Dynamic risk assessment for underground gas storage facilities based on Bayesian network |
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| Institution: | 1. College of Ocean and Safety Engineering, China University of Petroleum-Beijing, China;2. Department of Mechanics and Electrics Engineering, Hainan University, China;1. School of Environmental and Chemical Engineering, Jiangsu Ocean University, Lianyungang, 222005, China;2. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, 430070, China;3. Jiangsu Institute of Marine Resources Development, Jiangsu Ocean University, Lianyungang, 222005, China;1. School of Safety Science & Engineering, Xi''an University of Science and Technology, 58, Yanta Mid. Rd., Xi''an, 710054, Shaanxi, PR China;2. Department of Safety, Health, And Environmental Engineering, National Yunlin University of Science and Technology, 123, University Rd., Sec. 3, Douliou, Yunlin 64002, Taiwan, ROC;1. Division 2.1 ‘‘Explosion Protection Gases and Dusts’’, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, D-12205, Berlin, Germany;2. Otto von Guericke University, Universitätsplatz 2, D-39106, Magdeburg, Germany;3. China Academy of Safety Science and Technology, Beijing, 100012, China;4. Physikalisch Technische Bundesanstalt (PTB), Bundesallee 100, 38116, Braunschweig, Germany;5. Department of Process Engineering & Applied Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada;1. International Center for Chemical Process Safety, Nanjing Tech University, Nanjing, 211816, China;2. College of Safety Science and Engineering, Nanjing Tech University, Nanjing, 211816, China;3. School of Environmental & Safety Engineering, Changzhou University, Changzhou, 213164, China;4. Department of Safety, Health, And Environmental Engineering, National Yunlin University of Science and Technology, 123, University Rd., Sec. 3, Douliou, Yunlin, 64002, Taiwan;1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China;2. China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China |
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| Abstract: | Loss of the underground gas storage process can have significant effects, and risk analysis is critical for maintaining the integrity of the underground gas storage process and reducing potential accidents. This paper focuses on the dynamic risk assessment method for the underground gas storage process. First, the underground gas storage process data is combined to create a database, and the fault tree of the underground gas storage facility is built by identifying the risk factors of the underground gas storage facility and mapping them into a Bayesian network. To eliminate the subjectivity in the process of determining the failure probability level of basic events, fuzzy numbers are introduced to determine the prior probability of the Bayesian network. Then, causal and diagnostic reasoning is performed on the Bayesian network to determine the failure level of the underground gas storage facilities. Based on the rate of change of prior and posterior probabilities, sensitivity and impact analysis are combined to determine the significant risk factors and possible failure paths. In addition, the time factor is introduced to build a dynamic Bayesian network to perform dynamic assessment and analysis of underground gas storage facilities. Finally, the dynamic risk assessment method is applied to underground gas storage facilities in depleted oil and gas reservoirs. A dynamic risk evaluation model for underground gas storage facilities is built to simulate and validate the dynamic risk evaluation method based on the Bayesian network. The results show that the proposed method has practical value for improving underground gas storage process safety. |
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| Keywords: | Underground gas storage process Underground facilities of gas storage Dynamic risk assessment Database Bayesian network |
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