In-situ, real time micro-CT imaging of pore scale processes, the
next frontier for laboratory based micro-CT scanning
Boone, Marijn1,2, Bultreys, Tom2, Masschaele, Bert1, Van Loo, Denis1, Van Hoorebeke,
Luc3, Cnudde, Veerle2
1X-Ray Engineering bvba
2UGCT/PProGRess, Dept. of Geology, Ghent University
3UGCT, Dept. of physics and astronomy, Ghent University
This paper was prepared for presentation at the International Symposium of the Society of Core
Analysts held in Snowmass, Colorado, USA, 21-26 August 2016
Over the past decade, laboratory based X-ray computed micro-tomography (micro-CT)
has given unique insights in the internal structure of complex reservoir rocks, improving
the understanding of pore scale processes and providing crucial information for pore
scale modelling. Especially in-situ imaging using X-ray optimized Hassler type cells has
enabled the direct visualization of fluid distributions at the pore scale under reservoir
conditions. While sub-micrometre spatial resolutions are achievable in lab-based micro-
CT, the temporal resolutions are still limited to minutes or hours. This time restriction is
often a bottleneck for imaging dynamic in-situ processes, thus limiting the applicability
to relatively slow pore scale processes occurring in the order of hours to days, or to end
points in drainage-imbibition cycles.
To overcome this issue, X-ray Engineering (XRE) and Ghent University’s Centre for X-
ray Tomography (UGCT) have jointly developed a gantry-based micro-CT system. This
system’s X-ray tube and detector rotate continuously in a horizontal plane around the
fixed sample. The setup still allows to tune the geometrical magnification, with spatial
resolutions down to 5 µm. This fixed sample setup is also ideal for in-situ imaging, as the
flow cells can be directly connected to high pressure flow tubing and sensor lines,
without the need to allow rotational movement relative to the X-ray source and detector.
An efficient hardware design with a fast flat panel detector, combined with custom X-ray
transparent flow cells to increase X-ray flux and dedicated 4D software tools in
acquisition, reconstruction and analysis, allows to reach temporal resolutions in the order
The possibilities of this new approach in dynamic in-situ imaging are illustrated with
flow tests on a carbonate sample. We discuss the challenges in dynamic imaging and
present methods to improve X-ray flux and optimize image quality by means of this
experiment. Furthermore, we show that the integration of fast imaging experiments with
other information from peripheral sensors or from imaging data at different resolutions
can help to link behaviour at the pore scale to the effective properties at the core scale,
but also facilitates the experimental workflow.
X-ray computed microtomography (micro-CT) has the unique ability to obtain reliable
high resolution 3D information inside otherwise non-transparent objects in a non-
destructive manner. Over the past 2 decades micro-CT evolved from a synchrotron
technique to a standard laboratory microscopy technique. In this period, the achievable
spatial resolution of the laboratory based micro-CT systems has improved drastically,
reaching resolutions up to 50nm for some systems. This evolution has had important
implications in the field of geosciences, as it enabled detailed microscopic studies of the
internal structure of geological samples in 3D, while before this was limited to 2D
techniques like optical or scanning electron microscopy. It was especially challenging to
describe the pore space of a rock, which is essentially a 3D property of a rock, and
therefore required statistical or process based modelling to extrapolate the 2D
information to a 3D model . The evolution in micro-CT imaging enabled the direct
visualization and characterization of the pore space in 3D at scales below the micrometer,
even for complex carbonate pore systems.
Besides the static pore structure characterization, the non-destructive nature of micro-CT
also makes it possible to visualize different fluids inside the pore space and to monitor
how these fluids migrate through the rock. This is in recent years often referred to as in-
situ micro-CT and implies the imaging of a sample under certain well-constrained
external conditions [2-8]. In pore scale studies these conditions are usually increased
pressure and temperature in order to obtain an insight in for example reactive flow,
multiphase flow and rock mechanics under reservoir conditions. Traditional flow studies
at reservoir conditions are performed by placing reservoir samples in large stainless steel
Hassler type flow cells and monitoring the fluid transport through the samples using
different sensors (e.g. pressure, pH and electrical conductivity) and analyzing the
chemical composition of the fluid coming in and going out the cell. Based on the
information of these different sensors and chemical data, assumptions are made on the
pore scale processes occurring in the sample. However, what happens inside individual
pores in the sample remains inaccessible. In-situ micro-CT imaging makes it possible to
visualize fluid distribution in the pore space and if and how the pore structure of the rock
changes throughout the experiment.
