NHESSNatural Hazards and Earth System SciencesNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus PublicationsGöttingen, Germany10.5194/nhess-18-3153-2018Impact of wildfires on Canada's oil sands facilitiesImpact of wildfires on Canada's oil sands facilitiesKhakzadNiman.khakzadrostami@tudelft.nlhttps://orcid.org/0000-0002-3899-6830Faculty of Technology, Policy, and Management, Delft University of
Technology, Delft 2628BX, the NetherlandsNima Khakzad (n.khakzadrostami@tudelft.nl)23November201818113153316617May20186July201810November2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://nhess.copernicus.org/articles/18/3153/2018/nhess-18-3153-2018.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/18/3153/2018/nhess-18-3153-2018.pdf
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.
Introduction
Rising temperatures and climate change have increased the risk of
weather-related hazards in Europe (European Joint Research Centre, 2017).
Canada and the US are no exception as evident by the recent hurricanes,
floods, and wildfires which devastated the states of Texas and California in
the US and the provinces of British Columbia and Alberta in Canada. Aside
from the impact of such natural disasters on the environment and urban
areas, their effect on industrial plants and hazardous facilities (process
plants, nuclear plants, etc.) has started to raise concerns in academia, the
industry, and regulatory bodies.
Massive fires in a refinery in Turkey in 1999 during the Kocaeli earthquake,
substantial release of petroleum products and chemicals in the US during
Hurricane Katrina in 2005 and Hurricane Harvey in 2017, extensive damage to
coastal industrial complexes in Japan in 2011 during the Great Sendai
Earthquake and the following tsunami, and shutdown of oil sands plants which
incurred enormous oil production losses during massive wildfires in Canada
in 2016 are just some examples among others.
Although the hazard of wildfires in ecological and urban risk assessment
studies has long been recognized (Preisler et al., 2004; Scott et al., 2012,
2013;), the relevant work in the context of wildland-prone industrial
complexes has been very limited (FireSmart, 2012; Khakzad et al., 2018). In
Europe, for example, Seveso Directive III (2012) has only recently mandated
the member states to consider the probability of natural disasters in the
risk assessment of major accident scenarios when preparing safety reports
(Article 10), with an explicit mention of floods and earthquakes (the Annex
II) but not of wildfires. Most European countries that consider natechs (natural hazards triggering technological
disasters) have likewise limited their focus to only a few natural hazards (Krausmann
and Baranzini, 2012). Table 1 exemplifies some of such efforts.
Exponential growth of industrial facilities and the subsequent prolongation
of wildland–industry interfaces from one side and an anticipated increase of
global warming from the other are expected to increase the frequency and
severity of technological accidents caused by natural disasters, including wildfires.
In May 2015, a massive wildfire in northern Alberta, Canada, spread into the
oil sands areas, threatening several operations and keeping about 10 % of
the production offline. Two major petroleum companies, Canadian Natural and
Cenovus Energy, shut down their 80 000 and 135 000-barrel-a-day operations,
respectively, for safety precautions as the fires approached Foster Creek
oil sands facility and Caribou South natural gas plant (Mining.Com, 2015).
Natural hazards considered in safety assessment and management of
process plants in the European Union (Krausmann and Baranzini, 2012).
* It is not identified whether it accounts for wildfires.
In May 2016, a wildfire burned part of Fort McMurray, Alberta, Canada, and
spread towards oil sands plants north of the city where major oil sands
production plants Syncrude and Suncor Energy along with some smaller
petroleum operations were located, resulting in a 40 % drop in production
at nearby oil sands facilities (Fig. 1).
The operations shutdowns or reductions were also influenced by precautionary
shutdowns of pipeline carrying diluent, a flammable substance needed to thin
the oil sands' bitumen, resulting in a reduction of the oil sands' output of roughly as
much as 1 million barrels a day (Maclean's, 2016a). The wildfire did
not cause damage to oil sands plants and process equipment, but it burned
down a 665-unit worker accommodation camp in northern Fort McMurray (Global
News, 2016a). But what would have happened if the fire had reached the
oil sands mines and the production facilities?
Wildfire in Fort McMurray and the location of affected oil sands
plants: 1 Canadian Natural Resources, 2 Syncrude joint venture, 3 Imperial
Oil, 4 Shell Canada, 5 Husky Energy/BP, 6 Suncor, 7 Athabasca, 8 Nexen
(CNOOC), 9 Japan Canada Oil Sands, 10 Connacher Oil and Gas, 11
ConocoPhillips, 12 Statoil (Maclean's, 2016a).
