Decision Making Support Tools

BSEE Response Research Branch is undertaking the DSD Instrument Evaluation project to better understand how surface water dispersant monitoring, as specified by the NCP SubPart J Monitoring Rule, can be practically implemented with existing technology, with specific emphasis on detection of oil and dispersed oil droplets in depths of 1 to 5 meters. The three main objectives of this project are:

This project developed the ROSI calculator tool to facilitate assessment of an operator's oil spill response plan for a well blowout, tank failure, pipeline leak, or other spill that occurs during winter months and results in recovery operations on snow and solid ice using "yellow gear" equipment as described in the Alaska Clean Seas (ACS) Tactics Manual. Further, it asssed whether further research was recommended to verify and potentially update the formulas incorporated into the calculator tool.

Under OSRR Project 1091, the NRL performed preliminary experiments to assess pulsed laser light technology (Light Detection And Ranging - LiDAR) ability to detect oil and characterize oil thickness on water. Initial testing conducted at Ohmsett demonstrated the successful application of LiDAR remote sensing to detect and measure the presence of oil at the surface and underwater.

This project will continue the development of the LiDAR system's ability to detect and characterize oil on the surface and varying subsurface layers thickness values and depth in the water.

Although remote sensing technologies have been advanced for airborne and spaceborne sensors, it is still challenging to detect oil under/encapsulated in ice as well as on seafloor.

The objectives of this project are to:

Investigate and advance the current underwater technology to detect and measure thickness of oil under ice, encapsulated in ice and/or on the seafloor
Conduct testing at Ohmsett or CRREL to characterize the sensitivity of remote sensors for detecting and characterizing oil under ice, encapsulated in ice, and/or oil on the tank floor.

Under Interagency Agreement (IAA) E13PG00031, the Bureau of Safety and Environmental Enforcement (BSEE)/U.S. Army Night Vision team completed a program that demonstrated a compact, lightweight, multi-spectral airborne sensor payload capable of detecting the presence of oil on water, distinguishing oil from false alarms such as kelp forests, and providing a reliable estimate of the thickness of the oil present on the water.

This project will study and test the Electrical Capacitance Tomography (ECT) sensor to detect oil in/under ice. For oil detection and thickness estimation under/in ice, where the access to the imaged region is limited to above its surface, AUB proposes a planar sensor design where the electrodes are mounted on a single plane and placed at a relatively close distance above the ice surface. 

The current NOAA's WebGNOME platform displays the modeling bounds with available operational forecast models for selected areas. These areas are typically in shoreline areas. This project will expand the availability of forecast models to cover offshore areas where BSEE's regulated facilities reside. This added feature will enable the ability to run WebGNOME more easily, using available operational forecast models.

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.
 

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, the advanced, processing/analyzing data and imagery requires significant time (at least several hours to days). The purpose of this project is 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.