基于DBSCAN算法的孔深测量与地层反演理论研究
投稿时间:2021-05-27  修订日期:2021-05-27  点此下载全文
引用本文:
摘要点击次数: 71
全文下载次数: 0
作者单位邮编
李璇 吉林大学建设工程学院吉林 长春 130061 130061
赵大军 吉林大学建设工程学院吉林 长春 130061 130061
中文摘要:论文针对地质勘探取心工况复杂、钻进深度大、事故发生率高、岩性复杂、钻进参数难以获取、无法实时掌握钻进状态的难题,在充分调研现有工况识别技术的采集原理、功能种类、现场实测的基础上,结合钻探智能化、自动化的发展方向,运用硬件与软件结合的思想,设计了实时测量钻探参数的方案。论文首次提出使用DBSCAN(针对噪声空间基于分布密度进行聚类的算法)密度聚类法分析钻进参数,结合光电编码器增量的正负性进行工况判别,以获取钻头位置以及钻孔深度。该方案可以判别钻进状态,获取钻进工艺参数,将测量数据作为所钻地层的可钻参数来反演地层。钻机的自动化、智能化研究能获取大量地层资料,有利于推进钻进工艺学发展,实现从经验钻探到智能钻探进的突破。同时,为智能钻进系统奠定基础。
中文关键词:立轴钻机  孔深测量  工况识别  地层反演  DBSCAN密度聚类
 
Theoretical research on hole depth measurement and formation inversion based on DBSCAN algorithm
Abstract:In view of the problems of complex geological exploration coring conditions, large drilling depth, high accident rate, complex lithology, difficulty in obtaining drilling parameters, and inability to know drilling status in real time, with full investigation of existing working condition identification technologies in terms of collection principle, functional types and field measurement, a scheme for real-time measurement of drilling parameters is designed by the methodology of combining hardware and software in reference to the development direction of intelligent and automatic drilling. In this paper, the density clustering method of DBSCAN (clustering algorithm based on distribution density for noise space) is proposed for the first time to analyze drilling parameters, and to determine working conditions jointly with the positive and negative increment of the photoelectric encoder, so as to obtain the bit position and drilling depth. This scheme can distinguish drilling state, obtain drilling process parameters, and invert the formation with the measured data as drillable parameters of the drilled formation. Research on automatic and intelligent drilling rig can obtain a large amount of formation data, which is beneficial to promote the development of drilling technology and realize the breakthrough from experience drilling to intelligent drilling. At the same time, it lays a foundation for the intelligent drilling system.
keywords:spindle drill  hole depth measurement  working condition identification  formation inversion  DBSCAN density clustering
  查看/发表评论  下载PDF阅读器