1 -Classification: Internal 2010-05-27 Uncertainty in reservoirs.

1 - Classification: Internal 2010-05-27

Uncertainty in reservoirs

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!