西湖凹陷N构造花港组厚层砂泥岩地层测井岩性识别方法
Lithology identification method of thick sand mudstone formation logging in Huagang Formation,N Structure,Xihu Sag
靳九龙 1杨斌 1唐生寿 1刘洪瑞 1代兴宇 1蒲金成1
作者信息
- 1. 成都理工大学 四川 成都 610059
- 折叠
摘要
西湖凹陷位于东海,钻井取心比较困难,取心资料比较少,岩屑录井资料比较粗略,为了得到比较精细的地层岩性,在无监督机器学习的基础上,采用有监督机器学习的方法进行岩性识别.结果表明:该方法对研究区地层的岩性识别率可达 95%以上,能够快速准确地对目的层的岩性进行识别.
Abstract
The West Lake Depression is located in the East China Sea,where drilling and coring are relatively difficult,with limited coring data and rough cuttings logging data.In order to obtain more precise formation lithology,a supervised machine learning method based on unsupervised machine learning is adopted for lithology identification.The results show that the method has a lithology recognition rate of over 95%for the strata in the study area,and can quickly and accurately identify the lithology of the target layer.
关键词
西湖凹陷/花港组/岩性识别/多分辨率图形聚类/支持向量机Key words
Xihu Depression/Huagang Group/Lithological identification/MRGC clustering/Support Vector Machine引用本文复制引用
出版年
2024
NETL
NSTL
万方数据