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Research Article
Mar 30, 2020

Novel Quantitative Risk Assessment Interface for Fixed Offshore Oil and Gas Exploration Platforms

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 6, Issue 2

Abstract

Light, sweet crude oils are depleting, forcing oil and gas companies to explore and operate in increasingly deeper waters, remote geographical locations, and harsher environmental conditions with higher safety risks. This paper analyzed the common misuse and errors of typical quantitative risk assessments (QRAs) during identification, assessment, approval, and project implementation stages of a project for the case of optimization of facilities and manning fixed offshore oil and gas platform, as well as the scenario of a gas leak from riser pipeline of a floating production platform. The lessons learned were then applied to design an optimized QRA process for a real case, preproject assessment for a proposed addition of a riser platform (R-A) to a fixed offshore oil and gas platform complex using individual risk per annum (IRPA) and potential loss of life (PLL) analyses. Findings reveal that applying the standard hazard and effects management process (HEMP) and as low as reasonably practicable (ALARP) guidelines and tools alone are insufficient. The application of practical lessons learned from the past oil and gas disasters using IRPA and PLL parameters has helped this research to produce an optimized QRA. The optimized QRA process is a live process which could be further improved with future lessons learned. This article is available in the ASME Digital Collection at https://doi.org/10.1115/1.4044809.

Information & Authors

Information

Published In

Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 6Issue 2June 2020

History

Received: Jan 31, 2019
Revision received: Sep 3, 2019
Published online: Mar 30, 2020
Published in print: Jun 1, 2020

Authors

Affiliations

Professor Director of Intelligence and Automation in Construction Fujian Province Higher-educational Engineering Research Centre, College of Civil Engineering, Huaqiao University, Xiamen 361021, China e-mail: [email protected]
Patrick Ong [email protected]
College of Civil Engineering, Huaqiao University, Xiamen 361021, China e-mail: [email protected]
Lincoln C. Wood [email protected]
Department of Management,University of Otago, Dunedin 9054, New Zealand; Curtin Business School,Bentley, Western Australia 6102, Australia e-mail: [email protected]
Fengqiu Zou [email protected]
College of Civil Engineering, Huaqiao University, Xiamen 361021, China e-mail: [email protected]
Hamzah Abdul-Rahman [email protected]
Professor Faculty of Science, Vision College, Petaling Jaya, Selangor 47301, Malaysia e-mail: [email protected]

Funding Information

Huaqiao University Research Fund: 17BS201
Quanzhou Government: 600005-Z17X0234
Quanzhou Science and Technology Bureau Research Fund: 2018Z010

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