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Oil spill contingency and response (OSCAR) model system: Sensitivity studies

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OSCAR, an integrated system of models and databases, has undergone a sensitivity analysis as part of a calibration and testing program. A total of 48 simulations was performed in this sensitivity study, which focused on selected parameters that may directly or indirectly influence environmental evaluations: spreading, response actions, and threshold concentration for biological effects. The main conclusions from the study are as follows:The spreading coefficient, K, is a key parameter in calculating areal coverage and mass balance.The number of particles used to represent an oil slick has a minor influence on the mass balance, to the extent that it has an influence on the simulation of response actions.In calm weather, the effectiveness of mechanical cleanup only slightly affects the total amount of oil collected, but is decisive for the rate at which oil is collected. At higher wind speeds, the total amount of oil collected becomes a strong function of effectiveness.The amount of oil that is chemically dispersed by helicopter is more dependent on the delay due to trips for refueling and refilling than on the effectiveness of the dispersant.Oil spill response actions from boats may be sensitive to the number of particles used to represent the surface slick, depending on the search strategy specified. Response from aircraft is not sensitive to particle number.In the simulations performed here, the threshold concentration for biological effects only influences the fraction offish eggs and larvae in the interval 0 to 40 ppb-hours. The sensitivity study has shown the model to be numerically robust. The importance of specifying realistic search strategies for response vessels has also been made clear.
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Sensitivity analysis
A subset of the model’s system and user-defined parameters has been
studied in respect to how variations in them affect the model results. The
following scenario is used in the analysis:
Release of 200 tons of Troll crude
Constant wind speeds of 5 and 10 m/s
Constant temperature of 10°C
Mechanical cleanup based on minimum requirements given in
“Regulations relating to emergency preparedness in the petroleum
activities” (Norwegian Petroleum Directorate, 1994)
Dispersant application based on a recently developed helicopter
bucket
Mechanical cleanup is used when the text does not state differently.
Tables 1 and 2 summarize the parameters for mechanical cleanup and
dispersant application, respectively. The effectiveness given in the
tables is defined as the percentage of treated oil (that is, oil that has
entered the boom, or oil that has been treated with dispersants) that is
actually recovered or dispersed.
Spreading. Most processes that take place in the model are dependent
on the area of the oil slick. A slick covering a large area will be subject to
a larger number of breaking waves, and thereby will disperse faster than
a thicker slick containing the same amount of oil. Also, the process of
evaporation will be more rapid when the oil is spread over a larger area.
The size of the slick also determines the effectiveness of oil spill response
actions. In other words, the model that estimates the rate at which an oil
slick spreads will influence the results given by most of the submodels.
In OSCAR, spreading is calculated according to Mackay et al. (1980):
Where: Ais the area of the slick (m2)
his the thickness of the slick (m)
tis time (s)
K<5780 is an empirical constant (s21)
In the model, spreading stops when the slick has reached a minimum
thickness, depending on the oil type in question. In this case, the mini-
mum thickness is the following:
hmin .=05µm
dA
dt Kh A=133 033..
ABSTRACT: OSCAR, an integrated system of models and databases,
has undergone a sensitivity analysis as part of a calibration and testing
program. A total of 48 simulations was performed in this sensitivity
study, which focused on selected parameters that may directly or indi-
rectly influence environmental evaluations: spreading, response actions,
and threshold concentration for biological effects. The main conclusions
from the study are as follows:
The spreading coefficient, K, is a key parameter in calculating
areal coverage and mass balance.
The number of particles used to represent an oil slick has a minor
influence on the mass balance, to the extent that it has an influence
on the simulation of response actions.
In calm weather, the effectiveness of mechanical cleanup only
slightly affects the total amount of oil collected, but is decisive for
the rate at which oil is collected. At higher wind speeds, the total
amount of oil collected becomes a strong function of effective-
ness.
The amount of oil that is chemically dispersed by helicopter is more
dependent on the delay due to trips for refueling and refilling than
on the effectiveness of the dispersant.
Oil spill response actions from boats may be sensitive to the num-
ber of particles used to represent the surface slick, depending on
the search strategy specified. Response from aircraft is not sensi-
tive to particle number.
In the simulations performed here, the threshold concentration for
biological effects only influences the fraction of fish eggs and lar-
vae in the interval 0 to 40 ppb-hours.
The sensitivity study has shown the model to be numerically robust.
The importance of specifying realistic search strategies for response
vessels has also been made clear.
The OSCAR model system (Reed et al., 1995a; Aamo et al., 1996)
has been developed to supply the public and private sectors with a
tool for an objective analysis of alternative spill response strategies.
Key components of the system, shown schematically in Figure 1, are
IKU’s data-based oil weathering model (Aamo et al., 1993; Daling
et al., 1990, 1991), a three-dimensional oil trajectory and fates model
(Reed et al., 1995b), and an oil spill combat model (Aamo et al., 1995,
1996).
