Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life

Chang, Yuanjiang; Wu, Xiangfei; Zhang, Changshuai; Chen, Guo-Ming; Liu, Xiuquan; Li, Jiayi; Cai, Baoping; Xu, Li

doi:10.1016/j.ress.2019.03.040

Cited by 83 publications

(36 citation statements)

References 18 publications

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“…3,28,[50][51][52] Another source of probability, as well as the failure and repair rates of equipment in the RRC system, is obtained from the literature, handbook, and failure database, such as OREDA, HSE, and OGP. 3,28,[50][51][52] Another source of probability, as well as the failure and repair rates of equipment in the RRC system, is obtained from the literature, handbook, and failure database, such as OREDA, HSE, and OGP.…”

Section: Dbn Model Developmentmentioning

confidence: 99%

“…Khakzad [20][21][22][23] and it therefore cannot be used to represent the system's temporal evolution as it is time-independent. Chang et al 28 proposed a DBN-based approach for dynamic fatigue failure risk analysis of the subsea wellhead during service life. [24][25][26] Cai et al 27 presented a quantitative reliability and availability evaluation method for subsea BOP system by translating fault tree (FT) into DBN.…”

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confidence: 99%

“…3,28,42,43 The objective of the present paper is to propose a DBNbased methodology to predict the dynamic risk of RRC failure over time during production test of marine hydrate. For riser recoil control during production test of hydrate, the objective data available to estimate probabilities of basic events are scarce, and there are some events which have unknown failure data, due to less frequency of occurrence.…”

mentioning

confidence: 99%

“…,28 By applying diagnostic analysis technique, the posterior probability of each basic event at different time slices can be…”

mentioning

confidence: 99%

“…[24][25][26] Cai et al 27 presented a quantitative reliability and availability evaluation method for subsea BOP system by translating fault tree (FT) into DBN. Chang et al 28 proposed a DBN-based approach for dynamic fatigue failure risk analysis of the subsea wellhead during service life.…”

mentioning

confidence: 99%

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Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Chang

Zhang

et al. 2019

Energy Science & Engineering

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The marine natural gas hydrate is considered to have huge resource potential in the South China Sea, and a sixty‐day production test has been successfully conducted in 2017. As the crucial and most vulnerable connection of floating platform and subsea wellhead, the risers are of paramount importance in safe production test of the hydrate. Due to harsh environment and the possible drive off and drift off events induced by failure of dynamic positioning system of the platform, the unplanned emergency disconnect of risers is an integral preventive measure to avoid accidents and secure the well. Once the disconnect occurs, the huge potential energy stored in the riser column will release and consequently cause violent axial recoil, which will cause serious consequences if the recoil control fails. This paper presented a quantitative risk assessment approach on the basis of dynamic Bayesian networks to dynamically predict the risk of riser recoil control failure during production test of hydrate. By using the proposed method, the dynamic failure risk of the riser recoil control was figured out, and the most contributory factors were identified. The practical application of the proposed methodology was demonstrated through a case study. Results indicated that the probability of riser recoil control failure can be significantly decreased within one year through equipment repair, and the failure and repair rates of shutoff valves and main air isolation valves are the most contributory factors. Some corresponding safety control measures were proposed to further mitigate the risk of riser recoil control failure during the production test of hydrate.

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Section: Dbn Model Developmentmentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

“…,28 By applying diagnostic analysis technique, the posterior probability of each basic event at different time slices can be…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Chang

Zhang

et al. 2019

Energy Science & Engineering

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A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Chong

Razak

Biak

et al. 2022

Process Safety Progress

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The new Fourth Industrial Revolution (IR 4.0) trend is driven by the concept of automation and artificial intelligence (AI). However, Malaysia is slightly behind Singapore in terms of adopting AI innovation among ASEAN countries. This paper aims to conduct a literature review of machine learning to overcome subjectivity and bias in risk ranking decision‐making. An introduction to machine learning concerning accident risk analysis is presented, and the challenges of its application in Malaysia are discussed. Existing machine learning features were evaluated to identify the feasible application in industrial accident analysis and ensure safety decision‐making consistency. This review observed how the IR 4.0 approaches were used in the risk analysis, especially on supervised machine learning. This study also highlights the finding from the previous works on challenges in utilizing supervised machine learning, which is the need to have publicly accessible large database from industries and agencies such as the Department of Occupational Safety and Health (DOSH) Malaysia for the development of algorithms, which can potentially improve accident risk analysis and safety, especially for Malaysian industries.

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Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery

Ashrafi

2020

Quality & Reliability Eng

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In this paper, risk modeling was conducted based on the defined risk elements of a conceptual risk framework. This model allows for the estimation of a variety of risks, including human error probability, operational risk, financial risk, technological risk, commercial risk, health risk, and social and environmental risks. Bayesian network (BN) structure learning techniques were used to determine the relationships among the model variables. By solving a bi‐objective optimization problem applying the genetic algorithm (GA) with the Pareto ranking approach, the network structure was learned. Then, risk modeling was performed for a petroleum refinery focusing on HydroDeSulfurization (HDS) technology throughout its life cycle. To extend the model horizontally and make it possible to evaluate the risk trend throughout the technology life cycle, we developed a dynamic Bayesian network (DBN) with three‐time slices. A two‐way forward and backward approach was used to analyze the model. The model validation was performed by applying the leave‐one‐out cross‐validation method.

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Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life

Cited by 83 publications

References 18 publications

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

Dynamic risk assessment approach of riser recoil control failure during production test of marine natural gas hydrate

A review on supervised machine learning for accident risk analysis: Challenges in Malaysia

Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery

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