Abstract 2017-184:

Accidental release of pollutants such as offshore oil spills can cause significant negative impacts on the environment and socio-economy, and constitutes a direct hazard to marine life and human health. Particularly, deepwater blowout released spills are more challenging to study because the trajectory and behaviour of oil are difficult to be comprehensively simulated. Although there are many integrated or coupled models available, there still lacks open source deepwater oil spill models to predict not only the trajectory but also the mass balance of oil. It is the objective of this study to fill this gap by coupling the Texas A&M Oilspill Calculator (TAMOC) for nearfield simulation and the advanced oil spill module in the Modelo Hidrodinâmico (MOHID) 3D Water modeling system. In addition, the Weber number scaling approach is also integrated in both the near- and far-field simulation for oil droplet size prediction. The applicability of the proposed comprehensive system is tested by a case study of simulation of oil spills released from a depth of 3,500 m in the Scotian Shelf, Canada. The results demonstrate a high feasibility of the proposed system. By providing comprehensive simulation for oil spills, the developed system should provide significant support to the response to offshore oil spill, especially from deepwater blowout.

Introduction:

Oil and gas resources, which account for more than 70% of Canada’s primary energy production, are of significant national economic importance. The production of crude oil in Canada reached 167.4 million m3 (1,053 million bbl) in 2010 and the industry is still growing (Niu et al., 2016). Currently, there are a number of oil and gas platforms already in production off the east coast of Canada and several new explorations have either started or been proposed. Increasing oil and gas activities lead to increased risk of marine oil spills which can cause significantly negative impacts on the environment and socio-economy, and constitutes a direct hazard to marine life and human health (Chang et al., 2014). Efficient oil spill response is of importance to prevent or reduce these impacts. Studies in recent years have indicated that it is essential to have a readily available model for simulation of oil trajectory and behavior to help allocate the limited response resource (Thibodeaux et al. 2011; Mariano et al., 2011).

In the past decades, many oil spill models have been developed, such as the General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) (Beegle-Krause, 1999, 2001; Beegle-Krause and O’Connor, 2005), the oil spill contingency and response (OSCAR) model (Reed et al., 1995; Aamo et al., 1997; Reed et al., 1999; Reed et al., 2004), and the OILMAPTM (Gjosteen, 2003; ASA, 2009; SL Ross et al., 2010). These models were developed mainly focusing on simulation of oil spills released from surface water. Challenges still remained on the simulation of oil spills from deepwater blowout because the trajectory and behaviour of oil are difficult to be comprehensively simulated.

The recent example of the Deepwater Horizon oil spill has shown the importance of a comprehensive oil spill model with consideration of near- and far-field simulations to predict the trajectory of oil and help allocate the limited response resources (Thibodeaux et al. 2011; Mariano et al., 2011). Recently, there are some models developed for simulating oil spills from deepwater blowout, such as the DeepBlow model from SINTEF (Johansen, 2000), the Blowout and Spill Occurrence Model (BLOSOM) from the National Energy Technology Laboratory (NETL) (Sim et al, 2015), the OILMAPDeep module in the OILMAP and SIMAP from RPS ASA (RPS ASA, 2016), the Nearfield plume model in the MIKE oil spill module from DHI (DHI, 2015). Most of these models are commercial or non-open source models which would limited their applications in oil spill research. In addition, there are also some nearfield only models such as the Comprehensive Deepwater Oil and Gas (CDOG) blowout model (Zheng et al., 2003; Yapa and Chen, 2004) and the Texas A&M Oilspill Calculator (TAMOC) (Socolofsky et al., 2008, 2015). The TAMOC model has been integrated with the Larval TRANSport Lagrangian model (LTRANS) for simulating the subsurface transport of oil droplets (North et al., 2011). Because the LTRANS is a 3D particle-tracking system with advection, diffusion, and oil transformations, the integration of LTRANS and TAMOC can be further improved with consideration of oil weathering processes. Recently, the GNOME2 is also integrating the TAMOC for trajectory and behavior simulation of oil spill released from deepwater, but it is still in the initial stage of development (Barker, 2016). This study is aiming at developing a system for comprehensively simulating oil spills from deepwater blowout. Such a system consists of a nearfield model based on the TAMOC model and a far-field model based on the 3D Water modeling system (Modelo Hidrodinâmico, MOHID). The MOHID system consists of a Lagrangian module for the particle tracking and an oil spill module for the weathering simulation of oil spills. In addition, the oil droplet size distribution will be considered in both the near- and far-field simulations.

