Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices

The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero "hazardous" discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a n...

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Published in:Journal of Offshore Mechanics and Arctic Engineering
Main Authors: Ayele, Yonas Zewdu, Barabady, Javad, López Droguett, Enrique
Format: Article in Journal/Newspaper
Language:English
Published: 2016
Subjects:
Rif
Online Access:https://doi.org/10.1115/1.4033713
https://repositorio.uchile.cl/handle/2250/142923
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spelling ftunivchile:oai:repositorio.uchile.cl:2250/142923 2023-05-15T14:21:40+02:00 Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices Ayele, Yonas Zewdu Barabady, Javad López Droguett, Enrique 2016 application/pdf https://doi.org/10.1115/1.4033713 https://repositorio.uchile.cl/handle/2250/142923 en eng Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme. Volumen: 138 Número: 5 Número de artículo: 051302 doi:10.1115/1.4033713 https://repositorio.uchile.cl/handle/2250/142923 Attribution-NonCommercial-NoDerivs 3.0 Chile http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ CC-BY-NC-ND Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme waste handling risk influencing factors dynamic Bayesian network drilling waste Arctic Artículo de revista 2016 ftunivchile https://doi.org/10.1115/1.4033713 2023-01-29T00:51:52Z The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero "hazardous" discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season. Article in Journal/Newspaper Arctic Arctic Barents Sea Universidad de Chile: Repositorio académico Arctic Barents Sea Norway Rif ENVELOPE(-16.172,-16.172,66.526,66.526) Journal of Offshore Mechanics and Arctic Engineering 138 5
institution Open Polar
collection Universidad de Chile: Repositorio académico
op_collection_id ftunivchile
language English
topic waste handling
risk influencing factors
dynamic Bayesian network
drilling waste
Arctic
spellingShingle waste handling
risk influencing factors
dynamic Bayesian network
drilling waste
Arctic
Ayele, Yonas Zewdu
Barabady, Javad
López Droguett, Enrique
Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
topic_facet waste handling
risk influencing factors
dynamic Bayesian network
drilling waste
Arctic
description The increased complexity of Arctic offshore drilling waste handling facilities, coupled with stringent regulatory requirements such as zero "hazardous" discharge, calls for rigorous risk management practices. To assess and quantify risks from offshore drilling waste handling practices, a number of methods and models are developed. Most of the conventional risk assessment approaches are, however, broad, holistic, practical guides or roadmaps developed for off-the-shelf systems, for non-Arctic offshore operations. To avoid the inadequacies of traditional risk assessment approaches and to manage the major risk elements connected with the handling of drilling waste, this paper proposes a risk assessment methodology for Arctic offshore drilling waste handling practices based on the dynamic Bayesian network (DBN). The proposed risk methodology combines prior operating environment information with actual observed data from weather forecasting to predict the future potential hazards and/or risks. The methodology continuously updates the potential risks based on the current risk influencing factors (RIF) such as snowstorms, and atmospheric and sea spray icing information. The application of the proposed methodology is demonstrated by a drilling waste handling scenario case study for an oil field development project in the Barents Sea, Norway. The case study results show that the risk of undesirable events in the Arctic is 4.2 times more likely to be high (unacceptable) environmental risk than the risk of events in the North Sea. Further, the Arctic environment has the potential to cause high rates of waste handling system failure; these are between 50 and 85%, depending on the type of system and operating season.
format Article in Journal/Newspaper
author Ayele, Yonas Zewdu
Barabady, Javad
López Droguett, Enrique
author_facet Ayele, Yonas Zewdu
Barabady, Javad
López Droguett, Enrique
author_sort Ayele, Yonas Zewdu
title Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
title_short Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
title_full Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
title_fullStr Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
title_full_unstemmed Dynamic Bayesian Network-Based Risk Assessment for Arctic Offshore Drilling Waste Handling Practices
title_sort dynamic bayesian network-based risk assessment for arctic offshore drilling waste handling practices
publishDate 2016
url https://doi.org/10.1115/1.4033713
https://repositorio.uchile.cl/handle/2250/142923
long_lat ENVELOPE(-16.172,-16.172,66.526,66.526)
geographic Arctic
Barents Sea
Norway
Rif
geographic_facet Arctic
Barents Sea
Norway
Rif
genre Arctic
Arctic
Barents Sea
genre_facet Arctic
Arctic
Barents Sea
op_source Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme
op_relation Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme. Volumen: 138 Número: 5 Número de artículo: 051302
doi:10.1115/1.4033713
https://repositorio.uchile.cl/handle/2250/142923
op_rights Attribution-NonCommercial-NoDerivs 3.0 Chile
http://creativecommons.org/licenses/by-nc-nd/3.0/cl/
op_rightsnorm CC-BY-NC-ND
op_doi https://doi.org/10.1115/1.4033713
container_title Journal of Offshore Mechanics and Arctic Engineering
container_volume 138
container_issue 5
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