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The offshore oil and gas production is a very complex industrial system that includes different areas of engineering. Its performance can be improved by means of an availability analysis that assesses the effect of the reliability and maintainability characteristics of the equipment on the system production. The classical reliability tools (e.g., Reliability Block Diagram, Fault Tree, Event Tree) are unsuitable to analyse industrial production systems, since they do not account for the dependencies or the dynamic interactions [8]. These models are based on Boolean algebra (i.e., the values of the variables either true or false) [4, 34] and are designed to deal with rare events, but with severe sequences. This is the opposite of dependability analysis that deals with frequent events with low consequences (e.g., minor production or financial losses) [30]. Markov modelling is a standard technique for the mathematical representation of dynamic systems, since component failure interactions, as well as systems with independent failures, may be effectively modelled as Markov processes [14]. However, Markov model has two main limitations: the number of states increases with system size so fast that it can lead to state-explosion, limiting the approach to very complex systems. Moreover, the Markov model only works with exponentially distributed events, i.e. constant failure and repair rates [27]. Therefore, to capture the complexity of real systems and to model the dependencies and interactions between the system components, simulation techniques have been adopted by several authors, e.g., Santos et al. [27]. Monte Carlo Simulation (MCS) provides all necessary information to describe the behaviour of different realistic aspects of a production system, such as component degradation, corrective and preventive maintenances, limited number of repair teams and associated component repair priorities [38]. Zio et al. [39] presented a MCS model for availability evaluation of a multi-state and multi-output offshore installation. Besides, Naseri et al. [20] used MCS for availability assessment of oil and gas processing plants considering the time-dependent effects of Arctic weather conditions on the components’ failure and repair rates. Simulation based approaches have been adopted for dealing with condition monitoring of multi-component deteriorating systems [2] and condition-based maintenance optimisation problems [37] and can also be applied to assess condition-based maintenance strategies of other production systems such as offshore wind turbines [12]. Petri Nets (PN) is a tool that combines graphical to mathematical modelling capabilities in order to simulate and analyse discrete event systems [29]. It was first introduced by Carl Adam Petri in 1962 in his Ph.D. dissertation [23], where he discussed the problem of representKeywords

Availability analysis of an offshore oil and gas production system subjected to age-based preventive maintenance by Petri Nets