Risk assessment on deepwater drilling well control based on dynamic Bayesian network
Deepwater drilling involves complex operations and equipment, so it is faced with various operational challenges including well control accidents. This paper proposes a dynamic risk assessment model for evaluating the safety of deepwater drilling operations. The dynamic risk assessment process inclu...
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| Veröffentlicht in: | Process safety and environmental protection 2021-05, Vol.149, p.643-654 |
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| creator | Liu, Zengkai Ma, Qiang Cai, Baoping Liu, Yonghong Zheng, Chao |
| description | Deepwater drilling involves complex operations and equipment, so it is faced with various operational challenges including well control accidents. This paper proposes a dynamic risk assessment model for evaluating the safety of deepwater drilling operations. The dynamic risk assessment process includes three key steps: constructing fault tree models to analyze risk factors leading to a blowout accident, developing dynamic Bayesian network model based on the constructed fault trees, and performing dynamic risk analysis to evaluate the safety of well control operation. The proposed model includes risk factors about kick cause, kick detection, shut-in operation and kill operation, which covers the full process of a blowout. The proposed model could analyze the risk of blowout more comprehensively and the influencing degree of these four phases could also be clarified. Besides, the modular modelling method could update the structure and parameters of the developed model easily if new factors or data are added. The results show kick cause has the greatest impact on blowout accidents, followed by shut-in operation, kill operation and kick detection. Mutual information analysis and uncertainty analysis is performed to investigate the effects of risk factors on blowout. Finally, some corresponding preventive measures for blowouts are proposed. |
| doi_str_mv | 10.1016/j.psep.2021.03.024 |
| format | Article |
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This paper proposes a dynamic risk assessment model for evaluating the safety of deepwater drilling operations. The dynamic risk assessment process includes three key steps: constructing fault tree models to analyze risk factors leading to a blowout accident, developing dynamic Bayesian network model based on the constructed fault trees, and performing dynamic risk analysis to evaluate the safety of well control operation. The proposed model includes risk factors about kick cause, kick detection, shut-in operation and kill operation, which covers the full process of a blowout. The proposed model could analyze the risk of blowout more comprehensively and the influencing degree of these four phases could also be clarified. Besides, the modular modelling method could update the structure and parameters of the developed model easily if new factors or data are added. The results show kick cause has the greatest impact on blowout accidents, followed by shut-in operation, kill operation and kick detection. Mutual information analysis and uncertainty analysis is performed to investigate the effects of risk factors on blowout. Finally, some corresponding preventive measures for blowouts are proposed.</description><identifier>ISSN: 0957-5820</identifier><identifier>EISSN: 1744-3598</identifier><identifier>DOI: 10.1016/j.psep.2021.03.024</identifier><language>eng</language><publisher>AMSTERDAM: Elsevier B.V</publisher><subject>Accidents ; Bayesian analysis ; Blowout ; Blowouts ; Control equipment ; Data analysis ; Deep sea drilling ; Deepwater drilling ; Deepwater well control ; Drilling ; Drilling machines (tools) ; Dynamic Bayesian network ; Engineering ; Engineering, Chemical ; Engineering, Environmental ; Evaluation ; Fault trees ; Information management ; Kick ; Mathematical models ; Modular structures ; Risk analysis ; Risk assessment ; Risk factors ; Safety ; Science & Technology ; Technology ; Uncertainty analysis ; Well engineering</subject><ispartof>Process safety and environmental protection, 2021-05, Vol.149, p.643-654</ispartof><rights>2021 Institution of Chemical Engineers</rights><rights>Copyright Elsevier Science Ltd. May 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>72</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000646150300014</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c328t-abec87a859a7e75c0fe57255c80167dd2687342d5b6fa46e961fe8a48e32f1653</citedby><cites>FETCH-LOGICAL-c328t-abec87a859a7e75c0fe57255c80167dd2687342d5b6fa46e961fe8a48e32f1653</cites><orcidid>0000-0001-6475-7481</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.psep.2021.03.024$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,39263,46000</link.rule.ids></links><search><creatorcontrib>Liu, Zengkai</creatorcontrib><creatorcontrib>Ma, Qiang</creatorcontrib><creatorcontrib>Cai, Baoping</creatorcontrib><creatorcontrib>Liu, Yonghong</creatorcontrib><creatorcontrib>Zheng, Chao</creatorcontrib><title>Risk assessment on deepwater drilling well control based on dynamic Bayesian network</title><title>Process safety and environmental protection</title><addtitle>PROCESS SAF ENVIRON</addtitle><description>Deepwater drilling involves complex operations and equipment, so it is faced with various operational challenges including well control accidents. This paper proposes a dynamic risk assessment model for evaluating the safety of deepwater drilling operations. The dynamic risk assessment process includes three key steps: constructing fault tree models to analyze risk factors leading to a blowout accident, developing dynamic Bayesian network model based on the constructed fault trees, and performing dynamic risk analysis to evaluate the safety of well control operation. The proposed model includes risk factors about kick cause, kick detection, shut-in operation and kill operation, which covers the full process of a blowout. The proposed model could analyze the risk of blowout more comprehensively and the influencing degree of these four phases could also be clarified. Besides, the modular modelling method could update the structure and parameters of the developed model easily if new factors or data are added. The results show kick cause has the greatest impact on blowout accidents, followed by shut-in operation, kill operation and kick detection. Mutual information analysis and uncertainty analysis is performed to investigate the effects of risk factors on blowout. Finally, some corresponding preventive measures for blowouts are proposed.