Formation, Detection, and Modeling of Submerged Oil: A Review

Adaptive control for follower gliders mapping underwater oil patches

Adaptive control was applied to follower gliders in cooperating multiple glider teams on missions to delineate underwater oil patches. The influence of water currents on the motion of the oil patches was included. The cooperation strategy with adaptive control was compared with strategies without cooperation or adaptive control through simulation experiments. In addition, the optimal number of follower gliders in a team was assessed. From the simulations, strategies with adaptive control achieved a higher score of performance, defined as a measure of the percentage of valuable-rich information collected to the percentage of the mission area covered by information-rich patches; this measure was applied when the percentage of the area of information-rich patches was less than 60%. The cooperation strategy with adaptive control had a lower duty cycle and a longer mission duration, but had the best score of performance, especially for long-duration missions. Backseat driver hardware was installed on a Slocum glider to support adaptive control and a field experiment successfully realized cooperation between a simulated scout glider and the follower glider and the adaptive control of the follower. The experiment also indicated the limitations of timely completion of missions owing to the operating speed of the glider relative to that of ambient currents.

Fluorescence-estimated oil concentration (Foil) in the Deepwater Horizon subsea oil plume

Tracking the subsea oil plume during the 2010 Deepwater Horizon Oil Spill (DWH) was conducted using in situ fluorescence via vertical profilers (n = 1157) and discrete sample chemical analyses (n = 7665). During monitoring efforts, discrete samples provided a coarse picture of the oil plume footprint, but the majority of the samples were below standard analytical detection limits for petroleum hydrocarbons. In situ fluorescence data improved the spatial and temporal resolution of the subsea oil plume characterization. Here we synthesized millions of continuous fluorescence data points from hundreds of contemporaneously discrete samples collected to demonstrate how fluorescence could serve as a proxy for Benzene-Toluene-Ethylbenzene-Xylene (BTEX) concentration. Data mined from Gulf Science Data repository were well correlated, and geographically and temporally aligned to provide direct comparisons. Described here are the methods used to calibrate the fluorescence data and to spatially approximate the three-dimensional geographic extent of the oil plume.

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.

Design of Real—Time Sampling Strategies for Submerged Oil Based on Probabilistic Model Predictions

Locating and tracking submerged oil in the mid depths of the ocean is challenging during an oil spill response, due to the deep, wide-spread and long-lasting distributions of submerged oil. Due to the limited area that a ship or AUV can visit, efficient sampling methods are needed to reveal the real distributions of submerged oil. In this paper, several sampling plans are developed for collecting submerged oil samples using different sampling methods combined with forecasts by a submerged oil model, SOSim (Subsurface Oil Simulator). SOSim is a Bayesian probabilistic model that uses real time field oil concentration data as input to locate and forecast the movement of submerged oil. Sampling plans comprise two phases: the first phase for initial field data collection prior to SOSim assessments, and the second phase based on the SOSim assessments. Several environmental sampling techniques including the systematic random, modified station plans as well zig-zag patterns are evaluated for the first phase. The data using the first phase sampling plan are then input to SOSim to produce submerged oil distributions in time. The second phase sampling methods (systematic random combined with the kriging-based sampling method and naive zig-zag sampling method) are applied to design the sampling plans within the submerged oil area predicted by SOSim. The sampled data obtained using the second phase sampling methods are input to SOSim to update the model’s assessments. The performance of the sampling methods is evaluated by comparing SOSim predictions using the sampled data from the proposed sampling methods with simulated submerged oil distributions during the Deepwater Horizon spill by the OSCAR (oil spill contingency and response) oil spill model. The proposed sampling methods, coupled with the use of the SOSim model, are shown to provide an efficient approach to guide oil spill response efforts.