Evaluation of the Risk OMT model for maintenance work on major offshore process equipment

Gran, Bjørn Axel; Bye, Rolf Johan; Nyheim, Ole Magnus; Okstad, Eivind H.; Seljelid, Jorunn; Sklet, Snorre; Vatn, Jørn; Vinnem, Jan Erik

doi:10.1016/j.jlp.2012.01.001

Cited by 59 publications

(22 citation statements)

References 9 publications

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“…Another representative example is the previously mentioned "Risk Barometer", which was developed within the IO Centre. This tool is influenced by previous analogous methods, such as the ORIM (Organizational Risk Influence Model) [31] and Risk OMT (risk modellingintegration of organisational, human and technical factors) [32].…”

Section: Inheritance From the Centre For Integrate Operations In The mentioning

confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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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 on the basis of system changes/modifications 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 commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the lifecycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions. Direct causes of such events often differed, but...

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Section: Inheritance From the Centre For Integrate Operations In The mentioning

confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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“…Update status of causal factors of basic events and update corresponding likelihood of consequences Gran et al, 2012;Røed et al, 2009;Sklet et al, 2006;Vinnem et al, 2012;Øien, 2001a,b). These causal factors are termed as Risk Influencing Factors (RIFs), which are defined as ''aspects of a system or an activity that affect the risk level of the object'' (Øien, 2001a).…”

Section: Estimation Of Site-specific Average Riskmentioning

confidence: 99%

Classification of risk to support decision-making in hazardous processes

Yang¹,

Haugen

2015

Safety Science

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a b s t r a c tApplication of risk assessments developed for the design phase to support decision-making in operational settings has exposed weaknesses in how risk is analysed and expressed in an operational context. The purpose of this paper is to clarify what we actually need to express when we use risk information to support various decision scenarios. We distinguish decision scenarios into strategic decisions, operational decisions, instantaneous decisions and emergency decisions. This forms a basis for discussing the different role risk and risk assessment plays in these decisions. Five categories of risk information (average risk, site-specific average risk, activity risk (activity performance risk and activity consequence risk), period risk and time-dependent action risk) are proposed and applications for different types of decisions are discussed. An example illustrates the use of the proposed risk types. The classification has novel aspects in providing a structure that should help in understanding how we need different aspects of risk and different ways of expressing risk in different situations. In addition, it improves communication among decision-makers by clarifying what aspects we are addressing when we use the term ''risk''.

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“…A further possibility would be to follow the relatively successful example of Vinnem et al (2012) and Gran et al (2012) (University of Stavanger, Norway), which comes closer to process risks, because it regards maintenance of offshore platform installations. The goal of the work is again design of cost-effective risk reducing measures.…”

Section: Human Reliability Modelingmentioning

confidence: 99%