Failure modeling and analysis of offshore process components

Deyab, Samir Massoud (2017) Failure modeling and analysis of offshore process components. Masters thesis, Memorial University of Newfoundland.

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Abstract

This thesis investigates the risk of offshore oil and gas processing equipment operating in a harsh environment. It comprises of two major studies which form the core of journal papers submitted for publication. The first study presented a new risk assessment methodology with applications to fire scenarios in compressor and heat exchanger units. A sensitivity analysis was conducted to identify the critical components, their interdependence, and importance in causing failure. In the second study, a risk assessment approach is proposed that demonstrates how process system failure risk could be assessed in the absence of complete data. The approach also highlighted the importance of interdependence of the failure causation factors. The Bayesian Network (BN) is used in the study to capture interdependence of and uncertainty the variables. Noisy-OR and Leaky Noisy-OR logics are used to improve uncertainty-handling capacity and overcome the data requirement. Application of the proposed approach is demonstrated on a subsea pipeline failure scenario. As a first step, a Bowtie (BT) was developed which captures all the possible failure causes of a leak and shows the potential consequences of a leak in the subsea pipeline. The BT was then mapped to a BN for OR, Noisy-OR and Leaky Noisy-OR logics. Failure probabilities of Subsea Pipeline and its Safety Barriers were calculated with Bow-tie and Bayesian Network for different Logics. Finally, importance analysis was performed for 21 basic events using OR, Noisy- OR and Leaky Noisy OR Logics to determine safety critical elements. In Summary, this thesis provides scientifically sound and applied approaches to conduct risk assessment of process components with limited data. Applications of these approaches demonstrated on different case studies. Use of the proposed approaches would help better understanding of failure and hence improving safety of process system.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/12949
Item ID: 12949
Additional Information: Includes bibliographical references (pages 80-91).
Keywords: Sensitivity analysis, Offshore safety analysis, Causal dependency, Noisy- OR Logic, Leaky Noisy- OR Logic, Dependency modeling, Uncertainty modeling, Probabilistic modelling
Department(s): Engineering and Applied Science, Faculty of
Date: October 2017
Date Type: Submission
Library of Congress Subject Heading: Risk assessment -- Mathematical models; Bayesian statistical decision theory; Offshore oil industry -- Risk assessment

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