Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework

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Optimizing oil spill cleanup efforts: A tactical approach and evaluation framework Tony H. Grubesica,⁎, Ran Weib, Jake Nelsona a Center for Spatial Reasoning & Policy Analytics, College of Public Service & Community Solutions, Arizona State University, 411 N Central Ave #600, Phoenix, AZ 85004, United States b School of Public Policy, University of California – Riverside, 900 University Ave, Riverside, CA 92521, United States

A R T I C L E I N F O

A B S T R A C T

Keywords: Oil spill response and cleanup Simulation Spatial optimization Contingency planning

Although anthropogenic oil spills vary in size, duration and severity, their broad impacts on complex social, economic and ecological systems can be significant. Questions pertaining to the operational challenges associated with the tactical allocation of human resources, cleanup equipment and supplies to areas impacted by a large spill are particularly salient when developing mitigation strategies for extreme oiling events. The purpose of this paper is to illustrate the application of advanced oil spill modeling techniques in combination with a developed mathematical model to spatially optimize the allocation of response crews and equipment for cleaning up an offshore oil spill. The results suggest that the detailed simulations and optimization model are a good first step in allowing both communities and emergency responders to proactively plan for extreme oiling events and develop response strategies that minimize the impacts of spills.

1. Introduction Over the past several decades, oil spills of varying duration and extent have impacted the coastal communities of the Gulf of Mexico (GOM), most notably the Deepwater Horizon spill of 2010 (Cruz and Krausmann, 2008; Graham et al., 2011). A mix of deleterious events, including those associated with Hurricanes Katrina, Harvey, and Rita have also caused significant damage to the communities surrounding the GOM. This combination of natural and anthropogenic disasters has affected the oil and gas, housing, aquaculture, forestry, tourism and fishing industries, with cumulative costs exceeding tens of billions of dollars (Baade et al., 2007; Smith et al., 2011). For example, recent estimates suggest that an average of 106,000 recreational fishing trips are taken per day along the Gulf of Mexico and when these trips are disrupted, economic losses are estimated at $9 million per day (Gentner, 2010). In fact, Sumaila et al. (2012) estimated that recreational fisheries in the Gulf of Mexico would suffer $1.9 billion of total revenue losses, $1.1 billion in total profit losses and $3.5 billion of total economic losses between 2010 and 2017 because of the Deepwater Horizon spill. Although these types of macro-level, economic assessments can be helpful, their tendency to focus on singular events for a specific sector (across an entire region) is myopic. The tangible economic, environmental, cultural and social effects of extreme events like the Deepwater

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Horizon oil spill habitually percolate through regions, impacting communities and individual ecosystems, households and businesses differentially. These varied “footprints” of damage (Muller and Stone, 2001; Boruff et al., 2005; Jepson, 2007) are strongly geographic, but predicting exactly where the footprints will manifest can be difficult. For instance, when large oil spills do occur, the structural and/or economic losses may be catastrophic in some locales, but damage in proximal areas may be minimal. The same can be said for the temporal implications of extreme events. Some disasters may have long-term implications, while others may only generate short-term shocks to the system. Further, there are significant challenges in developing the spatial and temporal analytics to anticipate and remediate disruptive events such as oil spills. For example, the ability to evaluate their potential outcomes, assess community vulnerability (broadly defined) and optimize cleanup efforts, including the allocation of human resources, equipment and supplies to areas impacted by an oil spill, is not trivial. With these challenges in mind, the purpose of this paper is to illustrate the application of advanced oil spill modeling techniques in combination with a newly developed mathematical model to geographically optimize the allocation of response crews and equipment for cleaning up an offshore oil spill in the Gulf of Mexico. This combination of simulation and response modeling is important because it provides a more holistic and well-rounded approach for anticipating the potential outcomes of oil spills, while simultaneously affording the

Corresponding author. E-mail address: [email protected] (T.H. Grubesic).

http://dx.doi.org/10.1016/j.marpolbul.2017.09.012 Received 12 July 2017; Received in revised form 1 September 2017; Accepted 6 September 2017 0025-326X/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Grubesic, T.H., Marine Pollution Bulletin (2017), http://dx.doi.org/10.1016/j.marpolbul.2017.09.012

