石油钻采工艺2023，Vol.45Issue(5)：532-539,8.DOI:10.13639/j.odpt.202210038
基于梯度提升决策树算法的钻井工况识别方法
Drilling condition identification method based on gradient boosting decision tree
摘要
Abstract
Drilling condition identification is a crucial measure to ensure operational safety and improve drilling efficiency.Currently,drilling conditions are commonly determined based on manual empirical formulas and threshold methods,leading to issues such as large data volume,low identification accuracy and slow decision-making speed.In order to enhance the efficiency of drilling condition identification,two important features,namely well depth variation and drill bit variation,were established by integrating domain knowledge.In view of the instability of on-site data transmission,a mobile window method was employed to select the most suitable window,thereby improving data stability.Additionally,an intelligent drilling condition identification model was constructed based on multiple algorithms,which uses evaluation indicators for analysis and model selection.The research results show that the model based on LightGBM performs exceptionally well in drilling operation condition identification,achieving a high accuracy of 98.9%with a processing time as short as 4.6 seconds.This affirms the efficiency and reliability of the proposed method,providing crucial theoretical and technical support for the efficient identification of drilling conditions.关键词
钻井工况/智能识别/LightGBM/移动窗口/特征建立/领域知识Key words
drilling conditions/intelligent identification/LightGBM/mobile window/feature establishment/domain knowledge分类
能源科技引用本文复制引用
毛光黔,宋先知,丁燕,崔猛,刘雨龙,祝兆鹏..基于梯度提升决策树算法的钻井工况识别方法[J].石油钻采工艺,2023,45(5):532-539,8.基金项目
国家重点研发计划"变革性技术关键科学问题"子课题"复杂油气智能钻井理论与方法"(编号:2019YFA0708300) （编号:2019YFA0708300）
中国石油天然气集团配套课题"复杂地层智能化破岩机理与导向控制方法"(编号:2021DQ0503) （编号:2021DQ0503）
中国石油天然气集团基础前瞻性重大科技专项攻关课题"地面-井下多目标协同优化控制机制"(编号:2023ZZ0601). （编号:2023ZZ0601）
