You made a good point about the different costs of sampling.
But perhaps I don't understand your example about offshore drilling. How
can you talk about geostatistics with n = 2 or 3? You are talking about
applying deterministic models to predict, not geostatistics, right?
One other point. The reason I'm so concerned with map errors is that for my
research I've been finding for a number of soil variables that ordinary
kriging with second order stationary data (fairly well structured) yield
very inaccurate maps. And that inverse distance weighted gave better
results than ordinary kriging most of the time. I didn't expect this.
Today, I would not trust an interpolated map without seeing some
quantification of map accuracy.
"At the extreme case, for example in an offshore
environment, two or three delineation wells are all
that one might afford (e.g., USD 30 million), and
the subsurface description might have to be built
deterministically, assuming different "scenarios",
and incorporating dense (but "soft") information.
And if one can afford such luxury, perhaps a Boolean
simulation of sand-shale distributions can be built,
the basis of which will be a dynamic fluid flow
model and the detailed design of a USD 500 million
dollar jackup platform. Whatever the case, samples
will generally be sparse, and variograms ill-defined,
and one would have to resort to deterministically
derived "scenarios" to assess the uncertainty.
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