As far as the oil sands mines are concerned, bitumen, the main component of
oil sands, does not easily catch fire (Global News, 2016b). Considering the
fact that 80 % of bitumen is buried deep underground, the bitumen in
oil sands mines is mixed with sand (similar to asphalt), and would probably
smolder if ignited (Maclean's, 2016b). However, oil sands projects rely on
two highly flammable substances for the extraction, processing and transport
of bitumen: natural gas and diluent, which is a very light petroleum
substance.
Natural gas is used to generate power for the plants and heat up the steam
used to liquefy the bitumen. Diluent, on the other hand, is used to dilute
the crude bitumen thin enough to flow through pipelines. Both the natural
gas and diluent can pose high risks if exposed to fire, though the pipes
carrying them are usually buried underground.
Oil sands process plants are usually accompanied by large tank terminals in
the vicinity to store oil products. Exposed to external fires (such as
wildfire), buckling of atmospheric storage tanks and spill of hydrocarbons,
tank fires, vapor cloud explosions, and explosion of pressurized tanks can
be recognized as potential risks (Heymes et al., 2013, Godoy 2016). In case
one or more storage tanks are ignited by the wildfire, the tank fire(s) can
impact adjacent storage tanks, leading to a fire domino effect.
In order to protect oil sands facilities from wildfires (and also protect the
forest from potential ignition sources at the facilities), there is a buffer
zone (safety distance in the form of vegetation-free ground) between
facilities and forest vegetation. In the absence of methodologies for
quantitative risk assessment and management in wildland–industrial
interfaces, such buffer zones are usually determined based on rule-of-thumb
guidelines (e.g., see FireSmart, 2012). Numerical simulations of storage
tanks exposed to wildfire have, however, demonstrated that in most cases
such safety distances would not suffice (Heymes et al., 2013).
Due to extensive oil sands operations in Canadian wildlands, in the present
study, we have developed a dynamic framework, mainly based on available
techniques and daily updated wildfire maps made available online by
the government of Canada, to assess the impact of wildfires on oil sands
facilities. Since the framework is modular, it can be tailored to assess the
risk of wildfires at process plants in wildfire-prone areas worldwide.
Section 2 revisits the Canadian wildland fire information system; in Sect. 3, the components of wildfire risk assessment are described and quantified;
Sect. 4 is devoted to the impact assessment of wildfires on process
facilities; Sect. 5 concludes the study.
Canadian Wildfire Information System
In Canada, two systems are being used to determine the characteristics and
the hazard of wildfires: the Canadian Forest Fire Weather Index (FWI) System and
the Canadian Forest Fire Behavior Prediction (FBP) System. The former is mostly
concerned with the estimation of wildfires' basic components (e.g.,
flammability of vegetation), whereas the latter deals with the dynamics of
wildfires (e.g., fire intensity). Since in the present study the
identification and quantification of wildfires in Canadian wildlands are
mainly based on the foregoing two systems, they will be recapitulated in
this section.
Forest Fire Weather Index System
Wildfires, like other types of fire, can be defined using the fire triangle
consisting of fuel (trees, grasses, shrubs), oxygen, and heat source. As
far as the fuel is concerned, parameters such as the Fine Fuel Moisture Code
(FFMC), which is the moisture content of litter and other crude fire fuels,
Duff Moisture Code (DMC), which is the moisture content of loosely compacted
organic layers of moderate depth and woody materials, and Drought Code (DC),
which is the average moisture content of deep compact organic layers and
large logs, are taken into account to determine both the ease of ignition
and the flammability of the available fuel.
DMC and DC are combined together to determine the total amount of
combustible materials in the form of a so-called Buildup Index (BUI).
Accordingly, the wind and the FFMC are combined to predict the rate of fire
spread in the form of a so-called Initial Spread Index (ISI). Having the BUI
and the ISI, the FWI, as an indication of fire danger,
can be determined as shown in Fig. 2 (Natural Resources Canada, 2018).
Figure 3a illustrates the FWI of Canada (0≤ FWI ≤30) on 1 May 2016, a day before the Fort McMurray wildfire. Based on
the FWI and the type of fire (surface fire, crown fire, intermittent crown
involvement), the fire danger index can be determined (low, moderate, high,
very high, extreme) as an indication of how easy it is to ignite the forest
fuel, how difficult it is to control the fire, and the type of firefighting
equipment needed (pumps, tanker trucks, bulldozer, aircraft, etc.) as shown
in Fig. 3b.
Identification of the Fire Weather Index (Natural Resources Canada).
