142
chemical dispersants, a common practice used to break up fresh surface oil slicks,
may also enhance the formation of oil-mineral aggregates, resulting in transport of
oil to the seafloor by marine snow. Subsea dispersant injection (SSDI) also alters not
only the droplet size distribution at the orifice but also the kinetics of aqueous dissolution of oil compounds, biodegradation rates, and aggregation and flocculation
processes. In the case of SSDI, jet breakup and near-far-field models must account
for these processes to accurately predict the oil distribution and fate. For surface
burning and surface dispersant application, far-field models must account for the
evolving oil composition and transport characteristics. Hence, oil spill models must
account for diverse intervention methods to accurately predict oil fate from accidental deepwater oil spills.
Here, we present an overview of plume and oil droplet dynamics in both the
near- and far-field domains, describing mechanisms underlying the biogeochemical and physical processes influencing the oil fate. We also discuss what the main
processes are that a coupled near-field/far-field model must present in order to
successfully improve the hindcast and forecast of oil transport. Finally, we present
a proof-of-concept model, which couples two numerical tools developed since the
Deepwater Horizon (DWH) blowout, each operating at different scales and coupled together: the near-field Texas A&M Oilspill Calculator (TAMOC) and the
far-field oil Connectivity Modeling System (oil-CMS). During deepwater spills, it
is fundamental to forecast the formation and propagation of lateral intrusion layers and the impact of response scenarios; thus, we focus our results on the vertical
distribution of hydrocarbons and on quantifying the effects of biodegradation
and SSDI.
9.2 Models Description and Coupling
9.2.1 Near-Field Modeling
In this chapter, we confine near-field modeling to those models designed to predict
the initial formation of gas bubbles and oil droplets in the jet breakup region immediately following the release at the orifice and those that predict the ensuing buoyant
dynamics of the near-field gas, oil, and entrained seawater plume. Two approaches
are used for jet breakup, and these include empirical equations that predict a characteristic bubble or droplet size at the end of the dynamic breakup region (e.g.,
Johansen et al. 2013; Li et al. 2017; Wang et al. 2018) and those that solve for the
competing physical and chemical processes regulating breakup and coalescence,
allowing for a dynamic solution of the entire bubble and droplet size distribution
throughout the jet break-up region (Bandara and Yapa 2011; Zhao et al. 2014, 2015,
2017). Likewise, for the buoyant plume, two types of models have been developed
to predict the multiphase oil spill plume dynamics at field scale: integral models,
which solve the cross-sectionally averaged flow along the centerline trajectory of
the plume (Johansen et al. 2003; Chen and Yapa 2003; Yapa et al. 2001), and
A. C. Vaz et al.
chemical dispersants, a common practice used to break up fresh surface oil slicks,
may also enhance the formation of oil-mineral aggregates, resulting in transport of
oil to the seafloor by marine snow. Subsea dispersant injection (SSDI) also alters not
only the droplet size distribution at the orifice but also the kinetics of aqueous dissolution of oil compounds, biodegradation rates, and aggregation and flocculation
processes. In the case of SSDI, jet breakup and near-far-field models must account
for these processes to accurately predict the oil distribution and fate. For surface
burning and surface dispersant application, far-field models must account for the
evolving oil composition and transport characteristics. Hence, oil spill models must
account for diverse intervention methods to accurately predict oil fate from accidental deepwater oil spills.
Here, we present an overview of plume and oil droplet dynamics in both the
near- and far-field domains, describing mechanisms underlying the biogeochemical and physical processes influencing the oil fate. We also discuss what the main
processes are that a coupled near-field/far-field model must present in order to
successfully improve the hindcast and forecast of oil transport. Finally, we present
a proof-of-concept model, which couples two numerical tools developed since the
Deepwater Horizon (DWH) blowout, each operating at different scales and coupled together: the near-field Texas A&M Oilspill Calculator (TAMOC) and the
far-field oil Connectivity Modeling System (oil-CMS). During deepwater spills, it
is fundamental to forecast the formation and propagation of lateral intrusion layers and the impact of response scenarios; thus, we focus our results on the vertical
distribution of hydrocarbons and on quantifying the effects of biodegradation
and SSDI.
9.2 Models Description and Coupling
9.2.1 Near-Field Modeling
In this chapter, we confine near-field modeling to those models designed to predict
the initial formation of gas bubbles and oil droplets in the jet breakup region immediately following the release at the orifice and those that predict the ensuing buoyant
dynamics of the near-field gas, oil, and entrained seawater plume. Two approaches
are used for jet breakup, and these include empirical equations that predict a characteristic bubble or droplet size at the end of the dynamic breakup region (e.g.,
Johansen et al. 2013; Li et al. 2017; Wang et al. 2018) and those that solve for the
competing physical and chemical processes regulating breakup and coalescence,
allowing for a dynamic solution of the entire bubble and droplet size distribution
throughout the jet break-up region (Bandara and Yapa 2011; Zhao et al. 2014, 2015,
2017). Likewise, for the buoyant plume, two types of models have been developed
to predict the multiphase oil spill plume dynamics at field scale: integral models,
which solve the cross-sectionally averaged flow along the centerline trajectory of
the plume (Johansen et al. 2003; Chen and Yapa 2003; Yapa et al. 2001), and
A. C. Vaz et al.