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evaporation, transport, dispersion, emulsification, entrainment, dissolution, volatilization, partitioning, sedimentation and degradation” (French-McCay, 2004, 2441), all of which is influenced by ambient conditions in the water body and local atmospheric conditions. One of the first comprehensive spill models, developed under the Department of Interior regulations and CERCLA, was the Natural Resource Damage Assessment Model for Coastal and Marine Resources (NRDAM/CMR) (Grigalunas et al., 1988). This included physical fates, biological effects and economic damages submodels and integrated a wide variety of chemical, biological, economic and toxicological data to estimate the potential impacts of a spill on marine, avian and mammalian communities, as well as important community resources (e.g. public beaches). A second and widely used model is the General NOAA Operational Modeling Environment (GNOME) developed by the National Oceanic and Atmospheric Association (NOAA, 2014) and its Office of Response and Restoration (OR & R). GNOME is a comprehensive spill modeling package that can be used to predict the trajectory of a pollutant in a body of water – from the spill site to its final fate. Much like NRDAM/ CMR, GNOME leverages ambient ocean conditions and local atmospheric conditions, as well as chemical and physical weathering to predict spill outcomes. However, given the stochastic nature of the ocean and local atmospheric conditions, GNOME also incorporate elements of uncertainty into the modeling package. Specifically, GNOME has two spill prediction functions: 1) best guess and, 2) minimum regret. Where the former is concerned, GNOME draws upon the modeling parameters set by a user to predict the final fate of the oil and assumes weather forecasts (e.g., wind speed, direction, frontal passing) are accurate. For the latter, the concept of “minimum regret” is salient because it includes a random component that will account for error and/ or uncertainty in things like weather, all of which can be embedded within alternate trajectory analysis results. In time, additional oil spill impact models were developed for the United States and elsewhere (McCay, 2003; Smith et al., 1982; Varlamov et al., 1999; Bollt et al., 2012), including freely available packages such as MEDSLIK-II (De Dominicis et al., 2013) and the proprietary contributions such as Oil Spill Contingency and Response (OSCAR) from Sintef (Aamo et al., 1997), MIKE from DHI Solutions (2016) and SIMAP, an integrated oil spill impact model system from RPS ASA (2016). A range of hazard and vulnerability assessment models for spills were also developed (Andrade et al., 2010; Garcia et al., 2013; Alves et al., 2014). For a thorough review of oil spill modeling, both past and present, see Spaulding (1988), Reed et al. (1995) and Fingas (2016).

geospatial intelligence necessary for more comprehensive scenario planning to reduce community vulnerability, increase resilience, and to decrease response costs. The remainder of this paper is organized as follows. In the next section, we provide an overview of a basic oil spill response framework, a review of existing spill simulation approaches, and detail the difference between strategic and tactical response models. The newly developed oil spill cleanup model, its formulation and solution approach are detailed in Section 3. Computational results for a case study are then provided and we close the paper with a discussion of the results and their implications for both policy and response planning. 2. Oil spill modeling At its core, oil spill modeling consists of a wide range of data inputs, numerical models and algorithms that attempt to either hindcast or forecast the transport and fate of oil in the environment. As detailed by Reed et al. (1995), oil spill models account for oil type, spill rates, location, ambient environmental conditions, advection, spreading, emulsification, evaporation, response actions and many other factors. However, oil spill modeling is only one element of a much larger planning and response framework. For example, spill response consists of a concrete set of strategies and plans for dealing with the oil once it is released into the environment (Galt, 1997). The general response framework and associated process is captured with a simple series of questions and answers (Galt and Payton, 1996):

• What oil is posing a threat to? • Where is it likely to go? • Who is likely to get hit by the oil? • What are the likely impacts? • What can be done with the available resources? For most spills, these questions are asked repeatedly and the responses are re-evaluated continually as new information is made available. This planning and response cycle is updated at frequent intervals until the spill is contained or is no longer a threat to critical assets, ecosystems and/or communities. Of course, elements of uncertainty permeate this process, especially when attempting to develop response strategies in data-sparse, stochastic environments (Galt and Payton, 1996; Nelson and Grubesic, 2017). 2.1. Oil spill simulation and modeling Over the past several decades, a wide variety of oil spill simulation models have been developed and implemented for both scenario planning and for developing coastal management and response strategies. Much of the motivation for this activity, at least in the United States, can be traced to the Santa Barbara Channel oil spill in 1969 and the Federal Water Pollution Control Act Amendments (FWPCA) of 1972, which charged the Coast Guard as the lead federal agency for oil spills in the coast zone (Burns et al., 2002). Subsequent legislation, including the Comprehensive Environmental Response Compensation and Liability Act of 1980 (CERCLA), the 1986 Superfund Amendments and Reauthorization Act (SARA) (Barr, 1990), and the Oil Pollution Act of 1990 (Sipes, 1991) also provided a foundation for the development of impact models. As detailed by French-McCay (2004), the bulk of the developed models are used to predict or hindcast the trajectory of the oil, estimate weathering, predict the final fate of the oil, and to inform spill response and contingency planning. Of course, this unassuming typology of spill models drastically simplifies their underlying complexity. For example, when considering physical fates models, one must account for the distribution of oil in the water column, on the water surface and shorelines, as well as within the shoreline sediments. But, in order to generate these outputs, the model must also account for “oil spreading,

2.2. Strategic and tactical oil spill response models As detailed previously, spill modeling, although complex, is only one element of the broader oil spill evaluation, planning and response framework. There are two additional elements of particular interest to this paper: 1) strategic planning, and 2) tactical response. The first deals with the strategic elements of the oil spill response problem, primarily focusing on where to locate adequate human resources and equipment for efficiently responding to oil spills. These decisions must be made prior to an oil spill occurring, which means that planners are forced to rely on probabilistic information concerning spill locations, frequency, size and duration. An evaluation of the consequences of oil spills is critical for contingency planning, which in turn drives policy and associated response strategies. The second element focuses on the tactical or operational decisions associated with oil spill response. These decisions are made after a spill occurs and necessarily includes choices concerning equipment dispatch origins/locations, optimal equipment mix for a spill (e.g., number of ships and skimmers), how long a particular set of equipment might be needed, as well as how it should be operated. There are a number of important contributions in these domains 2

