深圳大学学报（理工版）2026，Vol.43Issue(1)：7-16,10.DOI:10.3724/SP.J.1249.2026.01007
基于改进Stacking集成学习的深层油井管腐蚀预测
Corrosion prediction of deep oil well tubing based on improved Stacking ensemble learning approach
摘要
Abstract
To enhance the prediction accuracy for both average corrosion rate and pitting corrosion rate of oil well tubing in deep complex environments,and to address the issue of insufficient consideration of base learner heterogeneity in traditional Stacking ensemble learning,an improved Stacking ensemble learning algorithm based on the coefficient of determination(R2)is proposed.This algorithm integrates four machine learning models as base learners:extreme gradient boosting(XGBoost),random forest(RF),support vector regression(SVR),and gradient boosting decision tree(GBDT).The outputs of these base learners are weighted according to their respective R2,and the weighted combination forms the input dataset for the meta-learner.Experimental results demonstrate that,compared with the traditional Stacking ensemble method,the improved model achieves a 25.9%reduction in mean absolute error(MAE)and a 9.7%reduction in mean squared error(MSE)for average corrosion rate prediction,alongside a 2.3%increase in the R2.For pitting corrosion rate prediction,it yields reductions of 11.6%for MAE and 2.0%for MSE,respectively,with a 2.7%increase for R2.These results validate the effectiveness of the proposed algorithm,and the research findings provide valuable support for corrosion prevention,control and safe operational maintenance of deep oil well tubing.关键词
腐蚀科学与防护/Stacking集成学习/深层油井管材腐蚀/机器学习/XGBoost/随机森林/支持向量回归/梯度提升决策树Key words
corrosion science and protection/Stacking ensemble learning/corrosion of deep oil well tubing/machine learning/XGBoost/random forest/support vector regression/gradient boosting decision tree分类
能源科技引用本文复制引用
黄晗,陈长风,贾小兰,张玉洁,石丽伟,王立群..基于改进Stacking集成学习的深层油井管腐蚀预测[J].深圳大学学报（理工版）,2026,43(1):7-16,10.基金项目
National Natural Science Foundation of China(12171482,U23A20301) （12171482,U23A20301）
Project of State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum-Beijing(PRE/DX-2504) 国家自然科学基金资助项目(12171482,U23A20301) （PRE/DX-2504）
中国石油大学油气资源与工程全国重点实验室资助项目(PRE/DX-2504). （PRE/DX-2504）
