【摘要】 为了系统解决油气井套管异常损坏这一长期制约油气田安全高效开发的工程技术难题，并探索数智技术在套管完整性管理中的赋能路径，通过文献综述、现场调研并结合40余年跟踪研究，对套管磨损、缩径、错断、腐蚀等异常损坏现象的诱因机制与防治措施进行了归纳分析。研究结果表明：(1)套管损坏是地质因素（围岩蠕变、断层滑移、地层特性）与工程因素（钻井磨损、固井缺陷、射孔冲击、压裂扰动）及设计因素（井身结构、套管选材、套管制造）等复杂耦合的结果；(2)针对地质因素可通过优化井眼轨迹规避高风险层段、采用高钢级厚壁套管提升抗挤强度、改进水泥环封固质量实现均匀载荷传递；(3)针对工程因素需优化钻井参数控制狗腿度、应用抗磨技术降低套管内壁磨损、调控压裂注采参数抑制地层滑移；(4)针对设计因素需要通过优化井身结构、套管钢级及壁厚提高套管自身承载能力；(5)创新引入人工智能技术，构建基于CNN-LSTM的磨损预测、基于RGPSO-LightGBM的损伤程度评估、基于BPNN-Optuna-XGBoost的剩余强度预测等智能模型，突破样本不足与测井成本高的技术瓶颈。结论认为，套管防治需贯穿“设计-钻井-固井-完井-生产”全寿命周期，未来应发展基于多源数据融合的实时监测、智能诊断与风险预警一体化技术体系，推动套管安全管理从被动治理向主动防控转型。更多还原

【Abstract】 To systematically address the long-standing engineering challenge of abnormal casing damage in oil and gas wells and explore the empowerment path of digital-intelligent technologies in casing integrity management, a research methodology combining literature review, field investigations, and over 40 years of tracking studies was adopted to summarize the inducing mechanisms and prevention measures for abnormal casing damage phenomena such as wear, diameter reduction, shear failure, and corrosion. The results indicate that:(1) Casing damage results from the complex coupling of geological factors(surrounding rock creep, fault slip, formation properties) and engineering factors(drilling wear, cementing defects, perforation impact, fracturing disturbance), requiring a full-lifecycle prevention and control system;(2) For geological factors, high-risk layers can be avoided by optimizing well trajectory, collapse resistance can be enhanced using high-grade thick-walled casings, and uniform load transfer can be achieved by improving cement sheath sealing quality;(3) For engineering factors, it is necessary to optimize drilling parameters to control dogleg severity, apply anti-wear technologies to reduce inner wall wear, and regulate fracturing and injection-production parameters to suppress formation slip;(4) Artificial intelligence technologies were innovatively introduced, constructing intelligent models such as CNN-LSTM for wear prediction, RGPSO-LightGBM for damage degree evaluation, and BPNN-Optuna-XGBoost for residual strength prediction, breaking through the technical bottlenecks of insufficient samples and high logging costs. It is concluded that casing prevention and control must run through the entire "design-construction-production" process, and future efforts should develop an integrated technical system for real-time monitoring, intelligent diagnosis, and risk early warning based on multisource data fusion, promoting the transformation of casing safety management from passive treatment to active prevention and control.更多还原