Network based approach for predictive accident modelling

Baksh, Al-Amin; Khan, Faisal; Gadag, Veeresh; Ferdous, Refaul

doi:10.1016/j.ssci.2015.08.003

Cited by 49 publications

(20 citation statements)

References 15 publications

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“…Because the oilfield development has the basic characteristics of high risk and high investment as well as complicated process, it is very important to study oilfield development risk forecasting and early warning in order to keep the oil production safe and reduce decision-making mistakes to be caused by risk. Up to now, many scholars have studied the problems on safety related risk and risk-based decisionmaking [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For example, Khan et al summarized and reviewed methods and models on process safety and risk management in recent years; moreover, they figured out their present research trends and future research direction which includes dynamic risk assessment and management as well as advanced consequence modeling [1].…”

Section: Introductionmentioning

confidence: 99%

“…Khakzad et al developed a methodology based on event tree and hierarchical Bayesian analysis to establish informative distributions for offshore blowouts using data of near accidents and implemented the methodology in a Markov Chain Monte Carlo framework and applied it to risk assessment of offshore blowouts in the Gulf of Mexico [12]. Baksh et al improved the shortcomings of the existing accident model to propose predictive accident modeling methodologies through mitigating restrictive sequential progression assumption and using BN approach [13]. Yang et al performed a similar analysis as Kalantarnia et al (2009) [14] to reduce the uncertainty of fault tree calculation using Bayes' theorem [15].…”

Section: Introductionmentioning

confidence: 99%

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The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

Zhong

Liu

Lin

et al. 2016

Mathematical Problems in Engineering

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Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

Zhong

Liu

Lin

et al. 2016

Mathematical Problems in Engineering

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“… developed a dynamic risk management tool based on BBNs for the hydrocarbon industry to observe risk in real‐time by Baksh et al . have shown non‐sequential network by implementing BN to calculate end events probabilities using case studies from offshore process facility. Recently, Yeo et al .…”

Section: Brief Review Of Risk Assessment Methodsologiesmentioning

confidence: 99%

“…It is therefore essential to consider potential consequences as a leak or release event may lead to multiple consequences. A review of past accidents and models demonstrates the need to evaluate the entire accident sequence to mitigate the impact, develop appropriate response methods, and prevent accidents by designing safety into the system.…”

Section: Introductionmentioning

confidence: 99%

A network based approach to envisage potential accidents in offshore process facilities

Baksh

Garaniya

Khan

2016

Process Safety Progress

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Envisaging potential accidents in large scale offshore process facilities such as Floating Liquefied Natural Gas (FLNG) is complex and could be best characterized through evolving scenarios. In the present work, a new methodology is developed to incorporate evolving scenarios in a single model and predicts the likelihood of accident. The methodology comprises; (a) evolving scenario identification, (b) accident consequence framework development, (c) accident scenario likelihood estimation, and (d) ranking of the scenarios. Resulting events in the present work are modeled using a Bayesian network approach, which represents accident scenarios as causeconsequences networks. The methodology developed in this article is compared with case studies of ammonia and Liquefied Natural Gas from chemical and offshore process facility, respectively. The proposed method is able to differentiate the consequence of specific events and predict probabilities for such events along with continual updating of consequence probabilities of fire and explosion scenarios taking into account. The developed methodology can be used to envisage evolving scenarios that occur in the offshore oil and gas process industry; however, with further modification it can be applied to different sections of marine industry to predict the likelihood of such accidents.

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“…From the perspective of the occurrence probability of the causes of dangerous transportation accidents, Kazantzi has clarified the impact of the occurrence probability of different risk events made on the transport cost (Kazantzi et al, 2011);Bayesian et al have applied the Bayesian method to analyze the occurrence probability of the accidents (Schubert et al, 2012;Yang et al, 2015;Adedigba et al, 2016;Shishkina, 2015;Qaziet al, 2015); Baksh has employed the Bayesian method to calculate the posterior probability of the occurrence of adverse events (Baksh et al, 2015); Khakzad has used approximate reasoning of the major accidents as precursor data to predict the occurrence probability of the major accidents (Khakzad et al, 2015).…”

Section: In Terms Of the Relationship Between Indicatoridentificationmentioning

confidence: 99%

A Study on the Management Factors of Transportation Accidents of Dangerous Chemicals: from the Perspective of Bayesian Network

He¹

2016

Revista de la Facultad de Ingeniería

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Management directly impactsthe occurrence of the direct factors of the accidents.Unfulfilled management is the incentive of transportation accidents of dangerous chemicals. In order to investigate the influence path and the degree of the management factors in the accident cause system, with the help of the structural equation model, a hypothesis relationshipbetween the management factors and the influence of direct accident factors is constructed, analyzed and verified. Furthermore, via the Bayesian network model, the cause system is intuitively described, the posterior probability of each management factor under each state of the accidents is calculated, and suggestions are provided to reduce the accident rates and to strengthen the safety production management.

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Network based approach for predictive accident modelling

Cited by 49 publications

References 15 publications

The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

A network based approach to envisage potential accidents in offshore process facilities

A Study on the Management Factors of Transportation Accidents of Dangerous Chemicals: from the Perspective of Bayesian Network

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