Abstract
The main objective of this paper is to model an offshore oil production system subjected to age-based preventive maintenance strategies by Petri Nets and to evaluate its availability by Monte Carlo Simulation. The oil processing and the separation equipment with their reliability and maintainability characteristics, the corrective and preventive maintenance policies and the operational dependencies that lead to the reconfiguration of the system after the failure are implemented. A special attention is given to the effect of age-based perfect and imperfect preventive maintenance strategies on the system availability. The maintenance actions consider the components’ age thresholds and an age reduction ratio. Moreover, the variation of the oil and gas flows from the well over the years is accounted by the model. As case study, an offshore production installation that operates in a Brazilian oilfield is adopted. An elasticity analysis on the model parameters is conducted to assess influence of the maintenance policy on the system availability and on the oil production.
Highlights
The offshore oil and gas production is a very complex industrial system that includes different areas of engineering
When the techniques based on Generalized Stochastic Petri Nets are combined with Monte Carlo Simulation, they can model and analyse the complex behaviour of an industrial multi-unit system [29], due to their transitions that can fire deterministically, stochastically and be conditioned by predicates
At the initial instant of simulation, the Petri Nets (PN) model has all components of Floating Production System (FPS) in operation with null initial age, the corrective maintenance (CM) team is at the port and the preventive maintenance (PM) team is on-board the FPS
Summary
The offshore oil and gas production is a very complex industrial system that includes different areas of engineering. 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. 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]
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