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

• Published in: Journal of marine science and engineering Vol. 8; no. 9; p. 642
• Main Authors: Ji, Chao, Beegle-Krause, Cynthia Juyne, Englehardt, James D.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Accuracy; Biodegradation; Blowouts; Bubbles; Colour; Deep water; deepwater horizon oil spill; Detection; Dissolved organic matter; Dissolved oxygen; Droplets; Emergency preparedness; Emergency response; Hydrodynamics; isopycnals; Locating; Mathematical models; Methods; Modelling; Ocean currents; Oil; Oil spills; Probabilistic models; Real time; Reviews; Sensors; Statistical analysis; submerged oil; transport and fate processes; Water circulation; Water column; France
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse8090642

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.