Project Overview

Summary:

Dr. James Englehardt at the University of Miami was awarded an RFP-VI grant at $499,813 to conduct the RFP-VI project titled, “Inferential/Parametric Forecasting of Subsurface Oil Trajectory Integrating Limited Reconnaissance Data with Flow Field Information for Emergency Response”.  The project comprised two institutions (University of Miami and SINTEF Ocean), 1 principal investigator (Englehardt), 1 co-PI (Dr. Cynthia Beegle-Krause), 3 PhD students, and 1 Visiting Scholar.

When an oil spill or blowout occurs, immediate and pressing questions emerge as to where and when to dispatch response operations. Such questions become daunting when there is significant sunken (bottom) or submerged (water column) oil present, due either to intrinsically-high oil density, sediment entrainment/marine snow formation, and/or weathering. Subsurface oil cannot be spotted by air. Underwater visualization techniques provide only narrow area coverage, are further limited or prevented by water turbidity, contamination, and oil fouling, and cannot project oil trajectory in time. Existing oil spill trajectory models have not been implemented for sunken oil, in particular, due to (a) limitations in information on bottom currents, and (b) the effects of changes in temperature (oil density), salinity, weathering, and wave-induced sediment entrainment that cause resuspension and re-deposition of the oil mass. Therefore, a generalized model capable of exploiting available field reconnaissance data rapidly to locate and project oil mass trajectory in time, would complement today's models. In particular, side-scan sonar and autonomous underwater vehicle equipment is now available for rapid collection of approximate narrow-field data on bottom oil following a spill. While available models are not generally able to use such data directly and rapidly, the inferential SOSim model developed by the PIs group in 2010 can infer and project oil location in time based on limited field data. However, SOSim v1 was designed for assessment only of sunken oil on bay bottoms and continental shelves from instantaneous spills.

We propose to expand SOSim capability to allow tracking of submerged, water-column oil, and oil released continuously over a period of time, from available 2-D and 3-D field data, and demonstrate it versus field data from the Gulf of Mexico and elsewhere.
Objectives are to:

1. Develop capability for modeling continuous spills and blowouts;
2. Develop capability for 3-D modeling; and
3. Integrate with an existing parametric model to develop inferential/parametric capability, with uncertainty bounds, exploiting reconnaissance data with flow field and bathymetry information; and
4. Demonstrate the model versus data for the Gulf of Mexico and elsewhere.

The two-year project addresses the GoMRI research theme “Technology developments for improved response, mitigation, detection, characterization, and remediation associated with oil spills and gas releases.” Principal outcomes include (a) a model that can rigorously infer present and future location of sunken or subsurface oil on a bay bottom, river bottom, or continental shelf, resulting from a continuous or instantaneous spill, from field data on oil location and approximate concentration at one or more points in time; (b) a model or models that can likewise infer present and future location of submerged water-column oil in 3-D due to a continuous or instantaneous spill; (c) an integrated inferential/parametric model exploiting both available field data and prior flow-field information; and (d) model demonstration and verification based on data from previous spills in the Gulf of Mexico and elsewhere. Two primary Ph.D. students will be trained in inferential submerged oil modeling, and outreach activities will include an educational website on oil spill response and inferential modeling.

Research Highlights

Dr. Englehardt’s research to date included 10 outreach products and activities, 2 peer-reviewed publications, 7 scientific presentations,  and 7 datasets submitted to the GoMRI Information and Data Cooperative (GRIIDC), which are available to the public.  Additionally, there are 3 manuscripts under journal peer review and 1 under internal review.  Significant outcomes of their research (all related to GoMRI Research Theme 5) are highlighted below.

• SOSim v2 Sunken Oil module for locating and forecasting the movement of sunken oil in marine and river water based on available field concentration data and bathymetric data.
  
  
• SOSim v2 Submerged Oil module for locating and forecasting movement of submerged oil in marine waters in 3-D based on available field concentration data and trajectory model output.
  
  
• A new Bayesian approach to modeling slow-moving pollutant masses such as submerged oil, by inference of model parameters based on available field concentration data coupled with readily available prior information.
  
  
• Novel algorithms to exploit bathymetric, temperature, and salinity data and hydrodynamic model output, as input to account for the Coriolis Effect, gravity, and other fate/transport forcing.
  
  
• Statistically-optimal adaptive plans to guide sampling of submerged oil by ships and autonomous underwater vehicles in near-real time, using SOSim v2 output.

 Proposal Abstract - RFP-VI PI Englehardt