A novel engineering framework for risk assessment of Mobile Offshore Drilling Units

Natural oil and gas has become one of mankind’s most fundamental resources. Hence, the performance of mobile offshore drilling units (MODUs) under various conditions has received considerable attention. MODUs are designed, constructed, operated, and managed for harsh geographical environments, thus they are unavoidably exposed to a wide range of uncertain threats and hazards. Ensuring the operational safety of an MODU’s system is often a complex problem. The system faces hazards from many different sources which dynamically threaten its integrity and can cause catastrophic consequences at time of failure. The main purpose of this thesis is to propose a methodology to improve the current procedures used in the risk assessment of MODUs. The aim is to prevent a critical event from occurring during drilling rather than on measures that mitigate the consequences once the undesirable event has occurred. A conceptual framework has been developed in this thesis to identify a range of hazards associated with normal operational activities and rank them in order to reduce the risks of the MODU. The proposed methodology provides a rational and systematic approach to an MODU’s risk assessment; a comprehensive model is suggested to take into consideration different influences of each hazard group (HG) of an offshore system. The Fuzzy- analytic hierarchy process (AHP) is used to determine the weights of each HG. Fault tree analysis (FTA) is used to identify basic causes and their logical relationships leading to the undesired events and to calculate the probability of occurrence of each undesirable event in an MODU system. The BBN technique is used to express the causal relationships between variables in order to predict and update the occurrence probability of each undesirable event when any new evidence becomes available. Finally, an integrated Fuzzy multiple criteria decision making (MCDM) model based on the Fuzzy-AHP and a Fuzzy techniques for order preference by similarity to an ideal solution (TOPSIS) is developed to offer decision support that can help the Decision maker to set priorities for controlling the risk and improving the MODU’s safety level. All the developed models have been tested and demonstrated with case studies. An MODU’s drilling failure due to its operational scenario has been investigated and focus has been on the mud circulation system including the blowout preventer (BOP)

Advanced Quantitative Risk Assessment of Offshore Gas Pipeline Systems

This research has reviewed the current status of offshore and marine safety. The major problems identified in the research are associated with risk modelling under circumstances where the lack of data or high level of uncertainty exists. This PhD research adopts an object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the Offshore Gas Supply Systems (OGSSs) at both the component and system levels. Then based on the object-oriented approach, frameworks of aggregative risk assessment and fault tree analysis are developed. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall OGSS. Fault trees are then used to represent the cause-effect relationships for a specific risk in the system. Use of these two assessment frameworks can help decision makers to obtain comprehensive view of risks in the OGSS. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy aggregative risk assessment method to determine the risk levels associated with components, subsystems, and the overall OGSS. The fuzzy aggregative risk assessment method is tailored to quantify the risk levels of components, subsystems, and the OGSS. The proposed method is able to identify the most critical subsystem in the OGSS. As soon as, the most critical subsystem is identified, Fuzzy Fault Tree Analysis (FFTA) is employed to quantitatively evaluate the cause-effect relationships for specific undesired event. These results can help risk analysts to select Risk Control Options (RCOs) for mitigating risks in an OGSS. It is not financially possible to employ all the selected RCOs. Therefore, it is necessary to rank and select the best RCO. A decision making method using the Fuzzy TOPSIS (FTOPSIS) is proposed to demonstrate the selection of the best RCOs to control the existing risks in the system. The developed models and frameworks can be integrated to formulate a platform which enables to facilitate risk assessment and safety management of OGSSs without jeopardising the efficiency of OGSSs operations in various situations where traditional risk assessment and safety management techniques cannot be effectively applied