OSCAR has been applied to the analysis of alternative oil spill
response strategies for both offshore platforms (Aamo et al., 1995;
Reed et al., 1995a) and coastal terminals (Reed et al., 1996b). In
evaluating these analyses, the Norwegian State Pollution Authorities
addressed the importance of a calibration, testing, and sensitivity
study to establish model credibility. The work is reported in Reed
et al. (1996a; calibration and testing) and in this paper (sensitivity
studies).
OIL SPILL CONTINGENCY AND RESPONSE (OSCAR)
MODEL SYSTEM: SENSITIVITY STUDIES
Ole Morten Aamo, Mark Reed, and Keith Downing
IKU Petroleum Research
N-7034 Trondheim
Norway
429
In addition to the gravity-viscous spreading given in the preceding
equation, natural dispersion will also affect the spreading of oil, in that
oil is driven into the water column and resurfaces as blue sheen (with
thickness equal to hmin in the model) behind the main slick after a cer-
tain period of time. For a given wind scheme, the spreading will typi-
cally be dominated by one of the effects: in calm weather, the gravity-
viscous equation will govern the spreading, whereas in rough weather
the process of natural dispersion will be the dominating factor.
Figures 2 and 3 show the total area covered by oil and the area cov-
ered by thick oil (.20 µm), respectively, for 5 m/s wind and K5Korg/5,
K5Korg, and K52Korg, where Korg is the original value of Kø5780s21.
The figures show that a change in Kleads to large variations in both total
area and area of thick oil. From this we can expect differences in evap-
orative loss, natural dispersion, and mass recovered. Computed mass
balances show that the differences are considerable, with lifetimes for
the surface slick of approximately 18 hours, 3.5 days, and 5 days,
respectively. The mass balances show that the cleanup rate decreases,
430 1997 INTERNATIONAL OIL SPILL CONFERENCE
whereas the rate of evaporation and dispersion increases with increas-
ing K. In 10-m/s wind, these considerations become less evident, in that
natural dispersion now dominates areas and mass balances (Figures 4
and 5). An increasing Kgives a considerable increase in dispersed oil,
which in turn leads to large areas of sheen being formed.
Figures 6 and 7 show total area and area of thick oil, respectively, for
5-m/s wind and hmin 5hmin,org/5, hmin 5hmin,org, and hmin 52hmin,org, where
hmin,org is the original value of hmin 50.5 mm. The figures show that, for
the two thickest slicks, just small changes in area are recorded, whereas
for the thinnest slick the value of hmin is approaching a limit for calcu-
lating surface area, where the combination of the number of particles
used to represent the slick and the minimum thickness introduce con-
siderable noise into the calculations. This effect is discussed in more
detail in the next section. The mass balances show only small differ-
ences as a function of hmin. In 10-m/s wind, the difference in total area
between the two thickest slicks is evident in the beginning of the simu-
lation, and then decreases somewhat as the simulation proceeds. The
thinnest slick, however, has a much larger total area than the others. The
area of thick oil is practically equal in the three cases. As for the 5-m/s
case, the mass balances show only small variations.
Number of particles for representing the surface slick. In the
model, an oil slick is represented by many small particles that all are
subject to spreading, evaporation, and emulsification. Any given parti-
cle can either lie on the surface or be submerged in the water as droplets
of certain sizes. In this way, natural dispersion is also simulated by the
particle representation. To obtain a good representation of the oil slick
as a whole, a great number of particles is needed. The number of parti-
cles is especially important for the dispersion process, since dispersion
is discretized in a whole number of particles (a particle cannot be partly
submerged and partly on the surface).
Simulations were performed for three different numbers of particles
(100, 500, and 1000). For 5-m/s wind and 100 particles, the total area
calculations are noisy because of natural dispersion being discretized in
a whole number of particles. However, this problem vanishes when 500
and 1000 particles are used. The differences between the 500 and 1000
particles cases are small, and are caused by cleanup being somewhat
dependent on the number of particles (this problem is discussed in more
detail in the section dealing with search criteria for mechanical cleanup
equipment). It is worth noting that the noisy behavior in the area calcu-
lations does not influence the mass balances much, since the noise is
caused by sheen, which has a large area but a small mass. Sheen also has
a relatively short lifetime.
As for the 5-m/s wind, the 10-m/s cases are relatively similar. How-
ever, the total area calculations are heavily affected by noise when 100
particles are used. Some noise can also be seen for the 500- and 1000-
particle cases. The increased noise level is caused by the more domi-
nating dispersion process at higher winds.
Effectiveness of response equipment. The effectiveness of an oil
spill response action in OSCAR depends on a set of parameters that the
user has to specify. Most of the parameters are usually known, and they
describe the physical characteristics of the equipment. Such parameters
may be given by the manufacturer or may be based on tests performed
by third parties. However, one particular parameter that must be speci-
ed, which often is difficult to estimate, is the instantaneous effective-
ness of the equipment. In OSCAR, an effectiveness must be specified
for both equipment for mechanical cleanup and equipment for the appli-
cation of dispersants.
