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

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

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

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)

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

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.

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

Simulation of NMR response in Micro-CT images 1 z y x convert to binary z < 0 0 < z < Length z > Length Reference voxel X is surrounded by 26 neighbouring voxels

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

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

Experimental Data: Sandpacks Grain Size Distribution LV60 F42 Porosity: 0.37 0.33 Permeability (D): 32.2 41.8 Density (kg/m3): 2630 2635 Sand Plugs: 3cm (diameter) 9cm (length) Fluid: Brine Density: 1035 (kg/m3): Viscosity: 1.04cp 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

Experimental Data: NMR (Sandpacks) Magnetization Decay T2 - Distribution F42 LV60 Mean T2: 553ms Mean T2: 733ms

Simulation Results vs. Experimental Data LV60A LV60B Voxel Dimension: 3003 Image size: 3mm3 Comparison of Mean T2 Micro CT LV60A Experimental Network LV60B 553ms 578ms 577ms 482ms 509ms Sandpacks

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

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

Simulation Results vs. Experimental Data Comparison of Experimental Pc with Network Pc NMR simulation of Bentheimer network Network: Pores: 12,349 Throats: 26,146 Simulation Parameters Diffusion Coefficient: 1.9x10-9m2/s (Vinegar, 1995) Bulk Relaxivity: 2.84s (Vinegar, 1995) Surface Relaxivity: 9.3μm/s (Liaw et al., 1996)

Simulation results vs Experimental data Results Summary

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

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