A Closer Look at Pore Geometry

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A Closer Look at Pore Geometry
A Closer Look at Pore Geometry
X-ray computed tomography has advanced the field of medicine for more than 30 years.
For nearly as long, it has also been a valuable tool for geoscientists. Improvements in
the technology are helping geoscientists uncover greater detail in the internal pore
structure of reservoir rock and achieve a better understanding of conditions that
affect production.
Andreas Kayser
Cambridge, England
Mark Knackstedt
The Australian National University
Canberra, Australia
Murtaza Ziauddin
Sugar Land, Texas, USA
For help in preparation of this article, thanks to
Veronique Barlet-Gouédard, Gabriel Marquette,
Olivier Porcherie and Gaetan Rimmelé, Clamart, France;
Bruno Goffé, Ecole Normale Supérieure, Paris; and
Rachel Wood, The University of Edinburgh, Scotland.
Inside Reality and iCenter are marks of Schlumberger.
Information gained through core analysis is
invaluable for predicting the producibility of a
reservoir pay zone. While other methods enable
petrophysicists to estimate grain size, bulk
volume, saturation, porosity and permeability of
formations, core samples often serve as the
benchmark against which other methods are
calibrated. However, notwithstanding several
hundred thousand feet of whole or slabbed core
residing in core libraries around the world, most
wells are not cored.
4
The wealth of information obtained from
cores comes at a price. Coring often increases rig
time, lowers penetration rates and increases the
risk of sticking the bottomhole assembly. At some
wells, hostile downhole or surface conditions
make coring too risky. In other cases, correlations are not sufficient to allow geologists to
accurately and confidently pick coring points.
Instead, many operators rely on sidewall cores
obtained through prospective pay zones, and may
compensate for lack of whole core data by
supplementing their usual logging program with
a wider range of measurements.
Oilfield Review
As oil companies try to drain aging reservoirs
more efficiently, engineers and geoscientists may
come to regret earlier decisions to forgo coring.
Once a well has been drilled through a pay zone,
it is too late to go back to obtain whole cores,
unless the well is sidetracked. However,
mineralogy, grain size, saturation, permeability,
porosity and other measures of rock fabric can
sometimes be determined without coring.
With improvements on the early medical CATscan technique developed in 1972, geoscientists
can take a series of fine, closely spaced X-ray
scans through a rock sample to obtain important
information about a reservoir.1 Using a
nondestructive technique called microcomputed
tomography, a focused X-ray beam creates
“virtual slices” that can be resolved to a scale of
microns, not just millimeters.2 These refinements
also allow the option of examining smaller
samples of rock; instead of depending on whole
cores for porosity and permeability measurements, geoscientists can now use formation
cuttings to estimate these properties.3 Although
many companies do not core their wells, they
usually employ the services of a mudlogger to
catch formation cuttings as they come over the
shale shaker. When they don’t have core,
geoscientists are finding that a sliver of rock can
be highly revealing.
This article reviews the development of X-ray
computed tomography (CT) and the ensuing
technology transfer from medical to oilfield
applications. We describe how the data can be
evaluated using immersive visualization techniques and discuss a range of oilfield applications
that may benefit from this technology. Finally, we
will see how this technology served researchers in
their evaluation of casing cement and well
stimulation treatments.
CT Scan Technology
Originally developed for medical use by Godfrey
Newbold Hounsfield in 1972, computed tomography uses X-ray scans to investigate internal
structures within a body, such as those of soft
tissue and bone.4 CT overcomes the problem of
superimposition exhibited in conventional X-ray
radiography when three-dimensional features
of internal organs are obscured by overlying
organs and tissues imaged on two-dimensional
X-ray film.
Rather than projecting X-rays through a
patient and onto a film plate, as with
conventional X-rays, the CT process takes a
different approach. The CT scanner uses a
rotating gantry to which an X-ray tube is
mounted opposite a detector array. The patient is
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placed in the center of the gantry, while the
opposing X-ray source and detectors rotate
around the patient. With the patient positioned
roughly in the middle of the source-receiver
plane, the rotating gantry allows a series of
closely spaced radiographic scans to be obtained
from multiple angles. These scans, or
radiographic projections, can then be processed
to obtain a 3D representation of the patient
(below).
CT radiographic projections are based on the
differential attenuation of X-rays caused by
density contrasts within a patient’s body. This
patient from this equation, attenuation is a
function of the energy of the X-ray as well as the
density and atomic number of the elements
through which the X-ray passes. The correlation
is fairly straightforward: lower-energy X-rays,
higher densities and higher atomic numbers
generally result in greater attenuation.5
Digital projection data are converted into a
computer-generated image using tomographicreconstruction algorithms to map the distribution of attenuation coefficients.6 This distribution
can be displayed in 2D slices, composed of points
that are shaded according to their attenuation
> Thoracic CAT scan. Manipulating color and opacity values of different
tissues provides physicians with an unobstructed view of a patient’s lungs
and skeletal system. (Image courtesy of Ajay Limaye, VizLab, The Australian
National University.)
attenuation represents a decrease in energy as
X-rays pass through various parts of the body.
