广东石油化工学院学报2026，Vol.36Issue(3)：55-60,6.DOI:10.26962/j.cnki.1991.2026.0051
基于无监督学习的井漏风险层位预测方法
Prediction Method of Lost Circulation Risk Horizon Based on Unsupervised Learning
谭伟雄 1赵才顺 1李灿灿 1李文元1
作者信息
- 1. 中海油能源发展股份有限公司 工程技术分公司,天津 300452
- 折叠
摘要
Abstract
Lost circulation is a common engineering problem in drilling engineering,and particularly important to accurately predict the risk horizon of lost circulation before drilling for safe drilling.The prediction of lost circulation risk is based on the study of the matching relationship of seismic attributes.To reduce the dependence on the calibration results of lost circulation horizon attributes,the key parameters closely related to lost circulation events are extracted by unsupervised clustering algorithm.Aiming at the problem of anomaly detection in the case of unbalanced data processing,based on the support vector machine(one-class SVM for anomaly detection)algorithm,the lost circulation risk assessment model and lost circulation risk index are constructed to assess the lost circulation risk horizon of new drilling wells.The constructed lost circulation risk index corresponds to the probability of lost circulation in the predicted risk horizon,and the predicted lost circulation risk can be divided into four grades.The coincidence rate of the predicted results and the actual results can reach 85.8%.The results show that this method can solve the problem of abnormal detection under the condition of high dependence of the calibration results of lost circulation horizon attributes and unbalanced processing data,and provide a new technical idea for the prevention and treatment of lost circulation.关键词
裂缝预测/井漏/无监督学习/工程技术/地震属性Key words
fracture prediction/lost circulation/unsupervised learning/engineering technology/seismic attributes分类
天文与地球科学引用本文复制引用
谭伟雄,赵才顺,李灿灿,李文元..基于无监督学习的井漏风险层位预测方法[J].广东石油化工学院学报,2026,36(3):55-60,6.
