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: [EMAIL PROTECTED]
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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