ABSTRACT The phenomenon of annular pressure buildup (APB) is commonly observed in offshore gas wells, characterized by complex and coupled pressure types across multiple annuli. Field operations, including well shutdowns, pressure relief, and well interventions, further complicate the pressure dynamics, rendering traditional theoretical models incapable of accurately predicting annular pressure under the influence of these coupled conditions. Elevated annular pressure can lead to casing deformation and failure, posing significant risks to well integrity and production safety. This study addresses the challenge of predicting annular pressure under the coupling of multiple APB types by utilizing real‐time monitoring data reflecting the annular pressure state of production wells and incorporating the influence of field operations. A convolutional neural network (CNN) is employed to optimize feature extraction, and a bidirectional long short‐term memory (Bi‐LSTM) network is established based on the optimized CNN kernels to predict annular pressure under complex coupling conditions. A dynamic management chart for APB is developed by incorporating the dynamic variation of wellbore pressure. The results demonstrate that the prediction accuracy of the CNN‐optimized Bi‐LSTM model exceeds that of the standalone LSTM model, with a mean error of 4.03% when compared with field‐measured data. The inclusion of operational characteristic parameters enables the extraction of features related to human intervention, further improving the model's accuracy and reducing the mean error to 2.55%. The dynamic management chart, which incorporates the variation in wellbore pressure, provides effective guidance for field safety operations.
Liu et al. (2025) studied this question.