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R
E P R E S E N T A T I V E S C A L
Very few Petrophysicists get involved in any detail with plugs being
selected for Special Core Analyses (SCAL). As a consequence, most of
the SCAL data we get our hands on is NOT representative of the
reservoir. If you use poorly selected data in your studies without some
kind of scaling, the relationships developed will be skewed toward the
dominant data. Typically people oversample the best reservoir,
resulting in the poorer quality reservoir being undersampled. The
poorer reservoir is not even recognised in many cases. The result can
be both under or over estimation of volumes in-place and reserves,
depending on the circumstances.
The techniques described below have been developed by our Principal Petrophysicist .
They have been used regularly for a number of Operators over a period
of more than eight years.
Checking If Your SCAL is Representative
So you've got your hands on some SCAL data and want to see if it's been
representatively selected. As a first pass, plot a histogram of
log-derived porosity over your reservoir interval, next plot a
histogram of the porosities of your routine core measurements, finally
plot histograms of the porosities for each of your SCAL measurement
sets. If all those histograms have the same shape, then not only have
you sampled your reservoir well with core, you've also apparently done
a good job of selecting your SCAL samples.
Just to confirm that you have done well, make histograms of the routine
core permeabilities and the permeabilities of each of the SCAL
datasets. Are these also the same shape? If so, then you really have
done a good job of sample selection! If the permeability histograms are
not the same, but the porosity ones are, then you have a least two
different facies to deal with! The next section explains how to move
forward.
It's Poorly Sampled - What Now?
You've checked the data and it's got big undersampled areas. Can we use
it anyway? Or should we take more measurements to fill the holes?
If you've got time and budget to do so, then additional samples should
be selected. The routine plugs can be re-used for most SCAL purposes,
so picking a few more from that dataset is the easiest way ahead. You
can work out which samples to select by making a porosity versus
permeability crossplot. Subdivide the crossplot into an even grid
pattern, then count the number of core plug measurements in each. Give
each of these squares an identifier and plot the number of routine core
plug measurements in each group on a histogram. Ideally you want to
recreate that histogram shape with your SCAL samples, so select samples
accordingly, remembering which "group" you already have SCAL
measurements from (if any).
If you are poorly sampled, but don't have time to get additional SCAL
carried out, there is still hope. Look at the data using the above
technique. If scaling factors can be applied to the measurements you
have to get the correct histogram shape, then use these scaling factors
as "weights" in your SCAL analyses. To make this work where there are
no SCAL samples in some groups, use a little interpolation between
groups to fill the gaps. If you have to extrapolate, then only do so
along the same porosity-permeability trend! This work requires a good
uncertainty analysis to be really meaningful!
Choosing Samples for New SCAL
The thinking reader will have already noted that the above technique
can also be used to select samples for new SCAL work. Just look at your
budget, use that to restrict the number of measurements you can make,
then select the appropriate number of plugs from those groups exceeding
the threshold value of (total number of routine plugs)/(number of
measurements required).
Conclusion
The tips given here are very straightforward (at least to me), so I've
skipped the diagrams. If you're having trouble following this scheme,
let me know and I'll update this guide with some plots.
Stephen Adams
Principal Petrophysicist
email:
steve@welleval.com
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