Some tissues scatter or absorb X-rays better than
others: thick tissue absorbs more X-rays than
thin; bone absorbs more X-rays than soft tissue,
while fat, muscle or organs allow more X-rays to
pass through to the detectors. Removing the
values (see “Moving from 2D Points to 3D
Volumes,” page 6). Thus, in hospital scans, bone
would typically be assigned a light color to
correspond with its comparatively high
attenuation value, while air-filled lung tissue
might be assigned a darker color corresponding
to low attenuation values.
1. In the medical field, the computerized axial
tomography (CAT) scan is sometimes also called
computer-assisted tomography, and is synonymous
with computed tomography.
2. A micron, or micrometer, is equal to one millionth of a
meter, or more commonly, one thousandth of a millimeter.
It is abbreviated as µ, µm or mc. In English measure, a
micron equals 3.937 x 10-5 in.
3. Siddiqui S, Grader AS, Touati M, Loermans AM and
Funk JJ: “Techniques for Extracting Reliable Density
and Porosity Data from Cuttings,” paper SPE 96918,
presented at the SPE Annual Technical Conference and
Exhibition, Dallas, October 9–12, 2005.
Bauget F, Arns CH, Saadatfar M, Sheppard AP, Sok RM,
Turner ML, Pinczewski WV and Knackstedt MA: “What
is the Characteristic Length Scale for Permeability?
Direct Analysis from Microtomographic Data,” paper
SPE 95950, presented at the SPE Annual Technical
Conference and Exhibition, Dallas, October 9–12, 2005.
4. Hounsfield GN: “A Method of and Apparatus for
Examination of a Body by Radiation such as X- or
Gamma Radiation,” British Patent No. 1,283,915
(August 2, 1972).
5. For more on X-ray CT: Publication Services
Department of the ODP Science Operator. http://wwwodp.tamu.edu/publications/185_SR/005/005_5.htm
(accessed January 27, 2006).
6. Feldkamp LA, Davis LC and Kress JW: “Practical
Cone-Beam Algorithm,” Journal of the Optical Society
of America A1, no. 6 (June 1984): 612–619.
5
Moving from 2D Points to 3D Volumes
6
> Pixel resolution. The sharpness and clarity of an image are affected by pixel count and the size
of the pixels. To increase the number of pixels within a fixed space, pixel size must be reduced.
As pixel size (in white) progressively decreases (left to right), more pixels can be used to provide
greater detail in the image.
0
Color bar
,z
ber
256
Vertical coordinates, y
800
color
600
x
400
200
Pixel
y
um
en
c
1,000
1,000
Vertical coordinates, y
In the mid-1880s, Neo-Impressionist artist
Georges Seurat perfected a revolutionary
technique of painting with tiny dots of color.
Like Michel Chevrul before him, Seurat recognized that from a distance, the eye would
naturally blend together tiny dots of primary
colors to produce secondary shades. Using tiny
brush strokes, Seurat and his contemporaries
captured scenes of cityscapes, harbors and
people at work and leisure. This technique
came to be known as pointillism.
Computers use a similar technique to display
text and images; however, they work at a much
finer scale. Every image portrayed on a computer monitor or video screen is composed of
many, almost imperceptibly tiny dots, spaced
at extremely close intervals. In a 2D picture
screen, each dot, or pixel (a word formed
from the contraction of picture element) can
be defined by its horizontal (x) and vertical
(y) screen coordinates. It is also defined by its
color value. In color images, each pixel is also
assigned its own brightness.
The number of shades that a pixel can take
on depends on the computer and the number
of bits per pixel (bpp) it is capable of processing. Common values range from 8 bpp (28 bits,
which translates to 256 colors) to 24 bpp
(224 bits, or 16,777,216 colors). On an eight-bit
gray-scale image, for instance, each pixel
would be assigned a value corresponding to a
shade of gray, ranging from 0 to 255, where 0
represents black and 255 represents white.
The number of pixels used to create an
image controls its resolution (above right).
As more pixels are used, the image can be portrayed in greater detail, or higher resolution.
Resolution is thus initially impacted by the
image acquisition system and later, by the
image display system.
Resolution in digital image acquisition
systems is largely governed by the number of
light-sensitive photoreceptor cells, known as
photosites, which are used to record an image.
These photosites (more commonly referred to
Sli
800
600
z
400
x
Voxel
y
200
0
0
0
200 400 600 800
Horizontal coordinates, x
1,000
0
200 400 600 800
Horizontal coordinates, x
1,000
> Pixel to voxel. A flat pixel (left) takes on a new dimension when the slice on which it resides is
stacked with other slices to form a volume (right). Adding the z-coordinate of the slice number
essentially assigns a depth-value to the pixel, thus creating a voxel within the stack of slices.
as pixels) accumulate charges corresponding
to the amount of light passing through the
lens and onto each cell.1 As more light falls
onto a photosite, the charge grows. Light is
shut off to the lens once the shutter closes, at
which point the charge in each cell is
recorded by a processing chip and converted
to a digital value that determines the color
and intensity of individual pixels used to display the image on screen. Resolution in these
Oilfield Review
devices is often expressed not in terms of photosites, but rather in megapixels. A
1.2-megapixel device, for instance, might have
an area of 1,280 x 960 (1,228,800 pixels), while
higher resolution would be attained by a 3.1megapixel device measuring 2,048 x 1,536
(3,145,728 pixels).
