seismic inversion to flow simulation testing of two different methods of stochastic modelling of...

SEISMIC INVERSION TO FLOW SIMULATION

TESTING OF TWO DIFFERENT METHODS OF STOCHASTIC MODELLING OF FACIES

DISTRIBUTION, CONDITIONED TO SEISMIC DATA.

(Dalai FIELD, ANGOLA BLOCK 17)

Eirik Vik, Alfhild Lien Eide, Christophe Basire

Composite figure of the Dalai Field, showing seismic, channel envelope and “bodies” extracted from the seismic.

Why do stochastic reservoir modelling?

Uncertainty evaluation

Integration of data -> Better models -> Increased hydrocarbon recovery

Why use seismic data in reservoir modelling?

Spatial coverage of reservoir

Purpose of the modelling project: Test fast workflows from interpretation of inverted seismic data to stochastic reservoir modelling and flow simulation. One workflow using a Multipoint statistical method developed at Stanford, and one workflow using Marked Point methodology implemented in Irap RMS.

SEISMIC INVERSION TO FLOW SIMULATION - ANGOLA CASE

The Early to Middle Miocene Dalia field is located offshore Angola in water depths between 1200 and 1450m, 700 to 900 m below the sea bottom. The field can be divided into a number of different reservoir systems that are characterised by deposition within confined and unconfined turbidite systems. API gravity of the oil is in the order of 22 to 23° API.

The accumulations are composed of stacked channelised turbidites deposited within confined fairways, enclosed within hemipelagic shales. Located laterally to each of the main channels are a series of unconfined “flanking” turbidite deposits, believed to represent sediments that pre date the main channels that are now in apparent depositional juxtaposition due to the incision of the main channels into older sediments.


• TASKS


– MODEL BUILDING AND SIMULATION

• Simple sand/shale model derived from the more complex model based on the Dalia-1 well.

• Make correlation function for seismic to lithology based on Dalia-1 well petrophysics.

• Simulate sand bodies conditioned to lithology cube (seismic) bounded by channel ‘envelope’

– Stochastic object model conditioned to seismic data using Irap RMS

» Facies composite: Rectangular objects

– Stochastic object model conditioned to seismic data using Irap RMS

» Facies Composite: Channel form objects

– Alternative stochastic model: Using a multipoint geo-statistical model from Stanford Centre for Reservoir Forecasting (SCRF)

– FLOW SIMULATIONS

• Flow simulation using streamline simulator in Irap RMS.

– DATA ANALYSIS

– CONCLUSIONS

Seismic to lithology:Correlation based on Well: Dalia-1. Figure show relation between Poisson ratio and Acoustic impedance of log data, discriminated between interpreted lithology.

NB: Plot separates shale (black) and oil sand (green) fairly well, while shale and brine sand (blue) are not separated !

Lithology cube constructed by taking AI* PR^2 and rescaling (black curves of above plot). Colour scale gives shale as red and more sandy facies as green

(and blue), yellow is intermediate.


Depth converted lithology cube, bounded by the upper and lower bounds of the Dalia channel complex. Resolution 50*50*2 m (522 711 cells, the full resolution 25*25 cube would have been 2092 051 cells, which is too large for the modelling). Red/pink colours designate shaly facies while the blue/green indicate sand. Note the layered “sand” objects in the middle of the section. Channel complex is approximately 500 m wide. The display has a 5x vertical exaggeration.

Frequency plot of values of the lithology cube.


DALIA-1 well with facies breakdown, compared to the simplified 2-facies model used .Note correspondence between facies and LFP values in well position

Lithology cube values


Shale

Shale

Sand

WORKFLOWSeismic data mapped into the channel ’envelope’

Running 10 realisations of each of the 3 model versions. The only differences are in the facies modelling.

•Same workflow the two GMPP jobs•Facies composite in RMS, rectangles and channels, conditioned to seismic data

•Multipoint statistical method:•10 facies realisations conditioned to the seismic cube done outside RMS, and then imported into RMS.

•Stochastic modelling of flow parameters taken from the Dalia-1 well

•IPL script to make vertical permeability 1/10 of horizontal perm.

•Streamline simulations.


