为避免异常环空带压导致井筒失效，进而造成安全事故，准确预测环空带压值，在其超过控制值时提前采取预防和解决措施。提出自回归差分移动平均-长短期记忆模型（autoregressive integrated moving average-long short-term memory, ARIMA-LSTM），该模型针对实际环空带压时序数据和特征捕捉的数据集训练预测实例井的环空带压值，并与单一模型、循环神经网络（recurrent neural network, RNN）模型进行对比。研究结果显示：通过实际数据训练后，该模型在误差、拟合精度和整体性能上有着良好表现，可为提高环空带压值的预测精度和效率提供参考，对井筒完整性设计有一定帮助。

In order to avoid wellbore failure caused by abnormal annulus band pressure and resulting safety accidents, the annulus band pressure value is accurately predicted, and preventive measures are taken in advance when it exceeds the control value. An autoregressive integrated moving average-long short term memory (ARMI-LSTM) model was proposed. The model was trained to predict the annular band pressure of example wells based on actual annular band pressure time series data and feature capture data sets, and compared with a single model and recurrent neural network (RNN) model. The results show that the model has a good performance in error, fitting accuracy and overall performance after training with actual data, which can provide a reference for improving the prediction accuracy and efficiency of annular band pressure value, and is helpful to well integrity design.