Mechanical response. In the model, the instantaneous effectiveness
of mechanical cleanup equipment (skimmer/boom system) affects the
rate of cleanup as follows:
Figure 1. Schematic overview of the OSCAR system
Table 1. Parameters for mechanical cleanup
actions
Maximum recovery rate 50 tons/hour
Operational speed 1 knot
Cruise speed 8 knots
Boom opening 56 meters
sweep area 0.1 km2/hour
Effectiveness 60% at 10 m/s wind
80% at 5 m/s wind
Table 2. Parameters for chemical dispersion
Maximum application rate 450 L/min.
Onboard dispersant tankage 2.5 m3
Operational speed 58 knots
Cruise speed 100 knots
Application width 25 meters
Effectiveness 60% at 10 m/s wind
80% at 5 m/s wind
Where: ris the rate at which emulsion is removed from the sea (m3/s)
rmax is the maximum pump capacity, specified by the manufac-
turer (m3/s)
rboom is the rate at which emulsion enters the boom (m3/s)
bis the width of the boom opening (m)
vis the operational speed (m/s)
his the oil slick thickness at the boom opening (m)
eis the instantaneous effectiveness (%)
If the effectiveness is set to, for instance, 80%, the cleanup rate will
be 80% of the rate at which emulsion enters the boom, but will be lim-
ited to 80% of the maximum pump capacity. In other words, the effec-
tiveness specifies leakage from the boom when rboom is the limiting fac-
tor, and overloading of the pump when rmax is the limiting factor. The
rer
r bvh
==
{
100% min max
boom
1997 INTERNATIONAL OIL SPILL CONFERENCE 431
effectiveness of the skimmer/boom system will typically vary with wind
and sea state, the viscosity of the emulsion, and how well the operators
are trained in using the equipment. These factors have to be taken into
account when the instantaneous effectiveness is estimated.
Simulations over 5 days were performed for four different effective-
ness values. Figure 8 shows the amount of oil recovered at 5-m/s wind.
Figure 9 shows the corresponding surface exposure. Surface exposure
is calculated as follows:
Where: E(tn) is surface exposure at time tn(m2s)
wiis the width of the slick perpendicular to the direction of
drift at time ti(m)
siis the distance the slick has drifted since last timestep (m)
Dtiis the ith timestep (s)
tnis time (s)
E t w s t
n i i i
i
n
(
)
==
1
Figure 2. Change in total surface area over time for three values of the spreading coeffi-
cient, 0.2
K
,
K
, and 2
K
, in a 5-m/s wind
Figure 3. Change in area of thick oil (.20 µm) over time for three values of the spreading
coefficient, 0.2
K
,
K
, and 2
K
, in a 5-m/s wind
The surface exposure is assumed to be proportional to the number of
birds that will be affected by the slick, assuming an even distribution of
birds on the sea surface.
Figures 8 and 9 show that in calm weather, when the degree of nat-
ural dispersion is low, the lifetime of the oil and thereby the exposure
will be strongly dependent on the effectiveness of the oil spill response
action. However, the total amount of oil eventually recovered does not
depend much on the effectiveness.
Figures 10 and 11 show the corresponding information at 10-m/s
wind. In this case, the lifetime of the oil is less dependent on the
effectiveness of the recovery units, since natural dispersion now
makes an important contribution to the removal of oil from the sur-
face. This leads to small differences in surface exposure (see
the curves for 50%, 70%, and 90% in Figure 11). The amount of
oil eventually recovered is more dependent on the effectiveness in
this case.
432 1997 INTERNATIONAL OIL SPILL CONFERENCE
Chemical response. The instantaneous effectiveness of dispersant
application affects the rate of dispersion in the model as follows:
Where: ris the rate at which oil is successfully treated with dispersant
(m3/s)
rmax is the application rate (m3/s)
bis the application width (m)
vis operational speed (m/s)
his the oil slick thickness (m)
eis the instantaneous effectiveness (%)
DOR is the dispersant-to-oil ratio (typically 1:20)
rerbvh
=
{
100% min /
max DOR
Figure 4. Change in total surface area over time for three values of the spreading coeffi-
cient, 0.2
K
,
K
, and 2
K
, with a 10-m/s wind
Figure 5. Change in area of thick oil (.20 µm) over time for three values of the spreading
coefficient, 0.2
K
,
K
, and 2
K
, in a 10-m/s wind
If the effectiveness is set to, for instance, 80%, 80% of the dispersant
that is applied will successfully treat the underlying oil. The phrase suc-
cessfully treated means that the oil will disperse gradually with time. In
other words, the effectiveness specifies the effectiveness of the disper-
sant on the given oil, as well as the accuracy of the application itself. It
will typically vary with wind and sea state, the viscosity of the oil, and
how well the operators are trained in finding and spraying the thick parts
of an oil slick.
Oil that is successfully treated will disperse according to a first-order
decay process with a halftime of 1 hour.
