• Like
  • Save

Reservoir characterization - Enhancement using geostatistics

  • 893 views
Uploaded on

Find out why keeping control on the key geostatistical parameters is primordial for reliable reservoir models.

Find out why keeping control on the key geostatistical parameters is primordial for reliable reservoir models.

More in: Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
893
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
6

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Reservoir Characterization Enhancement using geostatistics
  • 2. Workflow for geomodeling Production forecast Reservoir grid Upscaling Flow simulation Integration of production data Geological model : Facies, porosity, permeability structural model Well and seismic data proportions of facies Stratigraphic model Integration of 4D seismic data (courtesy IFP)
  • 3. Facies Simulation
  • 4. Facies simulation  A facies is the representation of a rock type or flow unit • Petrophysical properties within facies should represent the same population  Subsequent petrophysical property modeling is determined by the location and amount of each facies • Facies simulation is a key step in reservoir characterization  Due to their discrete nature it is demanding to: • Compute variogram (need indicators) • Match the input data • Correlate with continuous properties such as seismic attributes
  • 5. Available facies modeling techniques  Typical approaches: • Object oriented: boolean • Pixel oriented: based on indicator simulations (SIS, TGS) • Process based approach  Plurigaussian simulations: • An extension of TGS to model more complex transition order between facies  Multiple-Point Statistics: • Intermediate between pixel-based and object oriented approaches
  • 6. Object-based simulations  Geological body modeling using a Boolean simulation CourtesyH.Beucher(CG) Remark: Difficult to constrain to wells or auxiliary data
  • 7. Truncated Gaussian simulation  Use of one Gaussian Random Function (GRF) • Simulate the GRF and truncate it to obtain facies code Lithotypes Indicators Gaussian Function and its truncation • Good to respect facies transition • But not all facies transitions can be modeled
  • 8. Plurigaussian simulations  Use of 2 GRFs to model more complex geological environments • Red Facies can be in contact with green and yellow but not blue • Green and yellow can be in contact with any facies • Blue can be in contact with green and yellow but not red.  Each GRF can have its own spatial structures
  • 9. Vertical Non-Stationarity  Use local vertical proportion curves to reflect the non stationarity of depositional environments • Essential for facies modeling Global VPC Local VPC
  • 10. Plurigaussian simulations  Model complex reservoirs with different structure orientations and heterogeneous deposits (channels, reefs, bars, …)  Provide realistic and detailed images of the reservoir geology Facies modeling displayed with ISATIS 3D Viewer
  • 11. Multiple-points overview  Two-steps approach: • Get multiple-point statistics from a geological training image • Create a pixel-based simulation by retrieving information from the multiple- point statistics  Key points: • Having a suitable training image! • Characterizing this training image in terms of facies relationships • No variogram needed
  • 12. Advanced Feature: auxiliary variable  An auxiliary variable may be added to account for non stationarity Simu GridTraining Image
  • 13. Multiple-points simulations  Examples: TI 2D channels 2D delta 3D channels Simu
  • 14. Petrophysical Modeling
  • 15. Property modeling  Petrophysical modeling techniques are simpler than the facies modeling ones  Main methods are: • Sequential Gaussian Simulation (SGS) • Turning Band  Multivariate techniques (Co-kriging) are particularly interesting to perform data integration, e.g: • Integrate seismic attribute for instance. However we need to take into account the change of resolution (support) between data • Co-simulate permeability from porosity
  • 16. Property Modeling Example Facies Porosity
  • 17. Variogram model QC  Having tools to check the consistency of the model of spatial correlation with the data is benificial • E.g. Cross-validation provides a way to derive local variogram model parameters for non-stationary field Base Map Histogram of the standardized errors Scatter diagram of Z vs Z* Scatter diagram (Z-Z*)/S* vs Z*
  • 18. Conclusion
  • 19. Conclusion  Geostatistics are used at every stages of the reservoir characterization workflow but too often as a black box  Understanding the statistics behind is key to reservoir characterization  Having the right set of tools to model or QC parameters is primordial  Geostatistics are key for data integration of geology, geophysics, petrophysics, reservoir engineering • However integrating all these information to better predict and minimize uncertainty still prove challenging
  • 20. Thank you for your attention For more information: Jean-Paul ROUX – Sales Manager jproux@geovariances.com www.geovariances.com