Abstract:
Offshore oil and gas platforms are well-known for their compact geometry, high degree of congestion, limited ventilation and difficult escape routes. The level of risk in such conditions, while operating in a remote and harsh marine environment, is very high. A small mishap under such conditions can quickly escalate into a catastrophe. Among all the accidental process related events occurring offshore, fire is the most frequently reported. It is therefore necessary to study the behavior of fires and quantity the hazards posed by them in order to complete a detailed quantitative risk assessment.
The focus of this work is to use Quantitative Risk Assessment Methodology for estimating the risk levels and assessing their significance in accident prevention. Here this methodology is used for the predefining the accident scenario. It is used as a design basis for fire protection and emergency evacuation equipment, or for emergency planning and training. Fire Consequence models have been developed offshore Quantitative Risk Assessment.
This work signifies the prediction of human error probabilities during the process of emergency musters on offshore oil and gas production platforms by using the expert judgment technique called as Success Likelihood Index Methodology (SLIM). Three muster scenario of varying severity (man overboard, gas release, and fire and explosion ) are studied in detailed.
SCAP methodology has been introduced for the risk-based process safety decision making for Offshore Oil Gas activities. This methodology is applied to various offshore process units, that is, the compressor, separators, flash drum and driers of an Offshore Oil Gas platform. Based on the risk potential, appropriate safety measures are designed for each unit. This paper also illustrates that implementation of the designed safety measures reduces the high Fatal accident rate (FAR) values to an acceptable level.
Keywords: Fire modeling, Quantitative risk assessment, Offshore risk modeling, Human Factors, Risk Assessment, Emergency Response
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