埕北35块东营组低阻薄油层的智能识别及应用OA

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.