Presentation on theme: "1 -Classification: Internal 2010-05-27 Uncertainty in reservoirs."— Presentation transcript:
1 -Classification: Internal Uncertainty in reservoirs
2 -Classification: Internal 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.
3 -Classification: Internal 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.
4 -Classification: Internal $£ $£ $£ $£ $£ 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.
5 -Classification: Internal 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 - Ill try out different values for a couple of parameters and see what happens.
6 -Classification: Internal 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.
7 -Classification: Internal 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.
8 -Classification: Internal 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.
9 -Classification: Internal 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
10 -Classification: Internal ~0.25 m ~10 m ~50 m Geo object Different scales
11 -Classification: Internal Different scales II ReservoirPores ~ 9 orders of magnitude
13 -Classification: Internal Geostatistics It is quite common to sample properties like permeability and porosity stochastically – with various constraints/trend parameters: Point measurements Spatial gradient Correlation length Different porosity realisations
14 -Classification: Internal 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.
15 -Classification: Internal 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:
16 -Classification: Internal Example – channel direction Prior: θ~100 O Conditioning the distribution P(θ|d) with McMC Posterior: θ~0 o
17 -Classification: Internal Main challenges 1.Model selection – and how to handle the Uknown unknowns. 2.Conditioning of coarse parameters like geostatistical trends. Thank you!