Integration of Seismic

1. Integration of Seismic Data and
Uncertainties in the Facies Model
P. Nivlet*, S. Ng, M.A. Hetle, K. Børset, A.B. Rustad (Statoil ASA),
P. Dahle, R. Hauge & O. Kolbjørnsen (Norwegian Computing Center)
1- Classification: Internal 2010-06-10

2. Motivation: 3D reservoir modelling
Reservoir
simulations
Production data
3D reservoir model
2- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010

3. The Snorre field
• Location: Blocks 34/4 and 34/7 in the
Tampen area, in the northern part of the
North Sea (191 km2)
• Production start: 1992
• Production (2009): ~180,000 bbl/day
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4. Motivations: Data integration
seismic amplitudes
(angle-stacks)
Well log data 3D reservoir model
Structure, stratigraphy
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5. Challenges in integrating the data
• Multi-scale issue
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6. Challenges in integrating the data
Shale
2.0
Vp/Vs
Sand
1.7
6,000 10,000
AI (g/cm3.m/s)
• Non-unique relationship between seismic amplitudes and geology
• A multivariate problem
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7. The data uncertainty challenge
• Random noise
• Acquisition / Processing footprint
• Angle Misalignments
• Imperfect physical model
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8. Geological setting
1,000 m
•Reservoir depth: 2-2.7 km
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9. Traditional workflow
Reservoir grid
(depth)
geometry
Seismic attribute
Well facies+extracted
(depth)
seismic attribute
conditioning
integration
Facies model
9- 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010

10. Proposed workflow
Reservoir grid
(depth)
geometry
Seismic attribute
(depth) Well facies+extracted
seismic attribute
conditioning
integration
Facies model
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11. Workflow from inversion to facies prediction
Bayesian wavelet
extraction
Seismic
modelling
Vp
Seismic facies
analysis
Vs
Seismic (partial angle-stacks)
Inversion
ρ
34/4-1
34/4-
m BCU
=
OWCLunde Increasing
probability
of shale
SN ML
SN LL Decreasing
probability
of shale
Lomvi Fm
m mBG mS mHF Facies probability
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12. Geostatistical seismic inversion
• 1D modelling of seismic amplitudes (Aki&Richards’ model): linear in
m=(log(vp), log(vs), log))
d  Gm  n
• Normal distribution of elastic properties m
mm|d = mBG+mG*(GmG* + e )-1(d - GmBG)
m|d = m - mG*(GmG* + e )-1G m
• Data (e) stationary uncertainties estimated from analysis of amplitudes
• Prior (m) stationary uncertainties estimated from well log analysis
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13. Advantages/limitations of the technique
Stationary uncertainty model:
Lateral correlations
- Global matrix
- Different stratigraphy settings - Lateral correlations
- Grid built from max. 2 horizons - Vertical correlations
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14. Inversion result: Elastic properties
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15. Impact on elastic parameter uncertainties
Seismic bandwidth (Near)
0 10 20 30
AI
0
Vp
Rho
-50
SI
Vs
Vp/Vs
0 20 40 60
Frequency (Hz)
Prior Posterior uncertainty Prior Posterior uncertainty
variation (%) variation AI (%)
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16. Inversion results QC
Band-pass filtered
AI SI Rhob
100 ms
Well
Inversion
Multivariate correlation (RV) between band-
pass well-logs and inversion results
 35% of wells RV > 0.8
 33% of wells 0.8 > RV > 0.7
 32% of wells RV < 0.7
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17. Workflow from inversion to facies prediction
Bayesian wavelet
extraction
Seismic
modelling
Vp
Seismic facies
analysis
Vs
Seismic (partial angle-stacks)
Inversion
ρ
34/4-1
34/4-
m BCU
=
OWCLunde Increasing
probability
of shale
SN ML
SN LL Decreasing
probability
of shale
Lomvi Fm
m mBG mS mHF Facies probability
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18. Supervised seismic facies analysis
Kernel
estimator
p(m | Sand)
p(Sand | m)
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19. Supervised seismic facies analysis
Different resolution scales
Raw Well logs
Filtered well logs
Inversion results at well position
Inversion filtered well logs
μ m|d = μm+(I- Σm/dΣm-1)(m – μm) + e*
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20. Cross plots: Inversion filtered well logs
1
2.0 2.0
Shale
Vp/Vs
Vp/Vs
Sand
1.7 1.7
0
6,000 10,000 6,000 10,000
AI (g/cm3 m/s) AI (g/cm3 m/s)
Inversion frequency filtered Predicted SAND probability
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21. Seismic facies analysis: Sand probability results
Sand
probability
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22. Inversion results QC: Finding optimal well
position
Confidence index (khi2):
Vertical sand proportion from well
100
ms compared with seismic sand probability
Seismic sand probability section
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23. Facies probability QC
 31% of wells: Good confidence
 61% of wells: Medium
 8% of wells: Bad confidence
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24. Inversion results QC
Potential factors impacting mismatch
Stratigraphic level ++
Position with respect to OWC +
Presence of faults +
Average shale proportion +
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25. 3D confidence index
• Measurement of prediction
• Weighting function in facies
modelling
1
Well Confidence
Inversion result [0,1]
0
25 - 72nd EAGE Conference and Exhibition – Barcelona, June 14th-17th 2010

26. Proposed workflow
Reservoir grid
(depth)
geometry
Well facies+extracted
seismic attribute
conditioning
integration
Facies model
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27. Snorre: Average proportion of channel
Average map estimated from 8 realizations
1 1
0 0
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28. Concluding remarks
• Integrated workflow from seismic inversion to consistent seismic constrained
facies modelling
• Fast geostatistical inversion approach and facies prediction
• Consistent resolution between inversion results and facies probabilities gives
realistic predictions and facies models
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29. Concluding remarks: Further work
• How to refine the upscaling of elastic parameters from well log to seismic
scales? How to have a more local approach?
• Constraining observed 4D signals by using predicted facies sand probability
(Ayzenberg and Theune, “Stratigraphically constrained seismic 4D inversion”
M017, Room 127/128, Wednesday, 9h30)
• Flow simulations of constrained facies models and history matching with 4D for
more predictive production prognoses
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30. Acknowledgements
Thanks to Statoil, Norwegian Computing Center and the
Snorre partners
Petoro, ExxonMobil Norge, Idemitsu Petroleum, RWE Dea
Norge, Total E&P Norge and Amerada Hess Norge
for discussions and permission to publish this work.
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31. Thank you
Integration of Seismic Data and Uncertainties in the
Facies Model
Philippe Nivlet
Principal Geophysicist –Petek Tyrihans
pniv@statoil.com, tel: +47 958 16 589
www.statoil.com
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