Risk analysis of an underground gas storage facility using a physics-based system performance model and Monte Carlo simulation
•A risk analysis model of underground gas storage facility operational reliability.•Physics-based performance model combined with Monte Carlo simulation of disruptions.•A multitude of potential events that can affect supply capacity can be analyzed.•Many combinations of systems states can be analyze...
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Veröffentlicht in: | Reliability engineering & system safety 2020-07, Vol.199, p.106792-17, Article 106792 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | •A risk analysis model of underground gas storage facility operational reliability.•Physics-based performance model combined with Monte Carlo simulation of disruptions.•A multitude of potential events that can affect supply capacity can be analyzed.•Many combinations of systems states can be analyzed in a single modeling framework.
This paper presents a quantitative risk analysis model to study operational reliability risk of an underground gas storage (UGS) facility. The model combines a thermo-hydraulic performance model for a gas storage facility consisting of a gathering system, compression system and transmission system with a Monte Carlo simulation of potential disruption events. The disruption events can impact the availability of one or more critical assets within the gas storage facility, which in turn affect the gas flow capability. The flow capability is compared against externally derived gas flow demand patterns to determine if shortfalls in supply can occur. The proposed model is highly configurable and can be used to quantitatively assess operational reliability risk in a UGS facility. A multitude of potential events that can affect a UGS facility can be analyzed. The integrated physics model means an analyst does not have to explicitly account for changes in system performance resulting from disruption events. As such, many combinations of asset configurations, system states and disruption events can be analyzed within a single modeling framework. |
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ISSN: | 0951-8320 1879-0836 |
DOI: | 10.1016/j.ress.2020.106792 |