Image resolution can then be affected by
the medium on which it is displayed. A relatively low-resolution computer monitor might
be described as a 640 x 480 display. This
means that the monitor has a width of 640 pixels, spread across a height of 480 lines,
totaling 307,200 pixels. If those pixels were
spread across a 15-inch monitor, then any
image displayed on that monitor would be
allotted 50 dots per inch. To increase resolution, either the screen size must be reduced
or more pixels must be packed into the
screen. Modern applications generally take
both approaches, squeezing a huge number
of pixels into a smaller area.
To image a 3D object, the pixel is expanded
into another dimension. A third coordinate
(z) is added to the x-y location to precisely
define the pixel’s position within the volume
of a 3D object, thereby creating a voxel—
short for volume pixel. In CT images, the
z-coordinate often denotes depth, and is dictated merely by the position that a
tomographic slice holds within a volume
formed by stacking together numerous closely
spaced slices (previous page, bottom). In
addition to x, y and z coordinates, a voxel can
define a point by a given attribute value. In
the case of CT scans, that value is density,
which is a function of the sample’s transparency to X-rays. Density values can be tied
to a color spectrum, while a range of intensities can control the opacity of a voxel on a
computer screen. With this information and
3D rendering software, a two-dimensional
image of a 3D object can be generated for
viewing at various angles on a computer screen.
Evolving to Industrial Strength
Density contrasts within a rock body can be
imaged just as they can within a human body
(below). By the mid 1980s, CT technology was
making significant inroads into geoscience
applications. In addition to quantitative
determination of bulk density of rock samples,
CT scanning was adapted to visualize microbial
desulfurization of coal, displacement of heavy oil,
and oil flow through carbonate cores.7
Mineral
Density, g/cm3
It didn’t take long for those outside the
medical field to recognize the potential of CT
technology for nondestructive evaluation of
materials. Geoscientists soon joined the ranks of
other researchers, particularly those in the field
of materials testing, who sought increasingly
finer detail for imaging internal structures. This
capability has largely been realized through
development of industrial-strength CT systems,
which can employ more powerful X-rays, a tighter
focal point and longer exposure times than those
used in the medical field.8
Mineral
Density, g/cm3
Quartz
2.64
Gypsum
2.35
Calcite
2.71
Dolomite
2.85
Anhydrite
2.98
Illite
2.52
Barite
4.09
Chlorite
2.76
Celestite
3.79
Hematite
5.18
> Density values of various minerals commonly found in sedimentary rock.
X-rays used to visualize rock structures are affected, in part, by differences
in density and mineralogy within a sample.
In the early days of CT rock scans, it was not
unusual for geoscientists to work out agreements
with the only institution in town that could
provide access to such sophisticated technology.
Often in the dark of night, with as little attention
as possible, core samples from the oilpatch would
be wheeled into the pristine and sterile setting of
a hospital CAT-scanning facility for imaging and
analysis (below).
7. Kayser A, Kellner A, Holzapfel H-W, van der Bilt G,
Warner S and Gras R: “3D Visualization of a Rock
Sample,” in Doré AG and Vining BA (eds): Petroleum
Geology: North-West Europe and Global Perspectives –
Proceedings of the 6th Petroleum Geology Conference.
London: The Geological Society (2005): 1613–1620.
Vinegar HJ: “X-ray CT and NMR Imaging of Rocks,”
Journal of Petroleum Technology 38, no. 3 (March 1986):
257–259.
8. For more on high-resolution X-ray CT: University of Texas
High-Resolution X-ray Computed Tomography Facility.
http://www.ctlab.geo.utexas.edu/overview/index.php#
anchor1-1 (accessed January 30, 2006).
1. Although experts may correctly assert that photosites
are not actually pixels, the terms are becoming
increasingly interchangeable in the popular vernacular, thanks largely to the broad appeal of digital
photography, in which manufacturers of digital cameras describe resolution in terms of megapixels.
> A different kind of patient. A section of whole core is placed on a sliding gurney prior to imaging at
a hospital CAT-scan facility.
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7
With the development of microCT (µCT),
researchers are attaining much higher
resolutions.9 Using µCT, researchers are sometimes able to image their samples with voxel
sizes as low as 2.5 µm. Depending on the size of a
sample and the number of pixels used to image it,
voxel sizes of one-thousandth of the sample size
are being attained. For example, a 1-megapixel
camera using 1,000 x 1,000 pixels could
conceivably resolve a 1-cubic centimeter sample
to about 10 µm. Similarly, a 16-megapixel camera
(4,000 x 4,000 pixels) can resolve the same
sample to 2.5 µm.
