Real-time risk assessment of a fracturing manifold system used for shale-gas well hydraulic fracturing activity based on a hybrid Bayesian network

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.