(a) Fire Weather Index and (b) fire danger index of Canada on 1 May 2016 (Natural Resources Canada).
Forest Fire Behavior Prediction System
To quantify the impact of wildfires on industrial plants, quantitative
estimates of head fire spread rate, fuel consumption and fire intensity are
needed. The FBP System employs PROMTHEUS –
a deterministic wildland fire growth simulation model based on Huygens' principle of wave propagation – to estimate the fire area, perimeter,
perimeter growth rate, and flank and back fire behavior (Tymstra et al.,
2010). The rate of spread (ROS) is the predicted speed (m min-1) of the fire
head (fire front), which is calculated based on the fuel type, ISI, BUI, crown base height and other
parameters based on the FWI and FBP subsystems of the Canadian Forest Fire Danger Rating
System.
Head fire intensity (HFI) is an estimate of the energy output per meter of
the fire front (kW m-1), calculated based on the ROS and
total fuel consumption (kg m-2). The ROS and HFI indices calculated by the Canadian Wildland Fire Information
System a day before the start of the Fort McMurray wildfire are shown in
Fig. 4a and b, respectively (Natural Resources Canada).
(a) Fire rate of spread and (b) head fire intensity in Canada on 1 May 2016 (Natural Resources Canada).
Wildfire risk assessment
In wildfire risk assessment, the ignition probability, burn probability (the
probability that wildfire reaches to a certain spot), type of fire (surface
fire, crown fire, intermittent crown involvement) and fire intensity are the
main factors to take into account (Scott et al., 2013).
Many methodologies have been developed to predict the lightning-induced
ignition probability (Latham and Schlieter, 1989; Anderson, 2002) and
human-induced ignition probability (Lawson et al., 1994) to model surface
fire spread (Rothermel, 1972), crown fire spread (Rothermel, 1991), and
the transition between surface and crown fire spread (van Wagner, 1977).
Accordingly, a number of software tools such as FARSITE (Finney, 1998),
FlamMap5 (Finney, 2006), FSPro (Finney et al., 2011a) and FSim (Finney et
al., 2011b) have been developed based on historical records of regional
wildfires, weather conditions, type and density of vegetation in the
landscape, and the topology of the landscape. Using the developed models and
software tools, the risk imposed by wildfires on an oil sands facility can be
modeled as the product of the wildfire probability, PW, and the
severity of consequences, preferably in monetary units as
wildfires' risk=PW⋅consequence.
Given the geographical location of the facility, the probability of wildfire
at the borders of the facility can be estimated as the probability of having
a small fire somewhere at the landscape (PI) times the probability of
the small fire growing to a wildfire larger than 400 m2 in area and
reaching the location of the facility (PB):
PW=PI⋅PB.
PI and PB are also known as ignition probability and burn
probability, respectively. Exposed to a wildfire, the potential consequences
and their severity depend on the wildfire intensity and the facility's
vulnerability to wildfire: C=f (fire intensity, facility's
vulnerability)
In the present study, we do not consider the
indirect risk incurred by, among others, loss of production due to the plant's
precautionary shutdowns, staff evacuation, or the like.
. In the following
sections we will describe the components of wildfire risk in further detail
and explain how they can be estimated or acquired from available (mostly
freely accessible) models and databases, with a particular emphasis on
the Canadian Forest Fire System.
Ignition probability
Wildfires can be categorized as hydrogeological events which are bound to
increase, especially due to global warming. Every degree in warming increases
the possibility of lightning, which is one of the major triggers of
wildfires, by 12 % (Romps et al., 2014). Likewise, 15 % more
precipitation would be needed to offset the increased risk of wildfires due
to a 1 ∘C increment of warming (Flannigan et al., 2016).
Nevertheless, man-made fires (burning campfires, cigarettes) account for
80 % of wildfires (National Geographic, 2018.).
Weather conditions such as temperature, relative humidity, and wind speed
are key factors in the probability estimation of an ignition (small fire)
which can lead to a wildfire. In addition to the weather conditions, the
vegetation moisture content (equal to FFMC) plays a key role, not only in the
initiation of fire (the ignition probability) but also in the continuation
and spread of fire (fuel flammability) (Chuvieco et al., 2004).
Based on the measurement of FFMC in consecutive time periods before the
start of a potential wildfire, the logistic regression has been used to
roughly predict PI based on FFMC (Larjavaara et al., 2004; Jurdao et
al., 2012). Similarly, Preisler et al. (2004) used the logistic regression
to predict the probability of small fires (fires in areas less than 0.04 ha) as an equivalent to PI based on, among others, the burning
index, fire potential index, Drought Code, wind speed, relative humidity,
dry bulb temperature, day of the year, and the elevation.