Currently, the OILMAP system is utilized by the Environment and Climate Change Canada (ECCC) for oil spill simulation, and some companies doing development have their systems for providing model output requirements for permitting. The proposed system with comprehensive near- and far-field simulation of oil spill trajectory and behaviors can not only provide an open-source model for the oil spill research, but also provide flexibility in oil spill modeling for specific situations in Canada (e.g., simulation of diluted bitumen in cold and harsh environments). In addition, well predicted droplet size distribution can improve the accuracy of oil spill simulation in near- and far-field areas (Li et al., 2016).

Methodology:

In order to comprehensively simulate the oil spill from deepwater blowout, a framework with dynamically linked near- and far-field simulations is required. In this study, the TAMOC model is used to simulation the nearfield plume of the oils and/or gases releasing from the deepwater jet. When the nearfield momentum disappears or the plume reaches the sea surface, the nearfield simulation will be ended and dynamically switch to the far-field simulation by the advanced oil spill module in the MOHID 3D water modeling system. Figure 1 shows the general framework of the proposed comprehensive oil spill model, where tset is the preset period of the oil spill simulation.

Figure 1

General framework of the compressive oil spill model.

Figure 1

General framework of the compressive oil spill model.

TAMOC Model

The TAMOC model was developed by Dr. Socolofsky from Texas A&M University (Socolofsky et al., 2008, 2015). It is a modeling suite for simulating the trajectory and behaviour of oil and gas released from subsea accidents, which is capable of simulating the spills in weak cross-flow (plume is mainly affected by buoyancy and dissolution) and strong cross-flow (plume is additionally governed by subsurface current) conditions. It has an advantage in its double plume integral model, which consists of an inner plume of entrained water, oil and gas and an outer plume of water and fine oil droplets exchanging with both the ambient environment and the inner plume.

The main modules in TAMOC include: 1) the Ambient Module for receiving environmental inputs such as temperature and salinity profiles, currents, and concentrations of oils and gases; 2) the Discrete Bubble Model (DBM) to track the behavior of individual bubbles, droplets, or particles and applies their predicted dynamics to a population of identical particles along the same path; 3) the Single Bubble Model to tracks the evolution of one bubble or droplet rising through the water column; and 4) the Integral Plume Models, with a Stratified Plume Model (SPM) and a Bent Plume Model (BPM), to simulate the flow rate, momentum flux, buoyancy flux. The TAMOC model is a robust nearfield modeling suite. However, it needs to be coupled with a far-field model for compressive simulation.

MOHID 3D water modeling system

The oil spill module in the MOHID 3D water modeling system is used in this study for far-field simulation. MOHID is a three-dimensional water modeling system developed by Marine and Environmental Technology Research Center (MARETEC) at Instituto Superior Técnico (IST), University of Lisbon (Miranda, 2000). The MOHID Water Modeling System uses finite volume approach and C-Grid in spatially discretization. It supports the orthogonal or curvilinear horizontal grid; sigma level or z-level vertical coordinate. The modeling system is based on its own hydrodynamic system with waves, sediments transport, sand, water quality, turbulence, Lagrangian transport, and oil spill models.

The MOHID oil spill model is a sub-model of the MOHID 3D Water Modeling System. The oil spill model works with the Lagrangian transport model for trajectory and behavior simulation of the spilled oil. The MOHID oil spill model considers oil as an aggregation of a large number of particles, and uses the Lagrangian method to describe the movement of these particles taking into account the following oil behaviors: spreading, evaporation, emulsification, dispersion, sedimentation, dissolution, oil-beaching, removal techniques, and chemical dispersion (Fernandes et al., 2013). In this study, the MOHID oil spill model is advanced by a new biodegradation approach based on the coupled pseudo-components and first-order biodegradation algorithms, a dispersion approach based on the modified weber number, and an oil-shoreline interaction approach improved by the consideration of beaching probability in different shoreline types (Li, 2017).

