144
water entering the plume from the crossflow (Lee and Chu 2003). Because oil and
gas particles advect within the control volume with their own rise velocity, they may
leave the plume on the upstream side. This effect has been observed in laboratory
experiments (Socolofsky and Adams 2002) and is considered by mainstream blowout models (Johansen et al. 2003; Chen and Yapa 2003; Dissanayake et al. 2018).
Eventually, the negative buoyancy of the entrained seawater will be greater than the
buoyant force from the remaining gas bubbles and oil droplets, the plume will stop
rising, and it will fall to form a lateral intrusion at a level of neutral buoyancy in the
density-stratified ocean. This region where the intrusion layer forms is generally
considered the end of the near-field and the beginning of the far-field oil and gas
transport (Socolofsky and Dissanayake 2016).
At the end of the near-field, the simulation results are used to provide initial
conditions to the far-field model. These initial conditions include the oil droplet and
gas bubble sizes, their composition, density, locations, and mass flow rates at the
end of the near-field domain. The near-field model also tracks the dissolved-phase
petroleum within the integral plume model control volume, and the mass flow rates
of dissolved hydrocarbons at the intrusion layer may also be passed to a far-field
Lagrangian transport model.
9.2.2 Far-Field Lagrangian Modeling
Once in the far field, the gas bubbles and oil droplets are apportioned to individual
Lagrangian particles. These models track the individual particles (i.e., gas bubbles
and oil droplets, each representing a given mass flow rate) geographical coordinates, depths, and compositions, while simultaneously simulating the physical and
biogeochemical processes influencing their distribution and fate. The explicit composition and characteristics of each oil droplet and gas bubble needs to be considered. The final petroleum distribution is primarily driven by the advection and
dispersion by the oceanic hydrodynamics, including currents, fronts, turbulence,
and internal waves. However, other important processes influence their distribution.
For instance, droplets reaching the far field originate from many different types (i.e.,
median size, hydrocarbon compositions), resulting in different phases, densities,
rates of ensuing fate processes, notably dissolution, and biodegradation. The physicochemical characteristics of each droplet will determine their terminal velocity and
vertical distribution (Pesch et al. 2020). Larger droplets ascend the water column
faster, while smaller ones can remain at depth, driven mostly by ocean stratification,
upwelling, and subduction currents.
Within the far field, the advection of individual droplets is represented by the
integration of the deterministic velocity field from hydrodynamic models, most
commonly using a spatiotemporal 4th order Runga-Kutta scheme (Paris et al. 2013).
The sub-grid-scale turbulent diffusion is resolved by adding a random displacement
scaled by a diffusivity coefficient derived from observations (Okubo 1987).
The wind-induced drift needs to be incorporated in the velocity field and explicitly
A. C. Vaz et al.
water entering the plume from the crossflow (Lee and Chu 2003). Because oil and
gas particles advect within the control volume with their own rise velocity, they may
leave the plume on the upstream side. This effect has been observed in laboratory
experiments (Socolofsky and Adams 2002) and is considered by mainstream blowout models (Johansen et al. 2003; Chen and Yapa 2003; Dissanayake et al. 2018).
Eventually, the negative buoyancy of the entrained seawater will be greater than the
buoyant force from the remaining gas bubbles and oil droplets, the plume will stop
rising, and it will fall to form a lateral intrusion at a level of neutral buoyancy in the
density-stratified ocean. This region where the intrusion layer forms is generally
considered the end of the near-field and the beginning of the far-field oil and gas
transport (Socolofsky and Dissanayake 2016).
At the end of the near-field, the simulation results are used to provide initial
conditions to the far-field model. These initial conditions include the oil droplet and
gas bubble sizes, their composition, density, locations, and mass flow rates at the
end of the near-field domain. The near-field model also tracks the dissolved-phase
petroleum within the integral plume model control volume, and the mass flow rates
of dissolved hydrocarbons at the intrusion layer may also be passed to a far-field
Lagrangian transport model.
9.2.2 Far-Field Lagrangian Modeling
Once in the far field, the gas bubbles and oil droplets are apportioned to individual
Lagrangian particles. These models track the individual particles (i.e., gas bubbles
and oil droplets, each representing a given mass flow rate) geographical coordinates, depths, and compositions, while simultaneously simulating the physical and
biogeochemical processes influencing their distribution and fate. The explicit composition and characteristics of each oil droplet and gas bubble needs to be considered. The final petroleum distribution is primarily driven by the advection and
dispersion by the oceanic hydrodynamics, including currents, fronts, turbulence,
and internal waves. However, other important processes influence their distribution.
For instance, droplets reaching the far field originate from many different types (i.e.,
median size, hydrocarbon compositions), resulting in different phases, densities,
rates of ensuing fate processes, notably dissolution, and biodegradation. The physicochemical characteristics of each droplet will determine their terminal velocity and
vertical distribution (Pesch et al. 2020). Larger droplets ascend the water column
faster, while smaller ones can remain at depth, driven mostly by ocean stratification,
upwelling, and subduction currents.
Within the far field, the advection of individual droplets is represented by the
integration of the deterministic velocity field from hydrodynamic models, most
commonly using a spatiotemporal 4th order Runga-Kutta scheme (Paris et al. 2013).
The sub-grid-scale turbulent diffusion is resolved by adding a random displacement
scaled by a diffusivity coefficient derived from observations (Okubo 1987).
The wind-induced drift needs to be incorporated in the velocity field and explicitly
A. C. Vaz et al.