25th International Meeting on Organic Geochemistry IMOG 2011

P-306
Evaluation of surface geochemistry and data mining in Brazilian
sedimentary basins
Jean R. Heckmann 1 , Débora A. Azevedo 2 , Luiz Landau 1
1 LAMCE/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro/RJ, Brazil, 2 Departamento de
Química Orgânica, Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro/RJ, Brazil
In recent years we have seen significant progress in
the advancement of geosciences. This have been
accomplished due to many aspects, such as, the
improvement in computer hardware, data integration,
quantification of uncertainties, effective use of
geochemistry data and reservoir properties, and the
consideration of unconventional statistical methods.
Thus, the use soft computing tools might be a keypoint
that will facilitate, in a substantial way, the
access to this dataset. Among other highlights, the
computational techniques to Data Mining (KDD -
Knowledge Discovery in Data Base) and / or simply
the discovery of robust data in a volume have an
implicit knowledge.
Stages of the Knowledge Discovery Database
technique (KDD), figure 1:
Data
Coords.
Grid
Clustering
Classifier
Geo-
Processing
Groups
Groups
Model
Groups
Mapping
Fig. 1: Knowledge Discovery Database
From the numerical analysis it is performed an
integrated evaluation of the geochemical parameters
that can provide a better understanding of the basin
formation.
The database corresponds to the data provided by
the BDEP/ANP (Banco de Dados de Exploração e
Produção/Agência Nacional do Petróleo) results
acquired from evaluations of the 500 piston cores of
the Santos basin: Fluorescence int, Emission max,
Excitation max (nm); Total alkanes, Total UCM (ppb);
Methane, Ethane, Propane, i-Butane, n-Butane and n-
Pentane (ppm).
One of the main tasks of data mining is the step of
grouping (clustering). This step identifies data sets
that have similar characteristics among themselves
and groups that have different characteristics.
In order to perform the cluster analysis it was used
software called Matlab (version 2007).
The assumption of the number of cluster database is
the first step in the process of clustering. Therefore,
the algorithm Fuzzy C-Means (FCM), were performed
to validate the Index (PBM).
PC2
PC1
Fig. 2: Pertinence functions FCM 2D
Fluorescence, emission, excitation and total alkanes
were the factors that had higher values of pertinence
for principal component 1 (PC1), while for component
2 (PC2) were the fluorescence, emission, excitation
and methane. Thus, the data mining resulted in well
resolved three main groups with the values of piston
cores with similar characteristics among themselves,
as highlighted in red, figure 2.
The authors would like to thank the Brazilian National
Petroleum Agency (ANP, PRH-02) for the financial
support.
References
[1] Jones, V. T., Burtell, S. G. Hydrocarbon Flux
Variations in Natural and Anthropogenic Seeps.:
.
Acessed: 28/01/2011.
[2] Aminzadeh, F., 2005. Journal of Petroleum
Science and Engineering 47, 5-14.
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