Abstract A fracturing manifold system used in the shale-gas hydraulic fracturing process involves significant risks, which result from its unique and harsh working conditions, such as high pressures of up to 105 MPa and a large displacement, along with the continuous erosion and corrosion of high-speed solid particles. Considering the high- or low-frequency demand modes of the components and the effect of any deviation in the state indicators on the real-time risk of the fracturing manifold system, we propose a real-time risk assessment method based on a hybrid Bayesian network (HBN) to provide decision support for supervisors that will prevent accidents. The proposed approach can be utilised to calculate the probability of each potential consequence in real time. The built HBN model was quantified by using the historical failure-related data of various components, specific monitoring data of multiple state indicators and expert judgment. An extensive case study focused on the real-time risk of a real-world fracturing manifold system and demonstrated the practical application of the presented methodology. We show by application that the proposed model can improve the situational awareness among operators, which is an effective method to control and reduce risk.

中文翻译：

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基于混合贝叶斯网络的页岩气井水力压裂压裂管汇系统实时风险评估

摘要 页岩气水力压裂压裂管汇系统具有高达105 MPa的高压、大排量等独特而恶劣的工况条件，以及持续不断的冲刷腐蚀。高速固体颗粒。考虑组件的高频或低频需求模式以及状态指标的任何偏差对压裂管汇系统实时风险的影响，提出一种基于混合贝叶斯网络的实时风险评估方法(HBN) 为主管提供决策支持，以防止事故发生。所提出的方法可用于实时计算每个潜在后果的概率。利用各部件的历史故障相关数据、多个状态指标的具体监测数据和专家判断，对构建的HBN模型进行量化。一个广泛的案例研究侧重于现实世界压裂管汇系统的实时风险，并展示了所提出方法的实际应用。我们通过应用表明，所提出的模型可以提高操作员的态势感知能力，是控制和降低风险的有效方法。