Subsea Blowout Preventer (BOP): Design, Reliability, Testing, Deployment, and Operation and Maintenance Challenges

Subsea blowout preventer (BOP) is a safety-related instrumented system that is used in underwater oil drilling to prevent the well to blowout. As oil and gas exploration moves into deeper waters and harsher environments, the setbacks related to reliable functioning of the BOP system and its subsystems remain a major concern for researchers and practitioners. This study aims to systematically review the current state-of-the-art and present a detailed description about some of the recently developed methodologies for through-life management of the BOP system. Challenges associated with the system design, reliability analysis, testing, deployment as well as operability and maintainability are explored, and then the areas requiring further research and development will be identified. A total of 82 documents published since 1980's are critically reviewed and classified according to two proposed frameworks. The first framework categorises the literature based on the depth of water in which the BOP systems operate, with a sub-categorization based on the Macondo disaster. The second framework categorises the literature based on the techniques applied for the reliability analysis of BOP systems, including Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Petri Net (PN), Markov modelling, Bayesian Network (BN), Monte Carlo Simulation (MCS), etc. Our review analysis reveals that the reliability analysis and testing of BOP has received the most attention in the literature, whereas the design, deployment, and operation and maintenance (O&M) of BOPs received the least

Advanced reliability analysis of complex offshore Energy systems subject to condition based maintenance.

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.Shafiee, Mahmood (Associate)PhD in Energy and Powe