Porosity Study of a Rock Sample Fire with Avizo Case Study

Case Study
Porosity Study of a Rock Sample
with Avizo® Fire
Abstract
The purpose of this document is to describe different ways of studying a rock sample with Avizo® Fire
software1.
The rock sample studied comes from asphalt. Data was acquired with a Nanotom scanner (advanced
3D X-ray micro Computed Tomography scanner) from Phoenix X-ray, with courtesy of Delft University of
Technology.
Avizo® Fire software can be applied the multi-step workflow for porosity analysis of rock samples. From
data loading to pores characterization, step-by-step methods can be applied, and further adjustments can
be done to refine results.
More generally, Avizo Fire enables 3D visualization and analysis of rocks, core samples, geomaterials,
porous and other multi-phase materials at any scale, from any source (micro-CT, FIB/SEM, etc.), for statistical
measurements, characterization or modeling.
1 This document includes previews of Avizo 6.2. However, the process described can also be achieved with Avizo 6.1
1. Data loading
Loading the data into Avizo Fire is easy. The CT-scan data consists of a stack of 2D images that
are read as a volume data set.
Fast volume rendering gives the user a quick overall perspective of the data set, while
instantaneous slice rendering provides a closer insight with accurate depiction of the porosity
in the rock sample.
2. Filtering
Three-dimensional image filtering is usually required before one can accurately segment.
Our goal is to accurately delineate the pore space inside the rock, which is made up of low density
values. It is critical to accurately preserve the boundaries between pores and rock because
mislabeling on the boundary regions could lead to over- or underestimating the pore space.
Looking at a portion of a slice, we notice that noise is present in both the pore space and the
surrounding rock material.
Before segmenting the pores, we can easily smooth out the noise without spoiling the pore-torock boundaries, by applying the edge-preserving smoothing filter in Avizo.
This is a powerful smoothing filter that models the physical process of diffusion without
smoothing the high gradient areas i.e. fast transitions between low and high densities.
In order to quickly determine the appropriate parameters, we use the FilteredObliqueSlice
module, which applies the filter to a single slice. This gives the user interactive response so he
or she can adjust the parameters and review the results, and the user can change the slice to
see the applied filter to other regions of the 3D volume.
Once parameters are correctly set we apply the filter to the whole data set.
3. Porosity segmentation
Avizo Fire offers an extensive set of tools for segmentation. We use a script-module Labeling/
SegWatershedOnGradient which enables fast and accurate pores and phases segmentation
using advanced algorithms.
A comprehensive description of this technique will soon be available in Avizo 6.2 under:
Example 6: segmentation of rocks using watershed and gradient thresholding.
As explained in the tutorial, we can’t just threshold pores, because some pore intensity values are
“melted” in rocks. Combined with high gradients masking, the performed watershed algorithm
provides fast and accurate porosity segmentation. In pore-rock boundary areas, the segmented
pore boundary accurately indicates the physical location of the separation i.e. where gradient
intensity is the highest.
We now have a labeled volume and an associated porosity value.
4. First analysis
By applying Avizo Fire’s interactive analysis tool to the segmented pore space, we instantaneously
get pore volumes and characteristics. Avizo Fire can provide statistics, plot distribution and
individual measures for the different regions, allowing the user to highlight, select and filter
objects corresponding to measures.
We notice that isolated pores are dispersed throughout the volume. As those pores have no
effect on fluid flow, we can discard them and focus on the connected pore space.
5. Advanced connectivity analysis
Our next goal is to try and define if pore connectivity is higher along an orientation through
the rock sample. This information can give useful indications on the fluid flow behavior in the
porous media by either giving the main fluid direction, or showing that there is no heterogeneity
in porosity.
By using the axis-connectivity module of Avizo 6.2, we can compute the pore connectivity along
any of the three orthogonal axes.
For this study case, axis-connectivity gives almost the same ratio along the X, Y or Z axis,
suggesting that the rock sample has a homogeneous porosity.
6. Presentation and reporting
Avizo helps you share, publish and present results, compute statistical reports, generate movies
and make live demos of your research findings.
7. More tools for rock porosity study
A number of additional tools are available to assist rock porosity analysis, such as morphometric
parameters and propagation calculation.
For accurate modeling of fluid behavior in rocks, users can apply computational tools of porosity
and permeability which rely on extraction of geometric models determined by Avizo, such as
the pore network skeleton or extraction of boundary surfaces and tetrahedral grids suitable for
simulation.
Avizo Fire is therefore the perfect platform for rock samples analysis and virtual experiment on
rock behavior. It can easily integrate your existing tools, be adapted to your needs and fit in
your specific workflow.
Inertia moment ellipsoids
About Avizo
Avizo® software is a powerful, multifaceted
tool for visualizing, manipulating, and
understanding scientific and industrial
data. Wherever three-dimensional data sets
need to be processed, in materials science,
geosciences, environmental science or
engineering applications, Avizo offers
abundant state-of-the-art features within an
intuitive workflow and easy-to-use graphical
user interface.
www.avizo3d.com
About VSG
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innovation to highly complex visualization
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industries.
We help our customers implement and
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Flow simulation results
Propagation in a plug
Skeletonization
Image courtesy: IFP
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© 2010 VSG, Visualization Sciences Group