1. Guang Yang, Amit Suman, Celine Scheidt, Jef Caers
Annual Meeting 2013
Stanford Center for Reservoir Forecasting
Generalized Sensitivity Analysis (GSA) applied to 4D seismic modeling -- Application to Norne Field
Guang Yang, Amit Suman, Celine Scheidt, Jef Caers

2. Coalesced calcite concretions in Eocene Formation, Egypt
Norne Field
Coalesced calcite concretions in Eocene Formation, Egypt
Antar, 2001
Heterogeneity induced by calcite concretions
Thin cemented horizon

3. Norne Field
After Hoffmann, 2005; Cheng, 2008

4. Previous Studies
Hoffmann, 2005

5. Seismic Surveys on Norne Field
4 Q-marine survey: July 2001, Aug. 2003, Aug. 2004, Aug. 2006
i-23 Section
Haven’t done the time to depth conversion. (avf for which software?? Maybe I can even do conversion in matlab?? Ask Tapan tomorrow). Petrel uses negative time value.
Observed relative impedance change ( )
layer 13

6. Objective of the Study
Understand the impact of calcite-cemented concretion on time lapse seismic modeling
Identify the influential parameters in 4D seismic modeling using GSA method

7. Design of Parameter Space
Group important parameters from previous studies together:
LHS (combinations of 8 parameters)
Fault transmissibility
Calcite proportions
Calcite distribution
Relative perm residual saturations
Clay content
Cement fraction
Coordination number
Fluid mixing
Reservoir Simulation
Hoffmann, 2005
Rock Physics Modeling
Suman, 2012

8. Parameters Varied in Calcite Modeling
Calcite proportion: triangular distribution [5% , 25%]
Calcite vertical probability curve
No trend
Scenario1: distributed among few specific layers
Scenario2: distributed among more layers
Scenario3
Layer index
Mention the scale issue

9. Representative Realizations
Calcite concretion
Modeled as zero transmissibility
Model A (21.5%,Scenario 3)
Model B (22.7%,Scenario 1)
46*112*22 cells

10. Calcite Cementation’s Impact on Drainage Pattern
i-23 section of calcite model A
Sw at year 2001
Sw at year 2003
Sw at year 2006
i-23 section of calcite model B
Sw at year 2001
Sw at year 2003
Sw at year 2006

11. Pattern Correlation between ΔSw and ΔAI
i-23 section of calcite model 1
ΔSw,
ΔAI,
i-23 section of calcite model 10
ΔSw,
ΔAI,

12. Pattern Correlation between ΔSw and ΔAI
Layer 11 of calcite model 1
ΔSw, ,layer 12
ΔAI, ,layer 12
ΔSw, ,layer 12
ΔAI, layer 12
Layer 11 of calcite model 10

13. Which distance measure?
GSA on ΔAI
Model responses
Distance Map
ΔAI, ,model 1 ?
Which distance measure? …
GSA
ΔAI, ,model 10 …

14. Distance Measure (An illustrative example)3
Distance Measure (An illustrative example)
ΔAI, ,model 1
Reference
Human judgment:
A>D>C>B 4 A B … C D
ΔAI, ,model 10

15. Manhattan distance Manhattan: local variation (location, magnitudes) √
global pattern X
Manhattan is not sensitive to the global pattern, (3 closer than 2, & 2 closer than 5), but sensitive to the location where it happens (local variations), and sensitive to the magnitudes.
Distance to reference case: B>A>D>C

16. Distance of cluster-based histogram of patterns (CHP)
CHP: global pattern √
local variation X
CHP detects global pattern (5 is closer to 2 because 5 has the exact same pattern), not sensitive to location where the changes happened, and also not very sensitive to the magnitudes of the changes ( 2 should be closer to 1 than 5)
Distance to reference case: C>D>A>B

17. Manhattan distance + CHP distance
D= D1/││D1││∞ + D2/││D2││∞
D1: Manhattan distance
D2: CHP distance
Match to reference case: A>D>C>B

18. GSA Results using Manhattan Distance
Fluid mixing:
Upper and lower bound of mixed fluid bulk moduli
Coordination number:
Stiffness of the rock
Clay content:
Moduli of the mineral

19. GSA Results using CHP Distance
Calcite proportions and VPC became sensitive in global patterns of ΔAI

20. Conclusion & Future Work
P(Model, Interpretation|Data)= ∑P(M|Interpretation,D)P(Interpretation|D)
Manhattan distance captures local variation; while CHP distance captures global pattern
Local variation of ΔAI is sensitive to rock physics parameters; Global pattern of ΔAI is sensitive to the calcite model scenarios
Future PhD work: Bayesian calcite model scenarios rejection for global pattern matching; Rock physics model tuning for local history matching