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approach that considers both economic and responsiveness (i.e. time) objectives for response operations. In addition to mechanical removal of oil, in-situ burning and chemical dispersants and coastal protection planning is also considered in the model. Given all of the work detailed above, it is clear that significant progress has been made in addressing both the strategic and tactical elements of oil spill response. However, much of this work is dated and it lacks the fine-grained spatiotemporal resolution required to ensure the most efficient and effective response to catastrophic oil spills. Again, the ability to account for individual components of an oil spill (i.e., droplets) and their trajectory would represent a major step forward in tactical response. Further, an ability to move away from the development and use of custom (but unvetted) oil transport and weathering models, such as those detailed by You and Leyffer (2011) and Zhong and You (2011), would be advantageous. To be sure, although the authors account for many of the key processes in these transport weathering modules, the underlying physics and associated ocean mechanics integrated into the larger oil spill modeling packages, such as those detailed in Section 2.1, are not only more complete, but these models can handle a much broader array of spill scenarios, from deep to ultra-deep water blowouts, to more innocuous surface spills from tankers. In short, the geospatial intelligence afforded by these packages would dramatically improve prescriptive strategies for both resource planning and allocation, as well as tactical strategies for cleaning up a spill.

worth noting. Where strategic planning is concerned, Psaraftis et al. (1986) developed a model for optimizing the location of oil spill cleanup resources in relation to a set of high risk oil spill points located off northeastern U.S. coastline. For each dispatch location, a finite set of response equipment is stockpiled and the cleanup capability of this equipment is known (e.g., skimming rate, pumping rate, storage capacity, containment boom length). Psaraftis et al. (1986) also considered aspects of the tactical response as they relate to strategic planning. For example, costs of operation, dispatch times for equipment, as well as estimated costs associated for the damage created by non-recoverable oil and equipment inefficiencies or delays were captured. The strength of this contribution is its comprehensive treatment of the response problem and an ability to capture the damages (i.e., costs) associated with non-recoverable oil. However, the way in which spill sites are modeled is unrealistic and highly aggregated. Although spills certainly occur at a discrete geographic location, the spatiotemporal dynamics of spills and the ambient environmental conditions driving each spill must be accounted for when developing prescriptions for optimizing the allocation of response resources. In other work, Belardo et al. (1984) used a multiobjective variation of the maximal covering location problem (MCLP) (Church and ReVelle, 1974) that optimized the location of response resources while accounting for the probabilities of covering spills categorized by group (e.g., high, medium and low potential for environmental/economic harm). Charnes et al. (1979) developed a chance-constrained, goal programming model to allocate response resources for a region. Although this work did include basic locational elements, it was more concerned with the budgetary restrictions and the capacity of cleanup equipment. Iakovou et al. (1996) propose a hybrid model in which strategic decisions regarding equipment are addressed simultaneously with optimized tactical response. The costs associated with opening a facility site, equipment storage and transport costs associated with equipment movement is considered, as well as spill types (e.g., crude, diesel), size, and response time. However, the spatiotemporal dynamics of the actual spills, including their transport and final fate, are not accounted for. Tactical models for oil spill response include the early work of Psaraftis and Ziogas (1985), who develop a decision algorithm for optimizing the dispatch of spill cleanup equipment sets. The model is used to recommend an aggregate cleanup capability for each time stage in the cleanup effort. The described tactical model is embedded within the larger MIT oil spill model (Psaraftis et al., 1983), but it does not attempt to optimize response of cleanup resources based on the individual components of the oil spill, known as spillets.1 Instead, Psaraftis and Ziogas (1985) mount an aggregate response, which assumes that equipment is distributed to spillets uniformly, irrespective of their location. Further, their model and the prescribed equipment mobilization can only control the volume, but not the trajectory of each spillet. Srinivasa and Wilhelm (1997) and Wilhelm and Srinivasa (1997) formulate a tactical decision problem for spill cleanup that minimizes total response time and integrates a degradation function for response system capability through time, but this work largely emphasizes problem reduction techniques and the exploration of solution alternatives. Gkonis et al. (2007) develop a tactical approach that also considers the oil weathering process and the costs (i.e., time) associated with response, as well as the damage associated with non-response. Spill incidents are categorized by damage potential (e.g., high, medium, low) and each spill is mathematically simulated to extract values associated with spill spread and weathering, but there is no explicit account of the spatial nature of the oil spread or its potential impact areas. In more recent work by You and Leyffer (2011), a mixed-integer dynamic optimization model is developed to minimize the total response cost and is integrated with a developed oil weathering model with multiple time horizons. Zhong and You (2011), develop a multiobjective optimization

