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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

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
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)
image acquisition
Image acquisition
  • MicroCT scanner
image acquisition5
Image acquisition
  • Specimen preparation
image acquisition6
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

image processing
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.

image processing8
Image processing
  • Thresholding

The original image from scanning is gray scale.

The thresholded image is only represented by 0 (void) and 1(grain).

image processing9
Image processing
  • Eliminate the small holes and small grains in the image.
ct images13
Shell CARBONATE1

resolution: 5.345 µm

µ-CT images …
network extraction
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].

network extraction18
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.

network extraction19
Network Extraction

Maximal balls superimposed on MicroCT images. These represent the pores.

network extraction20
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

sample case
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.

combination to berea network
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).
future work
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.
acknowledgement
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 )