基于ADASYN-IAO算法的改进Stacking集成模型在测井岩性识别中的应用OA

Well logging lithology identification is a critical step in oil and gas exploration and development.Due to the complex and diverse lithologies and strong heterogeneity of sedimentary strata,traditional identification me-thods struggle to achieve accurate and efficient lithology identification.Machine learning offers a new way to ad-dress this problem by rapidly modeling the nonlinear relationships between well-log response features and litho-logy.However,a single machine learning algorithm suffers from insufficient identification accuracy and poor sta-bility,while traditional ensemble methods still have considerable room for improvement in identification perfor-mance as they fail to fully account for the performance differences among base models.To address these issues,an improved Stacking ensemble model for lithology identification is constructed by combining the adaptive synthetic sampling(ADASYN)algorithm with the improved aquila optimizer(IAO)for synergistic optimization.First,the ADASYN algorithm is employed to process the original imbalanced sample data,generating a new balanced train-ing set to enhance the ability to identify minority lithology samples.Second,an improved Stacking ensemble model is built.By integrating multiple heterogeneous base models,dynamically assigning weights based on their accuracy,and using random forest(RF)as the meta-model to fuse their outputs,the improved Stacking ensemble model enhances classification performance and generalization ability.Finally,the IAO algorithm,which incorpo-rates a chaotic initialization strategy,a nonlinear convergence factor,and a dynamic weight adjustment mecha-nism,is introduced to simultaneously optimize the parameters of the base models.The results show that the overall identification accuracy of the proposed model reaches 89.64％,and its classification ability and generalization perfor-mance are significantly superior to those of other ensemble strategies and single identification models.The lithology identification results of two adjacent blind wells further verify the applicability and effectiveness of the model in ac-tual production.This method provides a new technical approach for intelligent lithology identification.