23 Jan 2009
An infrared sensor can now be used to detect oil spills around offshore platforms 24 hours a day say US researchers.
The research arm of a US oilfield technology company has unveiled a dual-band optical instrument that can detect oil spills at night for the first time. Commercialization of the instrument is currently underway and could provide year-round oil spill monitoring for offshore platforms (Optics Letters 33 3019).
"Current practice for oil spill detection for offshore platforms is mainly daytime helicopter fly-by surveillance, which doesn't work in the dark," Wei-Chuan Shih, a researcher from Schlumberger-Doll Research, told optics.org. "Night time capability provides the potential to monitor oil spills around an offshore platform 24/7 with a permanently installed sensor system and automated alert triggering."
Infrared oil spill detection relies on either temperature or emissivity contrast of oil-covered surfaces. During the day, sunlight heats up oil films, which leads to excellent contrast and easy detection.
Current limitations of the technique are false positive detection, which can occur during the day when the oil film becomes thin (less than 50-150 µum). In addition, detection becomes difficult on cloudy days or at night when there is little solar heating.
Shih and colleagues have developed a physical model, which demonstrates thickness-dependent infrared contrast of crude oil covered water. Their model is based on the radiative transfer theory and thin-film interference effect and reveals that thinner oil films provide better imaging contrast.
"Our findings have important implications to long-wavelength infrared instrument design and data interpretation for crude oil spill detection," commented Shih. "Our model suggests that thin oil slicks can be easier to detect in the long-wave infrared than thicker films, especially at night when differential heating is not effective."
The next steps for the team are to demonstrate 24/7 oil spill detection on an offshore oil platform and to improve detection accuracy. "We have to develop better algorithms to reduce false positive results, which is a current issue," concluded Shih.