Extension of operating lifetime of aging subsea pipelines is of great interest to oil and gas sectors. Corrosion and fatigue are the main causations of the condition degradation of subsea pipelines. A dynamic probabilistic condition assessment model based on continuous dynamic Bayesian network (DBN) is developed to support the life extension decision-making of aging subsea pipeline subjected to the corrosion-fatigue degradation. The methodology is built based on equivalent initial flaw size (EIFS) concept and a time-dependent prediction model implemented using DBN. The complex corrosion-fatigue degradation process is simplified by EIFS concept, and the crack propagation due to corrosion-fatigue is modelled using fracture mechanics model and DBN. A limit state function (LSF) is used to express the failure condition of subsea pipeline due to crack propagation. The dynamic reliability of subsea pipeline is estimated using Monte Carlo (MC) method with the probability distributions of the predicted crack sizes at different time slices. The estimated reliability is compared with the acceptable threshold to decide whether any measures are required to extend the life of subsea pipeline. The methodology is tested by a case study, and it is observed that it can be a useful tool to support life extension decision-making of aging subsea pipelines.@en

University chemical laboratory is a high-risk place for teaching and scientific research due to the presence of various physical and chemical hazards. In recent years, university chemical laboratory accidents occur frequently. This urges the need to enhance university chemical lab safety. A semi-quantitative methodology comprising Matter-Element Extension Theory (MEET) implemented with Combination Ordered Weighted Averaging (C-OWA) operator is proposed to assess the risk of a university chemical laboratory. First, an index-based risk assessment system of university chemical laboratory is built by identifying various risk factors from a system perspective. Then, C-OWA operator is used to calculate the weight of assessment indices, whereas MEET is employed to determine the correlation degree of assessment indices. Finally, the comprehensive risk of university chemical laboratories is assessed, and some safety measures are proposed to reduce the risk of university chemical laboratories. The applicability of the proposed methodology is tested using a practical case. It is observed that the methodology can be a useful tool for risk assessment and management of university chemical laboratories.

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Urban gas pipelines usually have high structural vulnerability due to long service time. The locations across urban areas with high population density make the gas pipelines easily exposed to external activities. Recently, urban pipelines may also have been the target of terrorist attacks. Nevertheless, the intentional damage, i.e. terrorist attack, was seldom considered in previous risk analysis of urban gas pipelines. This work presents a dynamic risk analysis of external activities to urban gas pipelines, which integrates unintentional and intentional damage to pipelines in a unified framework. A Bayesian network mapping from the Bow-tie model is used to represent the evolution process of pipeline accidents initiating from intentional and unintentional hazards. The probabilities of basic events and safety barriers are estimated by adopting the Fuzzy set theory and hierarchical Bayesian analysis (HBA). The developed model enables assessment of the dynamic probabilities of consequences and identifies the most credible contributing factors to the risk, given observed evidence. It also captures both data and model uncertainties. Eventually, an industrial case is presented to illustrate the applicability and effectiveness of the developed methodology. It is observed that the proposed methodology helps to more accurately conduct risk assessment and management of urban natural gas pipelines.

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Ultra-deep oil and gas wells have become a new development trend in onshore oil and gas exploitation. However, Ultra-deep oil and gas wellbore casing is with high failure risk due to the harsh environment. It is essential to evaluate the reliability of wellbore casing. This paper assesses the operational reliability of wellbore casing using data statistics and numerical simulation. Firstly, the theoretical model for reliability analysis of wellbore casing is established, and the variables in the model are determined, including rock mechanics, cement ring, and casing string strength factors. Subsequently, considering the random distribution of model variables, many statistics and analyses are performed to determine the distribution parameters of the model variables. Eventually, Monte Carlo based numerical simulations are carried out to obtain the residual strength distribution and the reliability of wellbore casing. The production casing in the ultra-deep well with a depth of 6.5 km in China as an industrial case is used to illustrate the present study. It is observed that this study can be useful to guide a more accurate assessment of the reliability of ultra-deep wellbore casing.

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