Real-time natural gas explosion modeling of offshore platforms by using deep learning probability approach

Natural gas explosion of offshore platform is prone to cause accidental disaster such as platform collapse and casualties etc. Real-time natural gas explosion consequence reconstruction is essential to support a quick accidental emergency response planning to prevent the accidental escalation to disaster. The widely-used CFD is computationally intensive and thereby has a significant delay. Machine/deep learning-based models offer a potential real-time alternative, which however are not able to quantify the uncertainty of spatial overpressure prediction. This study aims to propose a hybrid deep learning probability model to real-time predict spatial explosion overpressure of offshore platform by using sparsely-observed overpressures. In this hybrid model, Variational Bayesian inference is incorporated into deep learning backbone. Both natural gas explosion experimental and numerical modeling of offshore platform are conducted to construct the benchmark dataset. By using this benchmark dataset, sensitivity analysis of Monte Carlo sampling number N, drop probability p on model's performance is also conducted. The results demonstrated our model exhibits high accuracy with R² = 0.955 and real-time capability with inference time of 2.9s. Compared to the state-of-the-art model, the additional uncertainty estimation improves the accuracy and robustness of spatial overpressure prediction, which contributes to the reliable explosion accidental emergency decision-making. Overall, this study provides a reliable alternative for constructing digital twin emergency management system to effectively manage natural gas explosion risk of offshore platforms.