Reservoir characterization - Enhancement using geostatistics

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

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Reservoir characterization - Enhancement using geostatistics

  1. 1. Reservoir Characterization Enhancement using geostatistics
  2. 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. 3. Facies Simulation
  4. 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. 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. 6. Object-based simulations  Geological body modeling using a Boolean simulation CourtesyH.Beucher(CG) Remark: Difficult to constrain to wells or auxiliary data
  7. 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. 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. 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. 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. 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. 12. Advanced Feature: auxiliary variable  An auxiliary variable may be added to account for non stationarity Simu GridTraining Image
  13. 13. Multiple-points simulations  Examples: TI 2D channels 2D delta 3D channels Simu
  14. 14. Petrophysical Modeling
  15. 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. 16. Property Modeling Example Facies Porosity
  17. 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. 18. Conclusion
  19. 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. 20. Thank you for your attention For more information: Jean-Paul ROUX – Sales Manager jproux@geovariances.com www.geovariances.com

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