The application of Bayesian – Layer of Protection Analysis method for risk assessment of critical subsea gas compression systems
Article
Arewa, A., Ifelebuegu, A. O., Awotu-Ukiri, E. O., Theophilus, S. C. and Bassey, E. 2018. The application of Bayesian – Layer of Protection Analysis method for risk assessment of critical subsea gas compression systems. Process Safety and Environmental Protection. 113 (2), pp. 305-318. https://doi.org/10.1016/j.psep.2017.10.019
Authors | Arewa, A., Ifelebuegu, A. O., Awotu-Ukiri, E. O., Theophilus, S. C. and Bassey, E. |
---|---|
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. |
Journal | Process Safety and Environmental Protection |
Journal citation | 113 (2), pp. 305-318 |
ISSN | 0957-5820 |
Year | 2018 |
Publisher | Elsevier |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.psep.2017.10.019 |
Publication dates | |
Online | 08 Nov 2017 |
Publication process dates | |
Accepted | 31 Oct 2017 |
Deposited | 05 May 2021 |
https://repository.uel.ac.uk/item/894q9
116
total views0
total downloads0
views this month0
downloads this month