Input parameters for GMPP simulation:Facies: CompositeRECTANGLESGeometry of objects (rectangles):

Length: 250 m (100 m)With: 100 m (25 m)Thickness: 5 m (3 m)

Seismic:Lithology cubeResolution 50 ‘ 50 ‘ 2 m

Simulation parameters:Iterations: 3*10^6 Seismic factor : 2e+05Annealing function:

T0 = 1t1 = 1*e^-7a=0.999893

Volume fraction of sand: 0.3

Relationship between lithology cube value and probability for sand used in the simulation.Simulation cells with values below ~150-200 have a probability of 0.8 to 0.9 of being filled with some sort of oil-filled sand facies. Values higher than that are most likely shale., except for very high values.

Input parameters for GMPP simulation:Facies: CompositeCHANNELSGeometry of objects (channels):

Length: 20000m (6000m)With: 1000m (100m)Thickness: 10 m (3m)

Seismic:Lithology cubeResolution 50 ‘ 50 ‘ 2 m

Simulation parameters:Iterations: 3*10^6 Seismic factor : 2 e+05Annealing function:

T0 = 1t1 = 1*e^-7a=0.999893

Volume fraction of sand: 0.3


Results from conditioning: Input parameters for the conditioning to the left (sand probability as function of lithology cube value) and results to the right: Upper right figure show the probability graph calculated based on the results from the conditioning.Lover right figure show the fraction of sand and shale in the corresponding lfp value bins.

Observations:The graphs show that the input distribution is fairly well reproduced in the resulting facies cube. The misclassification of cells may be due to the differing size between modelled objects and the seismic resolution. Of greater concern is the lower graph showing that the larger number of cell have seismic values in the area where the sand and the shale distributions overlap !!


MULTIPOINT STATISTIC

•Developed at Stanford Centre for Reservoir Forecasting (SCRF)•Probability of facies in each grid cell is defined from the configuration of facies in a neighbourhood template

•Probabilities are inferred from a training image.• Multipoint geostatistics is better at reproducing curvilinear geometry than two-point geostatistics

•More flexible than object-based methods in situations with large amounts of conditioning data?

•Is called multipoint because it uses multiple points at a time (the template).

Pixel based methods (works on grids) Object based (drops ’objects’ randomly in space)

(Indicator) Kriging / Cokriging

•Uses variograms (two-point statistic)

Multipoint method

•Uses templates

Marked point models (GMPP)

Example: Facies: Geoplex in RMS

Does good reproduce curvilinear geometry

Can reproduce curvilinear geometry

Reproduces geometry (shapes) explicitly

APPLICATION TO DALIA STUDY•Model for two facies: Sand and shale

•The LFP cube was transformed to an input probability cube for sand/shale.

•Training images were generated by unconditional simulation from a stochastic object-based model to obtain stationarity (must have many repetitions of a pattern in the training image)

•A channel training image was chosen as training image for the simulation on the full grid.


SCRF model training image, constructed using unconditional modelling of ”channel objects”and example of resulting facies model (red is sand)


Porosity realisation

Permeability realisation

Facies realisationLFP cube

Typical results from stochastic facies and petrophysical modelling, using rectangular objects in Irap RMS.


Porosity

LFP cube

Typical results from stochastic facies and petrophysical modelling, using channel- formed objects in Irap RMS.

Facies realisation

Permeability


Porosity

LFP cube

Typical results from stochastic facies- and petrophysical modelling, using the SCRF methodology.

Facies realisation

Permeability


RMS-streamline simulation:A simple flow simulation useful for rapid assessment of flow properties of a model and for the ranking of models and realisations. Speed is accomplished by decoupling pressure modelling and flow modelling. The simple version implemented in RMS assumes single phase, thus avoiding the complexities of gravity flow and capillary effects.

Parameters:Simplified model, the same as used by Statoil in early RMS modelling of the Dalia field.

10 realisations.11 producer/ injector pairs (fixed positions):

Production rate: 1835 m^3/dayInjection rate: 1835 m^3/dayProducer bhp: 50 barInjector pressure: 10000 bar

Completed in all sand intervals for each realisation.Model run for 1e+6 days.

Fluid compressibility 0,0001 /bar; Rock compressibility 1e-5 /bar;Viscosity 0,25 cp.

