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.
Offshore drilling is an activity inherent to the oil and gas industry as it is essential for confirming the economic feasibility of hydrocarbon reservoirs. However, operational uncertainties and risks inherent to typical major accident hazards are associated with the performance of this activity, where blowouts are assumed to be one of the major contributors to risk in offshore drilling. This paper presents an accident precursor risk model for offshore drilling blowouts that integrates the blowout basic causes, safety critical barriers & elements, Risk Influencing Factors (RIF) including Human and Organizational Factors (HOF), and Operational Performance Indicators (OPI). The method, which was adopted to design the model and allow its customization for reflecting the characteristics of any drilling rig, well and operation is also described and demonstrated using a theoretical example. The model is hybrid, as it combines bow-tie diagram (fault tree and event tree) with directed acyclic graphs (DAG), to account for the effect of the RIF on the performance of safety barriers through the direct observation of pre-defined OPI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.