In the simulations, dispersant is applied continuously, only inter-
rupted for the refueling and refilling of dispersants. Simulations over
3 days were performed for two cases, each with varying values for the
instantaneous effectiveness:
1. Refueling and refilling of dispersants close to the spill site, result-
ing in nearly continuous application
2. Refueling and refilling at a distance from the spill site, such that
the trips are 3 hours apart
1997 INTERNATIONAL OIL SPILL CONFERENCE 433
Figure 12 shows the amount of dispersed oil as a function of time
for effectiveness of 10%, 50%, and 70% and approximately no delay
during refueling and refilling of dispersant. The figure shows nearly the
same result for 70% and 50%, whereas for 10% the dispersion process
is somewhat slower. The small differences are due to the small differ-
ences in the duration of the spraying operation compared to the time that
the dispersion process takes. However, the amount of dispersant used in
the three cases is very different.
Figure 13 shows the corresponding information when there is a
3-hour delay between each trip. The figure clearly shows that the sensi-
tivity of the system is strongly dependent on the delay between each
application trip. In other words, the instantaneous effectiveness given
for a system affects the results more the longer the entire operation lasts
compared to the rapidity of the dispersion process itself.
The effectiveness directly influences the amount of dispersant used.
In the cases in Figure 13, the amount of dispersant used is 11.4, 16, and
80 m3for 70%, 50%, and 10% effectiveness, respectively. If the stock
of dispersant was limited to, say, 16 m3in all cases, the response action
would have to stop after about 1 day. At that time, no oil would be left
Figure 6. Change in total surface area over time for three values of the minimum oil film
thickness, 0.2
h
min,
h
min, and 2
h
min, in a 5-m/s wind
Figure 7. Change in area of thick oil (.20 µm) over time for three values of the minimum
oil film thickness, 0.2
h
min,
h
min, and 2
h
min, in a 5-m/s wind
on the surface for 50% and 70% effectiveness, but for 10% effective-
ness, 100 tons of oil would still be left on the surface.
Search criteria for response actions. In OSCAR, response equip-
ment is positioned according to one of the following rules:
1. A certain limited geographical area is given in which the equip-
ment must operate.
2. Search for fresh oil. Makes the system move close to the source.
3. Search for old oil. Makes the system work at the downwind front
of the slick.
4. Search for thick oil.
5. Search for the closest oil that is thicker than some specified thick-
ness (e.g., the average thickness of the slick).
Simulations have shown that alternatives 4 and 5 are the most effec-
tive, so we will focus on those (other rules for positioning of equipment
are easy to incorporate into the OSCAR system, if needed).
434 1997 INTERNATIONAL OIL SPILL CONFERENCE
Mechanical response. Figure 14 shows the amount of oil recovered
for search criteria 4 and 5, using 500 and 1000 particles to represent the
oil slick. The highest effectiveness is obtained by searching for the clos-
est oil (search criterion 5). The reason for this is that, by moving the
equipment to the closest particle having a thickness over a certain limit,
instead of moving to the overall thickest particle, one spends less time
moving the equipment between particles and more time recovering oil.
Search criterion 5 is also more robust with respect to variations in the
number of particles used. When the number of particles is increased, the
equipment will have to move between particles more often. For search
criterion 5, this does not influence the results a great deal because the
equipment will have to move a shorter distance each time, since the par-
ticles are closer together. However, the distance to the overall thickest
particle will on average be the same. Thus search criterion 4 depends
strongly on the number of particles.
Chemical response. The search criteria are the same for chemical
response as for mechanical response, and the same considerations as in
Figure 8. Oil recovered over time for four values of instantaneous effectiveness for
mechanical response in a 5-m/s wind
Figure 9. Surface exposure over time for four values of instantaneous effectiveness for
mechanical response in a 5-m/s wind
text preceding are valid for chemical response. Variations in search
criteria and the number of particles do not affect the amount of oil
dispersed when dispersant is continuously applied from a helicopter.
This is caused by the rapid movement of the helicopter between
particles. If the dispersant is applied from a boat, which works at a
much lower speed, we will see the same effects as for mechanical
cleanup.
Limit for biological effects. The model calculates the exposure
to dissolved hydrocarbons that fish eggs and larvae are subject to
in an oil spill. The exposure calculations are based on a concen-
tration field, which is calculated as the sum of the concentration
fields around a number of particles. The concentration field around a
particle is assumed to be Gaussian-distributed, which in theory is
infinite in extent. Thus, when calculating a concentration field in
a finite grid, we have to choose a cutoff or threshold concentra-
tion, below which no biological effects are expected. The cutoff con-
centration is currently set to Cmin 52 ppb (this may be altered by
the user).
1997 INTERNATIONAL OIL SPILL CONFERENCE 435
Simulations were performed with Cmin 5Cmin,org/5, Cmin 5Cmin,org/2,
and Cmin 5Cmin,org where Cmin,org is the original value of 2 ppb. The stan-
dard scenario was used, with no response.
Within a defined working area that completely surrounds the concen-
tration field during the entire simulation period, fish eggs and larvae are
assumed to be evenly distributed down to a depth of 10 meters.
Figure 15 shows to which extent fish eggs and larvae have been
exposed to the contaminant. For Cmin 5Cmin,org 52 ppb, 10% of all fish
eggs and larvae have an exposure between 0 and 20 ppb-hours, while a
total of about 15% have been affected by the spill.
