首页|期刊导航|Petroleum Science|A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage

A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage

Juan Li Mandella Ali M.Fargalla Wei Yan Zi-Xu Zhang Wei Zhang Zi-Chen Zou Tang Qing Tao Yang Chao-Dong Tan Guang-Cong Li

Petroleum Science2025，Vol.22Issue(10)：P.4157-4173,17.

Petroleum Science2025，Vol.22Issue(10)：P.4157-4173,17.DOI:10.1016/j.petsci.2025.08.011

A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage

Juan Li ¹Mandella Ali M.Fargalla ²Wei Yan ²Zi-Xu Zhang ²Wei Zhang ²Zi-Chen Zou ²Tang Qing ¹Tao Yang ¹Chao-Dong Tan ²Guang-Cong Li²

作者信息

1. PetroChina Dagang Oilfield Petroleum Engineering Research Institute,Tianjin,300450,China
2. College of Safety and Ocean Engineering,China University of Petroleum,Beijing 102249,China
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摘要

关键词

Casing damage/Machine learning/Feature selection/Sand production/Boosting algorithm

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能源科技

引用本文复制引用

Juan Li,Mandella Ali M.Fargalla,Wei Yan,Zi-Xu Zhang,Wei Zhang,Zi-Chen Zou,Tang Qing,Tao Yang,Chao-Dong Tan,Guang-Cong Li..A novel fusion of interpretable boosting algorithm and feature selection for predicting casing damage[J].Petroleum Science,2025,22(10):P.4157-4173,17.

基金项目

funded by the National Natural Science Foundation Project(Grant No.52274015) （Grant No.52274015）

the National Science and Technology Major Project(Grant No.2025ZD1402205)。（Grant No.2025ZD1402205）

Petroleum Science

ISSN：1672-5107

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