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Automatic detection and classification of drill bit damage using deep learning and computer vision algorithms

Xiongwen Yang Xiao Feng Chris Cheng Jiaqing Yu Qing Zhang Zilong Gao Yang Liu Bo Chen

Natural Gas Industry B2025，Vol.12Issue(2)：P.195-206,12.

Natural Gas Industry B2025，Vol.12Issue(2)：P.195-206,12.DOI:10.1016/j.ngib.2025.03.004

Automatic detection and classification of drill bit damage using deep learning and computer vision algorithms

Xiongwen Yang ¹Xiao Feng ²Chris Cheng ¹Jiaqing Yu ¹Qing Zhang ³Zilong Gao ³Yang Liu ³Bo Chen³

作者信息

1. CNPC Houston Technology Research Center,Beijing,100028,China CNPC Engineering Technology R&D Company Limited,Beijing,102206,China National Engineering Research Center of Oil&Gas Drilling and Completion Technology,Beijing,102206,China
2. CNPC Engineering Technology R&D Company Limited,Beijing,102206,China National Engineering Research Center of Oil&Gas Drilling and Completion Technology,Beijing,102206,China
3. DataNova Analytics Incorporated,Houston,TX,77084,USA
折叠

摘要

关键词

IADC dull/PDC bit/Artificial intelligence/Deep learning/Damage assessment/Data augmentation

分类

能源科技

引用本文复制引用

Xiongwen Yang,Xiao Feng,Chris Cheng,Jiaqing Yu,Qing Zhang,Zilong Gao,Yang Liu,Bo Chen..Automatic detection and classification of drill bit damage using deep learning and computer vision algorithms[J].Natural Gas Industry B,2025,12(2):P.195-206,12.

基金项目

support of the CNPC International Collaborative Research Project(No.2022DQ0410)。（No.2022DQ0410）

Natural Gas Industry B

ISSN：2352-8540

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