稠油作为全球能源结构中的关键非常规油气资源，其开发效率提升备受关注。蒸汽驱技术虽然提高了稠油开采效率，但汽窜现象对热效率和油藏开发产生不利影响。传统的预测方法在处理油田长时间序列时存在精度不足和响应迟缓的问题。基于胜利油田稠油区块13个蒸汽驱井组的数据，采用生产井与注汽井间瞬时温度比作为见汽时间的表征方法，并通过皮尔逊相关系数分析，精选出与汽窜时间相关的关键因素。在此基础上，构建基于Transformer架构的深度学习模型，成功实现了瞬时温度比的精确预测，并与实地观测数据高度吻合，展现了比传统长短期记忆网络（long short-term memory, LSTM）LSTM模型更高的预测精度与稳定性。研究成果不仅为稠油油藏汽窜时间的精确预测提供了新的视角，也进一步验证了深度学习技术在油田开发领域应用的广泛潜力，为油田智能化管理和决策支持系统的构建提供支撑。

As a critical unconventional oil and gas resource within the global energy framework, heavy oil has garnered significant attention for its development efficiency. Although steam flooding technology has improved the efficiency of heavy oil production, the phenomenon of steam breakthrough negatively impacts thermal efficiency and reservoir development. Traditional prediction methods have shown inadequate precision and delayed response when dealing with long-term oilfield time series data. Data from 13 steam flooding well groups in the Shengli oilfield heavy oil block were utilized. An innovative approach was adopted, using the instantaneous temperature ratio between production and injection wells as an indicator of steam breakthrough time. Pearson correlation coefficient analysis was employed to select key factors related to steam breakthrough time. Based on these factors, a deep learning model built on the Transformer architecture was developed, achieving accurate predictions of the instantaneous temperature ratio. The predictions closely aligned with oilfield observation data, demonstrating higher prediction accuracy and stability compared to traditional long short-term memory(LSTM) models. The research results not only provide a new perspective for the precise prediction of steam breakthrough time in heavy oil reservoirs but also further validate the extensive potential of deep learning technology in oilfield development applications, supporting the construction of intelligent oilfield management and decision support systems.