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3. Methods, problem statement and data For the purposes of this paper, we introduce a basic (but extendable) tactical model for oil spill cleanup that minimizes the total dispatching time/cost of cleanup equipment while simultaneously meeting defined cleanup goals. Where this work deviates from previous efforts is in the use of a spatially and temporally explicit oil spill model to inform the tactical response. Specifically, our example application will focus on an accidental blowout scenario, much like the Deepwater Horizon accident in 2010. The spill modeling package used for this study, the Blowout and Spill Occurrence Model (BLOSOM), is an integrated oil spill simulation program written in the C++ programming language (Sim et al., 2015; NETL, 2017). It has been designed to simulate offshore oil spills emanating from deep and ultra-deep water well blowouts, making it an ideal simulation tool for modeling catastrophic events. BLOSOM is a spatially and temporally explicit model capable of modeling an oil spill from the initial release at the ocean floor to the eventual beaching and evaporation of the oil. It does so by incorporating seven submodels: 1) the Jet/Plume model, 2) transport model, 3) conversion model, 4) weathering model, 5) crude oil model, 6) gas/hydrates model, and the 7) hydrodynamic handler, all of which are able to run in tandem or individually. Although relatively new, a growing body of research suggests that BLOSOM is a highly robust and effective platform for modeling the spatial and temporal dynamics of oil spills (Nelson et al., 2015; Socolofsky et al., 2015; Nelson and Grubesic, 2017; Sim et al., 2015).2 As alluded to previously, the major advantage to using BLOSOM is that one can account for the individual components of the oil spill (i.e., droplets) and their disaggregate behavior, as well as things such as volume, trajectory, weathering and the spatial and temporal aspects of the oil's final fate. Given the astronomical costs associated with spill response and cleanup efforts, the enhanced spatial and temporal resolution afforded by BLOSOM allows planners to better anticipate spill outcomes, potentially impacted areas, as well as providing a more detailed framework for both crafting and improving tactical response plans. More importantly, BLOSOM excels in facilitating the exploration 2 Although the weathering submodel lacks an evaporation component for this application, it is under development and will be included in future analyses.

Today, “spillets” are commonly referred to as droplets or parcels.

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Fig. 1. Spill site and response staging areas.

of a “do-nothing” response, where no effort is made to recover any oil. As detailed by Psaraftis and Ziogas, 1985, this “benign neglect” is rarely an acceptable option, but it does represent a good way of modeling the upper bound of the damage associated with the spill and the potential vulnerability of areas (e.g., ecosystems and communities) proximal to the spill.

be accounted for in the model detailed below. Instead, we focus on the travel costs, specifically distance, as the costs to be minimized. In this particular scenario, we do not monetize these costs, but it can be assumed that as aggregate distance traveled increases, costs increase accordingly. Further, as more ships are dispatched, the costs associated with travel distance exhibit a commensurate increase. The cleanup capability of each vessel is assumed to be identical in this scenario, but there are capacity constraints associated for each vessel which must be considered when trying to reach the cleanup targets (i.e., total oil removal).3 The cleanup targets can range from 0 to 100%, although it is unrealistic to assume that all of the oil discharged from a blowout can be recovered. However, as mentioned earlier, the notion of benign neglect, where no response is made to a spill, ensures that 0% of the oil will be recovered – which represents a worst-case scenario and the upper bound of spill damage. BLOSOM simulations were run for a total of 40 days, which corresponded to the amount of time required for all of the discharged oil to beach (if no response was mounted). Once beached, the oil was not reentrained. The Navy Coastal Ocean Model American Seas (AmSEAS) ocean data was selected for use in the oil spill transport submodel. AmSEAS data has a temporal resolution of 3 h and a spatial resolution of about 3.3 km. For the diffusion submodel, a random walk with a Smagorinsky coefficient of 0.1 is chosen because of its previous use in

3.1. Problem statement The problem to be addressed is a relatively generic, tactical response scenario, similar, at least in spirit, to that defined by Psaraftis and Ziogas (1985). Specifically, at midnight, on June 1st, 2016, at 28.20° N, − 96.03° E and a depth of 150 ft., a blowout occurs at a wellhead in the Brazos Area, South Addition (Fig. 1). The simulated blowout lasted a total of 8 days, releasing 500 barrels of oil every 24 h, for a total spill amount of 4003.9 barrels or 168,165.7 gal. The blowout is detected on Day 1 and a response is mounted by Day 2. Staging areas proximal to the spill are notified and the vessels with response capabilities are readied for deployment. For the purposes of this analysis, the number of vessels available at each staging area corresponds to a random integer between 1 and 4. Each of these vessels is equipped with an integrated skimming system that consists of booms, pumps, and related mechanical extraction and storage equipment – and are dispatched, if necessary, as a tactical response to the spill area. Again, the use of these “equipment sets” for spill response is routine in both practice and the literature. For more details, see Psaraftis and Ziogas (1985). Neither the sunk, nor variable (opportunity) costs associated with the response equipment are considered, although these can easily

3 For this study, vessel storage capacity is based on the skimmer recovery rate: 130 gal per minute, or 7800 gal per hour. Assuming an 8 h cleanup effort per day, each vessel is assumed to have a storage capacity of 62,400 gal. This is easily adjusted in the model to reflect alternative capacities.