Nevertheless, the nearfield model in the MOHID system is developed for wastewater discharges which would lack of efficiency in simulating the oil spill cases (MARETC, 2017). Thus, the MOHID oil spill model is coupled with the TAMOC model to provide a comprehensive simulation of oil spills from deepwater blowout.

Oil droplet size simulation

Oil droplet size distribution is considered to be one of the most important factors governing the transport and fate of oil and gas released in deepwater. Well predicted droplet size distribution improves the simulation of transport and fate of the oil in both deep and shallow water, and supports decision making for oil spill response (Johansen et al., 2013). Thus, this study also takes the oil droplet size distribution into account for the comprehensive oil spill modeling. The Modified Weber Number Scaling approach response (Johansen et al., 2013) is used due to its efficiency and accuracy with less complex parameters, which is advantageous in real-world application.

A comprehensive oil spill simulation in Scotian Shelf:

The Scotian Shelf is located southwest of Nova Scotia, Canada. It covers an area of 120,000 km2, is 700 km long and ranges in width from 120 to 240 km. It has an average depth of 90 m. The northeastern boundary drops off to 400 m, while the further south sharply drops off to a depth of more than 3,000 m. Figure 2 shows the bathymetry and the model domain based on the Regional Nucleus for European Modeling of the Ocean (NEMO) Model. The NEMO model is widely used in oceanographic and climate research, operational ocean forecasts and seasonal weather forecasts (Madec and the NEMO team, 2012). The regional model has a resolution about 1/36° and curvilinear grid in horizontal direction, vertical layers varying from 0.7 m to 233 m and z-level coordinate in vertical direction (Brennan et al., 2016; Li, 2017).

Figure 2:

Bathymetry and domain of the regional model of the Scotian Shelf.

Figure 2:

Bathymetry and domain of the regional model of the Scotian Shelf.

A hypothetical oil spill case has been conducted in the Scotian Shelf to evaluate the feasibility of the near- and far-field simulation coupling. The release point is set at longitude: −60.98439, latitude: 40.942459, as indicated in Figure 2. In addition, the depth of the release point is 3,500 m. The settings of the releasing jet are as follows: diameter = 0.3 m, port angel = −90° (straight upward), temperature = 35 °C. There are two types of spills released from the jet: gases and oil. The gases consist of methane, ethane, and propane with molecular fractions of 0.93, 0.05, and 0.02, respectively. In addition, the mixed gases are categorized into large bubbles (diameter = 5 mm) and small bubbles (diameter = 0.5 mm). Each type of gas bubbles has a mass flux of 5 kg/s. Comparatively, the droplet sizes of oil spill are governed by modified Weber number approach in the TAMOC with a median diameter of 5 mm. The additional settings for the released oil is as follows: mass flux = 10 kg/s, density = 890 kg/m3, API = 30, and dynamic viscosity = 8.8 cP. The releasing period is from 00:30, Jan. 01 to 00:30 Jan. 02, 2010. Corresponding, the simulation period of the nearfield model is the same as the releasing period. The simulation period of the far-field simulation is 1 day longer than the nearfield model, which is from 00:30, Jan. 01 to 00:30 Jan. 03, 2010. Furthermore, because the temporal resolution is 1 hour for the data from the regional NEMO model, an assumption is made that the ambient conditions are unchanged in every hourly period. The vertical profiles of the salinity, temperature, and currents for some hourly periods are demonstrated in Figures 3 - 4. The profiles of the currents indicate that a strong cross-flow () is occurring in the modeling domain. Thus, this case study only considers the bent plume model in the TAMOC for the nearfield simulation (Socolofsky et al., 2015).

Figure 3

Vertical profile of (a) salinity and (b) temperature.

Figure 3

Vertical profile of (a) salinity and (b) temperature.

Figure 4

Vertical profile of (a) Zonal and (b) Meridional current velocities.

Figure 4

Vertical profile of (a) Zonal and (b) Meridional current velocities.

