Journal of Health and Safety at Work

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Introduction: Nowadays, gas and oil account for 60 percent of world energy resources. Transporting crude oil and its products are accomplished through a number of ways among which pipelines are of the utmost significance. Considering the extent of pipelines in installation and residential areas and also high potential for damage, the safety of these pipes and application of risk management principles have undeniable importance. Bow-tie risk assessment method is one of the ways to determine safety level. The tool is a qualitative and semi-quantitative method the data of which are implemented by specialists and experts via conducting surveys.
 

 Method and material: In the present study, Bow-tie technique was employed having been combined with fuzzy logic in which Likret Scale was applied to quantify the qualitative (verbal) data to reduce the uncertainty of risk evaluation of Amaak No.10 gas pipeline and Bongestan No.12 petroleum pipeline. In this way, factors affecting the pipeline safety were at first, recognized through the checklists. Then, the risk evaluation of pipplines was conducted using the mentioned method.
 

Results: The results of the study showed that third party damages, initial defects in materials, and constructing pipeline with failure possibility of 0.0484 stood at the highest in terms of importance (equivalent to 12.32%) in destroying oil and gas pipelines. In addition, the toxic impacts and environmental damages with occurrence possibility of 0.00327 were the most striking consequences of gas and oil leakage based on event tree analysis.
 

Conclusion: Considering the recognized factors leading to destruction of pipelines and their most notable outcomes, instructions on how to control and reduce the potential consequences were suggested, with emphasis on the removal of the most probable causes.

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Introduction: Pipelines are widely used to transport large volumes of oil and gas over long distances. Risk assessment can help identify risk factors and create an appropriate action plan and strategy to reduce or eliminate them. The main goal of this research is to provide a method for assessing the risk of pipelines based on the Fuzzy Inference System (FIS), creating a systematic format that is expected to be a more effective, accurate, and reliable model for controlling risks related to oil and gas pipelines.
Material and Methods: In this article, fuzzy logic is used to model uncertainty and present a model for assessing pipeline risk. The Muhlbauer method, one of the most common risk assessment methods for oil and gas pipelines, has been employed to determine critical factors affecting the lines. This method has been implemented using the Mamdani algorithm and based on expert knowledge in the fuzzy logic toolbox of MATLAB software. To validate the results of the proposed model, data from the interphase pipelines of the fifth refinery of the South Pars Gas Field have been used as a study sample.
Results: The findings from the implementation of the model created in South Pars Phases 9-10 pipelines (on shore) show that the studied pipelines are divided into three parts (A, B, and C) based on indicators such as population density and equipment deployment. Part C of the pipeline has the highest risk, with third-party damage and design being the most important factors affecting it. Part B has the lowest level of risk and results in the fewest consequences for human accidents. It was also observed that corrosion is essential in increasing leakage and risk in all three pipeline parts.
Conclusion: To verify the developed model, the inter-phase shore pipe of phase 9-10 refinery in the South Pars Gas Field was considered as a case study. The findings indicate that the proposed method provides more accurate and reliable results than traditional methods. Factors such as improper operation, dispersion, receptors, leakage volume, and product risk, which are other factors affecting pipeline risk, were not considered in traditional methods. Therefore, the risk level of oil and gas pipelines can be calculated using this model as a comprehensive and intelligent tool.

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Material and method:  In this study, industrial accidents during gas pipeline excavation and piping operation were analyzed using Bowtie method. The Fuzzy DEMATEL method was used to determine relationships between accident root causes, and the Fuzzy AHP method was used to compare pairs of causes and determine their weight. Finally, Bowtie and DEMATEL outputs were mapped in Bayesian networks and important risk factors of accident were determined.
Introduction: Identifying and modeling the root causes of accidents can play an important role in preventing them. The purpose of this study is to identify and model the causes of gas pipeline excavation and piping operation accidents using Bayesian network (BN) and fuzzy DEMATEL.
Results: The most important risk factors for trench collapse accident were risk management (16% impact weight), competency assessment (14.2% impact weight), supervision (13.8% impact weight), work permit system (13.7% impact weight), compliance with requirements and guidelines (13.4% impact weight), training (11.4% impact weight), HSE system (9.5% impact weight) and contractor management (8%impact weight).
Conclusion: From the results obtained in this study, it can be said that the risk management and competency assessment with a higher weight percentage play a more important role in the occurrence of trench collapse accident. The results of this study can be used to prioritize corrective actions to prevent trench collapse accidents in gas pipeline excavation and piping operations.