Lawson et al. (1994) developed an application called the Wildfire Ignition
Probability Predictor (WIPP) to predict, on an hourly or daily basis, the
PI of man-made wildfires in British Columbia forests, Canada.
Based on the calculations of FFMC and 10 m wind speed, WIPP estimates the
PI in three categories as low (0 %–50 %), medium
(50 %–75 %), and high (75 %–100 %). Considering lightning
as one of the main triggers of wildfires, Canadian Wildland Fire System
estimates the time-dependent probability of lightning-caused ignitions as
(Anderson, 2002):
PI=PLCC⋅Pign⋅Psur⋅Parr,
where PLCC is the probability of a long-continuing current (85 % for
positive flashes, 20 % for negative flashes across Canada); Pign is
the probability of ignition given a long-continuing current, determined by
fuel type, forest floor depth, and moisture conditions (Latham and Schlieter
1989; Anderson 2002); Psur is the probability that a smoldering
ignition will continue to survive as a smoldering fire, determined by the
fuel moisture, the bulk density, and the inorganic content of the forest
floor (Hartford 1989; Anderson 2002); Parr is the probability of a
smoldering fire escalating to a flaming fire (Lawson et al., 1994; Forestry
Canada Fire Danger Working Group, 1992; Anderson, 2002).
Wildfire-prone provinces in Canada such as Alberta and British Columbia
provide ignition probability maps on a daily basis both for the current day
and the next day. Figure 5 depicts the PI map for the province of
Alberta administrated by Alberta Agriculture and Forestry.
Wildfire ignition probability (PI) in Alberta, Canada
(http://wildfire.alberta.ca, last access: 17 October 2017).
Burn probability
Burn probability (PB) is the conditional probability that a small fire
somewhere in the landscape would escalate to a wildfire and burn somewhere
else in the landscape. Estimation of PB is challenging as the spread of
wildfire from one point to another is a complicated process affected by many
factors such as the type of vegetation (fuel), weather conditions, and land
topology. These factors, in turn, consist of several key parameters such as
the flammability of fuel, vertical arrangement of fuel, moisture content of
fuel, wind speed and direction, relative humidity, the orientation of fire
(downhill or uphill) and the type of fire (surface fire, crown fire,
surface–crown transition).
Considering the foregoing fire spread parameters, PB can be
estimated as the relative frequency of wildfires' burning a certain spot,
given a number of small fires at different spots of the landscape (Scott et
al., 2013). Models developed for wildfire spread simulation include
empirical, semi-empirical, and physical models (Pastor et al., 2003). Some of
these models such as FARSITE
FARSITE is available from
https://www.firelab.org/project/farsite (last access:
20 September 2018).
(Finney, 1998) and BehavePlus (Andrews, 2013) need
detailed spatial information on topography, fuels, and weather conditions,
not readily available for many locations of interest. A comprehensive review
of wildfire simulation models can be found in Papadopoulos and
Pavlidou (2011). Less
sophisticated models and software have also been developed for fire spread
modeling and investigation of whether a small fire at point A would evolve as
a wildfire at point B in the landscape.
To estimate PB, fire spread models should simulate thousands of
wildfires from various ignition points (Finney, 2002). For instance, Fig. 6
schematizes a fire spread model
The program is available from
http://www.shodor.org/interactivate/activities/Fire/ (last access:
20 September 2018).
in which a random small fire (ignition) somewhere in the landscape
(Fig. 6a) evolves to a wildfire (Fig. 6b) and reaches an oil sands plant
(Fig. 6c). The probability of the wildfire reaching the oil facility can thus
roughly be estimated as
PB=nN,
where N is the total number of simulations, that is, the total number of
random small fires at different spots of the landscape; and n is the total
number of simulations in which a small fire turned out as a wildfire and
reached the facility.
Wildfire spread in a hypothetical landscape. (a) Random ignition
of a small fire in the landscape. (b) The small fire escalates as a
wildfire. (c) The wildfire reaches an oil facility.
Similar attempts have been made, for example, using NetLogo (Wilensky,
1997), which is a multi-agent programmable modeling environment, to model
fire spread though it is based on simplistic assumptions and uses tree density as
the only parameter.
