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

Abstract

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.