The concept of wavelet cross-correlation is used to provide a new
approach to identify similar patterns in related data sets, which
largely improves the confidence of the results. The method amounts to
decompose the data sets in the wavelet space so that correlations
between wavelet coefficients can be analyzed in every scale. Besides the
identification of the scales in which two independent measures are
correlated, the method makes it possible to find patches of data sets
where correlations exist simultaneously in all scales. This allows to
extend the information of a small number of spots to larger regions.
Well-log data sets from two neighboring oil wells are used. We compare
similar measures at different probe sites, and also measurements of
different physical quantities taken on the same place. Although this is
a typical scenario for the application of classical geostatistical
methods, it is well known that such methods erase out local differences
in favor of smoother variability. In contraposition, this wavelet
cross-correlation takes advantage of the fluctuations to give
information about the continuity of the geological structures in space.
It works even better if no filtering procedure has been applied to the
original raw data. (C) 2014 Elsevier B.V. All rights reserved.
%0 Journal Article
%1 WOS:000345721800014
%A Henriques, M V C
%A Leite, F E A
%A Andrade, R F S
%A Jr., J S Andrade
%A Lucena, L S
%A Neto, M Lucena
%C PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
%D 2015
%I ELSEVIER SCIENCE BV
%J PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
%K Complex Noise Oil Wavelet data; reservoirs; suppression; systems} transform; {Seismic
%P 130-140
%R 10.1016/j.physa.2014.09.027
%T Improving the analysis of well-logs by wavelet cross-correlation
%V 417
%X The concept of wavelet cross-correlation is used to provide a new
approach to identify similar patterns in related data sets, which
largely improves the confidence of the results. The method amounts to
decompose the data sets in the wavelet space so that correlations
between wavelet coefficients can be analyzed in every scale. Besides the
identification of the scales in which two independent measures are
correlated, the method makes it possible to find patches of data sets
where correlations exist simultaneously in all scales. This allows to
extend the information of a small number of spots to larger regions.
Well-log data sets from two neighboring oil wells are used. We compare
similar measures at different probe sites, and also measurements of
different physical quantities taken on the same place. Although this is
a typical scenario for the application of classical geostatistical
methods, it is well known that such methods erase out local differences
in favor of smoother variability. In contraposition, this wavelet
cross-correlation takes advantage of the fluctuations to give
information about the continuity of the geological structures in space.
It works even better if no filtering procedure has been applied to the
original raw data. (C) 2014 Elsevier B.V. All rights reserved.
@article{WOS:000345721800014,
abstract = {The concept of wavelet cross-correlation is used to provide a new
approach to identify similar patterns in related data sets, which
largely improves the confidence of the results. The method amounts to
decompose the data sets in the wavelet space so that correlations
between wavelet coefficients can be analyzed in every scale. Besides the
identification of the scales in which two independent measures are
correlated, the method makes it possible to find patches of data sets
where correlations exist simultaneously in all scales. This allows to
extend the information of a small number of spots to larger regions.
Well-log data sets from two neighboring oil wells are used. We compare
similar measures at different probe sites, and also measurements of
different physical quantities taken on the same place. Although this is
a typical scenario for the application of classical geostatistical
methods, it is well known that such methods erase out local differences
in favor of smoother variability. In contraposition, this wavelet
cross-correlation takes advantage of the fluctuations to give
information about the continuity of the geological structures in space.
It works even better if no filtering procedure has been applied to the
original raw data. (C) 2014 Elsevier B.V. All rights reserved.},
added-at = {2022-05-23T20:00:14.000+0200},
address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS},
author = {Henriques, M V C and Leite, F E A and Andrade, R F S and Jr., J S Andrade and Lucena, L S and Neto, M Lucena},
biburl = {https://www.bibsonomy.org/bibtex/25fae92cb6ae9537836b0750bd3711cec/ppgfis_ufc_br},
doi = {10.1016/j.physa.2014.09.027},
interhash = {809c9a47415b991d60d429217b9a696c},
intrahash = {5fae92cb6ae9537836b0750bd3711cec},
issn = {0378-4371},
journal = {PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS},
keywords = {Complex Noise Oil Wavelet data; reservoirs; suppression; systems} transform; {Seismic},
pages = {130-140},
publisher = {ELSEVIER SCIENCE BV},
pubstate = {published},
timestamp = {2022-05-23T20:00:14.000+0200},
title = {Improving the analysis of well-logs by wavelet cross-correlation},
tppubtype = {article},
volume = 417,
year = 2015
}