Fire intensity
Head fire intensity (HFI) is the rate of heat release per unit length of the
fire head (kW m-1), regardless of the fire's depth. HFI, which is also known
as Byram's fire intensity or frontal fire intensity, can be calculated as
(Byram, 1959)
HFI=H⋅w⋅r,
where H (kJ kg-1) is the fuel's low heat of combustion, w (kg m-2) is the
fuel's combustion rate in the flaming zone, and r (m s-1) is the fire's spread
rate in the direction of the fire head (Fig. 7). H is equal to the high
heat of combustion minus the heat losses from radiation, incomplete
combustion, and fuel moisture. Compared to the other parameters in Byram's
fire intensity, H varies slightly from fuel to fuel and can thus be
considered as a constant. Alexander (1982) suggests a basic value of 18 700 kJ kg-1.
Different zones of a wildfire (adapted from Wikipedia).
Values of r and w, however, can vary significantly for different fuels.
Considering r, for instance, a grass fire may travel at a rate of r=5 km h-1, whereas fire
in a dry eucalypti forest may travel at a rate of r=1 km h-1 capable of throwing embers up to 1 km ahead of the fire (Cheney, 1990;
Cheney et al., 1998). As a result, HFI can vary from 15 to 100 000 kW m-1
(Byram, 1959), though it rarely exceeds 50 000 kW m-1, and for most crown fires lies in the range of 10 000–30 000 kW m-1 (Alexander, 1982).
Having the flame length, L(m), Byram (1959) has suggested Eq. (6) to
calculate the HFI of surface fires:
HFI=260L2.174.
In case of crown fires, one-half of the mean canopy height should be added
to L (Byram, 1959). Flame length (L), flame height (h), and the flame depth
(D) have been depicted in Fig. 8. At very low wind speeds on level
terrain, h and L can be considered to be the same. A thorough review of developed
relationships to calculate the fire intensity based on the fire length can
be found in Alexander and Cruz (2012).
Flame characteristics.
Based on the flame length (L), the fire intensity (HFI) can also be
classified into six classes (Scott et al., 2013) as listed in Table 2; this
way, the observations of L can be used to make rough estimates of HFI.
Flame length range associated with six standard fire intensity
classes.
The fire intensity classes in Table 2 can be associated with the wildfire
ranks used by the British Columbia Wildfire
Service
https://www2.gov.bc.ca/gov/content/safety/wildfire-status/about-bcws/wildfire-response/fire-characteristics/rank
(last access: 20 September 2018).
for a quick description of fire behavior based on wildfire visual
observations (Table 3). Similar classes to those in Tables 2 and 3 are also
provided by Canadian wildfire protection agencies such as Alberta Wildfire
(Fig. 9), which accordingly can be used to infer the flame length (L)
using Table 2 and then to estimate the fire intensity (HFI) using Eq. (6). As another option, the head fire intensity maps provided by the
Canadian Wildfire System (Fig. 4b) can be used to directly identify the
HFI.
Wildfire ranks used by the British Columbia Wildfire Service to
determine the fire intensity.
VisualizationRankDescriptionCharacteristics1Smouldering ground fire– Smouldering ground fire– No open flame– White smoke– Slow (i.e., creeping) rate of fire spread2Low vigor surface fire– Surface fire– Visible, open flame– Unorganized or inconsistent flame front– Slow rate of spread3Moderately vigorous surface fire– Organized flame front – fire progressing in organized manner– Occasional candling may be observed along the perimeter and/or within the fire– Moderate rate of spread4Highly vigorous surface fire with torch-– Grey to black smokeing, or passive crown fire– Organized surface flame front– Moderate to fast rate of spread on the ground– Short aerial bursts through the forest canopy– Short-range spotting5Extremely vigorous surface fire– Black to copper smokeor active crown fire– Organized crown fire front– Moderate to long-range spotting and independent spot fire growth6A blow up or conflagration;– Organized crown fire frontextreme and aggressive fire behavior– Long-range spotting and independent spot fire growth– Possible fireballs and whirls– Violent fire behavior probable– A dominant smoke column may develop which influences fire behavior
Having the flame depth (D), the frontal fire intensity (HFI) can be
converted to area-fire or reaction intensity Q (kW m-2) (Alexander,
1982):
Q=HFID.
Considering the flame as a solid body (Butler and Cohen, 2000; Heymes et
al., 2013), the amount of reaction intensity at a distance of x from the
flame's ground center (see Appendix B) can be calculated using the Solid Flame Model (Mudan, 1987) as
Qx=Q⋅Fview⋅τa,
where Fview, the view factor, is the fraction of the heat radiation
received by a receptor (Assael and Kakosimos, 2010), and τa∈
[0, 1] is the atmospheric transmissivity, corresponding to the fraction of
the thermal radiation received by the receptor considering the mitigation
effect of humidity and carbon dioxide as well as the dissipation due to the
distance. In the determination of safety zones, τa=1 is used for
conservative results (Heymes et al., 2013).
