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Chronology and backtracking of oil slick trajectory to source in offshore environments using ultraspectral to multispectral remotely sensed data
Institution:1. Davidson College, Environmental Studies, 209 Ridge Rd., Davidson, NC 28036, USA;2. Kansas Geological Survey, University of Kansas, Lawrence, KS 66047, USA;3. Department of Geography and Earth Sciences, University of North Carolina — Charlotte, McEniry 324, 9201 University City Blvd., Charlotte, NC 28223, USA;1. Key Laboratory of the Three Gorges Reservoir Region''s Eco-Environments of Ministry of Education, Chongqing University, Chongqing 400045, China;2. Key Laboratory of Reservoir Aquatic Environment of CAS, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China;1. Norwegian Geotechnical Institute, NGI, Sognsveien 72, N-0855 Oslo, Norway;2. SINTEF Building and Infrastructure, SINTEF, Richard Birkelands vei 3, 7034 Trondheim, Norway;3. WSP Group, Smedjegatan 24, 972 31, Luleå, Sweden
Abstract:Offshore natural seepage confirms the occurrence of an active petroleum system with thermal maturation and migration, regardless its economic viability for petroleum production. Ocean dynamics, however, impose a challenge for correlation between oil seeps detected on the water surface and its source at the ocean floor. This hinders the potential use of seeps in petroleum exploration. The present study aims to estimate oil exposure time on the water surface via remote sensing in order to help locating ocean floor seepage sources. Spectral reflectance properties of a variety of fresh crude oils, oil films on water and oil–water emulsions were determined. Their spectral identity was used to estimate the duration of exposure of oil–water emulsions based on their temporal spectral responses. Laboratory models efficiently predicted oil status using ultraspectral (>2000 bands), hyperspectral (>300 bands), and multispectral (<10 bands) sensors covering near infrared and shortwave infrared wavelengths. An oil seepage recorded by the ASTER sensor on the Brazilian coast was used to test the designed predictive model. Results indicate that the model can successfully forecast the timeframe of crude oil exposure in the ocean (i.e., the relative “age” of the seepage). The limited spectral resolution of the ASTER sensor, though, implies less accurate estimates compared to higher resolution sensors. The spectral libraries and the method proposed here can be reproduced for other oceanic areas in order to approximate the duration of exposure of noticeable natural oil seepages. This type of information is optimal for seepage tracing and, therefore, for oceanic petroleum exploration and environmental monitoring.
Keywords:Petroleum  Seepage  Offshore  Infrared spectroscopy  ASTER  Remote sensing
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