</description><subject>Accidents</subject><subject>Bayesian analysis</subject><subject>Blowout</subject><subject>Blowouts</subject><subject>Control equipment</subject><subject>Data analysis</subject><subject>Deep sea drilling</subject><subject>Deepwater drilling</subject><subject>Deepwater well control</subject><subject>Drilling</subject><subject>Drilling machines (tools)</subject><subject>Dynamic Bayesian network</subject><subject>Engineering</subject><subject>Engineering, Chemical</subject><subject>Engineering, Environmental</subject><subject>Evaluation</subject><subject>Fault trees</subject><subject>Information management</subject><subject>Kick</subject><subject>Mathematical models</subject><subject>Modular structures</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>Safety</subject><subject>Science & Technology</subject><subject>Technology</subject><subject>Uncertainty analysis</subject><subject>Well engineering</subject><issn>0957-5820</issn><issn>1744-3598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkE1rGzEQQEVJoU6aP9CToMeyW31Lhlwak6SFQKEkZyFrZ4uctbSR5Br_-8h1yLH0pMt7M6OH0CdKekqo-rrp5wJzzwijPeE9YeIdWlAtRMfl0pyhBVlK3UnDyAd0XsqGEEKZpgv08CuUJ-xKgVK2ECtOEQ8A895VyHjIYZpC_I33ME3Yp1hzmvDaFRj-gofotsHja3eAElzEEeo-5aeP6P3opgKXr-8Fery9eVh97-5_3v1YfbvvPGemdm4N3mhn5NJp0NKTEaRmUnrTvqSHgSmjuWCDXKvRCQVLRUcwThjgbKRK8gv0-TR3zul5B6XaTdrl2FZaJrmSgnDFGsVOlM-plAyjnXPYunywlNhjPbuxx3r2WM8Sblu9JpmTtId1GosPED28iS2fEopKwo8hxSpUV0OKq7SLtalf_l9t9NWJhhbqT4BsX40hZPDVDin8684XwHuZmg</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Liu, Zengkai</creator><creator>Ma, Qiang</creator><creator>Cai, Baoping</creator><creator>Liu, Yonghong</creator><creator>Zheng, Chao</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Science Ltd</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TB</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-6475-7481</orcidid></search><sort><creationdate>202105</creationdate><title>Risk assessment on deepwater drilling well control based on dynamic Bayesian network</title><author>Liu, Zengkai ; Ma, Qiang ; Cai, Baoping ; Liu, Yonghong ; Zheng, Chao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-abec87a859a7e75c0fe57255c80167dd2687342d5b6fa46e961fe8a48e32f1653</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accidents</topic><topic>Bayesian analysis</topic><topic>Blowout</topic><topic>Blowouts</topic><topic>Control equipment</topic><topic>Data analysis</topic><topic>Deep sea drilling</topic><topic>Deepwater drilling</topic><topic>Deepwater well control</topic><topic>Drilling</topic><topic>Drilling machines (tools)</topic><topic>Dynamic Bayesian network</topic><topic>Engineering</topic><topic>Engineering, Chemical</topic><topic>Engineering, Environmental</topic><topic>Evaluation</topic><topic>Fault trees</topic><topic>Information management</topic><topic>Kick</topic><topic>Mathematical models</topic><topic>Modular structures</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Risk factors</topic><topic>Safety</topic><topic>Science & Technology</topic><topic>Technology</topic><topic>Uncertainty analysis</topic><topic>Well engineering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Zengkai</creatorcontrib><creatorcontrib>Ma, Qiang</creatorcontrib><creatorcontrib>Cai, Baoping</creatorcontrib><creatorcontrib>Liu, Yonghong</creatorcontrib><creatorcontrib>Zheng, Chao</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Process safety and environmental protection</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Zengkai</au><au>Ma, Qiang</au><au>Cai, Baoping</au><au>Liu, Yonghong</au><au>Zheng, Chao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Risk assessment on deepwater drilling well control based on dynamic Bayesian network</atitle><jtitle>Process safety and environmental protection</jtitle><stitle>PROCESS SAF ENVIRON</stitle><date>2021-05</date><risdate>2021</risdate><volume>149</volume><spage>643</spage><epage>654</epage><pages>643-654</pages><issn>0957-5820</issn><eissn>1744-3598</eissn><abstract>Deepwater drilling involves complex operations and equipment, so it is faced with various operational challenges including well control accidents. This paper proposes a dynamic risk assessment model for evaluating the safety of deepwater drilling operations. The dynamic risk assessment process includes three key steps: constructing fault tree models to analyze risk factors leading to a blowout accident, developing dynamic Bayesian network model based on the constructed fault trees, and performing dynamic risk analysis to evaluate the safety of well control operation. The proposed model includes risk factors about kick cause, kick detection, shut-in operation and kill operation, which covers the full process of a blowout. The proposed model could analyze the risk of blowout more comprehensively and the influencing degree of these four phases could also be clarified. Besides, the modular modelling method could update the structure and parameters of the developed model easily if new factors or data are added. The results show kick cause has the greatest impact on blowout accidents, followed by shut-in operation, kill operation and kick detection. Mutual information analysis and uncertainty analysis is performed to investigate the effects of risk factors on blowout. Finally, some corresponding preventive measures for blowouts are proposed.</abstract><cop>AMSTERDAM</cop><pub>Elsevier B.V</pub><doi>10.1016/j.psep.2021.03.024</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-6475-7481</orcidid></addata></record> |
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| subjects | Accidents Bayesian analysis Blowout Blowouts Control equipment Data analysis Deep sea drilling Deepwater drilling Deepwater well control Drilling Drilling machines (tools) Dynamic Bayesian network Engineering Engineering, Chemical Engineering, Environmental Evaluation Fault trees Information management Kick Mathematical models Modular structures Risk analysis Risk assessment Risk factors Safety Science & Technology Technology Uncertainty analysis Well engineering |
| title | Risk assessment on deepwater drilling well control based on dynamic Bayesian network |
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