When the cutoff value decreases, the volume of water affected
increases, and we can expect a larger portion of fish eggs and larvae to
be exposed. This is confirmed by the figure, which shows that 25% and
20% of the fish eggs and larvae are exposed when Cmin 52/5 and Cmin
52/2, respectively. However, the difference is contained in the first two
intervals, 0 to 40 ppb-hours, and no shift toward higher exposure levels
is seen for the intervals above 40 ppb-hours as a result of lowering the
threshold concentration.
Figure 10. Recovered oil over time for four values of instantaneous effectiveness for
mechanical response in a 10-m/s wind
Figure 11. Surface exposure over time for four values of instantaneous effectiveness for
mechanical response in a 10-m/s wind
Discussion and conclusions
A total of 48 simulations was performed in this sensitivity study. The
study has focused on selected parameters that may directly or indirectly
influence environmental evaluations: spreading, response actions, and
threshold concentration for biological effects. The main conclusions
from the study are as follows:
The spreading coefficient, K, is a key parameter in calculating areal
coverage and mass balance. Kis currently constant, but should vary
with the viscosity of the oil (and thereby with the oil type and its
emulsion formation properties).
The minimum thickness for an oil slick has limited influence on
areal coverage of thick oil and practically no influence on the mass
balance. However, the total areal coverage, including sheen, is
greatly affected by the minimum thickness.
436 1997 INTERNATIONAL OIL SPILL CONFERENCE
The number of particles used to represent an oil slick has a minor
influence on the mass balance, to the extent that it has an influence
on the simulation of response actions. Noise is introduced into the
area calculations when too few particles are used. The noise is
caused by natural dispersion being discretized in a whole number
of particles, and increases with increasing wind speed (due to
increasing natural dispersion).
In calm weather, the effectiveness of mechanical cleanup only
slightly affects the total amount of oil collected, but is decisive for the
rate at which oil is collected. At higher wind speeds, the total amount
of oil collected becomes a strong function of effectiveness, in that it
competes with natural dispersion in removing oil from the surface.
The effectiveness of mechanical cleanup strongly affects surface
exposure in calm weather, but only slightly in rough weather.
The amount of oil that is chemically dispersed by helicopter is more
dependent on the delay due to refueling and refilling than on the
Figure 12. Dispersed oil over time for three values of instantaneous effectiveness for
chemical response. Nearly no delay between application trips.
Figure 13. Dispersed oil over time for three values of instantaneous effectiveness for
chemical response. Three-hour delay between application trips.
effectiveness of the dispersant. However, the amount of dispersant
applied is directly dependent on the effectiveness.
Oil spill response actions from boats may be sensitive to the num-
ber of particles used to represent the surface slick, depending on the
search strategy specified.
Oil spill response from aircraft, when the movement between dif-
ferent parts of the slick is very rapid, is not dependent on the num-
ber of particles used to represent the surface slick.
In the simulations performed here, the threshold concentration for
biological effects only influences the fraction of fish eggs and
larvae in the interval of 0 to 40 ppb-hours, indicating that a higher
fraction of the total population is affected with a decreasing thresh-
old. For the fish eggs and larvae affected at a certain threshold,
1997 INTERNATIONAL OIL SPILL CONFERENCE 437
no shift toward higher exposures is seen when the threshold is
lowered.
The sensitivity study has shown the model to be numerically robust.
The importance of specifying realistic search strategies for response
vessels has also been made clear.
Acknowledgments
This work was financed by the Norwegian State Pollution Agency,
the Norwegian Clean Seas Organization, Saga Petroleum a.s., Amoco
Norway Oil Company, and Norsk Hydro a.s., as well as by internal
nancing by IKU Petroleum Research.
Figure 14. Recovered oil over time for two search criteria (closest and thickest oil) and two
values for number of particles (500 and 1000)
Figure 15. Exposures of fish eggs and larvae for three values of the cutoff value for
effects
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... Recently, many models have been developed to simulate oil weathering processes and the changes of oil properties. Some of the widely used models include: the General National Oceanic and Atmospheric Administration (NOAA) Operational Modelling Environment (GNOME) with the Automated Data Inquiry for Oil Spills (ADIOS2) (Beegle-Krause, 1999, 2001Beegle-Krause and O'Connor, 2005;Elizaryev et al., 2018;Torres et al., 2020); the Oil Spill Contingency and Response (OSCAR) model (Reed et al., 1995;Aamo et al., 1997;Reed et al., 1999;Reed et al., 2004;Nordam et al., 2020;Barreto et al., 2021); and the OIL-MAP™ (Okalnd Gjosteen et al., 2003;Applied Science Associates Inc, 2009;SL Ross et al., 2010;Toz and Buber, 2018;Chen, 2021). Nevertheless, these models were developed mainly focusing on simulation of conventional oil spills. ...
... In addition to natural dispersion, the effectiveness of chemical dispersants (e.g., Corexit 9500) is further considered in the development of DBWM with the experimental data from the COOGER (EC, DFO and NRC, 2013) and the simulation model for the application of chemical dispersant from the Oil Spill Contingency and Response (OSCAR) Model (Aamo et al., 1997): ...