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Mendoza-Cantú et al., 2011). Furthermore, normalization results in a common scale so that the overall magnitude of impact can be compared between scenarios. For this analysis, impact was measured on a scale ranging from 0 to 100 calculated using the max impact score from the benign neglect scenario:

spill simulations in the Gulf of Mexico and has been determined as a reasonable value for estimating horizontal diffusion (Duran, 2016). In the western Gulf of Mexico, summer currents tend to push toward the western shoreline, which encourages the beaching of the spilled oil. This is why a start date of June 1st was selected. In short, this example scenario is one where both proximal communities and ecosystems are potentially vulnerable to the spill effects; meaning that benign neglect is not a viable option for a spill of this size and duration. The main BLOSOM output is a series of “parcels” that represent the oil plume. Each parcel is represented as a point in a shapefile format. All of the modeled parcels include a unique identifier used throughout the simulation, and each parcel has suite of unique characteristics that are a result of oceanic conditions, weathering, and initial start characteristics (see Section 3 for more detail on parcel characteristics). The individual parcels are used in both the impact calculations and the OSCOM model detailed below. For this particular case study, shapefiles of the spill plume parcels were generated every 24 h and used as inputs to the response model. It is important to note that this temporal resolution (i.e., 24 h) is flexible and can be adjusted depending on the situation. For example, ambient ocean data are available in three hour intervals. Thus, although the 24 h time interval for parcel generation (and associated outputs) chosen here provides a nice balance between computational costs and model resolution, one could generate the spill parcel layer every 3 h if desired. As oil was mechanically removed during the tactical response process, the corresponding droplets were permanently removed from consideration in the simulation package. This process represents a loose coupling between the simulation package, the spatial optimization model and a geographic information system. Beginning with the first day of the oil spill, the optimization model (detailed below) ingests the simulated, shapefile output of the spill during Day 1. Next, the optimization model determines which parcels are to be cleaned (or not), and each parcel is assigned a binary score (1-cleaned, 0-not). Using this information and the unique identifier associated with each spill parcel, the cleaned parcels are removed from the Day 2 simulation output, effectively simulating them being mechanically removed from the environment. The remaining Day 2 parcels represent a mixture of newly introduced oil from the blowout and residual parcels leftover from Day 1 that were not cleaned up. This basic, iterative process is repeated until the final spill day, when a calculation of spill impacts could be generated and compared to the non-intervention scenario. One compelling aspect of the BLOSOM simulation and its subsequent analysis in a geographic information system is the ability to incorporate a vector impact grid, where the final, geographic fate of the oil is accounted for. The GIS-derived impact grid is used to tabulate the amount of oil that is ultimately beached and the number of local assets (e.g., hotels, businesses) impacted by the oil in coastal locations. Specifically, the total impact grid (TIG) is the entire grid of cells subsuming the coastline. The value for each cell was calculated by using an oil modifier combined with the number of assets impacted for each cell. The oil modifier is a scaled value from 0 to 1, and is calculated on a per cell basis as follows:

⎛ oilk ⎞ × ⎝ oil max ⎠ ⎜

⎟

∑ Ak

maxnew −minnew × (v − max old) + maxnew max old−min old

(2)

where maxnew is 100, minnew is 0, maxold and minold are the maximum and minimum impact values from the benign neglect scenario (details below), and v is the individual impact score for each grid cell. Based on the problem statement detailed above, we define the input variables to the tactical decision problem as follows: I = set of staging areas indexed by i J = set of oil spill locations indexed by j or l Parameters: Γ = oil cleanup target Pe = operating capacity of cleanup equipment in the response vessel Vj = volume of spill oil at site j Dij = cost/time to dispatch vessel from staging area i to spill site j Ωj = set of spill locations that are within the containment area of site j Ni = available number of vessels in i Decision variables: xij = number of vessels dispatched from area i to spill site j 1; if spill site j is cleaned up by vessels uj = ⎧ ⎨ ⎩ 0;otherwise The staging areas used for index I correspond to the actual physical locations of boat ramps along the Gulf of Mexico. Many of these locations have a variety of vessels already equipped with spilled oil recovery systems (SORS), but also includes vessels that could be equipped with skimming systems and on-board storage should a catastrophe occur.4 During the cleanup process, each vessel is assumed to return to the staging area at night, unload all of the collected oil, and then depart from the staging area with a fully replenished storage capacity in the morning. It is important to note that the sites indexed in I are flexible and can easily be updated to reflect current equipment allocations and/ or ramps with vessel size constraints and the like. If a vessel is dispatched to spill site j, it is assumed that the vessel will collect all oil from the designated containment area. For the purposes of this analysis, the containment area is defined as a circle with a radius of 1769 m. This is not an arbitrary parameter selection. Containment booms are usually deployed at a speed of 0.75 knots. As a result, 11,116.8 m of booms can be deployed within an 8 h window, corresponding to the perimeter of circle with a radius of 1769 m. If there are additional environmental or operational constraints that require consideration, this parameter can be adjusted accordingly. Given these inputs, the Oil Spill Cleanup Operation Model (OSCOM) is defined as follows: Oil Spill Cleanup Operation Model (OSCOM):

