1. Predicting NMR Response in Micro-CT images and Networks
Olumide Talabi
Supervisor: Prof Martin Blunt

2. OUTLINE Motivation Modelling NMR Response
Simulation of NMR response in Micro-CT images
Simulation of NMR response in Networks
Comparison of simulation results with experimental data
Conclusions

3. Motivation
From pore scale modelling; relative permeability, capillary pressures, etc, have been successfully predicted.
We combine predictions of NMR, capillary pressure, resistivity and relative permeability to pin down wettability

4. Modelling NMR Response: Basics
NMR is a phenomenon that occurs when the nuclei of certain atoms are immersed in a static magnetic field and then exposed to a second oscillating magnetic field.
Relaxation Mechanisms:
Bulk Relaxation:
Surface Relaxation:
Diffusive Relaxation:
Relaxation mechanisms above all act in parallel and as such their rates add up.
(transverse relaxation)

5. Modelling NMR Response: Surface Relaxation
Analytical solution (sphere):
(Crank, 1956)
Random walk solution:
(Ramakrishnan et al. 1998).
Killing probability;
(Bergman et al. 1995)

6. Modelling NMR Response: Validation
Comparison:
Analytical Solution (sphere)
Random Walk Solution
D - 2.5x10-9m2/s
r - 5μm,
- 20μm/s.
- 10,000
Fig 1: Comparison of the magnetization decay for a spherical pore
obtained by random walk solution with the analytical solution.

7. Modelling NMR Response: Bulk relaxation
Surface + Bulk Relaxations
Pore Size
From Surface Relaxation
Pore Size Distributions
Inversion

8. Simulation of NMR response in Micro-CT images1 z y x
convert to binary
z < < z < Length z > Length
Reference voxel X is surrounded by 26 neighbouring voxels

9. Simulation of NMR response in Networks
Micro-CT
2mm
LV60
Maximal Ball
F42
Network elements, triangular, circular or square
cross-section have the same shape factor

10. Simulation of NMR response in Networks
START
Pore 1
Pore 2
Throat
Place N walkers randomly in network
Spherical 3D displacement of walkers
For all walkers; i = 1,2,3,4………(N - Nd)
Walker enters one of
connected throats.
yes
is z <0 or z>L no
walker in a throat?
yes no no
contact with
any surface? no
is z <0 or z>L
yes
yes no
is walker killed?
Walker enters new pore
yes
Generate new x, y values
return to previous position
retain x, y and z values
Nd = Nd + 1

11. Experimental Data: Sandpacks
Grain Size Distribution
LV F42
Porosity:
Permeability (D):
Density (kg/m3):
Sand Plugs: cm (diameter) 9cm (length)
Fluid: Brine
Density: (kg/m3):
Viscosity: cp
2-D Sections of Micro – CT Images of Sandpacks
Simulation Parameters
Diffusion Coefficient:
Vinegar, 1995
Bulk Relaxivity:
LV60A
LV60B
LV60C
Surface Relaxivity:
41μm/s
900um
F42A
F42B
F42C

12. Experimental Data: NMR (Sandpacks)
Magnetization Decay
T2 - Distribution
F42
LV60
Mean T2: ms
Mean T2: ms

13. Simulation Results vs. Experimental Data
LV60A
LV60B
Voxel Dimension: Image size: mm3
Comparison of Mean T2
Micro CT
LV60A
Experimental
Network
LV60B
553ms
578ms
577ms
482ms
509ms
Sandpacks

14. Simulation Results vs. Experimental Data
LV60C
F42A
Micro CT
LV60C
Experimental
Network
F42A
553ms
733ms
566ms
754ms
683ms
487ms
Sandpacks
Comparison of Mean T2

15. Simulation Results vs. Experimental Data
F42B
F42C
Micro CT
F42B
Experimental
Network
F42C
733ms
745ms
703ms
680ms
679ms
Sandpacks
Comparison of Mean T2

16. Simulation Results vs. Experimental Data
Comparison of Experimental Pc with Network Pc
NMR simulation of Bentheimer network
Network:
Pores: ,349
Throats: 26,146
Simulation Parameters
Diffusion Coefficient: x10-9m2/s (Vinegar, 1995)
Bulk Relaxivity: s (Vinegar, 1995)
Surface Relaxivity:
9.3μm/s
(Liaw et al., 1996)

17. Simulation results vs Experimental data
Results Summary

18. Conclusions and future work
Successful comparison of NMR simulation results with experimental data
Simulation results of micro CT images and extracted networks are consistent with a good degree of accuracy.
Validation of the method used in simulating NMR response in networks.
The slight differences observed between simulation results and experimental data is as a result of the information that is lost while processing the micro CT images and extracting networks
Future Work
Simulation of NMR response of two-phase fluid.
Wettability determination from NMR response
Combination of relative permeability, capillary pressures, electrical resistivity and NMR response to determine wettability.
Comparisons with benchmarked experimental data

19. Predicting NMR Response in Micro-CT images and Networks
Olumide Talabi
Supervisor: Prof Martin Blunt