Research Article Quantitative Risk Analysis Of Offshore Fire And .
Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015, Article ID 537362, 10 pageshttp://dx.doi.org/10.1155/2015/537362Research ArticleQuantitative Risk Analysis of Offshore Fire and ExplosionBased on the Analysis of Human and Organizational FactorsYan Fu Wang,1 Yu Lian Li,1 Biao Zhang,1 Pei Na Yan,1 and Li Zhang21Department of Safety Science and Engineering, China University of Petroleum, Qingdao 266580, ChinaJiangsu Academy of Safety Science and Technology, Nanjing 210042, China2Correspondence should be addressed to Yan Fu Wang; 66923203@qq.comReceived 12 May 2015; Accepted 30 June 2015Academic Editor: Xiaobo QuCopyright 2015 Yan Fu Wang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.A dynamic risk analysis model of offshore fire and explosion is proposed in this paper. It considers the effect of human andorganizational factors in a more explicit way than current traditional risk analysis methods. This paper begins with exploringthe recent advances on offshore fire and explosion risk analysis theories, followed by briefly introducing the research techniquesemployed in the proposed hybrid causal logic model which consists of event tree, fault tree, Bayesian network, and system dynamics.Thereafter, it proposes a quantitative risk analysis framework. At last, the applicability of this model to the offshore platform is alsodiscussed. It aims to provide guideline for risk analysis of offshore fire and explosion.1. IntroductionOver the past two decades, a number of serious accidentsincluding the Piper Alpha accident have attracted publicconcerns over offshore safety and reliability. Offshore platform is in a harsh environment due to highly concentratedequipment, a large amount of explosive substances, oil/gaspipelines, and flange leak sources. Hydrocarbon fires andexplosions are extremely hazardous on offshore platforms.The fire and explosion will not only result in significantcasualties and economic losses, but also cause serious pollution and damage to surrounding environment and coastalmarine ecosystems [1]. The high cost of offshore platforms,fire/explosion severity, and complexity of marine environment determine the necessity and difficulty of fire/explosionrisk analysis [2]. How to propose efficient safety measures toprevent the escalation of such accidents requires comprehensive knowledge of accident-related phenomena, as well as thetools of adequate risk analysis [3].Historical statistics show that the majority of offshore fireand explosion accidents are caused by human and organizational error (HOE) [4]. As a result, Norway and UK offshorelegislation and guidelines require that HOE analysis shouldbe included in quantitative risk analysis. However, the mainstream offshore fire/explosion risk analysis methods focusmore on probability of equipment and structures failure.Recently, some attempt to build human reliability analysismodels used in quantitative risk analysis chose to leverageexpert information due to lack of HOE data. There havebeen repeated calls to expand the technical basis of humanreliability analysis by systematically integrating informationfrom different domains. Thus refining and improving analyticmethods of human reliability analysis to more accuratelysimulate and quantify the impact of HOE have become amatter of urgency.This paper is organized as follows. Section 2 analyzes therecent advances on offshore fire and explosion risk assessment. Section 3 briefly introduces event tree, fault tree, Bayesian network (BN), and system dynamics. Then, in Section 4,a dynamic quantitative risk analysis framework is proposedand its applicability for the offshore platform is also discussed,followed by conclusions in Section 5.2. Recent Advances of Offshore Fire andExplosion Risk AnalysisQuantitative risk analysis involves four main steps: hazardidentification, consequence assessment, probability calculation, and finally risk quantification [5–8]. Three kinds of
2researches on offshore fire/explosion risk analysis are carriedout worldwide: the first one is using statistical methods topredict fire/explosion risk based on historical data [9]; thesecond one is carrying out risk analysis using commercialsoftware [10]; the third one is integrating new theory withtraditional risk assessment methods [11].In this section, a literature review is carried out to identifyexisting methods for risk assessment of offshore fire andexplosion.2.1. Consequence Analysis of Offshore Fire and Explosion.Some offshore fire/explosion risk assessment studies focuson consequences and impacts analysis: Krueger and Smithdemonstrate how a scenario-based approach of fire riskassessment can be effectively applied to calculate potentialimpacts of credible fire scenarios on the platform processequipment, structural members, and safety systems [12]. Pulaet al. considered fire