Title of article :
Reservoir Rock Characterization Using Wavelet Transform and Fractal Dimension
Author/Authors :
Partovi, Seyyed Mohammad Amin; Department of Petroleum Engineering - Faculty of Chemical Engineering - Tarbiat Modares University - Tehran, I.R. IRAN , Sadeghnejad, Saeid Department of Petroleum Engineering - Faculty of Chemical Engineering - Tarbiat Modares University - Tehran, I.R. IRAN
Pages :
12
From page :
223
To page :
234
Abstract :
The aim of this study is to characterize and find the location of geological boundaries in different wells across a reservoir. Automatic detection of the geological boundaries can facilitate the matching of the stratigraphic layers in a reservoir and finally can lead to a correct reservoir rock characterization. Nowadays, the well-to-well correlation with the aim of finding the geological layers in different wells is usually done manually. For a rather moderate-size field with a large number of wells (e.g., 150 wells), the construction of such a correlation by hand is a quite complex, labor-intensive, and time-consuming. In this research, the wavelet transform as well as the fractal analysis, with the aid of the pattern recognition techniques, are used to find the geological boundaries automatically. In this study, we manage to use the wavelet transforms approach to calculate the fractal dimension of different geological layers. In this process, two main features, the statistical characteristics as well as the fractal dimensions of a moving window, are calculated to find a specific geological boundary from a witness well through different observation wells. To validate the proposed technique, it is implemented in seven wells of one of the Iranian onshore fields in the south-west of Iran. The results show the capability of the introduced automatic method in detection of the geological boundaries in well-to-well correlations.
Keywords :
Well-log , Pattern recognition , Geological boundary , Fractal , Wavelet transform
Journal title :
Astroparticle Physics
Serial Year :
2018
Record number :
2449218
Link To Document :
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