1. Predictive Pore-Scale Modelling
Matching SCAL Experiments using Realistic Networks
Per Valvatne
Imperial College, London

2. Per Valvatne, Imperial College
Presentation Outline
Brief overview of pore-scale modelling
The importance of spatially correlated wettability when predicting mixed-wet data
Using non-specific networks to to predict experimental data for other rock types
Successfully predict experimental data for water-wet and mixed-wet conditions
Per Valvatne, Imperial College

3. What is Pore Scale Modelling
Describe the void space of
a rock as a network of
pores and throats
Rules determine fluid configuration and transport through network
Macroscopic properties like capillary pressure and relative permeability can be estimated
Per Valvatne, Imperial College

4. Primary Drainage Displacement Process
Oil invasion into a water-filled water-wet porous medium
Increase pressure in oil phase, keeping water pressure constant
Some water will still remain in the corners of pores with irregular shapes
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5. Primary Drainage Displacement Process
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6. Wettability Alteration
Drainage
Wettability Alteration
Water Flooding
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7. Water Flooding Displacement Events
Piston type displacement
Water in the body displaces oil in a neighbouring element
Snap off
When water in the corners no longer has a stable configuration, the element fills.
Spontaneous
Forced
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8. Per Valvatne, Imperial College
Water-Wet
Following primary drainage all elements contacted by oil have their wettability altered
The elements might remain water-wet
Considerable amount of trapped oil due to water snap-off
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9. Per Valvatne, Imperial College
Oil-Wet
All elements might become oil-wet
Low residual oil saturation due to oil escaping through layers
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10. Per Valvatne, Imperial College
Mixed-Wet
What if initial water saturation is higher than the residual?
Only pores and throats contacted by oil become oil-wet
Network exhibits mixed-wet characteristics
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11. Per Valvatne, Imperial College
Mixed-Wet
Rocks often exhibit mixed wet characteristics even if all elements have been contacted by oil
How does wettability vary spatially on the pore scale?
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12. Per Valvatne, Imperial College
Random Mixed-Wetting
Water filled elements poorly connected through network
Very low water relative permeability
High residual oil saturation
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13. Spatially Correlated Mixed-Wetting
Water filled elements well connected through network
Relative permeability “looks” correct
Same oil-wet fraction as in last example
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14. Predictive Pore Scale Modelling
Create the network from a geologically reconstructed Berea sandstone (in cooperation with Statoil)
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15. Water-Wet Experimental Data
Berea sandstone cores
0 degrees receding contact angle
50-80° advancing contact angle (uniform distribution)
Compared to experimental data by Oak
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16. Matching SCAL Experiments
Use existing realistic network for connectivity information
Pore locations, connection number, pore shapes etc.
Condition network to mercury injection data
Modify throat size distribution until match on capillary pressure curve
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17. Per Valvatne, Imperial College
Sandstone Example
Absolute permeability well predicted
669 mD predicted versus 750 mD found experimentally
Steady-state waterflood relative permeability available
Mixed-wet characteristics
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18. Per Valvatne, Imperial College
Sandstone Example
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19. Per Valvatne, Imperial College
Carbonate Example
Tight intergranular carbonate (2 samples matched)
1.7 mD predicted versus 1.4 mD found experimentally
Mixed-wet characteristics
Both aged and unaged results available
Sample 15
Sample 24
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20. Relative Permeability Prediction (15)
Both primary drainage and water flooding relative permeabilities were well predicted
USBM Index of 0.70 predicted versus 0.69 found experimentally
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21. Relative Permeability Prediction (24)
Experimental primary drainage data not available
Water flooding relative permeability, absolute permeability (1.37 vs mD) and USBM Index (1.05 vs. 1.55) reasonably well matched
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22. Per Valvatne, Imperial College
Conclusions
Relative permeability is sensitive to both the average network-scale wettability as well as it’s spatial distribution on the pore scale
Networks can be conditioned to successfully predict performance of a wide range of rock types
Successfully predicted relative permeability and recovery data for water-wet and mixed-wet cores
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23. Per Valvatne, Imperial College
Remaining Work
Using conditioning procedure to match more SCAL experiments
Would like to have more sandstone SCAL data
Sensitivities with respect to underlying connectivity information
Further investigation of wettability distribution
Is there a way to verify it’s distribution on the pore scale?
Hysteresis trends during secondary drainage and higher order water floods
Compare to experimental data
Per Valvatne, Imperial College