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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 5
Nonlin. Processes Geophys., 21, 1043–1049, 2014
https://doi.org/10.5194/npg-21-1043-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 21, 1043–1049, 2014
https://doi.org/10.5194/npg-21-1043-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 20 Oct 2014

Research article | 20 Oct 2014

Spatial analysis of oil reservoirs using detrended fluctuation analysis of geophysical data

R. A. Ribeiro1, M. V. M. Mata2, L. S. Lucena3, U. L. Fulco4, and G. Corso4 R. A. Ribeiro et al.
  • 1Centro Universitário-FACEX-UNIFACEX, Rua Orlando Silva, 2897, Capim Macio, CEP 59080 020, Natal, RN, Brazil
  • 2Programa de Pós-Graduação em Ciências Climáticas, Universidade Federal do Rio Grande do Norte, UFRN – Campus Universitário, Lagoa Nova, CEP 59078 972, Natal, RN, Brazil
  • 3Departamento de Física Teórica e Experimental, International Center for Complex Systems and Instituto Nacional de Ciência e Tecnologia sobre Sistemas Complexos, Universidade Federal do Rio Grande do Norte, UFRN – Campus Universitário, Lagoa Nova, CEP 59078 970, Natal, RN, Brazil
  • 4Departamento de Biofísica e Farmacologia, Centro de Biociências, Universidade Federal do Rio Grande do Norte, UFRN – Campus Universitário, Lagoa Nova, CEP 59078 972, Natal, RN, Brazil

Abstract. We employ the detrended fluctuation analysis (DFA) technique to investigate spatial properties of an oil reservoir. This reservoir is situated at Bacia de Namorados, RJ, Brazil. The data correspond to well logs of the following geophysical quantities: sonic, gamma ray, density, porosity and electrical resistivity, measured in 56 wells. We tested the hypothesis of constructing spatial models using data from fluctuation analysis over well logs. To verify this hypothesis, we compare the matrix of distances of well logs with the differences in DFA exponents of geophysical quantities using a spatial correlation function and the Mantel test. Our data analysis suggests that the sonic profile is a good candidate for representing spatial structures. Then, we apply the clustering analysis technique to the sonic profile to identify these spatial patterns. In addition, we use the Mantel test to search for correlations between DFA exponents of geophysical quantities.

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