1. SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION

2. SEISMIC ATTRIBUTES FOR RESERVOIR CHARACTERIZATION
Elastic Rock Properties
Seismic Velocity inversely correlated with Porosity
Rock Property Predictions from Seismic Attributes
Geometrical Attributes – Faults, Sedimentary Features, Channels
Bright Spot vizualization

3. FOR FLOW SIMULATION MODEL THE RESERVOIR ENGINEER NEEDS
Porosity: amount and spatial distribution
Permeability
Nature of fluids and saturation
Pressure
Temperature
Barriers to flow (faults, strat. barriers)

4. ELASTIC ROCK PROPERTIES
For isotropic media, (Sheriff, 1992) Compressional wave velocity
Vp = [λ + 2μ /ρ] 0.5
where Lamé’s constant λ, an elastic parameter sensitive to fluid content, is related to μ and κ by λ = κ - 2μ/3.
rigidity (shear modulus, μ),
Incompressibility (bulk modulus, κ)
density (ρ)

5. ELASTIC ROCK PROPERTIES
Shear wave velocity = Vs = (μ / ρ ) 0.5
where rigidity (shear modulus, μ),
density (ρ)

6. ELASTIC ROCK PROPERTIES
where σ is the Poisson ratio.

7. SEISMIC VELOCITY AND POROSITY
where φ is the porosity, Vf is the velocity of the interstitial fluid, and Vm is the velocity of the rock matrix (Wyllie et al. 1956).

8. SEISMIC VELOCITY AND POROSITY
a high negative correlation between porosity and acoustic impedance has a physical basis

9. SEISMIC VELOCITY MAP AND WELL POROSITY

10. SEISMIC VELOCITY AND WELL POROSITY

11. Snap 3D Seismic to Grid Impedance (Stochastic Inversion)
3D Inversion Cube

12. Acoustic Impedance Model

13. PREDICTION METHODS
Regression
Geostatistics
Neural networks

14. PREDICTION PROCESS Calibration Choice of seismic attribute(s)
Cross-validation
Management decisions

15. SEISMIC VELOCITY MAP AND WELL POROSITY

16. RISK MAP
based on 100 conditional simulations showing the probability that porosity is ≥ 9%.

17. FALSE CORRELATIONS Possiblity of false correlations increases with:
small number of well data
many seismic attributes

18. GEOMETRICAL ATTRIBUTES
depict patterns related to:
faults
fracture swarms
depositional patterns
channels

19. GEOMETRICAL ATTRIBUTES
MULTIPLE-POINT STATISTICS allows geological patterns integration through pixel-based modeling (Journel, 1997, 2002; Caers, 2000; Strebelle, 2000).
Training images depict the geological conceptual model
then a sequential-based simulation algorithm is used to generate multiple realizations

20. GEOMETRICAL ATTRIBUTES
left - seismic amplitude data showing channel-like features
center - conceptual model
right - pixel-based realization

21. Data Integration Geology Geophysics
Structural Map on Top Reservoir using G&G

22. Example Well Average Porosities Seismic Amplitude Map
Average Porosity Maps
Combined using
Collocated Cokriging
R = 0.75

23. Uncertainty Reduction
Average Porosities - Wells Only
Realization 1
Realization 2
Realization 3
Average Porosities - Wells plus Seismic
Realization 1
Realization 2
Realization 3

24. Example 3D sGs constrained by 2D seismic surface Cokriged Por. Map
Amplitude Map
Cokriged Por. Map
3D Porosity Model
Avg. of 3D Model

26. Reservoir Facies Using Seismic

27. A series of equally probable high resolution impedance models
Impedance Logs
3D Seismic Data
Deterministic Inversion
Stochastic Inversion
A series of equally probable high resolution impedance models

28. Reservoir Geometry Using Seismic
Channel geometries are locally controlled by the use of Seismic facies.

29. Bright Spot Vizualization

30. Bright Spots

32. Seismic attributes can help in characterizing and modeling:
Reservoir Geometry
Reservoir Facies
Reservoir Property (Porosity)
Bright spots