Correspondingly, the TAMOC model is applied for the simulation. Table 1 and Figure 5 indicate the location changes of the particles release at the beginning of each hour until the nearfield simulation terminates. In addition, the trajectories and plumes for the oil and gases in 1st, 8th, 16th, and 24th hour are shown in Figure 6. The nearfield results indicate that the large-bubble gases rise with a very high angle and leave the plume in 10 to 30 m of the downstream direction. The small-bubble gases leave the plume in about 100 to 150 m downstream. Comparatively, the oil travels 400 to 600 m until the nearfield model ends. The plume moves toward northwest with a small variation (Figure 5) during the 24-hour period. At the early stage, the plume rises with a significant slop and speed due to the high proportion of bubbles in the first 10 min. The plume becomes flatten and slowly rising after the leaving of bubbles. In addition, due to the entrainment of ambient water, the salinity, temperature, and the density of the plume are changed rapidly and become similar to the ambient water (Figure 7). Figure 7c also indicates that the density of the ambient water in about 300 m centerline distance (about 250 m downstream distance as show in Figure 6a) has an increase, which would cause a downward exchange with the lighter water in the deeper area. Correspondingly, the plume elevation has a slight drop in this area. Similar situations are also observed in the simulation of the other periods due to the same reason (Figure 6).

Figure 5

(a) Plume centerlines and (b) final locations of the oil released at the beginning of each hour in the nearfield simulation.

Figure 5

(a) Plume centerlines and (b) final locations of the oil released at the beginning of each hour in the nearfield simulation.

Figure 6

Trajectories and plume for the oil and gases in (a) 1st, (b) 8th, (c) 16th, and (d) 24th hour.

Note: the x axis indicates the downstream direction of the plume.

Figure 6

Trajectories and plume for the oil and gases in (a) 1st, (b) 8th, (c) 16th, and (d) 24th hour.

Note: the x axis indicates the downstream direction of the plume.

Figure 7

Changes of plume (a) salinity, (b) temperature, and (c) density along the centerline in the 1st hour.

Figure 7

Changes of plume (a) salinity, (b) temperature, and (c) density along the centerline in the 1st hour.

Table 1

Final location of particles when the nearfield simulation terminated.

Final location of particles when the nearfield simulation terminated.
Final location of particles when the nearfield simulation terminated.

The final locations of the plume and the corresponding mass fluxes are dynamically fed to the far-field model handled by the MOHID. The addition inputs to the MOHID model include the ambient conditions in sub-sea environment (Figures 3 and 4) since the terminated locations from the nearfield simulation are still deeper than 3,000 m. Furthermore, the surface conditions such as the wind conditions, surface currents, and waves are also supplied in case the plume reach the sea surface. The wind field used in this study is obtained from the CFSR (Climate Forecast System Reanalysis) produced by the NCEP (National Centers for Environmental Prediction) (National Center for Atmospheric Research Staff, 2016), with a time interval of 6 hours, and a horizontal resolution 0.312° (Figure 8). The surface currents are based on the same inputs from the NEMO Model (Figure 9). In addition, the significant wave height is set to 1.8 m, and the mean wave period is set to 4.7 s (Li, 2017).

Figure 8

Wind field in the Scotian Shelf on Jan. 01, 2010.

Figure 8

Wind field in the Scotian Shelf on Jan. 01, 2010.

Figure 9

Current field in the Scotian Shelf on Jan. 01, 2010.

Figure 9

Current field in the Scotian Shelf on Jan. 01, 2010.

The outputs of the far-field simulation in 8th, 24th, and 48th hours are shown in Figures 10 to 12. In the first 24 hours, there are continuous inputs from the nearfield model due to the jet releasing. Due to the close locations of the end points from the nearfield plume, a continuous trajectory path is observed in the far-field modeling during the first 24 hours (Figures 10 and 11). The jet stops releasing oil at 24th hour, and the nearfield model terminates at around 25th hour. Thus, after 25 hours, the continuous trajectory path disappears and the oil slick moves along with the currents (Figure 12). Because the release point is at a depth of 3,500 m and the ending locations of the nearfield plume are also still over 3,000 m depth, the far-field simulation is still carrying on in a deepwater environment. Thus, no evaporation occurs and the spreading is not as significant as in the surface area. In addition, the rising of the oil still relies on the buoyance and is affected by the turbulence (Figures 10b and 11b). Therefore, there will be a long period until the oil reaches the surface. Nevertheless, the far-field model integrates well with the nearfield model in a dynamical way, demonstrating a high feasibility of the proposed comprehensively modeling system.

Figure 10

Oil trajectory in (a) horizontal and (b) vertical direction after 8 hours.

Figure 10

Oil trajectory in (a) horizontal and (b) vertical direction after 8 hours.