At such resolutions, geoscientists can distinguish density or porosity contrasts inside a rock
sample and can study pore space and pore
connectivity in great detail. This µCT technology
permits recognition of grains or cements with
different mineralogical compositions (right). It
has even been used to differentiate grains of the
same type, such as those found in carbonates,
where microporosity may vary between different
grain types in the same rock.10
The Scanning Process
The scanning process to acquire µCT data is in
some respects analogous to acquiring 3D seismic
data. A seismic crew shoots a series of regularly
spaced seismic lines. Coordinates of the starting
and ending points of each line are surveyed,
making it possible to infer the distance between
each line in the series. It is therefore possible to
determine the position of any point along any
line as well as the distance between points
within the series of lines. With this knowledge, a
position between any two points or lines can be
interpolated when the data are processed.
For µCT, a regular series of closely spaced
scans are acquired to obtain high-resolution
virtual slices of a sample. Each pixel in the slice
represents a scanned point and has coordinates
that correspond to an actual point in the sample.
Because coordinates of each point are known,
distances between each point and each slice can
be determined. And just like the seismic line,
points or slices can be interpolated between
existing slices. By stacking the series of slices
close together to make up a volume of data, each
pixel in a slice becomes part of the stack and
takes on a third dimension. In this way, each
pixel can be treated as a voxel.
The scanning process is carried out by highly
specialized X-ray systems. Though several
companies offer research-grade systems, many
X-ray microtomography devices are custom-built.
Regardless of whether they are off-the-shelf or
specially designed, all rely on three primary
8
Barite cement: 1%
Pore space:16%
Sandstone grains and quartz cement: 78%
Calcite cement: 5%
> Three-dimensional quantification and spatial distribution of sandstone
components. While most sandstones consist primarily of quartz grains and
cement, X-ray imagery helps put other components into perspective.
Differences in X-ray attenuation throughout the sample indicate changes in
density caused by porosity and various mineral constituents of the rock.
Once mapped, these characteristics can be isolated for further scrutiny.
components: an X-ray source, a rotating stage on
which the sample is placed and an X-ray camera
to record the pattern of X-ray attenuation within
a sample.
To scan a sample, it must be placed on the
rotating stage, positioned between the X-ray
source and the camera. X-rays emitted from the
source are attenuated through scattering or
absorption before being recorded by the
camera.11 The camera then records a large series
of radiographs as the sample rotates incrementally on its stage through 360°. A computer
program stacks the digital projection data while
maintaining true spacing between pixels and
slices. CT algorithms are applied to these data to
reconstruct the internal structure of the sample
and preserve its scale in three dimensions.
One such device was built in 2002 by The
Australian National University in Canberra (next
page, top). Its source generates X-rays with a 2to 5-µm focal spot. The X-ray beam expands from
the focal point, creating a cone-beam geometry.12
Because magnification of the sample increases
with proximity to the X-ray source, the rotating
stage and camera are designed to slide
separately on a rail, allowing researchers to
adjust distances between source, sample and
camera. The sample stage can rotate the sample
with millidegree accuracy and can support up
to 120 kg [265 lbm] of sample and associated
test equipment.13
At this facility, the X-ray “camera” consists of
a scintillator that fluoresces green in response to
X-rays, and a charge-coupled device (CCD) that
converts this green light into electric signals.14
The camera has a 70-mm2 active area, containing
4.1 megapixels (2,048 x 2,048 pixels). The
system’s large field of view allows researchers to
Oilfield Review
Scintillator + CCD
Rotation stage
X-ray source
Approximately 1.5 meters
> A high-resolution X-ray tomography device at The Australian National University. The rotating
sample stage and charge-coupled device (CCD) camera slide on a track, enabling adjustment of the
distance between the camera, sample and X-ray source. With this device, a sample can be magnified
from 1.1 to more than 100 times its original size. The stage rotates with millidegree accuracy and can
be fitted with fluid pumps for imaging flow through porous media. (Figure courtesy of The Australian
National University.)
image a 60-mm specimen with a 30-micron pixel
size. They can also zoom in for high-resolution
scanning to image a 4-mm specimen with
2-micron pixels.
Approximately 3,000 projections are needed
to generate a 2,0483 voxel tomogram. Between
each projection, the sample stage is rotated
0.12°. The entire process takes 12 to 24 hours,
depending on the type of sample and the filtering
steps required to reduce sampling artifacts. The
resulting 24 gigabytes of projection data are
9. Abbreviations for microcomputerized tomography range
from µCT (where the Greek letter mu is a standard
symbol for the prefix “micro”) to uCT (where u is a
substitute for mu) to mCT (where the m stands for micro)
to XMT for X-ray Microtomography.
10. Kayser A, Gras R, Curtis A and Wood R: “Visualizing
Internal Rock Structures: New Approach Spans Five
Scale-Orders,” Offshore 64, no. 8 (August 2004): 129–131.
11. Ketcham RA and Carlson WD: “Acquisition, Optimization
and Interpretation of X-Ray Computed Tomographic
Imagery: Applications to the Geosciences,” Computers
& Geosciences 27, no. 4 (May 2001): 381–400.
12. Sakellariou A, Sawkins TJ, Senden TJ and Limaye A:
“X-Ray Tomography for Mesoscale Physics
Applications,” Physica A 339, no. 1-2 (August 2004):
152–158.