consequence modeling as a suite ofsubmodels such as individual fire models, radiation model,overpressure model, smoke and toxicity models, and humanimpact models. This comprehensive suite of models wasthen revised and its performance is compared with theones used in a commercial software package for offshorerisk assessment [13, 14]. Suardin et al. adopted a grid-basedapproach for fire and explosion assessment to enable betterconsequence/impact modeling and enhanced on-site ignitionmodel. This approach features built-in calculations for jetand pool fire size estimation for gas/liquid releases and theability to perform quantitative risk analysis to specify thepersonnel and equipment risk [15]. The Joint Industry Projectcarries out a series of experimental tests to evaluate the loadcharacteristics of steel and concrete tubular members underjet fire to investigate the jet fire load characteristics [16]and to examine the effect of wind on the thermal diffusioncharacteristics of floating production storage and offloading[17]. Moreover, a modeling technique for computational fluiddynamics simulations of hydrocarbon explosions and fire isdeveloped [18]. These insights developed will be very usefulfor the fire engineering of floating production storage andoffloading topsides.2.2. Frequency Analysis of Offshore Fire and Explosion. Thereare also few offshore fire/explosion risk assessment studies focused on frequency and probability analysis: Ronzaet al. [19] analyzed the correlations of ignition probabilityand the mass flow rate by analyzing tens of thousands ofrecords of hydrocarbon spills by HMIRS (a database abouthazardous materials incident reporting system). Moosemiller[20] develops the algorithms based on a variety of design,operating, and environmental conditions to calculate probabilities of immediate ignition and delayed ignition resultingin explosion. On the other hand, Vinnem proposes failuremodels for hydrocarbon leaks during maintenance work inprocess plants on offshore petroleum installations based onseventy hydrocarbon leaks accidents [21]. Because majoraccidents are always of low frequency, data collected fromthem are not sufficient. As a result, it is very difficult to useconventional statistical methods to analyze them. In orderMathematical Problems in Engineeringto improve the situation, a methodology has been proposedbased on hierarchical Bayesian analysis and accident precursor data to do risk analysis of major accidents [22].Based on the aforementioned works on leak frequencyand ignition modeling, many researchers have proposed newmethodologies for frequency analysis of offshore fire andexplosion. The Joint Industry Project introduces a number ofprocedures [9] and reviews the state-of-the-art technologies[23] for quantitative assessment and management of fireand gas explosion risks in offshore installations focusing ondefining the frequency of fires and explosions in offshoreinstallations. Aside from the application of the fault tree,event tree, and BN [24–27], a methodology [28] based onBow-tie and real time predictive models is proposed toconduct dynamic risk assessment of the drilling operations.The application of the Bow-tie model is used to analyzethe potential accident scenarios, their causes, and the associated consequences. Real time predictive models for thefailure probabilities of key barriers are developed to conductdynamic risk assessment of the drilling operations. Vinnemet al. [29] proposed the Risk OMT model combining the traditional quantitative risk analysis models and HOE analysisthrough Risk Influence Factor model, for quantitative riskanalysis of platform specific hydrocarbon release frequency.The risk of fire and explosion in floating production storageand offloading is quantitatively assessed by Dan et al., inwhich the consequence is simulated by PHAST and the frequency is analyzed based on the analysis of historical datausing event tree model [30].The current generation of quantitative risk analysis doesnot include the quantitative impacts of HOE on the safetyperformance of equipment and personnel. There are a number of technical challenges in developing an offshore fire andexplosion risk analysis model based on the effect analysis ofHOE.2.3. Effect Analysis of Human and Organizational Error to Offshore Fire and Explosion Risk. With the extension of humanreliability research field from human-machine systems tohuman inherent factors (psychology, emotion, and behavior),increasing attention has been paid to the relationship betweenHOE and offshore fire/explosion risk. The relevant referencesare shown as follows: an extensive survey [31, 32] is followedto identify the relationship between HOE and hydrocarbonleaks on Norwegian oil and gas producing platforms. Theresults from regression analysis on survey data show that thepsychosocial risk indicator significantly impacts frequency ofhydrocarbon leaks.Although it is generally acknowledged that HOE has asignificant impact on the fire and explosion risk, it is difficultto quantify how the HOE affects the fire and explosionrisk due to scarcity of HOE data and uncertainty of HOE.In order to deal with the problem, Musharraf et al. [33]addressed the issue of handling uncertainty associated withexpert judgments with evidence theory and described thedependency among the human factors and associated actionsusing BN. They also present a virtual experimental technique
