Risk management of offshore logistics support operations in remote harsh environments

Rahman, Md Samsur (2020) Risk management of offshore logistics support operations in remote harsh environments. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Activities in northern offshore regions are increasing due to proven reserves of natural resources. These regions are considered to have a harsh marine environment due to extreme weather conditions, namely low temperatures, frequent storms and the presence of sea ice. In general these activities are moving further offshore. Thus many new developments are faced with operations in extreme environments at long distances from shore support. Design, operational and regulatory planning for such offshore installations must consider the environmental challenges along with additional difficulties that arise due to remoteness. The most significant aspects of an offshore development that are affected by the factors of environment and remoteness, are the logistical support functions required for daily operations and the rapid response required for emergencies. In the early stages of design it would be beneficial to have a means of assessing the high risk elements of such operations and the risk reduction cost effectiveness of proposed solutions. This study presents an end-to-end risk reduction analysis of the logistical support functions for a typical remote harsh-environment offshore operation including; risk assessment to provide identification of most significant risks, risk reduction modeling and development of a solution to provide the identified most effective reduction strategy, and finally a cost benefit analysis that includes the costed initial risk factors, the solution cost and the costed net reduction in risk arising from implementation. This research serves three functions. It develops a procedure for evaluating offshore operations that have inherently high logistical risks due mainly to distance but also applicable to other factors. It provides a risk analysis based solution to the specific problem of remote operations in harsh environments. Finally it develops a method of determining the utility of a possible solution or of alternative solutions through rational risk based cost analysis. The study is divided into four phases, Risk Analysis, Risk Reduction, Specific Solution and Cost-Benefit Analysis. In phase one – risk analysis, an advanced probabilistic model is developed using fault trees to identify the main contributing factors of the logistical challenges. A fuzzy-based and evidence-based approach is implemented to address inherent data limitations. It is found that existing modes of logistics support such as marine vessel or helicopter are not sufficiently reliable and quick for remote offshore operations. Moving towards in phase two – risk reduction, a conditional dependence-based Bayesian model is developed that has integrated multiple alternative risk reduction measures. The analysis depicts that a nearby offshore refuge and an additional layer of safety inventory are found to the most effective measures. In phase three – specific solution, the concept of a moored vessel, which is termed as offshore resource centre (ORC) is proposed that can meet the functions of both these measures. The overall dimensions of the ORC are derived based on the functional requirements and the model is validated for stability and mooring requirements. In phase four – cost-benefit analysis, the life cycle costs of an ORC is estimated from historical vessel data using regression analysis. A loss model is developed for a hypothetical blowout incident, which is a function response time and the distance from shore support. These models are integrated into a single framework that can project the costed risk with or without the ORC. The analysis reveals that an ORC becomes more and more viable when the offshore distance becomes longer and if there is a higher probability of any platform incident, recognizing that it is desirable to keep the probability as low as possible. Taken together these phases form a full analysis from problem identification through solution cost-benefit.

Item Type: Thesis (Doctoral (PhD))
URI: http://research.library.mun.ca/id/eprint/14683
Item ID: 14683
Additional Information: Includes bibliographical references.
Keywords: Offshore logistics, Offshore risk management, Remote and harsh environments, Bayesian networks, Offshore resource centre
Department(s): Engineering and Applied Science, Faculty of
Date: October 2020
Date Type: Submission
Digital Object Identifier (DOI): https://doi.org/10.48336/2b8z-t004
Library of Congress Subject Heading: Offshore structures--Risk assessment; Offshore structures--Environmental aspects.

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