BORA.
Barrier and Operational Risk Analysis
Originators Aven, Sklet, Vinnem, Hauge & Seljelid (SINTEF / Univ. of Stavanger, 2006)
Paradigm Quantitative barrier-based risk analysis
Unit of analysis Initiating event → barriers → RIFs
Primary domain Offshore O&G (BORA-Release); adapted to aviation, rail, process

BORA is a semi-quantitative methodology for analysing how safety barriers and risk-influencing factors (RIFs) shape the probability of specific initiating events. It links classical PRA (fault and event trees) to organisational and operational conditions, producing installation-specific risk estimates rather than industry averages.

Overview of the framework

Developed in the Norwegian BORA-Release project (Aven et al., 2006; Sklet et al., 2006), the method proceeds through eight steps: (1) build a basic risk model of release scenarios; (2) model the performance of technical, human and organisational barriers; (3) assign industry-average base probabilities; (4) construct risk-influence diagrams linking RIFs to barrier elements; (5) score each RIF for the installation of interest; (6) weight the RIFs; (7) adjust base probabilities using the RIF scores; and (8) recalculate installation-specific risk. BORA thus operationalises a long-standing ambition of integrating managerial and operational factors into quantitative risk assessment (Vinnem et al., 2009).

Initiating Event B₁ B₂ B₃ B₄ Loss of Containment / Event Technical RIF Human RIF Operational RIF Organisational RIF Score & weight each RIF → adjust base probability → installation-specific risk (Industry average P₀) × f(RIF scores, weights) = P_platform
Figure 1. Stylised BORA structure: initiating event propagates through a sequence of barriers (B₁–B₄); performance of each barrier is modified by technical, human, operational and organisational risk-influencing factors.

When to use it

Typical applications

  • Quantitative risk analysis where site-specific conditions differ materially from industry averages.
  • Barrier management and demonstration under PSA/HSE regimes.
  • Benchmarking the effect of operational and organisational change on risk.
  • Analysis of planned maintenance and intervention work.

Aviation & cross-domain relevance

  • Originally offshore (hydrocarbon release, well control) — conceptually transferable to aviation barrier management, e.g., CFIT, runway excursion, loss of separation.
  • Useful complement to bow-tie analyses already used by ICAO/EASA.
  • Applied or adapted in process industries, LNG, maritime, and rail maintenance.

Benefits

Analytical strengths

  • Links technical, human, operational and organisational factors to quantitative probabilities.
  • Produces installation-specific risk estimates rather than relying on industry averages.
  • Compatible with existing QRA/PRA frameworks (event trees, fault trees, bow-ties).
  • Transparent weighting and scoring of RIFs supports sensitivity analysis.

Practical strengths

  • Well-documented stepwise procedure (Aven et al., 2006; Sklet et al., 2006).
  • Supports barrier management — a cornerstone of Norwegian offshore regulation.
  • Enables what-if analysis of organisational interventions.
  • Adaptable: criticality importance analysis (Bourareche & Said, 2017) and extensions to aviation are published.

Limitations

  • Expert-judgement heavy. RIF scoring and weighting introduce uncertainty and potential bias.
  • Model structure is largely linear (fault/event trees); less suited to highly emergent, non-linear interactions addressed by STAMP/FRAM.
  • Data requirements. Robust base probabilities and calibration data are needed; scarce for novel or emerging operations.
  • Originally domain-specific. Transferring from offshore to aviation requires careful re-definition of barriers and RIFs.
In short BORA is a pragmatic bridge between classical PRA and real operational conditions. It remains one of the clearest frameworks for embedding human and organisational factors into quantitative barrier analysis.

References (APA 7)

Aven, T., Sklet, S., & Vinnem, J. E. (2006). Barrier and operational risk analysis of hydrocarbon releases (BORA-Release): Part I. Method description. Journal of Hazardous Materials, 137(2), 681–691. https://doi.org/10.1016/j.jhazmat.2006.03.049

Sklet, S., Vinnem, J. E., & Aven, T. (2006). Barrier and operational risk analysis of hydrocarbon releases (BORA-Release): Part II. Results from a case study. Journal of Hazardous Materials, 137(2), 692–708. https://doi.org/10.1016/j.jhazmat.2006.03.048

Vinnem, J. E., Seljelid, J., Haugen, S., Sklet, S., & Aven, T. (2009). Generalized methodology for operational risk analysis of offshore installations. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 223(1), 87–97. https://doi.org/10.1243/1748006XJRR109

Vinnem, J. E. (2014). Offshore risk assessment: Principles, modelling and applications of QRA studies (3rd ed.). Springer. https://doi.org/10.1007/978-1-4471-5207-1

Aven, T. (2015). Risk analysis (2nd ed.). Wiley. https://doi.org/10.1002/9781119057819

Further reading

Bourareche, M., & Said, A. N. (2017). Improving barrier and operational risk analysis (BORA) using criticality importance analysis: Case study on oil and gas separator. Process Safety Progress, 36(3), 295–302.

Røed, W., Mosleh, A., Vinnem, J. E., & Aven, T. (2009). On the use of the hybrid causal logic method in offshore risk analysis. Reliability Engineering & System Safety, 94(2), 445–455. https://doi.org/10.1016/j.ress.2008.04.003

Haugen, S., Seljelid, J., Nyheim, O. M., Sklet, S., & Jahnsen, E. (2011). A generic method for identifying major accident risk contributors and their influencing factors. Risk, Reliability and Safety (ESREL proceedings).

Taibi, H., Hamaidi, B., Zerrouki, H., & Chergui, A. (2025). Modeling safety barriers and risk analysis in cement firing systems: A BORA-based approach incorporating risk influencing factors. Proceedings of the Institution of Mechanical Engineers, Part O. https://doi.org/10.1177/1748006X251325579

Petroleum Safety Authority Norway. Barrier management guideline. https://www.ptil.no