Visual results from streamline-simulation of model with rectangular objects. Black pillars designate pseudo-wells, coloured lines are streamlines connection produces and injectors while the yellow lines represent streamlines linked to ”black” and ”white” holes.


Results from facies modelling: Visual impression: The three models have different looks: the “rectangles model” seem to be more heterogeneous, with smaller sand bodies than the two other models. This was to be expected as the two other models are constructed using larger elementary bodies.

The rectangles methodology is thus better able to “mimic” the seismic, and would look more heterogeneous.

Rectangles

Channels

SCRF model

Results from volumetric modelling: Little variation between realisations within each series (standard deviation is 1 to 3 % of average!) Figure show sand volume for each of the 10 realisations of the three series, with standard deviation within the series. Note the difference between the series, this is difficult to explain as the target sand volume in all three series were set to 40 %

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Average time to breakthrough for the 11 producers of the three models. Note the lack of correlation between the channels model and the two others. This is interesting as all three models were conditioned to the same seismic data, and one could expect a larger similarityNote also that the time to breakthrough for the producers of the channel model is on average larger than for the two other models. This may be due to the channels being less continuous than the two other models: the rectangle model with a better fit because of the small size of the elementary building blocks , and the SCRF model with branching channel elements and therefore better connections!

(No 1 is not effective in the SCRF model and no 11 is shut in the two others)

Results from streamline simulations (1): Breakthrough:Days to ”breakthrough” for 10 producer wells (and average) for 10 realisations of the rectangles RMS model. Note the relatively large spread in values. Further analyses show that there is no correlation between the breakthrough times and the facies volumes show in the diagram on the last slide. Similar values and spread are observed for the channels and the SCRF model.

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Results from streamline simulations (2):Drained volumes as function of model and realisation at end of simulation.

Yellow lines represent total volumes for each realisation, the other lines represent volumes for each producer.Note that the total volume drained from the “SCRF” model is larger than for the other two models. At the same time the production as part of total fluid in place for the SCRF model is also greater than for the two others (51 % vs. 42% and 37%), all indicating that the SCRF model has the better internal connection.



Results and conclusions (1)

– An efficient work flow for using seismic data to condition stochastic modelling of facies distribution is explored on data from the Dalai field of offshore Angola block 17

– The workflow is used for 2 different Irap RMS composite models(GMPP) and one model derived by a Stanford University methodology (SCRF).

– The workflow is found to be feasible and easy to use. The SCRF model takes some more effort to implement but no big difficulties were encountered.



– 10 realisation of each model were made and RMS streamline simulations performed to compare and to rank the results.

– Comparison of visual and numerical results from the simulations show that

• the sand / shale fraction of the different model/realisations are very similar, This is most likely a function of the modelling set-up and as such inevitable.

• the RMS model using small rectangular objects in the stochastic modelling do look more heterogenic than the two other models. This is most likely caused by the small size of the elementary rectangles compared to the grid size of the conditioning fabric, which makes it easier to “follow” the minor variations” in the seismic pattern.

• time to breakthrough (as defined in RMS stream) is slightly larger for the channels model than for the two others (but “inter realisation” variation is very large), indicating that the channels models may be more heterogeneous (?) than the others.

• the SCRF model has slightly larger total drained volume and is producing a larger part of the total fluid in place that the two other model.This is most likely because of a greater connectivity.


Overall: The models are quite similar but have some differences. The most conspicuous feature is the very large variation between different realisations.

This may partly be due to the modelling set-up either wells in fixed positions and no conditioning to well results. The potential for such large variations is however something to bear in mind assessing more complex models with fewer realisations.


– The presented case is very simple (having only two facies!), the workflows should be implemented on more complex cases, either by developing the Dalai case or by continuing the work on another field.

– The SCRF methodology has been show to be feasible, this work should be continued on other cases with other deposits types and more complex facies relationship.

– Work on different ways of comparing and ranking models and realisations should be continuing.


Further work

seismic inversion to flow simulation testing of two different methods of stochastic modelling of...

Documents

tasks seismic inversion

lithology cube seismic

flow simulation testing

stochastic reservoir

complex model

dalia channel complex

angola block

offshore angola