Predictive Pore-Scale Modelling Matching SCAL Experiments using Realistic Networks Per Valvatne

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

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

5. Primary Drainage Displacement Process Per Valvatne, Imperial College

6. Wettability Alteration Drainage Wettability Alteration Water Flooding Per Valvatne, Imperial College

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 Per Valvatne, Imperial College

8. 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 Per Valvatne, Imperial College

9. Oil-Wet • All elements might become oil-wet • Low residual oil saturation due to oil escaping through layers Per Valvatne, Imperial College

10. 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 Per Valvatne, Imperial College

11. 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? Per Valvatne, Imperial College

12. Random Mixed-Wetting • Water filled elements poorly connected through network • Very low water relative permeability • High residual oil saturation Per Valvatne, Imperial College

13. Spatially Correlated Mixed-Wetting • Water filled elements well connected through network • Relative permeability “looks” correct • Same oil-wet fraction as in last example Per Valvatne, Imperial College

14. Predictive Pore Scale Modelling • Create the network from a geologically reconstructed Berea sandstone (in cooperation with Statoil) Per Valvatne, Imperial College

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 Per Valvatne, Imperial College

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 Per Valvatne, Imperial College

17. Sandstone Example • Absolute permeability well predicted • 669 mD predicted versus 750 mD found experimentally • Steady-state waterflood relative permeability available • Mixed-wet characteristics Per Valvatne, Imperial College

18. Sandstone Example Per Valvatne, Imperial College

19. 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 Per Valvatne, Imperial College

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 Per Valvatne, Imperial College

21. Relative Permeability Prediction (24) • Experimental primary drainage data not available • Water flooding relative permeability, absolute permeability (1.37 vs. 0.92 mD) and USBM Index (1.05 vs. 1.55) reasonably well matched Per Valvatne, Imperial College

22. 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 Per Valvatne, Imperial College

23. 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