Abstract
Subsea gas compression system (SGCS) is a new critical subsea-to-shore field development solution that could reduce costs and environmental footprint. However, this system is not without inherent and operational risks. It is therefore, vital to evaluate the possible risks associated with SGCS to ensure the safe operation of the system. To this end, Layer of Protection Analysis (LOPA) is a suitable method for the estimation of possible risks. However, the failure rate data from SGCS required for LOPA is sparse and mostly developed from experimental testing. Bayesian (BL) logic is an effective tool that could be used to resolve this shortfall. In this paper, generic data from a secondary database was updated with SGCS specific data using BL logic to give a better risk frequency value. The key findings show that the posterior values derived from the BL-LOPA methodology are safer and more reliable to implement for an event scenario when compared to literature, expert judgement and generic data; therefore recommending an improved judgement in the application of safety instrumented systems for a required safety integrity level. The case studies used demonstrated that the BL-LOPA risk assessment method is sufficiently robust for quantifying uncertainties in new process facilities with sparse data.
Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Process Safety and Environmental Protection. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Process Safety and Environmental Protection, [113, (2017)] DOI: 10.1016/j.psep.2017.10.019
© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial- NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Process Safety and Environmental Protection. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Process Safety and Environmental Protection, [113, (2017)] DOI: 10.1016/j.psep.2017.10.019
© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial- NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
| Original language | English |
|---|---|
| Pages (from-to) | 305- 318 |
| Number of pages | 14 |
| Journal | Process Safety and Environmental Protection |
| Volume | 113 |
| Early online date | 8 Nov 2017 |
| DOIs | |
| Publication status | Published - Jan 2018 |
Bibliographical note
NOTICE: this is the author’s version of a work that was accepted for publication in Process Safety and Environmental Protection. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Process Safety and Environmental Protection, [113, (2017)] DOI: 10.1016/j.psep.2017.10.019© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
- Bayesian
- Subsea Systems
- Risk Assessment
- LOPA