Figure 11

Oil trajectory in (a) horizontal and (b) vertical direction after 24 hours.

Figure 11

Oil trajectory in (a) horizontal and (b) vertical direction after 24 hours.

Figure 12

Oil trajectory in (a) horizontal and (b) vertical direction after 48 hours.

Figure 12

Oil trajectory in (a) horizontal and (b) vertical direction after 48 hours.

Conclusion:

This study has provided a comprehensive model for simulating the oil spills from deepwater blowout. The proposed model dynamically integrates a nearfield model based on TAMOC model and a far-field model based on the advanced oil spill module in the MOHID 3D water modeling system. The TAMOC model is capable of simulating the oil/gas trajectory and behaviour in nearfield area under weak cross-flow and/or strong cross-flow conditions. This model is dynamically coupled with the advanced oil spill module in the MOHID 3D water modeling system for far-field simulation. The existing oil spill module in the MOHID system has been advanced by a new biodegradation approach based on the coupled pseudo-components and first-order biodegradation algorithms, a dispersion approach based on the modified weber number, and an oil-shoreline interaction approach improved by the consideration of beaching probability in different shoreline types. In addition, the Weber number scaling approach is introduced in both models for predicting the oil droplet size to further improve the accuracy of oil spill simulation.

In order to test the applicability of the proposed comprehensively modeling system, a case study has been conducted for a hypothetical oil spill release in the deep-water area of the Scotian Shelf, Canada. The results have demonstrated a high feasibility of the near and far-field simulation coupling with the TAMOC and the advanced oil spill module in the MOHID system. By providing comprehensive simulation for oil spills, and once integrated in MOHID framework, the new model will be seamless integrated in existing desktop GIS (MOHID Studio) or web-based (mobile-friendly) operational decision support systems (Action Seaport), enhancing tactical and strategical support to offshore oil spills - especially from deepwater blowout.

In the future study, the behaviour of oil during the near and far-field simulation will be quantified. Case studies will also be conducted for validating the developed system under weak cross-flow conditions. In addition, the effects of dispersant applications will also be analyzed. Also, more general approaches for oil droplet size prediction such as the Reynolds number scaling approach will be introduced. Furthermore, the accuracy, sensitivity, and robustness of the proposed system will be tested with real-work case studies.

Acknowledgments:

This research was funded by the Natural Sciences and Engineering Research Council of Canada Discovery Program (NSERC DG), and the Marine Environmental Observation Prediction and Response Network (MEOPAR). Although the research was funded in part by the Department of Fisheries and Oceans Canada (DFO), it has not been subjected to any DFO review, and therefore does not necessarily reflect the views of the department, and no official endorsement should be inferred.

Reference:

