--> Abstract: Deconvolution and Mapping of Soil Gas Anomalies in Surface Prospecting: A New Approach Based on Bayesian Geostatistics; #90063 (2007)
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Deconvolution and Mapping of Soil Gas Anomalies in Surface Prospecting: A New Approach Based on Bayesian Geostatistics

 

Goncalves, Felix T. T.1, Fernando H. Pulgati1, Ricardo P. Bedregal1, Previous HitFlavioTop L. Fernandes1, Jason T. G. Carneiro1 (1) PGT-Petroleum Geoscience Technology Ltd, Rio de Janeiro, Brazil

 

Geochemical prospecting methods use surface or near-surface occurrences of hydrocarbons (micro seepage) provide direct evidence of the existence of an active petroleum system, helping in the identification of prospective areas and in the assessment and ranking of exploration leads and prospects. During the last decades, a significant advances in sampling and analytical techniques have allowed the detection of minute traces of hydrocarbons in a more accurate and effective way. Conversely, interpretative methods have been mostly limited to straightforward statistical approaches that define anomalously high values relative to a background usually by (a) arbitrarily assigning a background threshold, such as the mean or one standard deviation from the mean, (b) separating groups or populations of concentrations based on the plot of all the data as a histogram; or (c) plotting the data in a cumulative frequency diagram to delineate a break in the slope of the curve. This study presents a new integrated geostatistical approach that allows an enhanced definition of surface hydrocarbon anomalies in several areas from Subandean basins and Brazilian rifts. Analysis of variance was used to identify and filter the influence of near-surface environmental factors, such as the moisture content, grain size and land use, which can mask any relationship with subsurface hydrocarbon accumulations. Bayesian modeling with Monte Carlo-Markov Chain (MC-MC) simulation was applied to delineate areas of anomalous concentrations of thermogenic hydrocarbon gases by calculating the probability that gas concentration on each site is higher than the value expected by the stochastic process, recognizing spatial clusters of anomalous values, and assessing the risk of biogenic contamination based on molecular parameters.

 

AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California