Impact of wildfires on Canada's oil sands facilities

Abstract. Exponential growth of oil and gas facilities in wildlands from one side and an anticipated increase of global warming from the other have exposed such facilities to an ever-increasing risk of wildfires. Extensive oil sands operations in Canadian wildlands, especially in the province of Alberta, along with the recent massive wildfires in the province, require the development of quantitative risk assessment (QRA) methodologies which are presently lacking in the context of wildfire-related technological accidents. The present study is an attempt to integrate Canadian online wildfire information systems with current QRA techniques in a dynamic risk assessment framework for wildfire-prone process plants. The developed framework can easily be customized to other process plants potentially exposed to wildfires worldwide, provided that the required wildfire information is available.

[1]  Valerio Cozzani,et al.  The assessment of risk caused by domino effect in quantitative area risk analysis. , 2005, Journal of hazardous materials.

[2]  M. Flannigan,et al.  Fuel moisture sensitivity to temperature and precipitation: climate change implications , 2015, Climatic Change.

[3]  Fotini-Niovi Pavlidou,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE SYSTEMS JOURNAL 1 A Comparative Review on Wildfire Simulators , 2022 .

[4]  Ari Venäläinen,et al.  Variation in forest fire ignition probability in Finland , 2004 .

[5]  M. E. Alexander,et al.  Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height , 2012 .

[6]  Charles W. McHugh,et al.  A Method for Ensemble Wildland Fire Simulation , 2011 .

[7]  C. E. Van Wagner,et al.  Conditions for the start and spread of crown fire , 1977 .

[8]  Marc J. Assael,et al.  Fires, Explosions, and Toxic Gas Dispersions: Effects Calculation and Risk Analysis , 2010 .

[9]  M. Finney.  FARSITE : Fire Area Simulator : model development and evaluation , 1998 .

[10]  A. M. Birk,et al.  The boiling liquid expanding vapour explosion , 1994 .

[11]  E. Pastor,et al.  Mathematical models and calculation systems for the study of wildland fire behaviour , 2003 .

[12]  Nima Khakzad,et al.  Quantitative assessment of wildfire risk in oil facilities. , 2018, Journal of environmental management.

[13]  F. Heymes,et al.  Impact of a distant wildland fire on an LPG tank , 2013 .

[14]  N. Cheney,et al.  Quantifying bushfires , 1990 .

[15]  Martin E. Alexander,et al.  Calculating and interpreting forest fire intensities , 1982 .

[16]  Krishna S. Mudan,et al.  Geometric view factors for thermal radiation hazard assessment , 1987 .

[17]  N. Cheney,et al.  Prediction of Fire Spread in Grasslands , 1998 .

[18]  Charles W. McHugh,et al.  Numerical Terradynamic Simulation Group 10-2011 A simulation of probabilistic wildfire risk components for the continental United States , 2017 .

[19]  E. Chuvieco,et al.  Modelling Fire Ignition Probability from Satellite Estimates of Live Fuel Moisture Content , 2012 .

[20]  M. Finney.  Fire growth using minimum travel time methods , 2002 .

[21]  J. Molinari,et al.  Projected increase in lightning strikes in the United States due to global warming , 2014, Science.

[22]  E. Krausmann,et al.  Natech risk reduction in the European Union , 2012 .

[23]  Luis A. Godoy,et al.  Buckling of vertical oil storage steel tanks: Review of static buckling studies , 2016 .

[24]  Matthew P. Thompson,et al.  Probabilistic assessment of wildfire hazard and municipal watershed exposure , 2012, Natural Hazards.

[25]  Kerry Anderson,et al.  A model to predict lightning-caused fire occurrences , 2002 .

[26]  P. Andrews.  Current status and future needs of the BehavePlus Fire Modeling System , 2014 .

[27]  Nima Khakzad,et al.  Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures , 2015, Reliab. Eng. Syst. Saf..

[28]  David R. Brillinger,et al.  Probability based models for estimation of wildfire risk , 2004 .