Sakellariou A, Sawkins TJ, Senden TJ, Knackstedt MA,
Turner ML, Jones AC, Saadatfar M, Roberts RJ,
Limaye A, Arns CA, Sheppard AP and Sok RM: “An
X-Ray Tomography Facility for Quantitative Prediction
of Mechanical and Transport Properties in Geological,
Biological and Synthetic Systems,” in Bonse U (ed):
Developments in X-Ray Tomography IV, Proceedings of
SPIE—The International Society for Optical Engineering,
Vol. 5535. Bellingham, Washington, USA: SPIE Press
(2004): 473–474.
13. This test equipment includes pumps or other devices
used to study fluid flow or mechanical compaction.
14. Rather than exposing film to light, CCD technology
captures images in a technique similar to common
digital photography. A CCD uses a thin silicon wafer to
record light pulses given off by a scintillator. The CCD
silicon wafer is divided into several thousand individual
light-sensitive cells. When a light pulse from the
scintillator impinges on one of these cells, the
photoelectric effect converts the light to a tiny electrical
charge. The charge within a cell increases with every
light pulse that hits the cell. Each cell on the CCD silicon
wafer corresponds in size and location to an image
pixel. The pixel’s intensity is determined by the
magnitude of the charge within a corresponding cell.
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processed by supercomputer, and it takes 128
central processing units about 2 hours to
generate the tomogram.
Visualization Technology
Once individual radiographic projections have
been compiled into a 3D data volume file, the
data can be loaded into an immersive visualization environment for detailed examination.
With Inside Reality virtual reality technology, the
data can be imaged and manipulated like any
other volume of 3D data. Originally developed to
help visualize seismic volumes based on miles or
kilometers of data, Inside Reality technology can
also handle data volumes based on much finer,
submillimeter scales.
Geoscientists utilize this advanced visualization technology to view a data volume from any
direction. This capability enables bedding planes
and fracture planes of rock samples to be viewed
orthogonally, even when the physical sample has
been cut obliquely to these planes. Sedimentary
and structural features of the rock sample are
typically analyzed in the form of slices or
transparency views through a volume.
While the scanning process relies on density
differences to distinguish features within a
sample, the visualization process depends largely
on opacity differences. One way to expose
features deep within a volume comprising
millions of voxels is to render surrounding voxels
invisible. Opacity rendering is the key to
visualization. Each voxel is assigned a value along
a transparency-opacity spectrum, thus making
some voxels stand out while others fade away.
Without this capability, the opacity of outer voxels
would hide all features lying within the volume.
Voxel-based technology can be used to
determine the volume and geometry of rock
grains, cement, matrix and pore space within a
sample. Using Inside Reality opacity-rendering
tools, geoscientists can assign different values of
the opacity-transparency spectrum to various
components within a volume. This technique
allows geoscientists to distinguish between
materials of different density values. For
example, the distribution of cement between
mineral grains shows up as a distinctive color,
while setting pore space to zero-opacity makes it
transparent, thus showing the spaces between
grains. This allows the viewer to separate rock
grains from cement, matrix and pore space to
reveal internal sedimentary and structural
features (below).
1.0 mm
> Sandstone pores. An opacity filter is used to render different features in volume windows using
Inside Reality software. The left window above and behind the yellow arrow shows only quartz grains
(light green) in this eolian sandstone from the Rotliegendes formation in Germany. A volume showing
only pore space (blue) is in the background on the right. The smaller volume in the foreground on the
right shows late diagenetic barite cement (red). The slice making up the base image indicates quartz
(gray), pore space (blue), barite (red) and carbonate cement (orange). The yellow arrow for scale is
1 mm long.
9
1.0 m
m
> Sandstone tracking. An opacity filter has been used to highlight quartz grains in sandstone from a
Rotliegendes gas reservoir in Germany. In the volume (light gray), interconnected porosity (blue) is
imaged using the volume-grower tool provided by Inside Reality software. Fringe (red) along the edge
of the porosity indicates possible connections to neighboring pores detected automatically by the
software. Carbonate cement (orange) is also shown in the volume. The horizontal slice shows quartz
grains (dark gray), pore space (black), carbonate cement (medium gray), and barite cement (white).
> Visualization using Inside Reality technology. Bringing sample volumes into
an iCenter secure networked collaborative environment allows asset teams
to become immersed in their data. Stereo projection creates a perception of
depth, providing a different perspective on the 3D nature of the rock and its
microstructure. Inside Reality visualization software provides a detailed
image of a foraminifera fossil measuring 1.5 x 1.0 mm (inset). This 3D
visualization allows examination of the fossil from many different angles. The
animated avatar mirrors the pointing motions and actions of another viewer
who is interacting with these data from a remote site.
The ability to manipulate opacity values plays
an important role in the seedpoint and volumegrower tools featured as part of the Inside Reality
toolbox. Using the seedpoint tool, the viewer
selects a point within a slice or volume. This
10
point has a certain X-ray attenuation value. Once
a point is selected, the program can automatically pick all neighboring voxels of a similar
value that are connected to that point. This
feature can help a geoscientist pick a point
within a volume known to represent porosity, for
example, and the volume-grower tool will display all
interconnected porosity within the volume (left).
