新疆大学学报（自然科学版中英文）2026，Vol.43Issue(3)：257-268,12.DOI:10.13568/j.cnki.651094.651316.2025.06.18.0001
埕北35块东营组低阻薄油层的智能识别及应用
Intelligent Identification and Application of Low-Resistivity Thin Oil Layers of Dongying Formation in 35 Block of Chengbei
摘要
Abstract
Intelligent identification of low-resistivity thin oil layers is crucial for improving logging interpretation accuracy in complex reservoirs.In Dongying formation of Chengbei 35 block,Chengdao oilfield,the low-resistivity and thin interbed characteristics lead to ambiguous logging responses and minimal differences between productive and non-productive layers.This paper innovatively applies the Gradient Boosting Decision Tree(GBDT)model for intelligent identification of low-resistivity thin oil layers.By integrating logging curve characteristics,lithoelectric test results,production data,and reservoir physical properties,a logging feature set of low-resistivity layers is constructed through mathematical feature extraction.Key discrimination parameters are selected via a decision tree feature selection mechanism as input for the GBDT model,establish-ing an intelligent identification model for low-resistivity reservoirs.Combined with lithoelectric test data,the reservoir lower limit standards are determined.Application results show that the GBDT model achieves an identification accuracy of 89.5%,approximately 30%higher than the traditional logging numerical model,significantly reducing errors caused by manual inter-pretation and providing an intelligent solution for efficient exploration and development of low-resistivity thin oil layers.关键词
东营组/GBDT/决策树/智能识别/低阻油层Key words
Dongying formation/GBDT(Gradient Boosting Decision Tree)/decision tree/intelligent identification/low-resistivity oil layer分类
能源科技引用本文复制引用
李鸿蕊,韩长城,杨彬,陈杨,鲁新便,李淦,赵振宇,木妮热,陈婉君..埕北35块东营组低阻薄油层的智能识别及应用[J].新疆大学学报（自然科学版中英文）,2026,43(3):257-268,12.基金项目
新疆维吾尔自治区天山英才计划"准噶尔盆地西北缘油-铀同盆共生体系及铀矿富集机制研究"(2023TSYCCX0009). （2023TSYCCX0009）
