Emergency risk analysis of subsea capping stack in blowout scenario integrating numerical simulation with ANN model

The subsea capping stack is crucial equipment for emergency rescue in a blowout scenario, which determines the success or failure of the emergency rescue. The secondary risk prevention of the blowout emergency continues to face a persistent challenge. This paper proposes a methodology framework for emergency risk of the subsea capping stack, which aims to realize the quantitative risk assessment of installation operation by integrating numerical simulation and an Artificial neural network (ANN) model. Herein, the numerical model of fluid impact for the capping stack is constructed, and the impact forces of the blowout fluid are analyzed. The calculation results indicate that the blowout pressure, water depth, and installation height significantly affect the impact force during emergency operations. Furthermore, a genetic algorithm optimizing the neural network (GA-BP) model for quickly predicting the blowout impact force is established to improve emergency response efficiency based on sufficient simulation data. The prediction model has a minor mean absolute percentage error (MAPE), and its accuracy is also verified compared with the SVM model. Besides, the developed model speeds up the processing time from hours to seconds compared to numerical simulation. Therefore, it can provide a decisionmaking basis for emergency engineers at the site of blowout accidents more efficiently.