Because each voxel is defined in part by its
coordinates, the distance between any two voxels
can be measured. To facilitate this process, the
Inside Reality system uses a ruler tool to provide
a visual scale. This tool can be used to measure
grain or pore size in three dimensions, helping
geoscientists estimate pore-volume proportions
and connectivity.
Taking rock samples from the laboratory to an
immersive visualization environment enables an
asset team to share important information and
concepts about reservoir samples so they can make
more informed decisions. Inside Reality virtual
reality technology lets geoscientists share 3D
virtual core data with those in remote sites to help
asset teams collaborate with company experts and
partners around the world (below left).
Applications
Rock fabric and textural data provide geologists
with key information used in analyzing facies and
in determining depositional environments.
Geologists and petrophysicists can now obtain
important information about grain size, shape
and matrix from digital scans of core or core
fragments. A single core-fragment image can
yield thousands of individual grains. By digitally
disaggregating grains in a scanned sample,
analysts can obtain coordinates of all voxels
composing each grain, the number of neighboring
grains and grain-overlap information.15
From such a dataset, geologists can derive a
comprehensive analysis of grain sizes and
distribution to obtain a full suite of statistical
15. Saadatfar M, Turner ML, Arns CH, Averdunk H,
Senden TJ, Sheppard AP, Sok RM, Pinczewski WV,
Kelly J and Knackstedt MA: “Rock Fabric and Texture
from Digital Core Analysis,” Transactions of the SPWLA
46th Annual Logging Symposium, New Orleans,
June 26–29, 2005, paper ZZ.
16. Both the Udden-Wentworth and the Krumbein scales are
used to classify rock samples according to diameter; the
former is a verbal classification while the latter is
numerical. According to the Udden-Wentworth scale,
sedimentary particles larger than 64 mm in diameter are
classified as cobbles. Smaller particles are pebbles,
granules, sand and silt. Those smaller than 0.0039 mm
are designated as clay. Several other grain-size scales
are in use, but the Udden-Wentworth scale (commonly
called the Wentworth scale) is the one that is most
frequently used in geology. The Krumbein scale is a
logarithmic scale, which assigns a value designated as
phi to classify the size of the sediment. Phi is computed
by the equation: ø = –log2 (grain size in mm).
17. Arns CH, Averdunk H, Bauget F, Sakellariou A,
Senden TJ, Sheppard AP, Sok RM, Pinczewski WV and
Knackstedt MA: “Digital Core Laboratory: Analysis of
Reservoir Core Fragments from 3D Images,”
Transactions of the SPWLA 45th Annual Logging
Symposium, Noordwijk, The Netherlands, June 6–9,
2004, paper EEE.
18. Bennaceur K, Gupta N, Monea M, Ramakrishnan TS,
Tanden T, Sakurai S and Whittaker S: “CO2 Capture and
Storage—A Solution Within,” Oilfield Review 16, no. 3
(Autumn 2004): 44–61.
Oilfield Review
Grain Size
Very coarse
sand
Coarse
Medium
Fine
Silt
50
Frequency
40
30
20
10
0
-1
0
1
2
= -log2 (diameter)
3
4
> Statistics obtained from a single slice of a sample. More than 4,100 grains
were virtually disaggregated from a single slice, allowing researchers to
compile detailed statistical data used to characterize rock fabric and
texture. When compared with other samples, these statistical measures
can help geologists sort out the depositional environment of the rock.
(Adapted from Saadatfar et al, reference 15.)
measurements (above left). Grain volume is
measured by counting the voxels in each distinct
grain, from which size is derived and then graded
against standard Udden-Wentworth or Krumbein
scales of grain sizes.16 Automated programs can
track and classify individual grains according to
grain shape characteristics of sphericity and
roundness or classify according to textural
categories, such as sorting, grain contacts,
and matrix or grain-support. Some programs
can also measure anisotropy in grain orientation
to help geoscientists ascertain sedimenttransport direction.
More important than the detailed measurement of rock grains is the analysis of the space
between the grains and the contents therein.
Opacity-rendering tools work particularly well in
showing what is not rock—that is, its porosity.
Researchers can obtain a good picture of porosity
by decreasing the opacity of dense voxels
representing rock grains and cements, while
simultaneously increasing the opacity of lowdensity voxels (right). This same opacityrendering technique highlights the extent of
interconnected porosity within the rock. Once
the porosity is brought up on screen, geoscientists can measure the size of pore spaces
and pore throats using the ruler tool. Pore
interconnectivity can also be charted, using pore
network models based on tomographic imaging
(above right). Pore-throat and pore-size distribution, along with interconnectivity, figure
Spring 2006
> Pore-scale information derived from tomographic images. Pore centers
(blue spheres), connected by pore throats (blue cylinders), are used
to model porosity within a sample of carbonate rock (yellow). The size
and location of pore centers and pore throats in this network reflect
actual conditions within the rock microstructure. The complexity and
heterogeneity of carbonate pore networks are brought to the
forefront as part of the rock matrix is rendered semitransparent while
pore space is rendered opaque. (Image courtesy of The Australian
National University.)
prominently in determining relative permeability
and recovery estimates in reservoir samples—
parameters that can be hard to quantify when
different fluids compete to flow through the
same opening.
