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concentrations and oil mass in a 3D space grid. We used the Deepwater Horizon
(DWH) blowout as a case study and performed a sensitivity analysis of several
modeling key factors, such as biodegradation, sedimentation, and alternative initial
conditions, including droplet size distribution (DSD) corresponding to an untreated
and treated live oil from subsurface dispersant injection (SSDI) predicted experimentally under high pressure and by the VDROP-J jet-droplet formation model.
This quantitative analysis enabled the reconstruction of a time evolving threedimensional (3D) oil plume in the ocean interior, the rising and spreading of oil on
the ocean surface, and the effect of SSDI in shifting the oil to deeper waters while
conserving the mass balance. Our modeling framework and analyses are thus important technical advances for understanding and mitigating deep-sea blowouts.
Keywords Deep-sea blowout · Subsea oil spill · Oil transport modeling · Oil fate
modeling · Biogeophysical oil modeling · Connectivity Modeling System ·
Subsurface dispersant injection (SSDI) effects
11.1 Far-Field Modeling of Oil Spills
Modeling of a deep-sea oil spill can be divided into few stages that represent the
dominant processes of oil and gas during a deep-sea blowout. The initial stage deals
with the buoyant jet of oil and gas mixture, where the turbulence is dominant and
where the coalescence of bubbles and droplets is the key process in the plume
affecting the droplet and bubble size distribution (Johansen et al. 2003; Bandara and
Yapa 2011; Aman et al. 2015; Malone et al. 2020). This jet plume model is sometimes followed by near-field modeling, which deals with oil and gas mixture separation, hydrate formation, loss of buoyancy, and formation of intrusion layers (Vaz
et al. 2020). The latter occurs in stratified water column conditions, dictating the
trap height of oil and gasses above the oil well and establishing a relative equilibrium in a characteristic droplet size distribution (DSD). The information about the
attained DSD and the trap height can be further used for three-dimensional (3D)
far-field modeling of oil transport and fate in the ocean at scales of hundreds and
thousands of kilometers.
The far-field modeling typically employs Lagrangian-based methods to
advance oil droplets in the horizontal and vertical directions based on environmental conditions and droplet buoyancy, using 3D hydrodynamic models supplying ocean state input data and wave and surface wind data to support surface
transport modeling. The oil fate is modeled through individual-based algorithms
of weathering processes such as biodegradation, dissolution, adsorption, sedimentation, degassing, surface evaporation, and photooxidation (Spaulding et al. 2017;
Li et al. 2017). These algorithms are often empirically derived, and their accuracy
is limited to the laboratory or field observations of available case studies. The validation of hindcast model results is often limited to quantitative comparison with
observational data such as the surface oil slick extent detected by remote sensing
11 Far-Field Modeling of a Deep-Sea Blowout: Sensitivity Studies of Initial…
concentrations and oil mass in a 3D space grid. We used the Deepwater Horizon
(DWH) blowout as a case study and performed a sensitivity analysis of several
modeling key factors, such as biodegradation, sedimentation, and alternative initial
conditions, including droplet size distribution (DSD) corresponding to an untreated
and treated live oil from subsurface dispersant injection (SSDI) predicted experimentally under high pressure and by the VDROP-J jet-droplet formation model.
This quantitative analysis enabled the reconstruction of a time evolving threedimensional (3D) oil plume in the ocean interior, the rising and spreading of oil on
the ocean surface, and the effect of SSDI in shifting the oil to deeper waters while
conserving the mass balance. Our modeling framework and analyses are thus important technical advances for understanding and mitigating deep-sea blowouts.
Keywords Deep-sea blowout · Subsea oil spill · Oil transport modeling · Oil fate
modeling · Biogeophysical oil modeling · Connectivity Modeling System ·
Subsurface dispersant injection (SSDI) effects
11.1 Far-Field Modeling of Oil Spills
Modeling of a deep-sea oil spill can be divided into few stages that represent the
dominant processes of oil and gas during a deep-sea blowout. The initial stage deals
with the buoyant jet of oil and gas mixture, where the turbulence is dominant and
where the coalescence of bubbles and droplets is the key process in the plume
affecting the droplet and bubble size distribution (Johansen et al. 2003; Bandara and
Yapa 2011; Aman et al. 2015; Malone et al. 2020). This jet plume model is sometimes followed by near-field modeling, which deals with oil and gas mixture separation, hydrate formation, loss of buoyancy, and formation of intrusion layers (Vaz
et al. 2020). The latter occurs in stratified water column conditions, dictating the
trap height of oil and gasses above the oil well and establishing a relative equilibrium in a characteristic droplet size distribution (DSD). The information about the
attained DSD and the trap height can be further used for three-dimensional (3D)
far-field modeling of oil transport and fate in the ocean at scales of hundreds and
thousands of kilometers.
The far-field modeling typically employs Lagrangian-based methods to
advance oil droplets in the horizontal and vertical directions based on environmental conditions and droplet buoyancy, using 3D hydrodynamic models supplying ocean state input data and wave and surface wind data to support surface
transport modeling. The oil fate is modeled through individual-based algorithms
of weathering processes such as biodegradation, dissolution, adsorption, sedimentation, degassing, surface evaporation, and photooxidation (Spaulding et al. 2017;
Li et al. 2017). These algorithms are often empirically derived, and their accuracy
is limited to the laboratory or field observations of available case studies. The validation of hindcast model results is often limited to quantitative comparison with
observational data such as the surface oil slick extent detected by remote sensing
11 Far-Field Modeling of a Deep-Sea Blowout: Sensitivity Studies of Initial…