Reference:
Aamo
,
O.M.
,
Reed
,
M.
,
Downing
,
K.
,
1997
.
Oil Spill Contingency and Response (OSCAR) Model System: Sensitivity Studies
.
Proceedings. of 1997 International Oil Spill Conference
,
429
438
.
Applied Science Associates (ASA) Inc.
,
2009
.
OILMAP v6.4 User’s Manual
.
Applied Science Associates Inc.
,
South Kingstown, RI, USA
,
p
.
102
Barker
C.
,
2016
.
New Developments in the National Oceanic and Atmospheric Administration’s (NOAAs) Oil Spill Modeling Suite: GNOME and ADIOS
.
Proceedings of the 39th AMOP Technical Seminar on Environmental Contamination and Response
,
June 7 – 9, 2016
,
Halifax, Canada
Beegle-Krause
,
C.J.
,
1999
.
GNOME: NOAA’s Next-Generation Spill Trajectory Model
.
Oceans ’99 MTS/IEEE Proceedings
.
Escondido, CA
:
MTS/IEEE Conference Committee
.
Vol. 3
:
pp
.
1262
1266
.
Beegle-Krause
,
C.J.
,
2001
.
General NOAA Oil Modeling Environment (GNOME): A New Spill Trajectory Model
.
IOSC 2001 Proceedings
,
Tampa, FL
,
March 26–29, 2001
.
St. Louis, MO
:
Mira Digital Publishing, Inc.
Vol. 2
:
pp
.
865
871
.
Beegle-Krause
,
C.J.
,
O’Connor
,
C.
,
2005
.
GNOME Data Formats and Associated Example Data Files. NOAA Office of Response and Restoration
.
Emergency Response Division
,
Seattle, WA, USA
,
49
pp
.
Brennan
C.E.
,
Bianucci
L.
, and
Fennel
K.
2016
.
Sensitivity of Northwest North Atlantic Shelf Circulation to Surface and Boundary Forcing: A Regional Model Assessment
.
Atmosphere-Ocean
,
54
,
230
247
Chang
,
S.E.
,
Stone
,
J.
,
Demes
,
K.
,
Piscitelli
,
M.
,
2014
.
Consequences of oil spills: a review and framework for informing planning
.
Ecology and Society
19
(
2
):
26
51
.
Fernandes
R.
,
Neves
R.
,
Viegas
C.
, and
Leitão
P.
,
2013
.
Integration of an Oil and Inert Spill Model in a Framework for Risk Management of Spills at Sea– A Case Study for the Atlantic Area
.
Proceedings of 36th AMOP Technical Seminar on Environmental Contamination and Response
,
June 4–6, 2013
,
Halifax, Canada
Fernandes
,
R.
,
Neves
,
R.
, and
Viegas
,
C.
,
Leitão
,
P.
,
2013
.
Integration of an Oil and Inert Spill Model in a Framework for Risk Management of Spills at Sea – A Case Study for the Atlantic Area
.
36th AMOP Technical Seminar on Environmental Contamination and Response
,
June 4–6, 2013
,
Halifax, Canada
Gjosteen
,
J.K.O.
,
Loset
,
S.
,
Gudmestad
,
O.T.
,
2003
:
The Ability to Model Oil Spills in Broken Ice
.
Proceedings of the 17th International Conference on Port and Ocean Engineering under Arctic Conditions
,
June 16–19
,
Trondheim, Norway
.
Johansen
,
Ø.
,
2000
.
DeepBlow – a Lagrangian Plume Model for Deep Water Blowouts
.
Spill Science & Technology Bulletin
,
6
(
2
),
103
111
.
Johansen
,
Ø.
,
Brandvik
,
P.J.
,
Farooq
,
U.
2013
.
Droplet breakup in subsea oil releases - Part 2: Predictions of droplet size distributions with and without injection of chemical dispersants
.
Marine Pollution Bulletin
,
73
(
1
):
327
335
.
Li
S.H.
,
2017
.
Improving the MOHID Oil Spill Model with New Weathering Algorithms
.
Master Thesis, Dalhousie University
,
132
pp
.
Li
,
P.
,
Weng
,
L.L.
,
Niu
,
H.B.
,
Robinson
,
B.
,
King
,
T.
,
Conmy
,
R.
,
Lee
,
K.
,
Liu
,
L.
,
2016
.
Reynolds number scaling to predict droplet size distribution in dispersed and undispersed subsurface oil releases
.
Marine Pollution Bulletin (MPB)
,
doi: 10.1016/j.marpolbul.2016.10.005. (In press)
Madec
,
G.
and
the NEMO team
,
2012
.
NEMO ocean engine. Note du Pole de modélisation de l’Institut Pierre-Simon Laplace, France
,
No 27 ISSN No 1288–1619
.
Mariano
,
A.J.
,
Kourafalou
,
V.H.
,
Srinivasan
,
A.
,
Kang
,
H.
,
Halliwell
,
