--> ABSTRACT: Mapping Oil Prospectivity in the Northern Tucano Basin (Brazil): An Analysis of the Spectral-Spatial Patterns in Orbital Remote Sensing Data and Their Spatial Association with Geologic Features

Datapages, Inc.Print this page

Mapping Oil Prospectivity in the Northern Tucano Basin (Brazil): An Analysis of the Spectral-Spatial Patterns in Orbital Remote Sensing Data and Their Spatial Association with Geologic Features

Souza Filho, Carlos R.1; Lammoglia, Talita 1
(1) Department of Geology and Natural Resources, Geosciences Institute, University of Campinas, Campinas, Brazil.

The detection of hydrocarbons (HCs) on continental basins by optical remote sensing systems in all acquisition levels (i.e. terrestrial, sub-orbital and orbital) can be guided by the spectral response of features produced by the interaction of HCs with the stratigraphic column or directly by the HCs spectral response. This study focus on the characterization and prospectivity mapping of hydrocarbon microseepages in the northern Tucano Basin (Bahia State, Brazil) by means of geostatistical analysis of regional hydrocarbon geochemical data yielded from soil samples, digital processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and knowledge-driven and data-driven spatial modeling. The premise of the model used to detect microseepages is that such phenomenon is associated with structural lineaments and headstreams, causes bleaching of rocks and soils (i.e., Fe3+ is reduced to Fe2+), triggers geobotanical markers and increases the relative concentration of clays (kaolinite) and carbonates (calcite) at surface. ASTER data was processed by Spectral Mixture Analysis techniques in order to generate kaolinite, calcite and hematite abundance maps. The Normalized Difference Vegetation Index and proximity spatial analysis were also employed to yield, respectively, a map of vegetation abundance and drainage and lineaments density maps. Such evidential maps were combined by fuzzy logic, logistic regression (LR) and neural network (NN) techniques. Data training by LR and NN was guided by the gasometric anomalies. The yielded models enabled the simultaneous integration of the information extracted from the ASTER VIS, NIR, SWIR and TIR spectral bands and the additional combination of various datasets. The HC prospectivity maps obtained attest for the high potentiality of the regions equally mapped by gasometry, corroborating to the usefulness of the methods tested here for this aim. Other sites with similar characteristics but for which no geochemical data were available were also revealed. These sites are taken as new potential targets for the presence of seeps and oil reservoirs. The research demonstrated the applicability of ASTER data and methods of extracting spectral-spatial information to HC exploration in continental basins.

 

AAPG Search and Discovery Article #90135©2011 AAPG International Conference and Exhibition, Milan, Italy, 23-26 October 2011.