常州大学学报（自然科学版）2024，Vol.36Issue(2)：39-47,9.DOI:10.3969/j.issn.2095-0411.2024.02.005
基于异质集成的井漏预警模型
Lost circulation early warning model based on heterogeneous integration
摘要
Abstract
Drilling lost circulation accidents are characterized by abruptness and difficulty in control.Therefore,it is urgent to establish an effective lost circulation prediction.In this study,Stacking,a heterogeneous integrator combined with stochastic forest support vector machine and back propagation neural network model,was applied to Yingxi area of Qaidam Basin,Qinghai Province.Firstly,the data set of the target block is processed,and ten parameters with high correlation are selected by grey correlation,and then two layers of stacking integration are set up.The first layer selects random for-est,support vector machine and back propagation neural network model as the basic learning device,and the second layer selects logistic regression model as the meta-learning device.The results show that the heterogeneous ensemble model improves the prediction accuracy(0.981 accuracy,0.970 pre-cision,0.963 recall,and 0.960 F1 score)and overcomes the limitations of homogeneous classifiers.The importance of considering various geological factors in comprehensive lost circulation early warn-ing and prediction is emphasized.关键词
井漏/异质集成模型/随机森林/智能预警Key words
lost circulation/heterogeneous integrated model/random forest/intelligent early warning分类
能源科技引用本文复制引用
宫闻浩,李朝玮,李栋,邓嵩,徐明华,赵飞..基于异质集成的井漏预警模型[J].常州大学学报（自然科学版）,2024,36(2):39-47,9.基金项目
中国石油-常州大学创新联合体资助项目(2021DQ06) （2021DQ06）
江苏省高等学校基础科学(自然科学)研究面上项目(22KJD430001). （自然科学）
