Combining statistical rock physics and sedimentology to reduce uncertainty in seismic reservoir characterization Per Åge Avseth Norsk Hydro Research Centre FORCE seminar, June 5 - 2001, Stavanger

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

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

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.

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

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

Seismic properties

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

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 %

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

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

Monte-Carlo simulation and AVO probabilities

Bivariate pdfs: R(0) vs. G

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

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

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

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

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

Seismic lithofacies prediction (map-view)

Facies probability maps

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

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.

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.

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