1887

Abstract

Petrophysical mapping corresponds to defining the<br>correct spatial equivalence between petrophysical<br>properties of layers in oil wells, and the well logs<br>from which they are estimated from. We present a<br>methodology to do this kind of mapping – specifically<br>the estimation of porosity – that is entirely<br>independent of interpreter intervention.<br>Assuming a sand-shale geological model, we perform<br>an automatic interpretation of the ρB −φ N cross-plot<br>by starting with an algorithm based on an artificial<br>neural network with competitive layers. The objective<br>is to obtain a log zonation and a porosity<br>determination for all available wells, resulting in a<br>porosity zonation log. In a second step, the spatial<br>integration of all porosity zonation logs is<br>accomplished through an artificial neural network<br>with radial basis functions that automatically<br>correlates the porosity zonation logs and performs a<br>spatial porosity mapping.<br>The results are presented as cross sections, thereby<br>illustrating the geometric disposition of the layers.<br>The estimated porosity variations along the section<br>are highlighted through characteristic color maps.<br>This methodology is developed and tested using well<br>log data from the Lagunillas member in Lake<br>Maracaibo, Venezuela.

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/content/papers/10.3997/2214-4609-pdb.217.398
2001-10-28
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.217.398
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