(1)

min ∑

where oilk is the amount of oil in grid cell k on the final simulation day, and oilmax is the largest amount of oil recorded in any one grid cell. This is then multiplied by the total number of assets (A) in one particular grid cell (k). This process was repeated for each grid cell within the TIG. The final result is a value that reflects the amount of oil within the cell and the number of assets occurring in that same cell. A normalization procedure was used to scale the calculated impact values for each grid cell. Normalization is often used for impact assessments because final measurements reflecting the components of impact are represented using different units (Frazão Santos et al., 2013; Lan et al., 2015;

j∈J

∑i∈I

x ij Dij

(3)

Subject to:

∑j∈J

x ij ≤ Ni , ∀ i ∈ I , t ∈ T

∑i ∈I

x ij Pe ≤

4

5

∑l∈Ωj

Vl , ∀ j ∈ J

This is often referred to as vessel of opportunity skimming system (VOSS).

(4) (5)

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Fig. 2. Frequency of oiling in the Gulf of Mexico and along the Texas coastline.

∑i ∑l∈Ωj ∑j

x il ≥ uj , ∀ j

uj Vj ≥ Γ

important feature of the model because it may not be necessary to remove all of the oil mechanically or through other means (e.g., burning or chemical dispersants). Depending on the size of the spill, as well as ambient environmental conditions in the GOM, the weathering and evaporation process may eradicate much of the oil without a massive interdiction effort. As a result, tactical response may only require the removal of a fraction of the discharged oil – just enough to ensure that proximal communities and ecosystems do not suffer and that the costs associated with response remain low.

(6) (7)

The objective (3) is to minimize the total dispatching time/cost of cleanup equipment. Constraints (4) invoke the capacity limitation for cleanup equipment at each staging area. Constraints (5) set limitations on the amount of oil that can be removed by using cleanup equipment, which should not exceed the total volume of the spill within the containment area of each site. Constraints (6) ensure a spill site is cleaned up only if a vessel is sent to its containment area. Constraints (7) require that the total amount of removed oil should achieve the pre-specified oil cleanup target. Spill sites are determined based on the BLOSOM parcel output. As detailed previously, each of the parcels output by BLOSOM contain a suite of characteristics unique to each parcel. This includes the total amount of oil represented by the parcel which is taken into account by constraint (5) in the OSCOM model. Each spill site is either cleaned up or not by using the binary decision variable, μj, so that its corresponding volume is counted only once in summarizing the total amount of removed oil. There are a number of aspects of this model that merit additional discussion. First, this model can be solved optimally, guaranteeing that the costs associated with response can be minimized. Given the scarcity of resources and relatively limited budgets associated with cleanup efforts, the ability to ensure an optimal response is critical. Second, as mentioned previously, cost parameter, Dij is flexible and extendible. For example, monetizing the costs associated with distance traveled to a spill site is trivial. One could simply weight the distance parameter using ship fuel cost. Further, spill specific opportunity costs could be included in the objective function (3) to reflect the varying nature of spills and the costs of the equipment mix and vessels used for tactical response. Second, the oil cleanup target (7) is easily adjusted. This is an

4. Results In an effort to highlight the utility of using both BLOSOM and OSCOM, three scenarios were explored. Scenario 1 highlights the results of benign neglect, where the spill is allowed to occur, without any tactical response. As mentioned earlier, this serves as a worst-case scenario and the upper bound of spill damage. Scenario 2 represents an aggressive tactical response which seeks to mechanically recover 95% of the discharged oil. Lastly, Scenario 3 assumes an equipment limited response, but it still requires a 95% recovery of the discharged oil. Specifically, unlike Scenario 2, where staging areas house between 1 and 4 SORS vessels, Scenario 3 allows for a maximum of only 3 vessels at any staging location during the tactical response. OSCOM is structured using Python, and subsequently solved using a commercial optimization package, Gurobi. Processing was conducted on an Intel Core i7–4770 (3.40 GHz) computer running Windows with 16 GB RAM. Prior to exploring the results of each scenario, it is important to highlight both the spatial and temporal nature of the simulated spill. Fig. 2 illustrates the spill site and the frequency of oiling (in days), by location, along the Texas coast. The vast majority of locations in the Gulf of Mexico were exposed to the oil for less than 10 days, but there are sections of the Texas coast that were oiled for up to 26 days after the 6

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Fig. 3. Oil spill behavior through time.

high for this spill and this is confirmed by the large number of sensitive assets impacted (n = 2876). Fig. 4b displays the TIG for beached oil, once again emphasizing that Matagorda Island is the most impacted location. Again, the advantage of the TIG is that it allows analysts to compare spill impacts, without bias, between different scenarios and with geographic specificity across the impact grid.

spill. In particular, portions of Matagorda Island, which is located south of Port Lavaca, were hit very hard. Fig. 3 displays the geographic location of oil, through several time steps of the simulation; it starts with Day 2 and ends with Day 40. Of particular interest here is the acceleration of the spill toward the Texas barrier islands around Day 20 and the geographic spread of the spill by Day 40. At the very least, the time required for the spill to reach land is an instructive metric – suggesting that there is time for a meaningful tactical response before communities and shorelines are impacted. More importantly, it suggests that there is both a spatial and temporal window in which tactical responses would be most efficient and effective, and this is prior to Day 10, when the spill really starts to spread.

