测井技术2025，Vol.49Issue(2)：288-297,10.DOI:10.16489/j.issn.1004-1338.2025.02.016
基于机器学习的混积岩有机碳测井预测方法
Logging Prediction Method of Organic Carbon in Mixed Deposits Based on Machine Learning
摘要
Abstract
The quantitative evaluation and prediction of total organic carbon(TOC)content is an important part of source rock quality evaluation.However,the traditional TOC content prediction methods,such as geochemical analysis,nuclear magnetic resonance response and empirical formula,have some problems,such as high prediction cost and difficult to deal with complex strata and lithology overlap.The machine learning method has a significant advantage in solving the complex nonlinear relationship between data because of its powerful nonlinear mapping ability.In this paper,three machine learning methods,namely XGBoost,random forest and support vector regression(SVR),are used to predict TOC content in the study area by selecting the logging properties sensitive to TOC content,such as natural gamma ray,sonic time difference,neutron and compensation density.XGBoost,random forest and support vector regression were used to predict TOC content,with R2 of 0.77,0.76 and 0.77,and MAE of 1.25,1.25 and 1.21,respectively.The results show that the machine learning method can effectively evaluate the quality of source rocks and accurately identify the"sweet spot"horizon,which provides a reliable basis for reservoir evaluation and reservoir development.关键词
机器学习/总有机碳含量/烃源岩/XGBoost/随机森林/支持向量回归Key words
machine learning/total organic carbon content/source rock/XGBoost/random forest/support vector regression分类
天文与地球科学引用本文复制引用
陈良雨,胡浪,辛锦涛,李永贵,陈挚,付建伟..基于机器学习的混积岩有机碳测井预测方法[J].测井技术,2025,49(2):288-297,10.基金项目
国家自然科学基金项目"致密混积岩储层孔喉网络体系成因机理与多尺度表征评价研究"(41872133) （41872133）