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A R T I C L E I N F O Keywords: Diluted bitumen Oil weathering model Marine oil spill modelling DilBit behaviour A B S T R A C T To help better assist the management of Diluted bitumen (DilBit) spills in marine environment, a model named as DilBit Weathering Model (DBWM) was developed in this study to simulate DilBits weathering in marine environment. The DBWM was developed based on specific algorithms for evaporation, dispersion, biodegradation, as well as density and viscosity changes for DilBit weathering and other widely used algorithms for conventional oil weathering in marine environment. To validate the model, a series of DilBit weathering simulation were conducted and compared with the experimental data. Furthermore, the performance of DBWM was compared with a widely used oil weathering model (Automated Data Inquiry for Oil Spills, ADIOS2). The results demonstrated the feasibility and advantages of the developed DBWM in simulating the weathering of marine DilBit spills. Thus, the proposed DBWM can provide effective decision support to marine DilBit spill management.
... Oil-ice interaction is taken into account as a surface process [66]. Furthermore, into the water column, horizontal and vertical advection, and dispersion of oil are simulated via random walk Schemes [63,67]. As for degradation and sedimentation of oil are described as a first order decay method [68]. ...
... As for degradation and sedimentation of oil are described as a first order decay method [68]. Vital components of OSCAR are the data-based oil weathering model developed by SINTEF [69][70][71], a three-dimensional oil trajectory and chemical fates model [72] and an oil spill combat model [67]. OSCAR oil database contains oil properties and weathering data [73]. ...
Chapter
This chapter presents an overview and inter-comparison of the main characteristics of the known well-established oil spill models applied in the Black Sea and the Sea of Azov. Since the development of the MyOcean Black Sea Forecasting Centre and, later on, of the Copernicus Black Sea Monitoring Forecasting Centre (CMEMS Black Sea MFC), several oil spill models have been implemented during preparedness exercises and after oil slicks detected from satellite remote sensing Synthetic Aperture Radar (SAR) data. Particularly, in the framework of pilot projects with the European Maritime Safety Agency CleanSeaNet (EMSA-CSN), 24 h forward and backtracking spill predictions were initiated in near real time using the satellite data provided through EMSA-CSN portal. In addition, several European Commission projects, for example, the European Marine Observation and Data Network (EMODnet) Black Sea Check points addressed issues related to oil leakages in the Black Sea taking advantage of the operational met-ocean forecasting in the region, is summarized in the chapter. Examples of oil spills modelling applications in the Black Sea and the Sea of Azov are presented using met-ocean forecasting data and satellite data. The catastrophic Volgoneft-139 oil spill in the Kerch Strait, in November 2007, is described. Several contemporary well-established oil spill modelling systems, three of them, MEDSLIK, MEDSLIK-II, BlackSeaTrackWeb were applied during emergencies and warnings, while the rest examined modelling systems, GNOME, MOTHY, OILTOX, OILMAP, OSCAR were applied during dedicated risk assessments. All these oil spill modelling systems implemented in the Black Sea and the Sea of Azov were inter-compared in terms of their characteristics concerning the physical/chemical spill processes, number of different oil types, type of oil discharges, initial slick shape, type of slick discharges and areas of oil spill model’s applicability.
... The floating object was parametrised for the Oil Spill Contingency and Response (OSCAR) numerical model (Aamo et al., 1997) as a passive-drifting, floating particle transported by the surrounding current and wind conditions. To 'back-track' the drift trajectory of the object, the forces acting on the floating object were reversed in time, permitting the drifting history of the object to be re-traced (method discussed in Kako et al., 2014;Suneel et al., 2016). ...
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The long-distance transfer of non-native, potentially invasive species via floating marine debris is an increasing threat to biodiversity and conservation efforts. To address the lack of understanding around mechanisms and pathways of species transfer via marine debris, a novel modelling approach was applied to recreate the likely trajectory and source of a large piece of debris fouled by non-native species collected from UK marine waters. This approach applied the Oil Spill Contingency and Response (OSCAR) simulation tool, an adapted oil spill modelling programme, which was informed by a combination of biological trait information for the foulant species, marine debris characteristics and hydrodynamic data. The modelling output suggested an origin in the Western Atlantic, a scenario concurrent with the known distribution of the foulant species. This modelling approach represents a valuable tool with which to determine the origin and trajectory of invasive species transferred via marine debris.
... The response to contingency actions simulates the rate of emulsion removal due to mechanical response (e.g. skimmer or boom systems) and the rate of dispersion due to chemical response (dispersants), based on the input parameters of effectiveness of the response method (Aamo et al., 1997). OILMAP (Jayko and Howlett, 1992;Spaulding et al., 1994) estimates the trajectory of the oil particles according to the sea currents, wind and a random turbulent dispersion. ...
