1. Uncertainty in reservoirs
Classification: Internal

2. 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.
Classification: Internal

3. 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.
Classification: Internal

4. $€£ $€£ $€£ $€£ $€£ Maybe the reservoir is larger? Producing fields:
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.
Classification: Internal

5. An organisational challenge
Geological model
Structural model
OK; I pass my best result on to Deborah!
I am working hard to interpret the seismic and build a structural model.
I am doing flow simulations, and management even wants uncertainty estimates these days
I pass my best effort on to Phillip.
I am creating a geological model.
Well - I’ll try out different values for a couple of parameters and see what happens.
Classification: Internal

6. ”What happens happens”
We do our best to model and quantify uncertainty.
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.
The new information is used to infer that we were just wrong. Uncertainty estimates are not really challenged.
Classification: Internal

7. History matching – it is just plain stupid
Traditionally 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.
Classification: Internal

8. Geological concept Channel system Deep marine
Shallow marine
The choice of geological concept is an example of a choice which will have a profound effect on subsequent interpretations, and decisions.
Classification: Internal

9. Model/parameter selection II
Good agreement between model simulation and observation!
Two wrongs do not make a right – it is all to easy to get ”a match” for the wrong reasons:
Maybe the ”real” reason was that the oil-water interface was shallower?
Water rate
Time
Oil
Simulations show to little water.
Increase relative permeability of water
Water
Classification: Internal

10. Different scales Geo object 1 4 ~10 m 5 2 3 ~50 m ~0.25 m
Geological heterogeneities to model explicit using the REV concept
Classification: Internal

11. Different scales II Pores Reservoir ~ 9 orders of magnitude
Classification: Internal

12. Different scales III: Upscalingk1 k2 k3
Permeability: k1<< k2<<k3
Vertically:
Horisontally: k1 k2 k3
~k3
~k1
Classification: Internal

13. Different porosity realisations
Geostatistics
Different porosity realisations
It is quite common to sample properties like permeability and porosity stochastically – with various constraints/trend parameters:
Spatial gradient
Point measurements
Correlation length
Classification: Internal

14. Modelling – the full loop
Sample geostatistical parameters
Ideal approach:
Make all alterations on geo parameters, and keep everything syncronized.
Sample a geological realisation according to the parameters.
Traditional approach:
Cutting the link to geostatistical paramaters.
Direct updates of the properties of the realisation
Perform flow simulations and evaluate misfit.
Classification: Internal

15. 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:
Classification: Internal

16. Example – channel direction
Posterior: θ~0o
Prior: θ~100O
Conditioning the distribution P(θ|d) with McMC
Classification: Internal

17. Thank you! Main challenges
Model selection – and how to handle the ”Uknown unknowns”.
Conditioning of coarse parameters like geostatistical trends.
Thank you!
Classification: Internal