Accident Precursor Probabilistic Method (APPM) for modeling and assessing risk of offshore drilling blowouts – A theoretical micro-scale application

This paper proposes and explains the application of an Accident Precursor Probabilistic Method (APPM) that aims to overcome the usual limitations of existing Quantitative Risk Analyses (QRA), with a focus on offshore drilling blowouts. This limitation is implicit in generic QRAs that do not appropriately reflect the specificities of the rig and its environment, without considering systems arrangements, Risk Influencing Factors (RIF) or current operational conditions.

The proposed method is divided into three pillars: (i) a guideline for modeling the blowout probability considering specific conditions or well, rig, safety barriers and Risk Influencing Factors (RIF) objectives; (ii) a proposed axiom combined with a scoring system to quantify the RIF into the QRA; and (iii) a risk based plan framework, to allow risk update and sequential learning during the operational phase.

The APPM is based on a Bayesian Network (BN) mathematical framework. It allows the pre-defined axiom to be entered into a conditional probability table (CPT). This approach, combined with the assessment of the company’s safety management system, allows the incorporation of RIF into the QRA.

The developed APPM is applied to a theoretical micro-scale calculation. The result demonstrates its suitability for addressing common aspects inherent to the blowout phenomenon, including uncertainty, dependability between variables (common cause factors and redundant failures), and dynamism due to planned or unplanned operational changes in systems, drilling parameters and current conditions of RIF. Limitations of the APPM are also identified, and suggestions are made for future work on this topic.