An offshore platform is naturally vulnerable to accidents, such as the leakage of dangerous chemicals, fire and explosion. Oil and gas are explosive and all the equipment and pipes are squeezed into a limited area on a platform. Escape, Evacuation, and Rescue (EER) plans play a vital role as the last barrier to ensure the safety of personnel in the event of a major accident. As a result, the main contributors leading to evacuation failure need to be analyzed to prioritize technology development and select a robust EER strategy. This research aims to undertake the quantitative reliability analysis of various EER strategies on offshore platforms. First, a reliability prediction model of emergency evacuation is established for offshore platforms based on the K2 structure learning algorithm and a Bayesian network parameter learning method. The conditional probability table of each node is determined by combining the Bayesian estimation method and a junction tree reasoning engine. The reliability of emergency evacuation on a platform is predicted using a dynamic Bayesian network model. The transition probability is determined through a Markov method. The main factors leading to evacuation failure are investigated using the diagnostic reasoning method of Bayesian Network. The findings can provide insights for the development of cost effective EER strategies for an offshore platform.

离岸平台自然很容易发生事故，例如危险化学品的泄漏，火灾和爆炸。石油和天然气具有爆炸性，所有设备和管道都被挤压到平台上的有限区域内。逃生，疏散和救援（EER）计划在重大事故中作为确保人员安全的最后障碍发挥着至关重要的作用。因此，需要对导致疏散失败的主要因素进行分析，以优先考虑技术开发并选择可靠的EER策略。这项研究旨在进行海上平台各种EER策略的定量可靠性分析。首先，基于K2结构学习算法和贝叶斯网络参数学习方法，建立了海上平台应急疏散可靠性预测模型。通过结合贝叶斯估计方法和联合树推理引擎来确定每个节点的条件概率表。使用动态贝叶斯网络模型可预测平台上紧急疏散的可靠性。过渡概率通过马尔可夫方法确定。使用贝叶斯网络的诊断推理方法研究了导致疏散失败的主要因素。这些发现可以为开发具有成本效益的海上平台EER策略提供见解。使用贝叶斯网络的诊断推理方法研究了导致疏散失败的主要因素。这些发现可以为开发具有成本效益的海上平台EER策略提供见解。使用贝叶斯网络的诊断推理方法研究了导致疏散失败的主要因素。这些发现可以为开发具有成本效益的海上平台EER策略提供见解。