TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractWhile designing Deep Offshore fields developments, several decisions have to be made : defining the system architecture, selecting technologies, defining operating and maintenance policies, etc. The objective is to ensure production by mitigating risks while lowering costs and maximising profits.However, risks are numerous (e.g. equipment failures or Flow Assurance issues) and strongly interact with each other thus making the behaviour of the production process quite complex. Hence, in order to make correct decisions, engineers need a tool able to :− integrate various risks, especially those arising from Flow Assurance issues, − simulate accurately the complex behaviour of the subsea production system all along the field lifecycle, − estimate the performances of the candidate designs in order to choose the best one. This paper describes a risk management methodology based on the general framework provided by Dependability. After a hazard identification step, a model is built based on hybrid interpreted stochastic Petri nets which allow to model complex interactive systems and mix both discrete (e.g. equipment failures) and continuous (e.g. progressive degradations such as corrosion or deposits) aspects of the production process. The model provides fast computations of the availability and production availability of the system thus giving criteria for risk management.The methodology was applied to a simplified representative subsea production system and allowed to quantify the influence of major risks in terms of economic consequences and optimize a maintenance policy.
Les normes de sécurité fonctionnelle IEC 61508 et IEC 61511 requièrent de prendre en compte les incertitudes relatives aux données de fiabilité pour les mesures probabilistes concernant les systèmes instrumentés de sécurité et proposent deux méthodes pour ce faire: utilisation des bornes supérieures à 70% ou des distributions complètes des paramètres de calcul. Dans la présente communication, les deux méthodes sont appliquées à trois cas d'étude : un système série, un système parallèle et un cas industriel. Pour cela, la suite logicielle GRIF (© Total S.A.) a été utilisée afin d'évaluer la probabilité moyenne de défaillance dangereuse en cas de sollicitation (PFD avg). Les calculs montrent qu'une des méthodes est plus pessimiste que l'autre lorsque l'incertitude sur les données de fiabilité est grande.
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