4.2. Scenario 2 The results of an aggressive tactical response to the spill, with a target cleanup goal of 95% of the discharged oil, is summarized in Table 1. Specifically, the spill is detected during Day 1 and SORS vessels arrive by Day 2 for the cleanup. As detailed previously, each staging location houses between 1 and 4 SORS vessels, but there is no hard limit to the number of vessels that can be used in this cleanup effort. Ultimately, the OSCOM model selects the optimal number, location and allocation of response vessels. For example, during Day 2, six vessels are dispatched, each of which booms, extracts and stores discharged oil from a specific geographical area within the Gulf of Mexico (Fig. 5). Metrics associated with travel distance, time taken to arrive at the cleanup site and the quantity of captured oil are also included in Table 1. During Day 2, for example, the six SORS vessels managed to cleanup 19,956 gal of oil and their aggregate travel distance to the cleanup area(s) is 634.43 km. As the spill continues during Days 3 and 4, the cleanup process endures. However, the number of vessels dispatched and their effectiveness in capturing oil varies. For example, as the spill begins spreading more aggressively during Day 3, seven vessels are sent to the cleanup area(s), traveling an aggregate distance of 731.91 km and capturing 20,653 gal of oil (Fig. 6). Perhaps the most interesting result occurs during Day 4. In this instance, only four vessels are dispatched, but they manage to capture 21,275 gal of oil – the maximum daily capture for the eight-day cleanup effort (Fig. 7). How

4.1. Scenario 1 Fig. 4a and b highlight the results of the first scenario, where no tactical response was mounted for the spill. Because none of the discharged oil was mechanically removed, by Day 40, much of it was beached or remained in the Gulf. Fig. 4a and b summarize the shoreline impacts using a 2 km × 2 km vector impact grid. Fig. 4a displays the aggregate impact of the oiling for each grid. In total, 3678.9 barrels (154,513.8 gal or 95.24%) of the discharged oil made its way to shore. Again, Matagorda Island was the highest impact area. A fraction of the oil remained at sea (295.08 barrels; 12,393 gal), and 29.8 barrels were distributed outside of the modeling area.5 These results further suggest that the interaction between beaches and the discharged oil was very 5 BLOSOM designates an oil particle as “out of bounds” if the particle drifts off the known bathymetry data. Specifically, because the bathymetry data is of higher resolution than the current data, there are some locations on the edges of the study region where currents exist but no bathymetry. Although rare, the reverse can also occur, where bathymetry data exist but no current data.

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Fig. 4. Benign neglect and the spatial impacts of the simulated oil spills. Top (a): total barrels of oil beached. Bottom (b): total impact index of beached oil.

Table 1 Scenario 2 response and performance statistics. Vessel travel time (h) Day

Number of vessels

Travel distance (km)

Minimum

Average

Maximum

Total spill

Gallons cleaned

Percent cleaned

Model solution time (s)

2 3 4 5 6 7 8

6 7 4 5 5 5 5

634.43 731.91 370.30 481.76 508.83 499.07 461.28

4.32 4.22 3.53 3.87 4.27 4.29 3.52

4.76 4.70 4.17 4.34 4.58 4.49 4.15

5.12 5.35 4.48 4.63 4.92 4.83 4.41

20,998.86 21,736.28 22,394.72 22,121.76 22,104.79 22,103.14 22,089.51

19,956.09 20,653.49 21,275.23 21,019.48 20,999.77 21,020.47 20,986.35

95.03% 95.02% 95.00% 95.02% 95.00% 95.10% 95.01%

780.13 5298.32 2485.60 3307.22 2122.50 953.05 1264.22

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Day 2 cleaned spill Day 2 uncleaned spill Day 2 vessel dispatch

0

12.5

25

50 KM

0

25

50

100 KM

Fig. 5. Tactical response for Scenario 2, Day 2.

monetized for the application, this would require a significant increase in response budget.

can four ships be this effective? Simply put, there is more oil in the environment and the spatiotemporal distribution of the oil is clustered in certain parts of the spill area. As a result, fewer ships were required for Day 4, but given the distribution of oil in the environment, those ships were particularly effective and efficient in their cleanup efforts. Lastly, it is important to note that the aggressive cleanup effort for Scenario 2 ensured that far fewer sensitive assets were impacted in this scenario (n = 1530).

5. Discussion and conclusion Scenario planning and the development of efficient and effective tactical responses for oil spills continues to be a critical element of coastal planning, management and emergency response. The results of this paper suggest that the coupling of BLOSOM, an integrated oil spill simulation and modeling program, with OSCOM, a flexible and extendable tactical response model, provide a significant advance in the geospatial intelligence associated with deep and ultra-deep water blowouts. In particular, the combination of BLOSOM with OSCOM allows planning and response agencies to account for the individual components of the oil spill (i.e., droplets) and their disaggregate behavior, as well as things such as volume, droplet trajectory, weathering and the spatial and temporal aspects of the oil's final fate. More importantly, OSCOM should be considered a “base” model that can be modified, extended or enhanced for specific tactical applications. As mentioned earlier, the cost parameter can be adjusted and/or weighted to reflect the variable costs associated with response efforts, including the differences associated with an alternative equipment mix and fuel requirements. In addition, the oil cleanup target is easily adjusted, allowing response efforts to better reflect potential spill impacts. Where the results of this paper are concerned, it is clear that an aggressive response, such as the one detailed in Scenario 2, provides a

