河北地质大学学报2025，Vol.48Issue(2)：21-31,11.DOI:10.13937/j.cnki.hbdzdxxb.2025.02.003
主成分分析在低孔低渗储层孔隙度预测中的应用研究
Application of Principal Component Analysis in Porosity Prediction of Low Porosity and Low Permeability Reservoir
摘要
Abstract
In order to improve the accuracy of porosity prediction in low porosity and low permeability reservoirs and better serve the logging evaluation of low porosity and low permeability reservoirs,by combing the principle and properties of principal component analysis,combined with the common methods of machine learning and ensemble learning combination strategies,the idea of selective ensemble learning is proposed to construct a porosity prediction model based on principal component analysis.Firstly,the principal component analysis of the normalized logging curve data is carried out,and then the extracted principal components are used as the input attributes of four machine learning models:BP neural network,RF(random forest),XGBoost(extreme gradient boosting tree)and ridge regression.Finally,the optimization algorithm constructs an integrated model according to the specific gravity to predict the porosity.The research shows that the correlation coefficient R2 between the predicted value and the actual value of the porosity of the low porosity and low permeability reservoir is 0.948,and the prediction accuracy is higher,which is better than the single machine learning prediction model.It solves the problems of insufficient accuracy and poor generalization ability of traditional porosity prediction methods for low porosity and low permeability reservoirs,and lays a foundation for subsequent logging comprehensive evaluation of low porosity and low permeability reservoirs.关键词
主成分分析法/孔隙度预测/集成学习/低孔低渗储层Key words
principal component analysis/porosity prediction/integrated learning/low porosity and low permeability reservoir分类
天文与地球科学引用本文复制引用
张雨辰,赵军龙,孙婧,崔文洁,陈家鑫,金利睿..主成分分析在低孔低渗储层孔隙度预测中的应用研究[J].河北地质大学学报,2025,48(2):21-31,11.基金项目
国家自然科学基金面上项目(42172164) （42172164）
