Reliability of quantitative risk models: a case study from offshore gas production platform

In response to the competing factors governing the operation of oil and gas facilities, i.e., the stringent safety and environmental regulations, and the challenging business environment that entails minimizing the running cost, a risk-based inspection (RBI) program became a vital part of all Asset Integrity Management (AIM) frameworks. The objective is to ensure asset mechanical integrity while optimizing the maintenance and inspection resources and minimizing production downtime. There are different risk models being used to manage operational risk for equipment. The decision-maker should be attentive to the subjectivity and reliability of the risk results to establish an adequate risk target that can achieve the ultimate goal of RBI by determining the cost-effective inspection and maintenance plan without compromising plant safety, integrity or reliability. This paper presents evaluations of the most quantitative RBI models through a case study from an offshore gas producing platform. A case study was implemented for topside equipment on an offshore platform. The study analyzed the impact of contributing factors to the probability of failure (PoF) model through a sensitivity analysis to quantify the reliability and subjectivity in the failure probabilities. A sensitivity analysis and comparison between both API consequence modelling methodologies (i.e., CoF level 1 and 2) were performed to manifest the reliability of risk results. The sensitivity analysis revealed the variance in the calculated risk and demonstrated that a risk target/threshold should be established based on the deployed risk model. Using the same risk target for different risk models cannot effectively define all equipment items that actually need more resources to mitigate the risk. And can result in omitting critical equipment which can jeopardize asset integrity and lead to major losses, or spend resources on unnecessary equipment.