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 submerged/sunken oil, due to (a) limitations in information on subsurface 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 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 is 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:
- Develop capability for modeling continuous spills and blowouts;
- Develop capability for 3-D modeling; and
- 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 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 Ph.D. students will be trained in inferential submerged oil modeling, and outreach activities will include an educational Spanish language blog on oil spill response and environmental topics.