University of Tasmania
Browse

File(s) under permanent embargo

A hierarchical Bayesian approach to modelling fate and transport of oil released from subsea pipelines

The significant increase in global energy demand has drawn the attention of oil and gas industries to exploration of less-exploited resources. Arctic offshore region is reported to hold a great proportion of un-discovered oil reserves. While this can be a promising opportunity for the industry, more exploration activities will also increase the possibility of oil spill during the entire process including production and transport. A comprehensive risk assessment based on Ecological Risk Assessment (ERA) method is then required during the planning and operation stages of future Arctic oil production facilities. In the exposure analysis stage, ERA needs an evaluation of the oil concentration profile in all media. This paper presents a methodology for predicting the stochastic fate and transport of spilled oil in ice-infested regions. For this purpose, level IV fugacity models are used to estimate the time-variable concentration of oil. A hierarchical Bayesian approach (HBA) is adopted to estimate the probability of time to reach a concentration (TRTC) based on the observations made from a fugacity model. To illustrate the application of the proposed method, a subsea pipeline accident resulting in the release of 100 t of Statfjord oil into the Labrador Sea is considered as the case study.

History

Publication title

Process Safety and Environmental Protection

Volume

118

Pagination

307-315

ISSN

0957-5820

Department/School

Australian Maritime College

Publisher

Inst Chemical Engineers

Place of publication

165-189 Railway Terrace, Davis Bldg, Rugby, England, Cv21 3Br

Rights statement

Copyright 2018 Institution of Chemical Engineers

Repository Status

  • Restricted

Socio-economic Objectives

Oil and gas extraction

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC