Decision Making Support Tools

As Arctic ice has receded, exploration and development of oil reserves have increased, thereby requiring an effective strategy to mitigate oil spills. PNNL proposes demonstrating oil detection in and under sea ice via FMCW radar by leveraging recent advancements in commercial subcomponents and systems. Utilizing Commercial Off the Shelf (COTS) hardware will address hardware reliability issues and focus work on implementation challenges.
 

Real-time data processing is critical to decision making. Although various remote sensors to detect oil slicks have been developed, processing/analyzing data and imagery requires significant time (at least several hours to days). The purpose of this project was to develop/advance algorithm for (Near) Real-Time Data Processing and Mapping for Commercially available Off The Shelf (COTS) Remote Sensors to detect oil and measure slick thickness.

The goals of this project were to (1) integrate a suite of sensors on a REMUS AUV to quantify, characterize and determine droplet size of spilled oil, (2) demonstrate the utility of this technology for oil detection in the field, and (3) develop a schema for real-time data transfer into existing spill response data management and visualization tools.

This project is a follow-on to Project 1070 Developing an Innovative Dispersant Spray Model, where AMOG Consulting developed the Dispersant Spray Drift (DSD) tool.  The DSD is intended to help spill response planners identify operational windows and setback distances based on weather conditions, aircraft types, dispersant spray systems and release rates. Project 1115 will aim to validate any previous assumptions, expand the DSD to include new oil spill response aircraft,  and add concentration contours to the output display to improve the user interface.

The purpose of this circumpolar Arctic response viability analysis was to better understand the potential for different oil spill response systems to operate in the Arctic marine environment. There is increasing concern about the risk of oil spills as human activity increases in the Arctic. The Arctic Council’s Emergency Prevention, Preparedness, and Response (EPPR) Working Group commissioned this study of oil spill response viability for the circumpolar Arctic region, co-sponsored by Norway, the United States, and Denmark.

This project developed the Arctic Spill Response Database Query Tool. This tool is designed to assist spill reponse planning by gathering reliable data on response equipement and geographical reach, from the eight member nations of Arctic Council. The extent of damage resulting from an oil spill can be mitigated when the availability of resources is known, logistics channels are clear, and contingency plans are in place. 

The focus of this project will be on the design and implementation of two components, the UAS system, and the algorithms for the image processing used on the system. The project will be carried out in two phases:  Phase 1: Development/Implementation of the UAS platform/sensors and its algorithms for oil classification and image processing based on Ohmsett testing.  Phase 1 will involve the following sub-tasks: 1) UAS multisensory array implementation, 2) Controlled experiment (Ohmsett tank testing), and 3) Development of the oil classification and image processing algorithm.

The project team will use radar technology instead of optical or infrared methods in order to enable 24-hour, weather independent operation that can be deployed in inclement or difficult to access environments, and reduce dependence upon on-site personnel. The team will evaluate the capability of low noise L-band (1.26 GHz) synthetic aperture radar (SAR) imagery acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) sensor.
 

The purpose of this study was to assess and evaluate the capabilities and limitations of two above-water laser systems owned and operated by NRL, to detect and characterize oil layers of varying thickness on the surface of the water, in conjunction with an acoustic sensor for in-water detection (oil in the water column).

The accurate monitoring of subsea oil release droplet sizes and the effects of applying subsea dispersants is a technology gap with consequences for decision-making, impact assessment, and scientific understanding of blowout behavior. Measurements and knowledge of the actual droplet sizes that exist under differing blowout or subsea release scenarios are fundamental to response decision-making and understanding potential ecosystem impacts.