Article
This work proposes a procedure for the Computational Fluid Dynamics (CFD) simulation of an oil spill at a large domain. The use of CFD in a large domain usually has restrictions due to the discretization that is needed for good accuracy. The problem increases as the wind effects over a wavy sea surface must be taken into account. To overcome these difficulties, these effects are included in the boundary conditions, through analytical models or simplified simulations. The effect of the wind is commonly determined by a wind drift factor that estimates the wind induced sea surface velocity as a percentage of the wind speed. In this paper, rather than using fixed or random values for the wind drift factor, a methodology based on CFD simulations of the wind acting over the sea surface is used together with analytical formulations, in order to reduce the subjectivity in its estimation. In addition, to avoid modelling the waves and the air phase, the wind effects are simulated as an equivalent shear stress boundary condition. This procedure is used in the simulation of the Foss Barge — Point Wells oil spill. The results of the oil spill trajectory are compared to the observed trajectory.
... The weathering and trajectory models are commonly integrated to provide a comprehensive prediction of oil spills and facilitate oil spill management. Recently, several models have been developed or integrated for oil spill transport and fate simulation, such as the Oil Spill Contingency and Response (OSCAR) model (Reed et al., 1995(Reed et al., , 1999a(Reed et al., , 2004Aamo et al., 1997;Nordam et al., 2020;Barreto et al., 2021); the OILMAP ™ model (Mackay et al., 1980a;Mackay, 1980;Toz and Buber, 2018;Chen, 2021;RPS-ASA, 2022); the General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) with the Automated Data Inquiry for Oil Spills (ADIOS2) (Beegle-Krause, 1999Elizaryev et al., Frontiers in Environmental Science | www.frontiersin.org June 2022 | Volume 10 | Article 910365 2018; Torres et al., 2020); and the OpenDrift model integrated with the integrated oil spill transport and fate sub-module (OpenOil) Röhrs et al., 2018;Keramea et al., 2022). ...
Article
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Bitumen, an unconventional crude oil, has received much attention with the increasing consumption and the shrinking storage of conventional crude oils. Bitumen is highly viscous and, thus, is commonly diluted for transportation purposes. Spills of diluted bitumen could occur during the transportation from reservoirs to refineries via pipeline, rail, and marine vessels. Although some laboratory and numerical modeling studies have been contributed to study the spill of diluted bitumen from different aspects, there is no systematic review in the field yet. Therefore, this study first conducted a review on different types of diluted bitumen based on their physicochemical properties, followed by their weathering processes including spreading, evaporation, emulsification, photooxidation, biodegradation, and sinking. Second, the numerical modeling on the fate and behavior of spilled diluted bitumen was summarized and analyzed. Finally, the techniques for spilled oil recovery were discussed, as well as the disposal/treatment of oily waste. Currently, a rare attempt has been made to turn the recovered oily waste into wealth (reutilization/valorization of oily waste). Using the recovered oily waste as the feedstock/processing medium for an emerging thermochemical conversion technique (hydrothermal liquefaction of biomass for crude bio-oil production) is highly recommended. Overall, this article summarized the state-of-the-art knowledge of the spill of diluted bitumen, with the hope to create a deep and systematic understanding on the spill of diluted bitumen for researchers, relevant companies, and decision makers.
Article
Oil fate and exposure modeling addresses the complexities of oil composition, weathering, partitioning in the environment, and the distributions and behaviors of aquatic biota to estimate exposure histories, i.e., oil component concentrations and environmental conditions experienced over time. Several approaches with increasing levels of complexity (i.e., aquatic toxicity model tiers, corresponding to varying purposes and applications) have been and continue to be developed to predict adverse effects resulting from these exposures. At Tiers 1 and 2, toxicity-based screening thresholds for assumed representative oil component compositions are used to inform spill response and risk evaluations, requiring limited toxicity data, analytical oil characterizations, and computer resources. Concentration-response relationships are employed in Tier 3 to quantify effects of assumed oil component mixture compositions. Oil spill modeling capabilities presently allow predictions of spatial and temporal compositional changes during exposure, which support mixture-based modeling frameworks. Such approaches rely on summed effects of components using toxic units to enable more realistic analyses (Tier 4). This review provides guidance for toxicological studies to inform the development of, provide input to, and validate Tier 4 aquatic toxicity models for assessing oil spill effects on aquatic biota. Evaluation of organisms' exposure histories using a toxic unit model reflects the current state-of the-science and provides an improved approach for quantifying effects of oil constituents on aquatic organisms. Since the mixture compositions in toxicity tests are not representative of field exposures, modelers rely on studies using single compounds to build toxicity models accounting for the additive effects of dynamic mixture exposures that occur after spills. Single compound toxicity data are needed to quantify the influence of exposure duration and modifying environmental factors (e.g., temperature, light) on observed effects for advancing use of this framework. Well-characterized whole oil bioassay data should be used to validate and refine these models.
Article
Marine oil spill pollution have increased recent years, threatening the safety of the marine environment. This paper proposes a coupling technique: Fast Mapping & Addressing(FMA) that integrates the oil spill model with oceanographic model. The FMA technique is based on hash function and spatial quadtree algorithm to achieve efficient addressing from an unstructured to a structured grid. The oil spill model simulates the oil spill process, while the ocean model simulates the ocean currents. The efficiency is improved about ten times compared to the interpolation algorithm. Results reveal the difference between the simulation results of the ocean model and the measured data is minimal, with an MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of about 0.06 m. Moreover, two oil spill events in China's nearshore were selected to simulate and verify the results. Indeed, our model's results agree with the observed data, demonstrating that our model can achieve a satisfied simulation of oil spill.
