• Title of article

    Maximizing of the coverage and quality in micro resistivity image log by applying minimum weighted norm interpolation and anisotropic diffusion filter

  • Author/Authors

    Moradi Chaleshtori ، Yahya Petroleum Engineering Department - Petropars LTD Company , Yarmohammadi ، Saeed Petroleum Engineering Department - Petropars LTD Company , Mohebian ، Reza School of Mining Engineering, College of Engineering - University of Tehran , Azizzadeh Mehmandoust Olya ، Behnia School of Mining Engineering, College of Engineering - University of Tehran

  • From page
    221
  • To page
    227
  • Abstract
    The micro-resistivity imaging log is a crucial tool for measuring the heterogeneous features of a formation. It objectively and quantitatively describes various reservoir characteristics, including fine structures, thin strata, fissures, and sedimentary facies. In these imaging tools, measurements from button arrays create an electrical image of the wellbore. However, gaps between tool pads limit coverage, and damaged buttons may compromise image quality. In this study, image log data are examined for factors impacting data acquisition, followed by processing for basic correction, image enhancement, static, and dynamic image log creation. To achieve 100% coverage, the Minimum Weighted Norm Interpolation (MWNI) algorithm fills gaps between tool pads. Finally, the Anisotropic Diffusion Filter (ADF) reduces noise and enhances image log quality in MATLAB, providing a comprehensive image from logging tools. As image logs play a crucial role in illustrating the wellbore and reservoir, this study suggests a new workflow to successfully tackle the challenges associated with acquiring comprehensive image log coverage.
  • Keywords
    Micro resistivity imaging log , Formation features , Minimum weighted norm interpolation , Anisotropic diffusion filter
  • Journal title
    International Journal of Mining and Geo-Engineering
  • Journal title
    International Journal of Mining and Geo-Engineering
  • Record number

    2767256