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Lookup NU author(s): Dr Yihong Li, Professor Zhiqiang Hu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2023 The Author(s)With decades of development, some offshore facilities are reaching their design lives and need to be decommissioned. However, due to the limitation of regulations, technologies, and other aspects, which decommissioning options should be chosen is a challenging task. This paper introduces a multi-attribute quantitative evaluation system called MADM-Q (Multi-Attribute Decision Making-Quantitative), which can show decision makers with decommissioning options’ cost, risk, and environmental impact assessment results. The system consists of an input, output, and three processing modules. The Engineering Cost Evaluation System (ECES) is the first bottom-up approach cost assessment model in offshore facility decommissioning research. The Hierarchical Analyst Domino Evaluation System (HADES) acts as the risk assessment module, considering Domino Effect Accidents (DEAs) and providing accurate and rapid quantitative risk assessment results. The third sub-model, Composite Impact Evaluation System (CIES) assesses the environmental and socioeconomic impacts of the project. The system can obtain the basic cost range and the Individual Risk Per Annum (IRPA) value of the project quickly by using some simple inputs. The results of ECES and HADES reflects excellent accuracy - 12% cost evaluation average deviation and 1.43E-04 IRPA evaluation bias. Although currently, the HADES is a quasi-dynamic risk assessment method, it would be easy to develop it into a real-time dynamic risk monitoring and assessment system by combining advanced technologies such as digital twin technology and sensor technology.
Author(s): Li Y, Hu Z
Publication type: Article
Publication status: Published
Journal: Ocean and Coastal Management
Year: 2023
Volume: 243
Print publication date: 01/09/2023
Online publication date: 01/08/2023
Acceptance date: 27/06/2023
Date deposited: 12/09/2023
ISSN (print): 0964-5691
ISSN (electronic): 1873-524X
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.ocecoaman.2023.106732
DOI: 10.1016/j.ocecoaman.2023.106732
Data Access Statement: The data that has been used is confidential.
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