Wildfire intensity classes in Alberta, Canada (http://wildfire.alberta.ca, last access: 17 October 2017).
Impact of wildfire on oil storage tanks
During wildfires, the main threats to oil sands facilities – either the
process plant or the storage terminal – come from airborne embers and
radiant heat. The threat of airborne embers is even greater since they are
able to travel with wind for several kilometers ahead of the fire front. The
accumulation of airborne embers near tank openings and vents or under the
base of structures and process vessels, given enough vegetation or spilled
flammable hydrocarbons, can ignite a fire – also known as spotting
(FireSmart, 2012) – which may easily escalate to a major fire and possibly
a domino effect given the large inventory of flammable substances stored in
the facility.
Assessing the risk of wildfires' embers is very tricky considering several
influential parameters such as the direction and speed of the wind, the
trajectory of embers, the accumulation of embers near critical spots,
availability of on-site vegetation or spilled hydrocarbons, whose prediction
is subject to large uncertainties if not impossible. Despite the
difficulties in impact assessment of wildfire embers, simple protection and
mitigation measures can be taken to effectively reduce their threat. For
instance, limiting the use of floating roof tanks as the most common type of
tanks reportedly involved in tank fires (Godoy, 2016), encouraging the use
of cone roof tanks to prevent embers from landing around openings and vents,
turning the vents downward and covering the openings with wire mesh,
removing vegetation around tanks and combustible structures and equipping
the structures and storage tanks with sprinkler systems are some of the
measures to tackle the risk of airborne embers (FireSmart, 2012).
Aside from the impact of embers, the radiant heat emitted from the wildfire
can threat the integrity and safety of process vessels and storage tanks.
The type and severity of such an impact depends on the intensity of the radiant
heat received by target vessels as well as their type (atmospheric,
pressurized, pipeline, etc.) and dimension (usually their volume). Radiant
heat acts as a thermal load on the wall of the vessels, which are
categorized as thin-walled structures, and affects the stiffness and
strength properties of the wall material (usually steel in the oil and gas
industry).
In the case of atmospheric storage tanks such as oil and gasoline tanks,
this change in properties results in wall weakening and is usually followed
by large radial displacements in the form of buckling (Godoy, 2016).
Buckling of steel storage tanks subject to thermal loading has
been thoroughly investigated in Liu (2011) and Mansour (2012). A review of oil storage
steel tanks under different types of loads, including thermal loading, can
also be found in Godoy (2016). Exposed to external fires, empty or partially
filled storage tanks may receive temperatures up to 5 times higher than
completely filled tanks, and thus are more susceptible to buckling. For
partially filled tanks, there is even a jump between the temperature below
and above the liquid level (Liu, 2011).
In addition to the possibility of buckling, which endangers the integrity of
storage tanks, petroleum products may ignite spontaneously at their
auto-ignition temperatures in normal atmosphere without even direct
impingement of wildfire flames or airborne embers. The auto-ignition temperature
of most petroleum products is between 200 and 250 ∘C, well
below the temperature required for buckling of steel storage tanks and
easily reachable for storage tanks exposed to the radiant heat of wildfires. For
intact atmospheric storage tanks, the auto-ignition of flammable contents
would most probably lead to tank fires, while for damaged storage tanks with
spilled fuel in the catch basins, it would lead to pool fires.
For pressurized tanks such as LPG
Liquefied petroleum gas (LPG),
mostly consisting of propane and butane, is a flammable substance used as
fuel in heating, cooking, and vehicles.
tanks, on the other hand, a BLEVE (boiling liquid expanding vapor explosion) is the most
likely scenario. A BLEVE occurs when the increase in the internal vapor
pressure of the tank exposed to an external fire grows beyond the strength
of the already weakened tank wall, leading to the formation of a tear. If
the tear spreads to the entire length of the tank, a BLEVE occurs, followed
by a fireball; otherwise, a jet fire would be expected (Birk and Cunningham,
1994). In order to prevent an increase in the internal overpressure,
pressurized tanks are usually equipped with pressure relief valves or
fusible plugs, which are nevertheless likely to be damaged and fail to operate
(CSB, 2008). Furthermore, to prevent a BLEVE, the American Petroleum
Institute (API) has identified a maximum heat radiation intensity of 22 kW m-12
to which LPG tanks should be exposed (API, 1996). Performance
and safety of LPG tanks exposed to radiant heat of wildfires have been
investigated by Heymes et al. (2013).
