1 -classification: internal 2010-05-27 uncertainty in reservoirs
TRANSCRIPT
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1 - Classification: Internal 2010-05-27
Uncertainty in reservoirs
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2 - Classification: Internal 2010-05-27
Deepwater Horizon – Gulf of Mexico
The slightly more mundane situation I consider:
–We have a hydrocarbon reservoir
–We have a model for the reservoir which will be used for future decisions.
–The parameters in the model are uncertain.
What do we do with the uncertainty?
Operational uncertainties are unfortunately not a topic in this presentation.
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Uncertainty in the petroleum industry
Organisational issues:
– Strong financial inertia to ”stick with the truth”.
– Tradition for compartmentalized organisations where uncertainty information is not passed on.
– ”What happens happens” – limited tradition to reevaluate uncertainty estimates.
Current topics:
– Choice of parameters – model selection.
– Different scales.
– Stochastic modelling.
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$€£ $€£ $€£ $€£ $€£
Producing fields:
Maybe the reservoir is larger?
Or smaller?
There is so much money, financial regulations e.t.c. in these questions that there is a strong organisational urge to just ignore the uncertainty.
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An organisational challenge
I am working hard to interpret the seismic and build a structural
model.
OK; I pass my best result on to
Deborah!
Structural model
I am creating a geological
model.
I pass my best effort on to
Phillip.
Geological model
I am doing flow simulations, and
management even wants uncertainty estimates
these days
Well - I’ll try out different values for a
couple of parameters and see what
happens.
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”What happens happens”
1. We do our best to model and quantify uncertainty.
2. We make a decision to e.g. drill a well:
– Estimated oil volume: A +/- B – found nothing!
– Estimated gas volume: A +/ B – found both gas and oil.
3. The new information is used to infer that we were just wrong. Uncertainty estimates are not really challenged.
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History matching – it is just plain stupidTraditionally History Matching is percieved as an optimization problem – a very problematic approach:
–The problem is highly nonlinear, and severely underdetermined.
–The observations we are comparing with can be highly uncertain.
–The choice of parameterization is somewhat arbitrary – we will optimize in the wrong space anyway.
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Geological concept
Deep marine
Shallow marine
Channel system
The choice of geological concept is an example of a choice which will have a profound effect on subsequent interpretations, and decisions.
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Water rate
Time
Model/parameter selection II
Two wrongs do not make a right – it is all to easy to get ”a match” for the wrong reasons:
Water –Simulations show to little water.
–Increase relative permeability of water
Maybe the ”real” reason was that the oil-water interface was shallower?
Good agreement between model simulation and observation!
Oil
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~0.25 m
12
3
45 ~10 m
~50 m
Geo object
Different scales
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Different scales II
ReservoirPores
~ 9 orders of magnitude
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Different scales III: Upscaling
1321
11111
T
1
2
3
1
2
3
Permeability:
3321 T
Vertically:
Horisontally:
~
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Geostatistics
It is quite common to sample properties like permeability and porosity stochastically – with various constraints/trend parameters:
Point measurementsSpatial gradient
Correlation length
Different porosity realisations
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Modelling – the full loop
Sample geostatistical parameters
Sample a geological realisation according to the parameters.
Perform flow simulations and evaluate misfit.
Traditional approach:
1. Cutting the link to geostatistical paramaters.
2. Direct updates of the properties of the realisation
Ideal approach:
Make all alterations on geo parameters, and keep everything syncronized.
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McMC and stochastic modelling – attempt 0
The geo modelling process is not a closed form PDF; it can only be observed from the created realizations.
We have tried to update update geo parameters; initial attempts show some success!
Uncertainty:
SCdd CCCC 0
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Example – channel direction
Prior: θ~100O
Conditioning the distribution P(θ|d) with McMC
Posterior: θ~0o
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Main challenges
1. Model selection – and how to handle the ”Uknown unknowns”.
2. Conditioning of coarse parameters like geostatistical trends.
Thank you!