1. Combining statistical rock physics and sedimentology to reduce uncertainty in seismic reservoir characterization
Per Åge Avseth
Norsk Hydro Research Centre
FORCE seminar, June , Stavanger

2. Seismic reservoir characterization - Problem statements
Where are the good quality sands?
Where is the oil?
What are the uncertainties?

3. Outline Link rock physics to sedimentary facies
Well log interpretation, rock physics modeling, cross-plot analysis
Quantify the uncertainty in seismic response related to facies and fluid variability
Facies classification, MC-simulation, seismic modeling
Predict most likely facies and fluids and their occurrence probabilities from seismic amplitudes
3-D AVO inversion, seismic reservoir mapping

4. Why facies? Well Vertical stratigraphy Depositional system
Clean sand
Shale
Shaly sand
Facies = characteristic sedimentary units that occur in predictable patterns/geometries within a depositional system.
Walther’s law: Facies that are conformably stacked on top of each other, can also be found laterally to each other.

5. I II III IV V VI FACIES I: FACIES II: FACIES III: FACIES IV: FACIES V:
Gravels and conglomerates
FACIES II:
Thick-bedded sandstone
FACIES III:
Interbedded sandstone-shale
FACIES IV:
Silty shale and
silt-laminated shale
FACIES V:
Pure shale
FACIES VI:
Chaotic deposits I II
III IV V VI

6. Identification of seismic lithofacies from well logs (= training data)

7. Seismic properties

8. Seismic lithofacies classification (quadratic discriminant analysis)
Well # 1
Well # 2
Well # 3
2100 V IV
III
2200
Depth (m)
IIc
IIb
IIa
2300

9. Validation analysis of training data
IIa
IIb
IIc
III IV V
Success-rate (%)
100 50
Vp and GR
Vp only
GR only
45 %
60 %
82 %

10. Cumulative distribution functions of seismic properties (brine facies)
1.0
IIb V
Cum. Frequency
0.5
III IV
IIa
IIc
5.0
5.5
6.0
6.5
7.0
Acoustic Impedance
1.0
IIa
IIb
Cum. Frequency
0.5
IIc
III IV V
1.8
2.0
2.2
2.4
2.6
2.8
Vp/Vs-ratio

11. Cumulative distribution functions: Expanding data base to include oil facies
0.5
1.0
1.0
IIb
IIc
Cum. Frequency
Cum. Frequency
0.5
IIa
IIb-oil
IIc-oil
IIa-oil
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Acoustic Impedance
1.0
IIa-oil
IIb
Cum. Frequency
Cum. Frequency
0.5
IIa
IIc
IIb-oil
IIc-oil
1.6
1.8
2.0
2.2
2.4
2.6
Vp/Vs-ratio

12. Monte-Carlo simulation and AVO probabilities

13. Bivariate pdfs: R(0) vs. G

14. Bivariate pdfs: R(0) vs. G (Grouped facies)

15. 3-D seismic topography of top reservoir horizon (travel-time)
Turbidite system outline
Feeder-channel
Lobe-structure
100ms
1km

16. R(0) at Top Reservoir horizon (3D seismic topography)
Relatively high R(0)
(blue)
Relatively low R(0)
(yellow)

17. G at Top Reservoir horizon (3D seismic topography)
Relatively large negative
gradient G (yellow)
Relatively small
gradient (blue)

18. Seismic lithofacies prediction (3D seismic topography)
Interbedded
sand-shales
Oil sands
Brine sands
Shale

19. Seismic lithofacies prediction (map-view)

20. Facies probability maps

21. Limitations of methodology
Tuning and thin-bed effects
Noise and processing effects
Lateral velocity trends in overburden
Cap-rock assumptions
Representative statistics?
Seismic interpretation of top reservoir

22. Conclusions
ROCK PHYSICS INNOVATIONS: The link between rock physics and sedimentology gives better understanding of depositional systems from seismic amplitudes.
INTEGRATED METHODOLOGY: Based on statistical rock physics and facies classification we can create probability maps of facies and pore fluids from seismic inversion results.
BUSINESS IMPLICATIONS: Facies probability maps can be used as inputs for various decision and risk analyses in hydrocarbon exploration and reservoir development.

23. The road ahead: Extended integration
GEOSTATISTICS: Expand on one-point uncertainty analysis and include spatial statistics (Eidsvik et al., 2001)
RESERVOIR MODELING: Apply the methodology to constrain reservoir modeling, flow simulation and production forecasting (Caers et al., 2001).
SEISMIC IMAGING: Include other uncertainties related to seismic processing, tuning effects, overburden, anisotropy, etc.

24. End quote
“The language of probability allows us to speak quantitatively about some situation which may be highly variable, but which does have some consistent average behavior....
Our most precise description of nature must be in terms of probabilities.”
- RICHARD FEYNMAN