Mathematical Problems in Engineering3Event treesFault tree1Fault tree2Fault treenFault treesBayesiannetworkSystemdynamicFigure 1: The proposed hybrid framework.for data collection for BN to human reliability analysis. Grothet al. [34] proposed the use of BN to formally incorporatesimulator data into estimation of human error probabilities.3. Quantitative Risk Analysis of OffshoreFire and Explosion Based on Human andOrganizational ErrorsIn this section, a dynamic quantitative risk analysis model ofoffshore fire and explosion is built by incorporating the effectof HOE.3.1. Incorporating Human and Organizational Factors intoQuantitative Risk Analysis. A method of applying BN inrisk analyses has been suggested in the hybrid causal logicframework and fully developed by letting BN be logically andprobabilistically integrated into fault tree. Thereafter, someparts of the risk analysis can be addressed in fault tree, whileothers are addressed in BN. Event tree/fault tree are oftenconsidered as the best option for technical aspects, whileHOEs in many cases fit better into a BN. By taking theadvantages of the three techniques, the result of combiningevent tree/fault tree and BN is normally a more detailed riskmodel with higher resolution.The above hybrid causal logic model can express the staticrelationships between logical variables. However, it cannotdeal with dynamic characteristics of process variables andhuman behavior. In order to quantify the dynamic influenceof HOEs on the total risk, system dynamics is combined withthe above hybrid causal logic model. The new framework isillustrated in Figure 1, which shows the link between systemdynamics, BN, event tree, and fault tree. The first interfacein this hybrid framework is the one between BN and eventtree/fault tree. System dynamics model describes dynamicdeterministic relations and provides a dynamic integrationamong the other two models. In the proposed framework,different models will have both inputs and outputs to thesystem dynamics model to allow the entire hybrid frameworkto capture feedback and delays.
4Mathematical Problems in EngineeringHOE and fault modeParameter learning andconstruction for BNCollection and statisticanalysis of HOE dataAnalysis frameworkof HOEParameter learning andconstruction for SDProbability of HOEBayesian network modelSystem dynamic modelEffect of HOERiskRisk analysis model of technicalsystem by integrating ET with FTDynamic risk analysis modelintegrating human, organizationalfactor with technical factorsFigure 2: Dynamic risk analysis model integrating human and organizational factors with technical factor.Based on Figure 1, a dynamic risk analysis model isproposed by integrating human and organizational factorswith technical factor as shown in Figure 2.Firstly, historical HOE data are collected and statisticallyanalyzed to build a HOE framework using the proposedhybrid causal logic. Parameter learning and construction ofBN and system dynamics model are researched separately tobuild a more refined HOE model. The interface of systemdynamics with BN, event tree, and fault tree can be capturedby importing and exporting the data from system dynamicsmodel. The target node calculated from BN is importedto system dynamics and processed inside system dynamics,and the estimated values from system dynamics can beexported to BN. The interaction effects of various factorscould be researched in this cyclic process. At last, a dynamicquantitative risk analysis model is built by integrating humanand organizational factors with technical factors.3.2. Quantitative Risk Analysis Model of Offshore Fire andExplosion Based on the Effect Analysis of HOEs. As shown inSection 3.1, the dynamic effects of human, organizational, andtechnical factors to system risk are quantitatively simulatedby integrating system dynamics and BN with event treeand fault tree. Based on this, a quantitative risk analysismodel for offshore fire and explosion is discussed from bothconsequence and probability perspective in this section andis illustrated in Figure 3.3.2.1. Consequence Analysis of Offshore Fire and Explosion.Firstly, a set of hydrocarbon release scenarios, including theposition, the size of latent leak source, and ignition source,are defined by hazard identification study using FMEAmethod. The purpose is to identify and describe the scenariosthat may lead to fire/explosion