An Integrated Approach to Support the Dynamic Risk Assessment of Complex Industrial Accidents

• Main Authors: Nicola Paltrinieri, Gabriele Landucci, Pierluigi Rossi
• Format: Article
• Language: English
• Published: AIDIC Servizi S.r.l. 2019-09-01
• Series: Chemical Engineering Transactions
• Online Access: https://www.cetjournal.it/index.php/cet/article/view/10073

Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation based on the evolution of external conditions has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not frequently carried out. Dynamic risk assessment methods were recently developed for the O&G offshore industry with the purpose of evaluating risk on a real-time basis, either implementing the periodic frequency update or providing a dynamic estimate of the consequences with specific tools. Periodic update is possible through collection and process of specific indicators. However, its effectiveness relies on continuous monitoring activity and real-time data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in (or around) an offshore O&G system; in particular, the specific application of sensor networks is described. Examples of coupling real-time data acquisition and dynamic risk assessment, with particular reference to evaluation of safety barriers performance, are shown with specific cases. The analysed cases demonstrate that this risk and impact assessment approach may provide effective support to safety-critical decisions.