Abstract
Vuggy porosity is a key pore space type associated with the characterization of any carbonate reservoir. This porosity has significant influence on many parameters in reservoirs such as permeability, oil recovery, pressure drop and production. In this paper, two data-driven methods (i.e., Bayesian and Parzen classifiers) are used to identify vuggy porosity zones from well logs. Four well logs namely gamma ray (GR), neutron porosity (NPHI), bulk density (RHOB) and sonic (DT) from three oil wells drilled in Sarvak Formation, west of Iran, were integrated to examine the capability of the methods. The results show that the Parzen classifier gives better results than Bayesian in terms of classification accuracy. The Parzen technique classifies the given depth with an average accuracy of 77.7 % in single-well analysis and average accuracy of 67.3 % in generalization step.
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The authors would like to express their sincere thanks to the Exploration Directorate of National Iranian Oil Company (NIOC) for their assistance in providing data and information.
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Mollajan, A., Asgarinezhad, Y., Tokhmechi, B. et al. A comparative study of two data-driven methods in detection of vuggy zones: a case study from a carbonate reservoir, west of Iran. Carbonates Evaporites 30, 181–185 (2015). https://doi.org/10.1007/s13146-014-0201-x
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DOI: https://doi.org/10.1007/s13146-014-0201-x