G.R.
,
Ryan
,
E.H.
,
Roffer
,
M.
,
2011
.
On the Modeling of the 2010 Gulf of Mexico Oil Spill
,
Dynamics of Atmospheres and Oceans
,
52
,
322
340
Marine Environment and Technology Center (MARETEC)
,
2017
.
MOHIDJET – Technical manual
.
Miranda
,
R.
,
Braunschweig
,
F.
,
Leitao
,
P.
,
Neves
,
R.
,
Martins
,
F.
,
Santos
,
A.
,
2000
.
MOHID 2000 - A Coastal Integrated Object Oriented Model
.
Hydraulic Engineering Software VIII
,
C.A.
Brebbia
&
W.R.
Blain
(
Editors
),
WIT Press
,
393
401
.
National Center for Atmospheric Research Staff
(Eds). Last modified
05
Jul
2016
.
The Climate Data Guide: Climate Forecast System Reanalysis (CFSR)
.
Niu
,
H.B.
,
Li
,
S.H.
,
King
,
T.
,
Lee
,
K.
,
2016
.
Stochastic Modeling of Oil Spill in the Salish Sea
.
Proceedings of the Twenty-sixth (2016) International Ocean and Polar Engineering Conference
,
Rhodes, Greece
,
June 26–July 1, 2016
.
North
,
E.W.
,
Adams
E.E.
,
Schlag
S.
,
Sherwood
C.R.
,
He
R.
,
Socolofsky
S.
,
2011
.
Simulating oil droplet dispersal from the Deepwater Horizon spill with a Lagrangian approach
.
AGU Book Series: Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record Breaking Enterprise
.
Reed
,
M.
,
Aamo
,
O.M.
,
Daling
,
P.
,
1995
.
OSCAR: A Model System for Quantitative Analysis of Oil Spill Response Strategies
.
Proceedings of the 18th AMOP Technical Seminar on Environmental Contamination and Response
,
Ottawa, Environment Canada
,
815
835
.
Reed
,
M.
,
Daling
,
P.
,
Lewis
,
A.
,
Ditlevsen
,
M.K.
,
Brors
,
B.
,
Clark
,
J.
,
Aurand
,
D.
,
2004
.
Modelling of Dispersant Application to Oil Spills in Shallow Coastal Waters
.
Environmental Modelling & Software
,
19
(
7–8
),
681
690
.
Reed
,
M.
,
Ekrol
,
N.
,
Rye
,
H.
,
Turner
,
L.
,
1999
.
Oil Spill Contingency and Response (OSCAR) Analysis in Support Environmental Impact Assessment Offshore Namibia
.
Spill Science & Technology Bulletin
,
5
(
1
),
29
38
.
RPS ASA
,
(
2016
).
OILMAP RELEASE NOTES, Version: 7.0
.
Sim
,
L.
,
Graham
,
J.
,
Rose
,
K.
,
Duran
,
R.
,
Nelson
,
J.
,
Umhoefer
,
J.
,
Vielma
,
J.
Developing a Comprehensive Deepwater Blowout and Spill Model; NETLTRS-9-2015; EPAct Technical Report Series
;
U.S. Department of Energy, National Energy Technology Laboratory
:
Albany, OR
,
2015
;
p
44
.
SL Ross Environmental Research Ltd., DF Dickins Associates LLC., Envision Planning Solutions Inc.
2010
.
Beaufort Sea Oil Spills State of Knowledge Review and Identification of Key Issues
,
p
.
126
,
Report prepared for ESRF
.
Socolofsky
,
S.A.
,
Bhaumik
.
T.
,
Seol
,
D.G.
,
2008
.
Double-Plume Integral Models for Near-Field Mixing in Multiphase Plumes
.
ASCE Journal of Hydraulic Engineering
,
134
,
772
783
Socolofsky
,
S.A.
,
Dissanayake
,
A.L.
,
Jun
,
I.
,
Gros
,
J.
,
Arey
,
J.S.
,
Reddy
,
C.M.
,
2015
.
Texas A&M Oilspill Calculator (TAMOC): Modeling Suite for Subsea Spills
.
Proceedings of the Thirty-Eighth AMOP Technical Seminar
,
Environment Canada, Ottawa, ON
,
153
168
.
Thibodeaux
,
L.J.
,
Valsaraj
,
K.T.
,
John
,
V.T.
,
Papadopoulos
,
K.D.
,
Pratt
,
L.R.
,
Pesika
,
N.S.
,
2011
.
Marine Oil Fate: Knowledge Gaps, Basic Research, and Development Needs; A Perspective Based on the Deepwater Horizon Spill
.
Environmental Engineering Science
,
28
(
2
),
87
93
.
Yapa
,
P.D.
,
Chen
,
F.H.
,
2004
.
Behavior of Oil and Gas from Deepwater Blowouts
.
Journal of Hydraulic Engineering (ASCE)
,
130
(
6
):
540
553
.
Zheng
,
L.
,
Yapa
,
P.D.
,
Chen
,
F.H.
,
2003
.
A Model for Simulating Deepwater Oil and Gas Blowouts - Part I: Theory and Model Formulation
.
Journal of Hydraulic Research
,
41
(
4
),
339
351
.