information Science&Technology University,Beijing 100101,China;School of Automation,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学信息与通信工程学院,北京100101 [2]北京信息科技大学自动化学院,北京100192

出　　处：《北京信息科技大学学报（自然科学版）》2019年第6期59-63,共5页Journal of Beijing Information Science and Technology University

基　　金：北京信息科技大学师资补充与支持计划(5029011102);北京信息科技大学科研水平提高重点研究培育项目(5221835901);北京市教委一般项目(KM201811232011);北京市属高校高水平教师队伍建设支持计划-青年拔尖人才培育计划(CIT&TCD201804057)

摘　　要：针对井下异常振动造成的钻头损坏、钻具疲劳、钻进效率慢等问题,提出了一种基于经验模态分解算法的井下异常振动识别方法;通过对井下近钻头振动数据进行经验模态分解,得到近钻头振动数据的本征模态分量;对本征模态分量进行希尔伯特变换,得到井下近钻头振动数据的瞬时频率,从而识别出井下异常振动;相较于传统的基于时域分析和快速傅里叶分析的井下异常振动识别方法,提高了识别速率、识别准确率,有助于缩短建井周期,节省钻井成本。To overcome the problems of drill bit damage, drilling tools fatigue and slow drilling efficiency caused by abnormal vibration of underground mine, a method for identifying abnormal vibration of underground mine based on empirical mode decomposition algorithm is proposed. By empirical model decomposition of near-bit vibration data of well, the intrinsic modal components of near-bit vibration is obtained. Through Hilbert transformation of the eigenmode component, the instantaneous frequency of the near-bit bit vibration data are obtained, and the abnormal vibration of the well is identified. Compared with the traditional time domain analysis and fast Fourier, the underground abnormal vibration identification method improves the recognition rate and recognition accuracy, helps shorten the well construction period, and saves drilling costs.

关 键 词：近钻头振动数据 经验模式分解 频域分析 异常振动识别 

分 类 号：TE28[石油与天然气工程—油气井工程]