Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.
Reservoir
Characterization
Enhancement using geostatistics
Workflow for geomodeling
Production
forecast
Reservoir grid
Upscaling
Flow simulation
Integration
of production
data
Geolo...
Facies Simulation
Facies simulation
 A facies is the representation of a rock type or flow unit
• Petrophysical properties within facies sh...
Available facies modeling techniques
 Typical approaches:
• Object oriented: boolean
• Pixel oriented: based on indicator...
Object-based simulations
 Geological body modeling using a Boolean simulation
CourtesyH.Beucher(CG)
Remark: Difficult to ...
Truncated Gaussian simulation
 Use of one Gaussian Random Function (GRF)
• Simulate the GRF and truncate it to obtain fac...
Plurigaussian simulations
 Use of 2 GRFs to model more complex geological environments
• Red Facies can be in contact wit...
Vertical Non-Stationarity
 Use local vertical proportion curves to reflect the non
stationarity of depositional environme...
Plurigaussian simulations
 Model complex reservoirs with
different structure orientations
and heterogeneous deposits
(cha...
Multiple-points overview
 Two-steps approach:
• Get multiple-point statistics from a
geological training image
• Create a...
Advanced Feature: auxiliary variable
 An auxiliary variable may be added to account for non
stationarity
Simu GridTrainin...
Multiple-points simulations
 Examples:
TI
2D channels 2D delta 3D channels
Simu
Petrophysical Modeling
Property modeling
 Petrophysical modeling techniques are simpler than the facies
modeling ones
 Main methods are:
• Sequ...
Property Modeling Example
Facies Porosity
Variogram model QC
 Having tools to check the consistency of the model of spatial
correlation with the data is benificial...
Conclusion
Conclusion
 Geostatistics are used at every stages of the reservoir
characterization workflow but too often as a black bo...
Thank you for your attention
For more information:
Jean-Paul ROUX – Sales Manager
jproux@geovariances.com
www.geovariances...
Próxima SlideShare
Cargando en…5
×

Reservoir characterization - Enhancement using geostatistics

5.269 visualizaciones

Publicado el

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

Publicado en: Tecnología, Empresariales
  • Sé el primero en comentar

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

×