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Micro-CT analysis of porous rocks and transport prediction

Micro-CT analysis of porous rocks and transport prediction. Hu Dong and Martin Blunt Department of Earth Science and Engineering Imperial College London. To generate the Pore Network, we need …. The rock samples (11 samples) Image acquisition and image processing

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Micro-CT analysis of porous rocks and transport prediction

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  1. Micro-CT analysis of porous rocks and transport prediction Hu Dong and Martin Blunt Department of Earth Science and Engineering Imperial College London

  2. To generate the Pore Network, we need … • The rock samples (11 samples) • Image acquisition and image processing • Image analysis based on maximal ball algorithm • Results’ verification (compare with experiments’ results, LBM simulation)

  3. Samples we have

  4. Image acquisition • MicroCT scanner

  5. Image acquisition • Specimen preparation

  6. Image acquisition Digital Detector 16 Bit Resolution 512 x 512 (or Virtual 1024 x 512) Pixel Size 0.4 mm x 0.4 mm X-Ray Tube 770 mm

  7. Image processing • Segmenting Due to the memory size limitation and some side effects of the image itself, we currently use a 1283 cubic image for analysis.

  8. Image processing • Thresholding The original image from scanning is gray scale. The thresholded image is only represented by 0 (void) and 1(grain).

  9. Image processing • Eliminate the small holes and small grains in the image.

  10. Fontainebleau Sandstone resolution: 7.5 µm µ-CT images …

  11. Berea Sandstone resolution: 10 µm µ-CT images …

  12. Saudi Aramco SAMPLE1 resolution: 8.683 µm µ-CT images …

  13. Shell CARBONATE1 resolution: 5.345 µm µ-CT images …

  14. Saudi Aramco SAMPLE4 resolution: 8.96 µm µ-CT images …

  15. Saudi Aramco SAMPLE3 resolution: 9.1 µm µ-CT images …

  16. Saudi Aramco SAMPLE2 resolution: 11.497 µm µ-CT images …

  17. Network Extraction • Maximal ball algorithm (SPE84296 - Silin and Patzek) • Maximal ball In the 3D image, from a voxel (voxel [i, j, k]=0) in the void space, the radius is increase by one step until the ball hits a solid phase voxel(1). We call the ball a maximal ball at voxel [i, j,k].

  18. Network Extraction • Building the hierarchy After finding all the maximal balls, we compare them to build the hierarchy. If two balls are overlapped, the bigger one is the smaller’s master, and recognize the smaller a slave. If a ball has no master, it is a supermaster and defined as the pore body; if a ball has no slaves, it is a superslave and gives information for minimum radius of the throat.

  19. Network Extraction Maximal balls superimposed on MicroCT images. These represent the pores.

  20. Network Extraction • To build the skeleton of pore network: Finished work: • Find all the effective Maximal Balls to fill in the void space in the image and build the hierarchy. • Calculate the distribution of pore size and the co-ordination number. Ongoing work: Configure the throats to connect the pores and get the throat size distribution

  21. Sample case We did a test on a sandstone SAMPLE1. The core-plug we used is 38mm in diameter and 26.5 mm in length. A cylinder drilled from the sandstone that is 8 mm in diameter has been scanned to get the 3D image and a set of processing and analysis has been done to the image. The image we used for simulation is 1283 voxels which represents a piece of rock of 1.13 mm3.

  22. Coordination number distribution

  23. Pore size distribution

  24. Combination to Berea network • Our network code at present does not output a full network. To predict relative permeability we took a network based on Berea sandstone and adjusted the pore size distribution to match that measured on the image. We preserved the spatial locations and rank order of pore size. The coordination number 4.2 of the network is close to that estimated from the image (5.2).

  25. Result comparison

  26. Predicted oil flood relative permeability (primary drainage)

  27. Predicted water flood relative permeability

  28. Future Work • Experiments • Traditional experiments; • MicroCT scanning (optimize the parameters during the scanning for correction and calibration to get high quality images); • Sample preparation (drill the sample into proper size and shape to meet the requirement of scanning) • 3D image library • Network generation • Identify the throats correctly; • Use the skeleton from a thinning algorithm [W.B. Lindquist] as a quality control.

  29. Acknowledgement • Supervisor: Martin J. Blunt • Members of Pore-Scale Modelling Group, Mariela Araujo-Fresky, Carlos A. Grattoni, Stefano Favretto, Hiroshi Okabe • Members of Imperial College Consortium on Pore-Scale Modelling (BHP, ENI, JOGMEC, Saudi Aramco, Schlumberger, Shell, Statoil, Total )

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