Abstract:
As the demand for energy in our world today continues to increase and
conventional reserves become less available, energy companies find themselves
moving further offshore and into more remote locations for the promise of higher
recoverable reserves. This has been accompanied by increased technical, safety
and economic risks as the unpredictable and dynamic conditions provide a
challenge for the reliable and safe operation of both oil and gas (O&G) and
offshore wind energy assets. Condition-based maintenance (CBM) is growing in
popularity and application in offshore energy production, and its integration into
the reliability analysis process allows for more accurate representation of system
performance. Advanced reliability analysis while taking condition-based
maintenance (CBM) into account can be employed by researchers and
practitioners to develop a better understanding of complex system behaviour in
order to improve reliability allocation as well as operation and maintenance
(O&M). The aim of this study is therefore to develop models for reliability analysis
which take into account dynamic offshore conditions as well as condition-based
maintenance (CBM) for improved reliability and O&M.
To achieve this aim, models based on the stochastic petri net (SPN) and dynamic
Bayesian network (DBN) techniques are developed to analyse the reliability and
optimise the O&M of complex offshore energy assets. These models are built to
take into account the non-binary nature, maintenance regime and repairability of
most offshore energy systems. The models are then tested using benchmark
case studies such as a subsea blowout preventer, a floating offshore wind turbine
(FOWT), an offshore wind turbine (OWT) gearbox and an OWT monopile. Results
from these analyses reveal that the incorporation of degradation and CBM can
indeed be done and significantly influence the reliability analysis and O&M
planning of offshore energy assets.
