物探与化探2024，Vol.48Issue(2)：489-497,9.DOI:10.11720/wtyht.2024.1492
基于不同机器学习模型的石油测井数据岩性分类对比研究
Comparative study on lithology classification of oil logging data based on different machine learning models
摘要
Abstract
Specific computational tools assist geologists in identifying and classifying the lithology of rocks in oil well exploration,reduc-ing costs,and enhancing operational efficiency.Machine learning methods integrate a vast amount of information,enabling efficient pat-tern recognition and accurate decision-making.This article categorizes the lithology of five oil wells in the Norwegian Sea,randomly di-viding the data into a training set(70%)and a test set(30%).Using multivariate well log parameter data for training and validation,the application effectiveness of models such as Multilayer Perceptron(MLP),Decision Tree,Random Forest,and XGBoost is com-pared.The research results indicate that the XGBoost model outperforms others in terms of data generalization,achieving an accuracy of 95%.The Random Forest model follows with an accuracy of 94%.Meanwhile,Multilayer Perceptron(MLP)and Decision Tree models exhibit good robustness,with accuracies of 92%and 90%,respectively.关键词
岩性识别/机器学习/石油测井/XGBoost算法/随机森林Key words
lithology identification/machine learning/oil logging/XGBoost gorithm/random forest分类
天文与地球科学引用本文复制引用
江丽,张智谟,王琦玮,封志兵,张博程,任腾飞..基于不同机器学习模型的石油测井数据岩性分类对比研究[J].物探与化探,2024,48(2):489-497,9.基金项目
放射性地质与勘探国防重点学科实验室开放基金(2020RGET06) （2020RGET06）
江西省教育厅科学技术研究项目(GJJ220075) （GJJ220075）
中国铀业有限公司—东华理工大学核资源与环境国家重点实验室联合创新基金项目(2023NRE-LH-08) （2023NRE-LH-08）
中国核工业地质局生产中科研项目(202311-5) （202311-5）
东华理工大学博士科研启动基金项目(DHBK2019087) （DHBK2019087）
