Occupational risk model for offshore oil and gas operations

Arevalo Aranda, Consuelo Berenice (2014) Occupational risk model for offshore oil and gas operations. Masters thesis, Memorial University of Newfoundland.

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    Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
    (Original Version)

Abstract

The rapid expansion of the offshore oil and gas activities into deeper waters and harsher environments has burden the industry with taking higher risks for developing fields. The lessons learned from past major accidents have shaped the today’s health, safety and operational requirements of the industry. Major efforts have been invested in development of analytical approaches to address occupational accidents of catastrophic proportions (high-severity, low-frequency). However, there is a lack of similar tools for accidents characterized as low-severity, high-frequency, which impose similar risks. To address this gap, the Attwood's reliability model (Attwood 2006) which was originally developed for the quantification of occupational accidents in the oil and gas industry has been revised and enhanced from a deterministic framework to a probabilistic approach. In addition, Attwood's model was extended to be used as an occupational risk estimation tool. The following important modifications were made: development of a probabilistic approach and use of Monte Carlo simulation, development of appropriate model calibration procedures, implementation of mathematical, computational codes and statistical tools, modification of expert survey analysis and finally, risk estimation. The final product is a useful tool for: prediction of occupational accidents likelihood on a specific offshore platform, estimation of accident rate, allocator of resources to specific key entities of the model to produce optimal safety results as well as occupational risk estimator. At the end, recommendations are provided to further advance the state-of-theart.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/6485
Item ID: 6485
Additional Information: Includes bibliographical references (pages 132-133).
Department(s): Engineering and Applied Science, Faculty of
Date: May 2014
Date Type: Submission
Library of Congress Subject Heading: Industrial safety--Mathematical models; Offshore oil industry--Accidents--Mathematical models; Offshore oil industry--Risk management--Mathematical models; Probabilities; Monte Carlo method

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