Operational risk analysis of blowout scenario in offshore drilling operation
Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Kun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Direct visualisation of thrombi for diagnosis of tissue valve thrombosis - Karthikeyan, Ganesan ELSEVIER, 2018, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:149 ; year:2021 ; pages:422-431 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.psep.2020.11.010 |
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ELV053826353 |
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| 520 | |a Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. | ||
| 520 | |a Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. | ||
| 650 | 7 | |a Bayesian, probability updating |2 Elsevier | |
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10.1016/j.psep.2020.11.010 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001375.pica (DE-627)ELV053826353 (ELSEVIER)S0957-5820(20)31869-3 DE-627 ger DE-627 rakwb eng Chen, Kun verfasserin aut Operational risk analysis of blowout scenario in offshore drilling operation 2021transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model Elsevier Wei, Xin oth Li, Hui oth Lin, Hao oth Khan, Faisal oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:149 year:2021 pages:422-431 extent:10 https://doi.org/10.1016/j.psep.2020.11.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 149 2021 422-431 10 |
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10.1016/j.psep.2020.11.010 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001375.pica (DE-627)ELV053826353 (ELSEVIER)S0957-5820(20)31869-3 DE-627 ger DE-627 rakwb eng Chen, Kun verfasserin aut Operational risk analysis of blowout scenario in offshore drilling operation 2021transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model Elsevier Wei, Xin oth Li, Hui oth Lin, Hao oth Khan, Faisal oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:149 year:2021 pages:422-431 extent:10 https://doi.org/10.1016/j.psep.2020.11.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 149 2021 422-431 10 |
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10.1016/j.psep.2020.11.010 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001375.pica (DE-627)ELV053826353 (ELSEVIER)S0957-5820(20)31869-3 DE-627 ger DE-627 rakwb eng Chen, Kun verfasserin aut Operational risk analysis of blowout scenario in offshore drilling operation 2021transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model Elsevier Wei, Xin oth Li, Hui oth Lin, Hao oth Khan, Faisal oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:149 year:2021 pages:422-431 extent:10 https://doi.org/10.1016/j.psep.2020.11.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 149 2021 422-431 10 |
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10.1016/j.psep.2020.11.010 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001375.pica (DE-627)ELV053826353 (ELSEVIER)S0957-5820(20)31869-3 DE-627 ger DE-627 rakwb eng Chen, Kun verfasserin aut Operational risk analysis of blowout scenario in offshore drilling operation 2021transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model Elsevier Wei, Xin oth Li, Hui oth Lin, Hao oth Khan, Faisal oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:149 year:2021 pages:422-431 extent:10 https://doi.org/10.1016/j.psep.2020.11.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 149 2021 422-431 10 |
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10.1016/j.psep.2020.11.010 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001375.pica (DE-627)ELV053826353 (ELSEVIER)S0957-5820(20)31869-3 DE-627 ger DE-627 rakwb eng Chen, Kun verfasserin aut Operational risk analysis of blowout scenario in offshore drilling operation 2021transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model Elsevier Wei, Xin oth Li, Hui oth Lin, Hao oth Khan, Faisal oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:149 year:2021 pages:422-431 extent:10 https://doi.org/10.1016/j.psep.2020.11.010 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 149 2021 422-431 10 |
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Enthalten in Direct visualisation of thrombi for diagnosis of tissue valve thrombosis Amsterdam volume:149 year:2021 pages:422-431 extent:10 |
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Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
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Elsevier Bayesian, probability updating Elsevier Drilling blowout Elsevier Dynamic risk analysis Elsevier Offshore safety Elsevier Data-driven model Elsevier Bow-tie model |
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Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
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Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
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Operational risk analysis of blowout scenario in offshore drilling operation |
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Operational risk analysis of blowout scenario in offshore drilling operation |
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Chen, Kun |
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Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
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10.1016/j.psep.2020.11.010 |
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operational risk analysis of blowout scenario in offshore drilling operation |
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Operational risk analysis of blowout scenario in offshore drilling operation |
| abstract |
Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. |
| abstractGer |
Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. |
| abstract_unstemmed |
Offshore drilling is a complex and hazardous operation. The safety of the drilling operation is a strong function of many time-dependent parameters. The traditional risk analysis model fails to capture the impact of spatial and temporal variations of these parameters. This paper presents a Bayesian Network (BN) model for the offshore drilling operation. The model uniquely considers the evolution of hazards as a function of time and space, and failure of the safety barriers. The model development is explained using the bowtie approach, which is routinely used in the industry for risk management. The bowtie model is subsequently transformed into a BN model and simulated for the well blowout scenarios. The blowout risk is updated based on operational field observations. An uncertainty analysis is also conducted to capture the spatial variability of the parameters. The results of the BN model provide a dynamic risk profile of the blowout accident during the drilling operation. Other possible accident scenarios, such as lost circulation, can also be analyzed using the proposed model. The proposed BN model serves as a robust tool for risk management of offshore drilling operations. |
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| title_short |
Operational risk analysis of blowout scenario in offshore drilling operation |
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Wei, Xin Li, Hui Lin, Hao Khan, Faisal |
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