几种基于随钻参数地层识别方法的对比分析

Comparative analysis of several formation identification methods based on parameters while drilling

摘要:

地层岩性的实时识别对及时调整钻井参数、有效控制井眼轨迹、寻找地下储层都具有十分重要的作用。与传统岩性识别方法相比，通过监测随钻参数变化进行岩性识别，具有便捷、高效、实时、准确、环保以及节能等优点。围绕基于随钻参数的地层岩性识别技术，按照煤矿勘探、油气藏开采等不同岩性识别应用领域对随钻参数进行分类；通过对随钻测控技术及装备的研究现状分析，介绍随钻参数采集及传输技术；介绍了机器学习算法、多元统计分析法、灰色关联法、交会图法的特点及应用情况；结合应用案例对4种基于随钻参数的地层识别方法进行对比分析。最终，归纳总结了随钻岩性识别研究的关键技术问题，分析了在研发及工程应用中存在的不足及面临的挑战，并给予建议。

关键词:地层识别;随钻参数;数据采集;机器学习算法;多元统计分析法;灰色关联法;交会图法

Abstract:

Real-time recognition of formation lithology is critical for promptly adjusting drilling parameters， effectively controlling wellbore trajectory， and identifying subsurface reservoirs. Compared to traditional methods of identifying lithology， real-time recognition through monitoring parameters while drilling offers advantages such as convenience， efficiency， real-time accuracy， environmental compatibility， and energy efficiency. In this paper， around the lithology identification technology based on real-time parameters while drilling， the parameters according to different applications such as coal exploration and oil and gas reservoir exploitation are classified. Through the analysis of the current research status of drilling measurement and control technology and equipment， the technology for collecting and transmitting real-time parameters while drilling is introduced. Additionally， the characteristics and applications of machine learning algorithms， multivariate statistical analysis， grey relational analysis， and cross-plotting methods are also discussed. Through application cases， it compares and analyzes four types of lithology identification methods based on real-time parameters while drilling. Ultimately， the key technical issues in real-time lithology identification research is summarized， the deficiencies and challenges in development and engineering applications are analyzed， and the recommendations are provided.

Key words:lithology recognition;parameters while drilling;data acquisition;machine learning algorithms;multivariate statistical analysis;grey relational analysis;cross-plotting methods