Measuring oil residence time with GPS-drifters, satellites, and Unmanned Aerial Systems (UAS).

As oil production worldwide continues to increase, particularly in the Gulf of Mexico, marine oil spill preparedness relies on deeper understanding of surface oil spill transport science. This paper describes experiments carried out on a chronic release of crude oil and aims to understand the residence time of oil slicks using a combination of remote sensing platforms and GPS tracked drifters. From April 2017 to August 2018, we performed multiple synchronized deployments of drogued and un-drogued drifters to monitor the life time (residence time) of the surface oil slicks originated from the MC20 spill site, located close to the Mississippi Delta. The hydrodynamic design of the two types of drifters allowed us to compare their performance differences. We found the un-drogued drifter to be more appropriate to measure the speed of oil transport. Drifter deployments under various wind conditions show that stronger winds lead to reduce the length of the slick, presumably because of an increase in the evaporation rate and entrainment of oil in the water produced by wave action. We have calculated the residence time of oil slicks at MC20 site to be between 4 and 28 h, with average wind amplitude between 3.8 and 8.8 m/s. These results demonstrate an inverse linear relationship between wind strength and residence time of the oil, and the average residence time of the oil from MC20 is 14.9 h.

Remote sensing assessment of oil spills near a damaged platform in the Gulf of Mexico.

An oil platform in the Mississippi Canyon 20 (MC-20) site was damaged by Hurricane Ivan in September 2004. In this study, we use medium- to high-resolution (10-30 m) optical remote sensing imagery to systematically assess oil spills near this site for the period between 2004 and 2016. Image analysis detects no surface oil in 2004, but ~40% of the cloud-free images in 2005 show oil slicks, and this number increases to ~70% in 2006-2011, and >80% since 2012. For all cloud-free images from 2005 through 2016 (including those without oil slicks), delineated oil slicks show an average oil coverage of 14.9 km2/image, with an estimated oil discharge rate of 48 to ~1700 barrels/day, and a cumulative oil-contaminated area of 1900 km2 around the MC-20 site. Additional analysis suggests that the detected oil slick distribution can be largely explained by surface currents, winds, and density fronts.

Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula.

Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean's surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time.

SAR observation and model tracking of an oil spill event in coastal waters.

Oil spills are a major contributor to marine pollution. The objective of this work is to simulate the oil spill trajectory of oil released from a pipeline leaking in the Gulf of Mexico with the GNOME (General NOAA Operational Modeling Environment) model. The model was developed by NOAA (National Oceanic and Atmospheric Administration) to investigate the effects of different pollutants and environmental conditions on trajectory results. Also, a Texture-Classifying Neural Network Algorithm (TCNNA) was used to delineate ocean oil slicks from synthetic aperture radar (SAR) observations. During the simulation, ocean currents from NCOM (Navy Coastal Ocean Model) outputs and surface wind data measured by an NDBC (National Data Buoy Center) buoy are used to drive the GNOME model. The results show good agreement between the simulated trajectory of the oil spill and synchronous observations from the European ENVISAT ASAR (Advanced Synthetic Aperture Radar) and the Japanese ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) images. Based on experience with past marine oil spills, about 63.0% of the oil will float and 18.5% of the oil will evaporate and disperse. In addition, the effects from uncertainty of ocean currents and the diffusion coefficient on the trajectory results are also studied.