Progress in operational modeling in support of oil spill response

Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies

Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers

Sunken oil transport processes in rivers differ from those in oceans, and currently available models may not be generally applicable to sunken oil in river settings. The open-source Subsurface Oil Simulator (SOSim) model has been expanded to handle spills of sunken oil in navigable rivers, utilizing Bayesian inference to integrate field concentration data with bathymetric data to predict the location and movement of sunken oil. A novel prior likelihood function incorporates bathymetric input, with sampling grid and default parameters adapted appropriately for rivers. SOSim v2 was demonstrated versus field observations taken following the M/T (Motor Tanker) Athos I oil spill. The model was also modified to operate in 1-D, to assess the longitudinal distribution of sunken oil in a non-navigable river using available poling data collected following the Enbridge Kalamazoo River oil spill in 2010. Results of both case studies were consistent with observed data and local bathymetry in 2-D and 1-D, and the model is suggested as a complement to deterministic models for oil spill emergency response in rivers

Application of the SOSim v2 Model to Spills of Sunken Oil in Rivers

Sunken oil transport processes in rivers differ from those in oceans, and currently available models may not be generally applicable to sunken oil in river settings. The open-source Subsurface Oil Simulator (SOSim) model has been expanded to handle spills of sunken oil in navigable rivers, utilizing Bayesian inference to integrate field concentration data with bathymetric data to predict the location and movement of sunken oil. A novel prior likelihood function incorporates bathymetric input, with sampling grid and default parameters adapted appropriately for rivers. SOSim v2 was demonstrated versus field observations taken following the M/T (Motor Tanker) Athos I oil spill. The model was also modified to operate in 1-D, to assess the longitudinal distribution of sunken oil in a non-navigable river using available poling data collected following the Enbridge Kalamazoo River oil spill in 2010. Results of both case studies were consistent with observed data and local bathymetry in 2-D and 1-D, and the model is suggested as a complement to deterministic models for oil spill emergency response in rivers

Bayesian sunken oil tracking with SOSim v2: Inference from field and bathymetric data

Sunken oil is often difficult to detect, and few oil spill models are designed to locate and track such oil. Therefore, the multi-modal Bayesian inferential sunken oil model, SOSim (Subsurface Oil Simulator), was expanded in this work for use during emergency response and damage assessment. Rather than requiring hydrodynamic data as input, SOSim v2 accepts available field concentration data, along with default or custom bathymetric data, for inference of the location and trajectory of sunken oil. Novel aspects include inference based on bathymetry and the Coriolis Effect, by constructing a prior likelihood function from sampled bathymetric data, scaled proportionally with field concentration data. SOSim v2 is demonstrated versus field data on the ITB DBL-152 oil spill in the Gulf of Mexico, with sensitivity analysis. Results suggest that the inferential approach presented can be effective for modeling relatively slow-moving pollutant masses such as sunken oil, when field concentration data are available. •SOSim (Subsurface Oil Simulator) is a multi-modal Bayesian sunken oil model.•SOSim was expanded to accept field concentration data and bathymetric data.•Results suggest that SOSim can be an effective model when field data are available

A review on the sinking mechanisms for oil and successful response technologies

A rise in the shipping of heavier hydrocarbon products increases the potential for an oil to sink after a spill. Further, sunken oil is difficult to locate and recover, and appropriate response technologies depend on the sinking mechanism. In this review, principal sinking mechanisms for oil are described and appropriate response technologies are suggested. Then, models appropriate for tracking sunken oil are compared. Oil may sink as burn residue, microscopic oil-particle aggregates (OPAs) or macroscopic oil-sediment mixtures (OSMs), marine oil snow during a MOSSFA event, or due to its high density. The most common mechanism is by sediment entrainment, and in such scenarios manual recovery has been reported as a successful response option. Among oil tracking models, trajectory models and Bayesian oil search models are compared for sunken oil capabilities. Many oil spill models require hydrodynamic inputs, whereas Bayesian models infer parameters based on available field concentration and bathymetric data. •Oil may sink due to its high density or by mechanisms that make the oil denser.•Processes include sediment interaction and the formation of MOSSFA and burn residue.•Successful detection and recovery depend on the specific sinking mechanism.•Advancements in sunken oil spill models are being made to assist during response

Bayesian submerged oil tracking with SOSim: Inference from field reconnaissance data and fate-transport model output

When spilled oil collects at depth, questions as to where and when to dispatch response equipment become daunting, because such oil may be invisible by air, and underwater sensing technology is limited in coverage and by underwater visibility. Further, trajectory modeling based on previously recorded flow field data may show mixed results. In this work, the Bayesian model, SOSim, is modified to locate and forecast the movement of submerged oil, with confidence bound, by inferring model parameters based on any available field concentration data and the output of one or more deterministic trajectory models. Novel aspects include specification of a prior likelihood function, and generation of results in 3-D from data in the 2-D density space of the isopycnal layer containing oil. The model is demonstrated versus data collected following the Deepwater Horizon spill. This new inferential modeling approach appears complimentary to deterministic methods when field concentration data are available. •A data-driven Bayesian statistical model SOSim is developed to find submerged oil in 3D from data of the isopycnal layer•SOSim assimilates the real time field observations as ground truth and utilizes the predictions from OSCAR as priors•SOSim is validated against the collected data from the DWH spill and a synthetic case with inertial current