An exemplary storage plant exposed to the heat of wildfire.
Despite the fact that the risk of radiant heat seems easier to quantify
(than the risk of airborne embers) based on current techniques and available
databases, it is missing in the available directives and guidelines. For
instance, FireSmart®, a Canadian field guide
for protecting oil and gas facilities against wildfires, identifies a
rule-of-thumb minimum safety distance of 3 m for propane tanks (pressurized
tank) from forest vegetation (FireSmart, 2012). However, Heymes et al. (2013) showed that even a
small fire of 2 m high and 5 m wide is able to
increase the internal pressure of LPG tanks and eventually lead to a BLEVE
and subsequent fireball.
To quantify the impact of a wildfire on an oil and gas facilities, the
damage probabilities of the process vessels exposed to the wildfire's
radiant heat (i.e., the primary vessels) as well as the damage probability
of neighboring vessels exposed to the heat radiation of fires at the primary
vessels need to be assessed. In this regard, dose–response relationships
which associate the damage probability of process vessels with the intensity
of received heat radiation can be used.
For instance, Cozzani et al. (2005) developed simplified probit functions to
correlate the time to failure (ttf) of vessels to their size and the
intensity of received heat (a minimum required value of 15 kW m-2 for
atmospheric vessels and 50 kW m-2 for pressurized vessels). Equations (9)–(11)
can be used to assess the damage probability of atmospheric process
vessels, including the storage tanks:
lnttf=-1.13lnQx-2.67×10-5V+9.9Y=12.54-1.85ln(ttf)P=ϕ(Y-5),
where ttf(s) is the time to failure of the exposed vessel (due to
the wildfire's heat or a primary tank fire's heat); QX (kW m-2) is the
received heat radiation by the vessel, calculated using Eq. (8); V (m3) is the volume of the vessel; Y
is the probit value; P is the
damage probability of the vessel; ϕ(.) is the cumulative standard normal
distribution. For the sake of exemplification, consider the hypothetical
tank farm in Fig. 10, where atmospheric storage tanks T1 and T2 are
exposed to the wildfire's radiant heat of greater than 15 kW m-2 and may
catch fire. Tank T3 is too far to be damaged directly by the wildfire's heat
radiation but may be damaged via a domino effect given wildfire-induced fires at
T1 or T2.
Given the characteristics of the wildfire, the location of the tank farm
(e.g., using Fig. 4b) and the distance of the storage tanks from the
head fire, the amount of radiant heat received by T1 and T2 can be
calculated using Eqs. (7) and (8); accordingly, the conditional damage
probabilities of the tanks given the wildfire, i.e., P(T1|wf) and
P(T2|wf), can be estimated using the probit functions given in
Eqs. (9–11). Given that the wildfire would ignite tank fires at
either T1 or T2, three mutually exclusive domino effect scenarios can be
envisaged in which T3 would be damaged and catch fire from either T1 or T2
(Fig. 11).
Wildfire-induced domino effect scenarios. (a) T1 catches fire
as it is exposed to the heat of the wildfire, and triggers secondary fires at T2 and T3
via a domino effect. (b) T2 catches fire as it is exposed to the heat of the wildfire, and
triggers secondary fires at T1 and T3 via a domino effect. (c) Both T1 and T2
catch fire as they are exposed to the heat of the wildfire, and trigger a secondary fire at T3
via a domino effect. Tanks directly impacted by the wildfire have been
highlighted yellow.
As a result, P(T3|wf) can roughly be estimated as the aggregation of
the three domino effect scenarios as P(T3|wf) =P(T3|wf)a+P(T3|wf)b+P(T3|wf)c, where
according to Fig. 11a,
P(T3|wf)a=P(T1|wf)⋅(1-P(T2|wf))⋅P(T3|T1)∪P(T2|T1)⋅P(T3|T2);
according to Fig. 11b,
P(T3|wf)b=(1-P(T1|wf))⋅P(T2|wf)⋅P(T1|T2)⋅P(T3|T1)∪P(T3|T2);
according to Fig. 11c,
P(T3|wf)c=P(T1|wf)⋅P(T2|wf)⋅{P(T3|T1)∪P(T3|T2)}.