Article
The ocean floor is littered with thousands of wrecks containing hazardous fuels, lubes, armaments and cargoes that have polluted, are polluting, or will pollute the marine environment. The UK Ministry of Defence manages the environmental risk associated with its ~5,700 military wrecks through the Wreck Management Programme. This paper explores the methods used to assess the environmental risk associated with the wreck of the Royal Fleet Auxiliary (RFA) tanker RFA War Mehtar , which lies at approximately 39 m depth, 15 nm east of Great Yarmouth, United Kingdom. The methods employed include archival research, computational oil spill modelling combined with sensitivity mapping, environmental sampling and hydrocarbon concentration analysis, multibeam echosounder sonar surveys and neutron backscatter probe measurements. Each method is described, and its efficacy discussed. The archival research was essential to determine what fuel and cargo the RFA War Mehtar sank with; but only when high-resolution multibeam sonar images were combined with the ship’s plans and neutron backscatter measurements from individual tanks could one confidently conclude that the wreck does not pose a significant pollution risk.
Chapter
Decision support tools for managing marine oil spills allow us to analyze hypothetical polluting events that could affect the coasts. This can aid in proposing prevention or mitigation measures against accidental oil spills. This chapter about oil spill models highlights its potential as management tool in contingency plans.
Article
Oil spills during offshore operations are likely to cause severe contamination of the sea. The identification of the environmental effects of accidental releases from offshore oil and gas facilities plays a crucial role in the prevention and mitigation of marine pollution. Key Performance Indicators (KPIs) are largely recognized as an effective tool to address and communicate multifaceted issues related to accidental events in the framework of risk management. In the context of Oil Spill Risk Assessment (OSRA) studies, this study proposes a set of KPIs addressing the potential environmental contamination caused by on-surface oil spills from offshore oil and gas installations. A layered approach was defined, based on three different levels of KPIs having an increasing complexity and providing an increasing amount of information. The environmental KPIs defined allow a preliminary quantitative assessment of the environmental contamination due to the oil spill scenarios defined in the ENVironmental hazards IDentification (ENVID) studies carried out for oil and gas installations, providing a preliminary ranking of the expected environmental effects of the spills and supporting their prioritization in order to select those which should undergo a more accurate Environmental Risk Assessment study (ERA).
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A comprehensive model of the dynamic, three-dimensional physical fates of contaminants in the marine environment has been developed. For oil spills, dissolution of aromatics from surface slicks and entrained oil droplets are the source of potential effects for biota in the water column. Oil on the surface and along shorelines provides the basis for evaluation of impacts on birds, marine mammals, and recreational activities. A graphical user interface couples the model to a variety of environmental databases and tools to facilitate specific applications and viewing of simulation results.
Article
A three-dimensional numerical model of the physical and chemical behavior and fate of spilled oil has been coupled to a model of oil spill response actions. This coupled system of models for Oil Spill Contingency and Response (OSCAR), provides a tool for quantitative, objective assessment of alternative oil spill response strategies. Criteria for response effectiveness can be either physical (‘How much oil comes ashore?’ or ‘How much oil have we recovered?’) or biological (‘How many biologically sensitive areas were affected?’ or ‘What exposures will fish eggs and larvae experience in the water column?’). The oil spill combat module in the simulator represents individual sets of equipment, with capabilities and deployment strategies being specified explicitly by the user. The coupling to the oil spill model allows the mass balance of the spill to be affected appropriately in space and time by the cleanup operation as the simulation proceeds. An example application is described to demonstrate system capabilities, which include evaluation of the potential for both surface and subsurface environmental effects. This quantitative, objective approach to analysis of alternative response strategies provides a useful tool for designing more optimal, functional, rational, and cost-effective oil spill contingency solutions for offshore platforms, and coastal terminals and refineries.
Article
A new approach for predicting the behaviour of oil spilled on the sea has recently been developed at IKU, Sintef-Group. The approach includes an extensive laboratory investigation of an oil's properties when exposed to weathering. Parameters especially tested are the tendency of the oil to form water-in-oil (w/o) emulsion (mousse), and the susceptibility of the w/oemulsion or water-free weathered oil to disperse using oil spill dispersants. The laboratory results are transformed to field conditions in a numerical model which predicts the rate of weathering processes at sea under different weather conditions. The computer system displays graphical chartsfor the development of each property with time, and estimates the ‘time window’ e.g. for effective application of dispersants under a chosen set of sea conditions. The system may represent an important tool, for contingency planning andfor ‘on-scene’ commanders to facilitate decision-making concerning the use of different countermeasure techniques during oil spill combat operations. This approach may form the basis for a standard method for future characterization of the weathering properties of different oil types which may be spilled under a variety of environmental conditions.
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