1. Establishing Patterns Correlation from Time Lapse Seismic
Jianbing Wu[1], Andre Journel[1], Tapan Mukerji[2]
[1] Stanford Center for Reservoir Forecasting
[2] Stanford Department of Geophysics

2. Objective
Establish patterns correlation between seismic and water saturation variables
Predict the water saturation field (future work)

3. Outline The challenge of 4D seismic The Stanford V reservoir
Flow simulation
Seismic amplitude simulation
Point-to-point correlation
Pattern correlation
Conclusions and future work

4. The challenge of 4D seismic
4D seismic can be used to detect fingering, monitor fluid movement, improve recovery and locate new wells
Success stories are limited to clastic reservoirs, shallow reservoirs, some carbonate reservoirs and reservoirs with great density differentiation
This study aims at establishing correlation between seismic and water saturation variables in intermediary less favorable cases

5. The challenge of 4D seismic (cont.)
. . .
The challenge of 4D seismic (cont.)
Seismic
time
lapse
Water
saturation
time
lapse
t1=Jan.1, t2=Jan.1, 2002
Overall correlation: -0.11
yet excellent visual pattern correlation

6. The Stanford V reservoir (Layer 2)
Clastic fluvial channels
injector
producer
Facies
Proportion
mudstone
47%
sandstone
45%
crevasse 8%

7. Introduction to Stanford V reservoir (cont.)
stratigraphic grid
true depth grid

8. The Stanford V reservoir (cont.)
(md)
Channel sand
0.27
648
Crevasse
0.24
521
Mudstone
0.07
1.5
All layer
0.17
314

9. Flow simulation Reservoir parameters: Oil density 45API0
Pressure 1100psi
Temperature 1800F
Water viscosity cP
GOR 850scf/STB
Reservoir parameters:
Initial water saturation:
in sandstone and crevasse
in mudstone

10. Flow simulation (cont.)
Relative permeability curves:
mudstone
sandstone & crevasse

11. Vertical Section at x=20
Oil saturation field on Dec. 29, 2013 (before breakthrough)

12. Seismic amplitude simulation
Normal incidence 1D convolution model
with Fresnel zone lateral averaging
Seismic amplitude should be able to
distinguish velocity difference when
saturated with 100% brine vs. 100% oil

13. Forward simulate seismic amplitude (cont.)
Sandstone
Crevasse
Mudstone

14. Forward simulate seismic amplitude (cont.)
lithofacies
Impedance
Mudstone
Crevasse
Sandstone
Amplitude
Horizontal resolution
Fresnel zone: 225m
Vertical resolution: 15m
Layer 2 mean thickness: 152m
Vertical section X=1
Initial ( Jan. 1, 2000)

15. Point-to-point correlation
. . .
Point-to-point correlation
Different volume supports
Seismic amplitude: Fresnel zone
Saturation: grid node
Seismic amplitude shows vertical impedance contrast

16. Point-to-point correlation (cont.)
Colocated correlation between seismic amplitude and a vertical contrast of spatially averaged saturation values.
with:
total nodes within a moving window

17. Point-to-point correlation (cont.)
seismic amplitude
vertical water contrast
(Jan. 1, 2000)
Overall 3D colocated correlation: poor
but excellent visual pattern correlation

18. Spatial pattern correlation
Principal component analysis (PCA)
Canonical analysis (CA)
Well understood, easy to apply
Linear combinations of data within 3D moving windows
PCA aims at max. within-window variance contribution
CA aims at defining pairs of max. correlated linear combinations

19. Spatial pattern correlation (cont.)
Template sizes:
Seismic: only vertical
Water saturation: 3D

20. Spatial pattern correlation (cont.)
3D correlation (space only) :
PCA applied to data recorded on Jan. 1, 2000:
% within-template variance explained by
1st seismic PC: 84%
1st saturation difference PC: 84%
Overall colocated correlation 0.39
PCA repeated on data recorded on Jan.1, 2002:
correlation is 0.32
Correlation values improve, but still low!

21. Spatial pattern correlation (cont.)
4D correlation:
PCA and CA performed on time lapse data:
time difference of seismic amplitude:
time difference of water saturation vertical contrast:
with:

22. Spatial pattern correlation (cont.)
. . .
Spatial pattern correlation (cont.)
1st PC
1st PC
t1=Jan.1, t2=Jan.1, 2004
Overall 4D correlation: 0.78
!! Significant

23. Spatial pattern correlation (cont.)
1st CC
1st CC
t1=Jan.1, t2=Jan.1, 2004
Overall 4D correlation: 0.82
!! Significant

24. 4D colocated point-to-point correlation
t1=Jan.1, t2=Jan.1, 2004
Overall 4D correlation: 0.34

25. Conclusions
Colocated point-to-point correlation between seismic amplitude and water saturation variables is low because of different resolutions
1st PC and 1st CC can capture the spatial patterns of seismic and saturation time lapse variables
High correlation between PC’s or CC’s can be used towards predicting saturations from time lapse seismic data

26. Saturation prediction (Future work)
Available data:
Water saturation at well locations (hard) :
Time lapse of seismic amplitude (soft) :
Water saturation at present time obtained from a flow simulator :   

27. Saturation prediction (Future work), cont.
Predict time lapse by regression
from known 
Use as an external drift for kriging water
saturation time lapse  
with
Predict water saturation 

28. Thank you!

29. 4 year time lapses of amp, imp, Sw and Pres. at vertical section X=20
amplitude diff.
impedance diff.
Sw diff.
pressure diff. (Mpa)
4 year time lapses of amp, imp, Sw and Pres. at vertical section X=20

30. Original 1st PC 2nd PC Correlation: 0.34 0.78 0.55
t1=Jan.1, t2=Jan.1, 2004