Similar to P(T1|wf) and P(T2|wf), the conditional
probabilities P(T1|T2), P(T2|T1), P(T3|T1) and
P(T3|T2) can be estimated using probit functions in Eqs. (9)–(11) based on the amount of heat radiation a secondary tank receives
from fire at a primary tank. Having the conditional damage probabilities of
the storage tanks (conditioned on the occurrence of a wildfire of given
characteristics), the marginal damage probabilities, e.g., for T3, can be
calculated as P(T3)=Pw⋅P(T3|wf)=PI⋅PB⋅P(T3|wf).
For large oil and gas facilities with many process vessels of different types
and dimensions, for which complicated interaction among the process vessels
would not allow a manual calculation of damage probabilities, more
sophisticated techniques such as a Bayesian network (Khakzad, 2015) can be
employed.
Conclusions
The present study has been inspired by recent massive wildfires in the
province of Alberta, Canada, jeopardizing the operation and safety of
oil sands facilities as a key contributing factor to the nation's economy.
Despite the extensive oil sands operations in Canadian wildlands and an
ever-increasing risk of wildfires, mainly due to global warming,
quantitative methodologies for assessing and managing the risk of wildfires
in the context of natechs (i.e., technological accidents triggered by natural disasters) are lacking.
In the present study, we made an attempt to develop a risk assessment
methodology for wildfire-prone oil sands facilities by integrating the
Canadian online wildfire information system and available quantitative risk assessment (QRA) techniques.
Since the wildfire information system is updated on a daily basis, providing
forecasts for the same day and the next day, the developed methodology can
help facilities owners and safety managers predict the risk of wildfires at
least a day ahead of time and thus devise appropriate protection and
mitigation measures.
In most wildland oil and gas facilities, the separation distances (buffer
zones) between oil facilities and forest vegetation are usually determined
based on approximate analyses (e.g., in Canada, it is based on
FireSmart® guidelines). As such, similar
methodologies to the one proposed in the present study can be developed, not
only for the risk-based identification of more dependable buffer zones, but also
for the design of oil facilities so as to increase their robustness against
wildfire-induced damage and potential domino effect scenarios.
No data sets were used in this article.
Nomenclature
API:American Petroleum InstituteBUI:Buildup IndexD:flame depthDC:Drought CodeDMC:Duff Moisture CodeFBP:Fire Behavior PredictionFFMC:Fine Fuel Moisture CodeFWI:Fire Weather IndexFview:view factorh:flame heightH:fuel's low heat of combustionHFI:head fire intensityISI:Initial Spread IndexL:flame lengthP(.):marginal damage probability of target vesselP(.|wf):conditional damage probability of target vessel given a wildfireParr:probability of a smoldering fire escalating to a flaming firePB:burn probabilityPign:probability of ignition given a long-continuing currentPI:probability of ignitionPLCC:probability of a long-continuing currentPsur:probability that a smoldering ignition survivesPw:probability of wildfireQ:reaction intensityQx:heat radiation at the distance of xr:fire's rate of spread in the direction of the fire headROS:rate of spreadttf:time to failure of target vesselV:volume of target vesselw:fuel's combustion rate in the flaming zoneWIPP:wildfire ignition probability predictorx:horizontal distance from the flame's centerY:probit valueτa:atmospheric transmissivityϕ:cumulative standard normal distribution
Identification of view factors in the Solid Flame Model
Fview can be calculated as a function of vertical Fv and
horizontal Fh view factors as (Assael and Kakosimos, 2010)
Fview=Fv2+Fh2,
where
πFv=-Etan-1∅+Eα2+β+12-2β1+αsinθABtan-1A∅B+cosθCtan-1αβ-F2sinθFC+tan-1FsinθCπFh=tan-11∅+sinθCtan-1αβ-F2sinθFC+tan-1FsinθC-α2+β+12-2β+1+αβsinθABtan-1A∅Bα=LRβ=XRA=α2+β+12-2αβ+1sinθB=α2+β-12-2αβ-1sinθC=1+(β2-1)cos2θ∅=(β-1)/(β+1)E=αcosθβ-αsinθF=β2-1.
The angle of tilt, θ, can be calculated as a function of wind speed
uw as (Pritchard and Binding, 1992)
tanθcosθ=0.666Fr0.333Re0.117,
where Fr is the Froud number Fr=uw2g∅, and Re is
the Reynolds number Re=uwρa∅ηa, both
non-dimensional numbers. ρa and ηa are, respectively, the
density (∼1.21 kg m-3) and viscosity (∼16.7µPa s) of air; g is gravitational
acceleration (∼9.81 m s-2).
Flame as a tilted cylinder.
The author declares that there is no conflict of
interest.
Edited by: Rosa Lasaponara
Reviewed by: two anonymous referees
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