The objective of this project is to provide Ohmsett with the capability of remotely calculating oil and ice coverage, as well as determining thickness. Accurate, rapid, and facilitated assessment of cold water and ice testing parameters such as total surface oil versus ice area coverage and oil layer thickness in the Ohmsett facility test tank can best be accomplished using imaging technologies.
The goal of this project was to develop a strap-on navigation, sensor, and computer control system that could be used to direct a variety of commercial off the shelf (COTS) skimmers and vessels to autonomously maneuver and skim the oil from a given area with automatic tracking and reporting of progress and performance. This autonomous oil skimmer (AOS) system consisted of a commercial of the shelf (COTS) skimmer and vessel, a COTS autopilot system, a high precision navigation package, oil thickness and recovery efficiency sensors, and a custom computer algorithm.
This project’s objective was to develop and test a prototype sensor that integrates two partial solutions (fluorescence and sonar) with a goal of operating at a suitable standoff distance and interrogating a wide area, while providing real-time data feed from the subsea environment. The project was conducted in two phases.
Phase 1 objectives:
Principal Investigator has worked on: (1) Polycyclic Aromatic Hydrocarbon (PAH) sensing by Surface-Enhanced Raman Scattering (SERS) and (2) Hydrocarbon and crude sensing by Localized Surface Plasmon Resonance (LSPR) and Surface-Enhanced Near-Infrared Absorption (SENIRA). The final test was conducted at Ohmsett in November 2016, and the research team demonstrated the detection and quantification of various polycyclic hydrocarbon species.
The objective of this project was to develop and test a next generation in-situ mass spectrometer payload that operates on an autonomous underwater vehicle (AUV) glider for real-time subsea hydrocarbon detection and classification. The system is designed to operate for long-term subsea inspection, monitoring, and incident response.
The goal of this project is to develop innovative, cost effective, and robust tagging devices and an automatic tracking system for use in tracking spilled oil under, or encapsulated within ice. This will be accomplished via the following key novel developments:
This research project will develop a portable, easy-to-operate, aerial sensor to detect and accurately map the thickness and distribution of an oil slick in coastal and offshore waters in real-time. Building on previous research the technical plan, consisting of five phases will lead to the deployment of an operational system estimated to be completed with 18 months. The five phases are: Addition and testing of infra-red camera to the detection system.
Refinement and implementation of the neural network and fuzzy ratio-based oil discrimination software.
Ocean Imaging's current aerial thickness mapping system has been deployed during oil spills in California and during the response to the Deepwater Horizon in the Gulf of Mexico. For this to occur in other U.S. geographic areas the technology needed to be tested and validated under other oceanographic and environmental conditions. The existing system was developed and operationally tested under temperate sea and atmospheric conditions with reasonable water clarity. Many geographic regions with oil and gas activities experience conditions outside of this realm.
The objective was to significantly expand the practical operating window for oil detection with Ground Penetrating Radar (GPR) to cover a wider range of sea ice and climate conditions. This project was a direct continuation of TAR projects 348, 517, 547, 569, and 588. Funding partners for this project were: Alaska Clean Seas, BSEE, ConocoPhillips, ExxonMobil Upstream Research, Shell International E&P, and StatoilHydro.