Importance analysis of assumptions in QRAs for oil and gas industry
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Original versionconfidential until july 2014
A quantitative risk analysis (QRA) should provide a broad, informative and balanced picture of risk, in order to support decisions. To achieve this, a proper treatment of uncertainty is a prerequisite. Most approaches to treatment of uncertainty in QRA seem to be based on the thinking that uncertainty relates to the calculated probabilities and expected values. This causes difficulties when it comes to communicating what the analysis results mean, and could easily lead to weakened conclusions if large uncertainties are involved. An alternative approach is to hold uncertainty, not probability, as a main component of risk, and regard probabilities purely as epistemic-based expressions of uncertainty.  In this work, we have relied on the latter approach and have limited our scope to investigating one sources of uncertainty in QRAs; assumptions made for QRAs. We have pointed out the main components of risk description in a QRA and later defined them with respect to assumptions made. We emphasize on the role assumptions play in a QRA and the impacts they have on the total risk level and what consequences they cause if they are not valid. An important issue addressed is how to communicate the shortcomings and limitations of presenting results only by probabilities and expected values. Sensitivity analysis plays a key role in this regard. Finally the intention is to rank the assumptions based on their importance according to their corresponding degree of uncertainty and sensitivity in a QRA. In order to achieve this goal, we have selected some examples of assumptions from current QRAs provided by Statoil ASA for our review and study. We have discussed the assumptions description and their impacts on other parts of the QRA. Based on the investigation of assumptions and their relation to the results of QRA and by following a checklist suggested by Aven  we have assigned an importance factor to each assumption and ranked them accordingly. The suggestion for further work is that assumptions will be presented in detail and preferably together with a sensitivity and uncertainty analysis to provide cleared pictures of risk results. This will help the stakeholders in a QRA to understand and interpret the results beyond just probabilities and expected values.
Master's thesis in Offshore Technology, Risk management