西南石油大学学报（自然科学版）2025，Vol.47Issue(5)：39-48,10.DOI:10.11885/j.issn.1674-5086.2024.06.12.01
基于XGBoost算法的砂砾岩储层测井岩性识别
Lithology Identification in Glutenite Reservoir Based on the XGBoost Algorithm
王英伟 1赵军 2覃建华 3张景 4汪峻宇 2冯月丽1
作者信息
- 1. 中国石油新疆油田公司勘探开发研究院,新疆克拉玛依 834000
- 2. 西南石油大学地球科学与技术学院,四川成都 610500
- 3. 怀柔实验室新疆研究院,新疆乌鲁木齐 830000
- 4. 中国石油新疆油田公司玛湖勘探开发项目部,新疆 克拉玛依 834000
- 折叠
摘要
Abstract
In glutenite reservoirs,the complexity of logging responses due to rock granularity poses challenges for traditional lithology identification methods.With technological advancements,combining logging data with computer technology for lithology research has become a new trend.The optimized version of the gradient boosting decision tree,the XGBoost algo-rithm,is widely applied in lithology identification for its efficient and accurate prediction capabilities and excellent general-ization performance.This study uses the XGBoost algorithm to identify the lithology of glutenite reservoirs in the M block to improve identification accuracy.By analyzing the lithological characteristics and logging responses of the Baikouquan forma-tion reservoirs,four logging curves(GR,AC,DEN,RT)were selected as feature variables.A total of 468 sample data sets were divided into training and testing sets in 4:1 ratio,and the key parameters of XGBoost were optimized through cross-validation,determining the optimal values for iteration times,learning rate,and other model parameters.The experimental results show that the XGBoost algorithm performs well in lithology identification,achieving a final accuracy rate of 91.05％,an improvement in both accuracy and efficiency compared to the C4.5 decision tree algorithm.The study results demonstrate the effectiveness of the XGBoost algorithm in improving lithology identification accuracy,providing guidance for the exploration and development of glutenite reservoirs.关键词
XGBoost算法/砂砾岩储层/岩性识别/测井评价/百口泉组储层/玛湖凹陷Key words
XGBoost algorithm/glutenite reservoir/lithology identification/logging evaluation/Baikouquan Formation Reser-voir/Mahu Depression分类
石油、天然气工程引用本文复制引用
王英伟,赵军,覃建华,张景,汪峻宇,冯月丽..基于XGBoost算法的砂砾岩储层测井岩性识别[J].西南石油大学学报（自然科学版）,2025,47(5):39-48,10.
