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dc.contributor.advisorMashuga, Chad
dc.contributor.advisorMannan, Sam M.
dc.creatorZeng, Ming
dc.date.accessioned2016-05-04T13:21:16Z
dc.date.available2018-12-01T07:21:28Z
dc.date.created2015-12
dc.date.issued2015-11-30
dc.date.submittedDecember 2015
dc.identifier.urihttps://hdl.handle.net/1969.1/156443
dc.description.abstractHuman factors (HFs) are important factors to the Macondo well blowout, but traditional risk assessment has not addressed them. Some common methodologies for offshore drilling risk assessment are fault tree, event tree and Bow-tie analysis, which are static structure and cannot consider common causes or conditional dependent factors. The Hybrid Causal Logic (HCL) model is a multi-layered, dynamic ever green model that can incorporate human factors. The HCL model enables the prediction of the probability of human errors and explains the reasons of human errors occurrence. This research applied the HCL model to offshore blowout risk assessment by using swabbing induced kick as a case study. The contribution of human factors to accidents in offshore industry has been identified based on literature review. They were categorized as individual factors, group factors and organization factors. The sub-heading human factors was considered as influencing factors in the HCL model. In the HCL model, an event tree was developed to display the links between kick and blowout. The safety barriers were identified as kick detection, kick control and shear ram. Basic events that could contribute to kick scenario, failure of kick detection, kick control and shear ram to seal the well were developed in fault trees. Then, the fault trees and event tree were mapped into Bayesian networks (BN). The human factors that could contribute to causal events in fault trees were also linked with BN. Objected-oriented BN was applied to link the fault trees models into a higher-level model with input and output nodes. This higher-level model was able to evaluate the impact of different HFs’ levels on the probability of kick and blowout. The most influencing factors could also be tracked in this model for risk control and mitigation. Based on the assumptions and structure of this model, competence, pressure, communication and management were identified as the most influencing factors for blowout escalating by swabbing induced kick. The blowout probability could be decreased four times if the competence level of an operator was increased from a low level to a high level.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectHuman Factorsen
dc.subjectBlowouten
dc.subjectRisk Assessmenten
dc.subjectBayesian Networken
dc.subjectHybrid Causal Logicen
dc.titleIntegration of Human Factors in Offshore Blowout Risk Assessmenten
dc.typeThesisen
thesis.degree.departmentChemical Engineeringen
thesis.degree.disciplineSafety Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelMastersen
dc.contributor.committeeMemberPeres, Camille S.
dc.type.materialtexten
dc.date.updated2016-05-04T13:21:16Z
local.embargo.terms2018-12-01
local.etdauthor.orcid0000-0002-0324-6685


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