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AI-GEOSTATS: Kriging versus inv. Dist. Weighting

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  • Tomislav Malvic RGNF
    Dear all, This is my first try at geostat mailing list, and maybe my question will not be very professional . I work with data set of porosity in one oil
    Message 1 of 3 , Nov 15, 2002
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      Dear all,


      This is my first try at geostat mailing list, and maybe my question will not be very "professional".


      I work with data set of porosity in one oil reservoir. Interpolations were done with three interpolation methods: Inverse distance weighting, Kriging (ordinary) and Cokriging (collocated). I done spatial analysis with semivariogram modelling for (co)Kriging.


      After all, I calculated true error for every included point as difference between real value and estimated value at the same place. I was confused when I saw that Kriging error was higher of Inverse Distance Weighting error! The lowest errors were gained by Cokriging (with the same semivariogram modell as used in Kriging).


      What could be reason for that? Maybe 14 points is too low set for proper modelling of directional semivariogram analysis (directions=0 and 90 degrees). I tested several lag distances and distance with the highest range was chosen. If chosen distance is too low interpolation map contains mostly areas of "bull-eyes". Also, input points are moderately clustered.


      Thank you and best regards,

      Tomislav



      [Non-text portions of this message have been removed]
    • Pierre Goovaerts
      Hi Tomislav, Don t be surprised. It is my experience that cross-validation might sometimes indicate that best interpolation results are obtained using the
      Message 2 of 3 , Nov 15, 2002
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        Hi Tomislav,

        Don't be surprised. It is my experience that cross-validation
        might sometimes indicate that best interpolation results are obtained
        using the simplest techniques. If your observations are not
        too clustered and display no anisotropy, inverse square
        distance could yield good results.
        Now, you didn't explain which secondary information was used
        for cokriging and how many neighboring values were used
        in the different interpolators.

        Regards,

        Pierre Goovaerts
        <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

        Dr. Pierre Goovaerts
        Consultant in (Geo)statistics
        and Senior Chief Scientist with Biomedware Inc.
        710 Ridgemont Lane
        Ann Arbor, Michigan, 48103-1535, U.S.A.

        E-mail: goovaert@...
        Phone: (734) 668-9900
        Fax: (734) 668-7788
        http://alumni.engin.umich.edu/~goovaert/

        <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

        On Fri, 15 Nov 2002, Tomislav Malvic RGNF wrote:

        > Dear all,
        >
        >
        > This is my first try at geostat mailing list, and maybe my question will not be very "professional".
        >
        >
        > I work with data set of porosity in one oil reservoir. Interpolations were done with three interpolation methods: Inverse distance weighting, Kriging (ordinary) and Cokriging (collocated). I done spatial analysis with semivariogram modelling for (co)Kriging.
        >
        >
        > After all, I calculated true error for every included point as difference between real value and estimated value at the same place. I was confused when I saw that Kriging error was higher of Inverse Distance Weighting error! The lowest errors were gained by Cokriging (with the same semivariogram modell as used in Kriging).
        >
        >
        > What could be reason for that? Maybe 14 points is too low set for proper modelling of directional semivariogram analysis (directions=0 and 90 degrees). I tested several lag distances and distance with the highest range was chosen. If chosen distance is too low interpolation map contains mostly areas of "bull-eyes". Also, input points are moderately clustered.
        >
        >
        > Thank you and best regards,
        >
        > Tomislav
        >
        >




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      • Adrian Martínez Vargas
        As it is usual in Oil reservoir modelling, it seem you have a very dense geophysical information (may be acoustic impedance) and low dense information from
        Message 3 of 3 , Nov 15, 2002
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          As it is usual in Oil reservoir modelling, it seem you have a very dense
          geophysical information (may be acoustic impedance) and low dense
          information from wells.



          In these case is natural that the best result are obtained from collocated
          cokriging, similar result can be obtained from kriging with external drift
          and the homologous in the simulation scope.



          I think that 14 pint is to short for correct variogram modelling, and it is
          recommendable to use cross variogram in collocate cokriging. A solution for
          know the shape of the variogram could be using the background information,
          if it is close correlate to the wells information, bat I´m not sure that it
          is a good solution.



          Form more help please be more specific



          King regards



          Adrian Martínez Vargas

          Instituto Superior Minero Metalúrgico.

          Moa Holguín Cuba.

          CP 83329


          ----- Original Message -----
          From: Tomislav Malvic RGNF
          To: ai-geostats@...
          Sent: Friday, November 15, 2002 2:24 PM
          Subject: AI-GEOSTATS: Kriging versus inv. Dist. Weighting


          Dear all,
          This is my first try at geostat mailing list, and maybe my question will not
          be very "professional".
          I work with data set of porosity in one oil reservoir. Interpolations were
          done with three interpolation methods: Inverse distance weighting, Kriging
          (ordinary) and Cokriging (collocated). I done spatial analysis with
          semivariogram modelling for (co)Kriging.
          After all, I calculated true error for every included point as difference
          between real value and estimated value at the same place. I was confused
          when I saw that Kriging error was higher of Inverse Distance Weighting
          error! The lowest errors were gained by Cokriging (with the same
          semivariogram modell as used in Kriging).
          What could be reason for that? Maybe 14 points is too low set for proper
          modelling of directional semivariogram analysis (directions=0 and 90
          degrees). I tested several lag distances and distance with the highest range
          was chosen. If chosen distance is too low interpolation map contains mostly
          areas of "bull-eyes". Also, input points are moderately clustered.
          Thank you and best regards,
          Tomislav




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