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

Abstract

Submerged oil, oil in the water column (neither at the surface nor on the bottom), was found in the form of oil droplet layers in the mid depths between 900–1300 m in the Gulf of Mexico during and following the Deepwater Horizon oil spill. The subsurface peeling layers of submerged oil droplets were released from the well blowout plume and moved along constant density layers (also known as isopycnals) in the ocean. The submerged oil layers were a challenge to locate during the oil spill response. To better understand and find submerged oil layers, we review the mechanisms of submerged oil formation, along with detection methods and modeling techniques. The principle formation mechanisms under stratified and cross-current conditions and the concepts for determining the depths of the submerged oil layers are reviewed. Real-time in situ detection methods and various sensors were used to reveal submerged oil characteristics, e.g., colored dissolved organic matter and dissolved oxygen levels. Models are used to locate and to predict the trajectories and concentrations of submerged oil. These include deterministic models based on hydrodynamical theory, and probabilistic models exploiting statistical theory. The theoretical foundations, model inputs and the applicability of these models during the Deepwater Horizon oil spill are reviewed, including the pros and cons of these two types of models. Deterministic models provide a comprehensive prediction on the concentrations of the submerged oil and may be calibrated using the field data. Probabilistic models utilize the field observations but only provide the relative concentrations of the submerged oil and potential future locations. We find that the combination of a probabilistic integration of real-time detection with trajectory model output appears to be a promising approach to support emergency response efforts in locating and tracking submerged oil in the field.

Tracking a Surrogate Hazardous Agent (Rhodamine Dye) in a Coastal Ocean Environment Using In Situ Measurements and Concentration Estimates Derived from Drone Images

“…The limits of the dye plume are discernible from the difference of red ratio. The buoys on the drifters, which were white and have no rhodamine, had lower ratios of red, around 1 3 . To construct the heat maps of the dye distribution, the assumptions were as follows: first, the rhodamine's signature on the images corresponds to a high ratio of red; second, the ambient seawater's signature on the images corresponds to a ratio of red approaching zero due to the ocean light absorption.…”

“…New tools and technology are needed to understand the transport of hazardous agents and track them in our oceans and waterways [1]. Such technology can be used to determine when people can safely return to their homes and work after a natural disaster and when it is safe for first responders to conduct rescue and recovery efforts in and around contaminated floodwaters.…”

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

“…This need was highlighted during the DWH oil spill emergency response period, when oil was detected by ship in only a few collected samples each day, with each sample taking ~2-3 h [7] to collect. Although oil spill models may help guide the sampling procedure, oil spill model predictions varied in their accuracy [8], before the required temperature and salinity profiles were available to calibrate the velocity field of ocean models [9,10].…”

“…Oil spill models are useful in estimating submerged oil distributions and thus assist in designing sampling plans. However, most models are based on results obtained from hydrodynamic models, which may not reflect conditions during the emergency response period reliably [10]. For example, during the DWH spill, there was no particle tracking model that was used by U.S. Coast Guard for the submerged oil response [9].…”

“…Alternatively, SOSim is a statistical model and is designed for real-time predictions of the location of the droplet layer using field observations of oil locations and concentrations as input, together with the output of another oil spill model if available, to infer the spatial distribution of submerged oil within the isopycnal as a function of time [34][35][36][37] using Bayesian methods [10,38]. Instead of using velocity field from hydrodynamic model, SOSim infers the velocity field by assimilating the field observations on submerged oil concentration and locations.…”