A variety of other measurements can be taken
from tomographic images, from which important
information is derived. Analysts can directly
correlate image data on pore structure and
connectivity to measures of formation factor,
permeability and capillary drainage pressures.
Comparisons of results obtained from µCT
images and conventional laboratory measurements on the same core material have generally
shown good agreement.17
Studying Effects of Carbon Dioxide
on Casing Cement
In an important application beyond the realm of
conventional petrophysics, µCT was used to study
the effects of carbon dioxide [CO2] on casing
cement. Greenhouse gases, particularly CO2,
have been linked to rising temperatures around
the world. Capturing CO2 emissions and
sequestering them in the subsurface have been
proposed as a measure to reduce atmospheric
greenhouse-gas concentrations until lowemission energy sources become viable.18
However, CO2 becomes supercritical when
temperature and pressure conditions exceed
Opacity change
Grains and quartz cement
Pores and pore throats
> A whole lot of nothing. By manipulating the opacity of a scanned sample image, it is easy to visually
examine either sand grains (green) or pore space (blue). In many evaluations, this detailed analysis of
pore space can reveal critical clues to future performance of a reservoir.
11
31.1°C and 73.8 bar [87.9°F and 1,070 psi]—
conditions that are easily exceeded in most
medium to deep wells.19 Therefore, an important
aspect of any CO2 sequestration project is to
know how downhole materials will react to
supercritical CO2 (scCO2).
Scientists at Schlumberger Cambridge
Research in England have collaborated with
their counterparts at Schlumberger Riboud
Product Center in Clamart, France, to investigate long-term effects of CO2 storage on wellbore
integrity. One such experiment sought to
determine how scCO2 would react with casing
cement.20 Long used in oil and gas wells to
hydraulically isolate pay zones from the surface
and other permeable zones, portland-based
cements play a critical role in wellbore integrity.
This study focused on a sample of neat
cement.21 The cylindrical cement sample was
cured for three days at 90°C and 280 bar [194°F
and 4,061 psi]. Scientists obtained CT scans of
the cement cylinder before exposing it to scCO2.
The cement was then subjected to a wet scCO2
environment and kept at 90°C and 280 bar for
30 days. Two sample plugs were cut from the
original cylinder and then scanned.
Using Inside Reality software, researchers
were able to manipulate the data volume to
visualize porosity and microfractures and arbitrarily slice through zones of interest. By
comparing scans acquired before and after
treatment, researchers noted significant
changes to the cement plug, resulting from scCO2
attack. Of particular interest were the formation
and distribution of microfractures, along with a
zone of aragonite replacement and a zone of
mineral alteration characterized by high
secondary porosity.
The reaction between scCO2 and cement
produced an irregular carbonation front,
extending 4 mm [0.16 in.] from the outer edge of
the core toward its center. This lighter colored
carbonation front was readily apparent in the
gray-scale 3D volume, and in a color-coded slice
(above right). Subsequent X-ray diffraction
analysis determined that the alteration front had
a different composition than the original cement,
which had been replaced by aragonite. Porosity
was clearly enhanced in the regions around
the microfractures and the aragonite front (right).
The tests suggested that exposure to scCO2
may cause conventional cement to lose more
than 65% of its strength after only six weeks.
These important observations provided an
impetus for creating new blends of cement.
Schlumberger researchers have developed new
scCO2-resistant cementing materials that display
12
good mechanical behavior after exposure to
scCO2 gas. Laboratory tests on these new
materials show only a slight decrease in
compressive strength during the first two days,
and essentially no loss for the subsequent
three months.
Sample Plug
Examining Wormholes Caused
by Stimulation Treatments
Researchers have also used CT imaging to study
the effects of heterogeneity on carbonate matrix
stimulation. In one experiment, it was instrumental in visualizing the effects of the original
porosity distribution on acid-dissolution patterns.
CT Image
Alteration front
Carbonation front
Zone of very low porosity
Air bubble
(Diameter 0.5 mm)
Dissolution front
Zone of very high porosity
Filled microfracture
0
1
cm
2
> A sample plug of neat cement. Only a few centimeters in length, this sample revealed important
information concerning the behavior of supercritical CO2 on portland cement. The tomographic grayscale image of the cement sample (right), scanned with a resolution of 18.33 µm, shows a high
concentration of aragonite along the edge of a carbonation front, accompanied by an alteration front.
An additional dissolution front of high porosity extends farther into the core. Circular holes with a
diameter of 500 µm may represent air bubbles. Microfractures are filled with aragonite crystals.
Lighter features represent higher CT values, signifying different mineralogy in the case of the filled
microfracture, or different amounts of microporosity, in the case of the alteration front.