4.3. Scenario 3 Although the third scenario represents an equipment-limited response, it remains an aggressive one, with a target cleanup goal of 95% of the discharged oil. For this scenario, the number of vessels available at each staging area corresponds to a random integer value between 1 and 3. However, similar to Scenario 2, there are no hard limits on the number of ships that can respond. As detailed by Table 2, this did not prove to be a significant limitation for the cleanup effort. 95% of the discharged oil was extracted from the Gulf and the resulting statistics mimic that of Scenario 2 for beached oil, open water oil and the TII. However, there is one significant divergence between the Scenarios 2 and 3. The travel cost associated with response, when staging areas are equipment-limited, is significantly larger when compared to Scenario 2 where more equipment was available. Specifically, the travel costs for Scenario 2 are 526,797.12 and those for Scenario 3 are 695,525.57, representing a 32.02% increase. Again, although these costs were not

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Day 3 cleaned spill Day 3 uncleaned spill Day 3 vessel dispatch

0

12.5

25

50 KM

0

25

50

100 KM

Fig. 6. Tactical response for Scenario 2, Day 3.

Second, it is also important to consider how OSCOM could interact with oil spill modeling packages, other than BLOSOM. Again, future work will integrate the coupling of other platforms such as GNOME, for helping inform a tactical response. Lastly, given the stochastic nature of the Gulf of Mexico, the results of this application could change significantly if the timeframe was switched to December, September, or some other time of year. However, the inherent power of simulation modeling would allow for planners to consider alternatives and develop tactical responses accordingly. In conclusion, the coupling of powerful oil spill modeling packages and tactical response models holds great promise for coastal management, planning and the mitigation of catastrophic spill events. Although significant research and development effort has been directed toward developing advanced oil spill modeling packages, much less attention has been paid to enhancing tactical response efforts from an explicitly spatial and temporal perspective. The model detailed in this paper, OSCOM, represents an effort to reinvigorate this domain and build upon the legacy of strong work in the fields of environmental management, operations research, management science, engineering and geography.

clear benefit to sensitive assets and coastal communities that are potentially exposed to offshore blowouts. By removing 95% of the spilled oil, the impacts to the local coastline are significantly reduced when compared to benign neglect (e.g., Scenario 1). Of course, there are significant costs associated with this type of cleanup effort. When evaluating the efficacy of these tactical responses, one outstanding question is how much spilled oil needs to be captured? Is 95% optimal? 85%? Less than half? There is no easy answer to this question, but it is clear that this type of evaluation framework and contingency analysis can help planners and emergency managers consider a range of response options. It is also important to highlight the computational effort associated with OSCOM. As detailed in Tables 1 and 2, although the required solution times are not trivial, they are not prohibitive. For example, the most time consuming solution corresponded to Scenario 2, Day 3, and required about 5300 s (or 88 min) to solve (Table 1). In other instances, an optimal solution was obtained in less than 460 s (about 7.5 min). The solution times for problem instances in Scenarios 2 and 3 averaged about 30 min. Of course, it is possible that a heuristic solution approach could lower computational effort, but there are no guarantees of optimality. There are several limitations to the results of this paper that are worth mentioning. First, emergency response efforts for oil spills often include measures for shoreline protection. For example, boom allocation along sensitive shorelines can help protect delicate ecosystems, critical infrastructure (e.g., boat ramps) and other important assets. Future work will integrate these objectives for tactical response.

Acknowledgements This work was supported by the National Academies of Science Gulf Research Program (# 2000007349). The authors would also like to thank the BLOSOM team at the National Energy Technology Laboratory for helping with oil simulations and offering guidance where needed.

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Day 4 cleaned spill Day 4 uncleaned spill Day 4 vessel dispatch

0

12.5

25

50 KM

0

25

50

100 KM

Fig. 7. Tactical response for Scenario 2, Day 4.

Table 2 Scenario 3 response and performance statistics. Vessel travel time (h) Day

Number of vessels

Travel distance (km)

Minimum

Average

Maximum

total spill

Gallons cleaned

Percent cleaned

Model solution time (s)

2 3 4 5 6 7 8

6 7 4 5 5 5 5

822.85 919.39 505.81 653.23 678.91 665.49 623.00

5.58 5.59 5.67 5.51 5.73 5.64 5.53

6.17 5.91 5.69 5.88 6.11 5.99 5.61

6.75 6.31 5.70 6.15 6.54 6.56 5.77

20,998.86 21,735.78 22,394.23 22,121.27 22,108.48 22,102.33 22,088.78

19,956.59 20,653.49 21,275.22 21,015.30 21,004.27 21,020.39 20,986.35

95.04% 95.02% 95.00% 95.00% 95.01% 95.10% 95.01%

459.30 1299.49 1827.72 1956.91 1268.63 580.58 760.50

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