随着页岩气开发周期逐渐增加,受管柱腐蚀、冲蚀等因素的影响,页岩气井井筒完整性问题愈发突显,严重影响页岩气井带液生产,抑制了气井的产能。针对该问题,利用现场监测数据,通过逻辑回归算法,建立了一套井筒完整性实时诊断模型,在此基础上采用长短时记忆神经网络算法提高技术方面准确性,最终实现井筒完整性的实时精准诊断。应用该方法实现试验区23口页岩气井油管穿孔断脱在线自动诊断,准确率达到100%,井筒完整性问题诊断效率提升96.7%,异常频率明显降低,因井筒完整性影响导致的产量降低得到有效控制,受井筒完整性影响的产量降低了78%。该研究为页岩气井井筒完整性的高效、快速识别以及气井生产监控、诊断、分析的智能化应用提供了参考。
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 perforation 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 analysis.
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