System Me
nu – Main
Menu
To o l s
System Menu
Colormap
Fa u l t
Fe n c e
Growing
Reservoir
Ruler
Sketch
Slice
Su r f a c e
ume Estimation
do w
Vo lum e Win
We ll
Aragonite front
Save Scene
S n a p s h ot
Restore Scene
Stereo
AU T O S AV E
S C R _ 0 4 0 917 _ 17 3 6 _ 1
SCR _04 091 7_1
847 _1
Neat cement
Inside Reality
[90 ]
Ver sion 5.1
> Highlighting the extent of supercritical CO2 alteration. Color-coding enhances features that may not
be readily apparent in gray-scale imaging. Microfractures formed during the supercritical CO2 attack
served as conduits for further aragonite alteration. The concentration of aragonite along the fractures
and the edge of the alteration front can be visually distinguished using color-coding provided by
Inside Reality software. Materials imaged are unaltered neat cement (green), an alteration front
(yellow), and mineral-filled microfractures or carbonation front (red). Increased porosity (blue) marks
the extent of various dissolution patterns.
Oilfield Review
Stimulation treatments are commonly
performed in wells where poor permeability
limits production due to naturally tight
formations or formation damage. A common
stimulation technique involves the injection of
acid into carbonate formations. Acid dissolves
some of the formation matrix material and
creates flow channels that increase the
permeability of the matrix.
The efficiency of this process depends on the
type of acid used, reaction rates, formation
properties and injection conditions. While
dissolution increases formation permeability, the
relative increase in permeability for a given
amount of acid is greatly influenced by injection
formation to facilitate the flow of oil. Better still,
wormholes require only a small volume of acid to
produce significant increases in permeability.
Researchers are therefore investigating factors
that influence production of wormholes.
CT scanning has proved instrumental in
determining the effects that injection rate and
spatial distribution of porosity have on dissolution
patterns formed during stimulation experiments
(below). Because it is nondestructive, this
technique allows for characterization of the core
before and after the treatment experiment so
the development and shape of the wormhole can
be evaluated.
applications, it is easy to envision the potential
spread of new applications for µCT.
The technology will no doubt prove
instrumental in improving the interpretation and
application of laboratory and log data. As an
increasingly important tool in nondestructive
testing, its application can be extended to
laboratory testing of unconsolidated or friable
formation samples. The combination of µCT
imaging with numerical calculations may lead to
more accurate predictions of a wide range of rock
properties crucial to exploration, reservoir
characterization and recovery calculations.
Further applications include development of
improved cross-property correlations and
development of libraries of 3D images that will
> Visualizing wormhole development. A sample of Winterset limestone was scanned by CT before (bottom) and after (top) acid injection. This data volume
is displayed using Inside Reality visualization technology, in which pore space is rendered opaque, while surrounding voxels are rendered transparent.
Initial distribution of pores (bottom) shows discrete clusters of pores (blue) along the long axis of the core. After acidizing (top), the core shows increased
porosity, with a dissolution pattern extending from right to left that further marks the flow of acid during injection.
conditions. At extremely low injection rates, acid
is spent soon after it contacts the formation,
resulting in relatively shallow dissolution along
the face of the injection zone. High flow rates
produce a uniform dissolution pattern because
the acid reacts over a large region. In either case,
the resulting gains in permeability require
relatively large expenditures of acid.
However, at intermediate flow rates, long
conductive channels known as wormholes are
formed. These channels penetrate deep into the
Peering into the Future
Tomography is not new to the oil industry. At the
upstream end of the tomography spectrum lies
crosswell seismic tomography; at the downstream
end is industrial process tomography for
refineries. As a research tool, µCT is used across
a broad suite of industrial applications to monitor
performance of polymer-enhanced foams and
polyethylene resins or to view phase separation
and pore-space characterization in formation
samples. Across this range of tomographic
19. Above its critical point at 31.1°C and 73.8 bar, CO2
becomes a supercritical fluid. In this compressed state,
its properties lie between those of a gas and a liquid.
With a lower surface tension than its liquid form,
supercritical CO2 can easily penetrate cracks and
crevices. Unlike CO2 gas, however, it can dissolve
substances that are soluble in liquid CO2.
20. Barlet-Gouédard V, Rimmelé G, Goffé B and Porcherie O:
“Mitigation Strategies for the Risk of CO2 Migration
Through Wellbores,” paper IADC/SPE 98924, presented
at the IADC/SPE Drilling Conference, Miami, Florida,
USA, February 21–23, 2006.
21. Neat cement has no additives that would alter its setting
time or rheological properties.
Spring 2006
allow a more rigorous and quantitative description of rock type and texture. These quantitative
descriptions can be integrated with classical
sedimentological descriptions. The technology
can also make a significant contribution to the
study of elastic behavior, porosity-permeability
trends and multiphase flow properties such as
capillary pressure, relative permeability and
residual saturations.
Future technological innovations will probably
include higher resolution to overcome problems
in predicting porosity when micropores fall
below the detection capability of the present
technique. With the improving resolution of their
samples, µCT technology is helping today’s
geoscientists to better see their world in a grain
of sand.
—MV
13

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