of offshore platform. Themost credible fire/explosion scenarios are fixed via LatinHypercube sampling methods.Every scenario is simulated using FLACS to characterizethe fire/explosion load profiles according to temperature andheat amount. The structure consequences of fire/explosionare simulated via nonlinear structural analysis. The personnelconsequences of fire/explosion are analyzed considering theexposed individuals and the fire/explosion load of everyaccident scenario. For example, potential loss of life (PLL) canbe calculated by the following equation:𝑁 𝐽PLL 𝐹𝑛𝑗 𝐶𝑛𝑗 ,𝑛 𝑗(1)where 𝐹𝑛𝑗 annual frequency of scenario 𝑛 with personnelconsequence 𝑗, 𝐶𝑛𝑗 the expected number of fatalities forscenario 𝑛 with personnel consequence 𝑗, 𝑁 the totalnumber of scenarios, and 𝐽 the total number of the consequence types, including immediate fatalities, escape fatalities, and rescue fatalities.3.2.2. Probability Analysis of Offshore Fire and Explosion.Historical data about leak, fire, and explosion of offshoreplatform are collected and statistically analyzed. Based onthis, the fire and explosion frequency model is constructedby integrating event tree/fault tree and BN with systemdynamics: the failures of safety barriers preventing offshorefire and explosion are analyzed using event tree/fault treeto develop a risk analysis model for technical systems; thedynamic effects of HOE on leak frequency and ignitionprobability are quantitatively simulated by integrating systemdynamics with BN. The system dynamics module depicts
Mathematical Problems in Engineering5Hazard identification foroffshore fire and explosionCredible fire/explosionscenarios are defined usingLatin Hypercube samplingCollection and statistic analysisof historical data on offshorefire and explosionSimulate each offshorefire/explosion scenario usingCFD softwareLeak frequency model is built byintegrating ET, FT, and BN with SDFire/explosion loadcharacteristic (temperature,heat radiation, and blast pressure)Design fire/explosion loadusing probabilistic andstatistical methodsCollection andanalysis of HOE dataHOE modelIgnition probability model isbuilt by integrating ET, FT, and BNwith SDOffshore fire/explosionprobability prediction modelconsidering the effect of HOENonlinear structuralconsequence analysis usingfinite element technologyRisk probability consequenceNoRisk ALARPStopYesDesign risk control measuresfrom the roots of HOECost-Benefit analysis of riskcontrol measuresQuantitative risk analysis modelof offshore fire/explosionFigure 3: Quantitative risk analysis model of offshore fire and explosion.dynamic deterministic relations in the proposed hybridcausal logic model and provides a dynamic integration amongevent tree/fault tree and BN model.Review and analysis of historical documents are performed to get the offshore industry average probabilities/frequencies of human, organizational, and technical factor,which are assigned to the initiating events and the basicevents in the hybrid causal logic model and carry out a quantitative analysis of the offshore fire and explosion frequency byusing these data. The results of this calculation may to somedegree reflect offshore platform specific conditions since specific data should be applied when possible. Offshore platformdata may be found in, for example, incident databases, logdata, and maintenance databases. In practice, extensive useof industry average data is necessary to be able to carryout the quantitative analysis. Several databases are availablepresenting offshore industry average data like OREDA forequipment reliability data and THERP and CORE-DATA forHOE data. In some cases, neither offshore platform specificdata nor generic data may be found, and it may be necessaryto use expert judgment to assign probabilities.3.2.3. Risk Analysis of Offshore Fire and Explosion. As shownin Figure 3, the fire and explosion risk of offshore platformcan be calculated as the product of frequency and consequences. If the calculated risk level is greater than the acceptable risk level, then the risk control options should be adoptedfrom the root of human and organizational factors. Theadopted risk control options should be evaluated using CostBenefit analysis method to get the most beneficial measuresto reduce the fire/explosion risk of offshore platform.“As low as reasonably practicable” principle is definedin terms of exceeding damage probability for main safetyfunctions or probability of accident escalation. The “as low
