特种油气藏2026，Vol.33Issue(1)：154-159,6.DOI:10.3969/j.issn.1006-6535.2026.01.018
基于人工智能算法的页岩气井筒完整性实时诊断技术探索与应用
Exploration and application of real-time diagnostic technology for shale gas wellbore integrity based on artificial intelligence algorithms
摘要
Abstract
As the development period of shale gas reservoirs extends,factors such as string corrosion and erosion have led to increasingly serious wellbore integrity problems in shale gas wells,which seriously affects the liquid unloading process and suppresses gas well productivity.To address this issue,a real-time wellbore integrity diagnostic model was established using a logistic regression algorithm and field monitoring data.On this basis,a long short-term memory(LSTM)neural network algorithm was applied to improve diagnostic accuracy,ultimately achieving real-time,precise diagnosis of wellbore integrity.Applying this method enabled automatic and effective online diagnosis of tubing perfo-ration and break-off in 23 shale gas wells in the test area,with an accuracy of 100%.The diagnostic efficiency for wellbore integrity issues was improved by 96.7%,the frequency of anomalies was significantly reduced,and production declines caused by wellbore integrity issues were effectively controlled,with the production loss attributable to wellbore integrity problems reduced by 78%.This study provides reference for the efficient and rapid identification of wellbore integrity issues in shale gas wells and for the intelligent application of gas well production monitoring,diagnosis,and a-nalysis.关键词
井筒完整性/实时诊断/深度学习/逻辑回归算法/页岩气Key words
wellbore integrity/real-time diagnosis/deep learning/logistic regression algorithm/shale gas分类
能源科技引用本文复制引用
陈学忠,李鹴,陈满,朱昆,陈超,高上钧,彭远进,刘志恒..基于人工智能算法的页岩气井筒完整性实时诊断技术探索与应用[J].特种油气藏,2026,33(1):154-159,6.基金项目
中国石油科技专项"页岩气规模增储上产与勘探开发技术研究"(2023ZZ21) （2023ZZ21）