6Mathematical Problems in EngineeringJet fireT0 IgnitionJet releaseM1M2 Upstream X6X7X8Leak from compressorM5 Release from pumpM3 Release fromupstreampipelineRelease fromupstreampipeline jointsX5X0DownstreamreleaseM4 Casing ofcompressorReleasefrom sealReleasefromimpellerCompressorfailedJunction ofpumppipelineX4X3X2X1X9M6 Releasefrom rotorPump failedto operateX10Release fromdownstreampipelineReleasefrom casingRelease fromdownstreampipeline jointsX12X11X13X14Figure 4: Fault tree of jet fire.compressor failedokay 93.0faulty 7.00release from impel.okayfaulty90.010.0release from compres.release from sealokay 88.0faulty 12.0release from juncti.95.05.00okayfaultyokayfaulty99.01.00release from rotorokay 94.0faulty 6.0085.015.0release from casi.okay 80.0faulty 20.0release from pumptrue 36.7false 63.3release from compres.truefalsepump failed to operateokayfaulty30.070.0downstream pipeli.okayfaultydownstream pipeline joi.99.30.65okayfaulty91.09.00upstream pipelineokayfaultyrelease from .454.6upstream pipeline joi.okayfaulty95.54.50release from upstre.true4.79false95.2jet releasetrue99.70.30jet fireignitiontruefalseexternal heatokayfaulty80.020.073.426.6electric sparkokayfaulty75.025.0explosion energyokay 85.0faulty 15.0Figure 5: Bayesian network of jet fire.as reasonably practicable” principle in conjunction with theresults of quantitative risk analysis can be used to manageand reduce the risks to the lowest level that is reasonablypracticable.4. Case Study for Fire ProbabilityAnalysis on Offshore PlatformIn this section, the proposed model in Section 3.1 is demonstrated on a simple case study of jet fire on offshore platform.The detailed scenarios are analyzed using fault tree as shownin Figure 4. The probability data of basic events were collectedfrom Offshore Reliability Data Handbook [35], World-WideOffshore Accident Databases, and reports by HSE [2].The basic events, their important degree of probability,and failure probability are shown in Table 1.The probability of jet fire is 0.505938 according to thecalculation results of fault tree. From the calculation forimportance degree of probability, it can be seen that theeffects of X6, X7, and X8 on the probability of jet fire are largerthan other basic events. The fault tree shown in Figure 4 canbe converted into BN, which is drawn in Figure 5.
Mathematical Problems in Engineering7Table 1: The failure probability and basic events of fault tree.X0X1X2X3X4X5X6X7X8X9X10X11X12X13X14Basic eventRelease from joints of upstream pipelineCompressor completely failed causing releaseRelease from impellerRelease from sealRelease from casing of compressorRelease from upstream pipelineIgnition due to explosion energyIgnition due to external heat from surroundingIgnition due to electric sparkRelease from junction of pump and pipelineRelease from rotorPump failed to operate causing releaseRelease from casingRelease from downstream pipelineRelease from joints of downstream pipelineFailure 10.060.150.20.00650.09Importance degree of 50.008650.00865Table 2: Conditional probability and mutual information of basic event.Basic eventRelease from joints of upstream pipelineCompressor completely failedRelease from impellerRelease from sealRelease from compressorRelease from upstream pipelineIgnition due to explosion energyIgnition due to external heatIgnition due to electric sparkRelease from junction of pumpRelease from rotorPump failed to operateRelease from casingRelease from downstream pipelineLeak from joints of downstream pipelinePrior probability(%)Posteriorprobability 67.772𝑒 0.0063152.175𝑒 0050.002478From BN inference shown in Figure 5, it can be seenthat the occurrence probability of jet fire is computed as0.0917 per year. The prior probability, posterior probability,and mutual information (entropy reduction) of each basicevent are compared, which is shown in Table 2.From Table 2, it can be concluded that “external heat,”“explosion energy,” and “electric spark” have the larger contribution to the occurrence of jet fire. The BN shown inFigure 5 is extended by incorporating the effect of human andorganizational factors as shown in Figure 6.The prior probability, posterior probability, and mutualinformation (entropy reduction) of each human and organization factor are compared as shown in Table 3.From Table 3, it can be seen that human factor “notcomply with instruction” and organizational factor “inefficient emergency plan” have the largest contribution to theoccurrence of the eventual fire accident. This analysis showsthat particular attention should be paid to “comply withinstruction” and “efficient emergency plan.”Dynamic quantitative risk analysis is carried out by integrating human and organizational factors with technicalfactors (see Figure 7).The simulation results of Figure 7 are shown in Figure 8.Figure 8 displays the probability trend that could betraced in jet fire, ignition, and jet release in a period of 10years. The advantage of the dynamic model over those of
8Mathematical Problems in Engineeringcompressor failedokay 93.0faulty 7.00release from impel.okayfaulty90.010.0release from sealrelease from compres.okay 88.0faulty 12.0okayfaultyrelease from juncti.95.05.00pump failed to operaterelease from rotor99.01.00okayfaultyokay 94.0faulty 6.0085.015.0okayfaultyrelease from pumptrue 36.7false 63.3release from compressortrue30.0false70.0downstream pipeli.downstream pipeline joi.99.30.65okayfaulty91.09.00okayfaultyupstream pipelineokayfaultyrelease from downstre.true9.59false90.4no check rulesyesnorelease from casi.okay 80.0faulty 20.05.0095.099.70.30yesnodeficient trainingyes 5.00no 95.023.676.4jet releaseinefficient timely controlyes29.2no70.8not comply with instructi.yes20.0no80.0external heattrue 30.3false 69.7truefalse15.284.8truefalsejet ment agingyes27.5no72.5adopt unsuitable equipmentyes25.7no74.3not obey standar.yesnoimproper safety organizat.yes10.0no90.015.085.0explosion energytrue36.9false 63.1electric sparktruefalse95.54.50release from upstreamtrue4.79false95.2deficient checkinefficient emergency planyes8.00no92.0upstream pipeline joi.okayfaultywrong risk assess.deficient maintenanceyesno8.0092.0yesno25.075.0Figure 6: Extended Bayesian network considering human and organizational factors.Release from sealCompressorfailedRelease from casingof compressorRelease fromimpellerRelease from Release fromjunctionrotorPump failed tooperateRelease fromcasingDownstream Downstreampipeline pipeline jointsRelease fromRelease from Upstream pipeline Upstream pipelinecompressor Downstream pumpjointsreleaseDeficient checkUpstreamreleaseNo check rulesInefficientemergency planInefficient timelycontrolDeficient trainingJet releaseJet fireNot complying withinstrumentIgnitionTechnician errorprobabilityExternal heatElectric sparkAdopt unsuitableequipmentGTTExplosion energyEquipment agingEPC experienceEPC moralEPC timepressureNot obeyingstandardsImproper safetyorganizationDeficientmaintenanceWrong riskassessmentFigure 7: Dynamic risk analysis of jet fire using system dynamics.
Mathematical Problems in Engineering9Table 3: Conditional probability and mutual information of human and organization factor.Posterior probability 250.0150.00750Prior probability (%)52085101582502.55Time (year)Jet releaseJet fireBasic eventno check rulesnot comply with instructioninefficient emergency plandeficient trainingimproper safety organizationnot obey standardsdeficient maintenancewrong risk 250.550.4750.45Time (year)02.57.55Time (year)Mutual 0.00028710.00007020.00010547.51010Figure 8: Jet fire probability varies with time during a period of ten years.static models is that it can show the dynamic probability variation with time.5. ConclusionThe exploration of accidents in light of human error linkedto underlying factors related to the human and organizationwork has been established as a major priority. In order toimprove traditional risk assessment methods without considering HOE, new risk assessment methods need to beresearched further based on analyzing HOE. The purposeof this paper is to simulate the dynamic effect of HOE onoffshore fire/explosion risk, and its contributions can besummarized as follows:(1) A hybrid framework, including an integration of system dynamics, BN, event tree, and fault tree, is builtto analyze the dynamic offshore fire/explosion riskbased on the effect analysis of HOE. The proposedmodel is demonstrated on a simple case study of jetfire on offshore platform.(2) The hybrid framework integrates deterministic andprobabilistic modeling techniques, which can be usedto analyze the dynamic effects of HOEs on the risk ofcomplex social-technical system.(3) The quantitative risk analysis framework for offshorefire and explosion is discussed from both consequence and probability perspective.(4) The effective prevention measures to reduce the riskof offshore fire/explosion can be designed from theroot of HOE. This will provide guideline for riskmanagement of offshore platform.Conflict of InterestsThe authors declare that there is no conflict of interestsregarding the publication of this paper.AcknowledgmentsThis paper is supported by the National Natural ScienceFoundation of China (Grant no. 51409260), the FundamentalResearch Funds for the Central Universities (Grant no.14CX05035A), and Shandong Province Natural Science FundProject (Grant no. ZR2012EEM023).References[1] HSE, “Offshore accident,” IncidentOTO96.954, HSE, London, UK, 1996.StatisticsReports[2] HSE, Accident Statistics for Floating Offshore Units on the UKContinental Shelf (1980–2003), HSE, 2005.[3] J. K. Paik and J. Czujko, “Explosion and fire engineering and gasexplosion of FPSOs (phase I): hydrocarbon releases on FPSOs—review of HSE’s accident database,” Final Report No. EFEFJIP-02, Research Institute of Ship and Offshore Structural
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integrating human, organizational factor with technical factors Eect of HOE Risk Probability of HOE F : Dynamic risk analysis model integrating human and organizational factors with technical factor. Based on Figure , a dynamic risk analysis model is proposed by integrating human and organizational factors withtechnicalfactorasshown in Figure .
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