Traditional Hassler type flow cells are however not practical for in-situ imaging due to
their size and constitution. An in-situ set-up for X-ray imaging requires a custom built,
miniature version of a traditional Hassler type cell. The first requirement of such a
custom built is the diameter of the cell. This diameter has to be kept as small as possible,
as most laboratory based micro-CT systems use a geometrical magnification to obtain
high resolutions. The second requirement is the composition of the cell. The cell needs to
be made from X-ray transparent materials, in order to ensure that a sufficient X-ray flux
reaches the detector. These in-situ cells are therefore usually constructed out of weakly
X-ray attenuating metals like aluminium  or strong polymers like PEEK or carbon
fiber [8,11,12] fiber instead of stainless steel.
When a fluid is injected into a reservoir sample in an in-situ setup, the fluid moves trough
the pore space and can interact with other fluids in the pore space or with the rock itself.
By monitoring these interactions over time using micro-CT imaging it is possible to
capture the dynamics of pore scale processes. In-situ imaging is therefore often related to
dynamic or 4D (3D + time) imaging. However, to acquire reliable pore scale 3D images,
the sample needs to remain unchanged during the micro-CT acquisition, otherwise
motion artefacts and image blurring can occur. To avoid these motion artefacts, the
temporal resolution of the micro-CT system should be high enough to capture the
dynamic changes occurring inside the sample while maintaining e.g. flow conditions.
While the spatial resolution of laboratory micro-CT system has improved during the last
years, the temporal resolution has remained in the order of minutes to hours. This limits
the type of pore scale processes that can be visualized using traditional laboratory micro-
CT systems to slow processes (hours to days), like mineral-fluid interactions in CCS
studies , or to quasi-static fluid distributions during drainage-imbibition cycles .
Monitoring fast (seconds to minutes) pore scale processes mostly remained restricted to
synchrotron facilities where temporal resolutions below 1 second can be obtained.
To tackle this issue, we have developed a benchtop scale gantry-based micro-CT system
which is optimized for dynamic in-situ imaging of pore scale processes (figure 1). The
temporal resolution of the system is around 12 seconds, which is an order of magnitude
higher compared to standard micro-CT systems. This is illustrated in figure 2, where an
overview of the spatial and temporal resolution of laboratory and synchrotron based
micro-CT systems is given.
In this manuscript the possibilities of this gantry-based micro-CT system are illustrated
by a solute transport experiment using a tracer salt inside a carbonate sample.
Traditionally, a tracer salt would be pumped through the sample (while mounted in a
flow cell) and the conductivity at the outlet would be monitored. Based on these
measurements one can investigate the dispersive behavior of the porous sample, and
determine if the transport of a solute through the sample is dominated by advective or
diffusive processes. Here we augment this data by directly visualizing – spatially
resolved - how the tracer salt is moving through the pore space of the carbonate.
While this is an example of single phase flow visualization, dynamic in-situ imaging is
also applicable to multiphase flow. As an example of fast pore scale imaging of
multiphase flow, Bultreys et al. (2015) presented the visualization of Haines jumps in a
sandstone sample with the same laboratory setup as used this work.
Experimental setup and optimizations for dynamic imaging
For the in-situ solute transport experiment a simple flow cell with confining pressure was
used (figure 1). Because this experiment is conducted at low pressure conditions, the used
flow cell was constructed out of polymethylmethacrylate (PMMA), which can be
considered as quasi transparent for X-rays, especially in comparison to the carbonate
sample. In the flow cell, a carbonate sample of 6 mm diameter and 16 mm in height was
mounted in a viton sleeve. A confining pressure of 10 bar was placed around the sleeve
using a manual syringe pump. A MilliGAT high-precision continuous flow pump
controlled the flow through the sample. To the outlet of the cell an electrical conductivity
sensor was placed as an indication to the brine salinity. The diameter of the entire flow
cell is 25 mm, allowing to obtain a spatial resolution of 7 µm and a field of view which
covers the entire diameter of the carbonate sample.
The carbonate sample investigated in this experiment was the Savonnières limestone,
which has a porosity ranging from 22% to 40% and a permeability from 115 to more than
2000 mD, depending on local variations . It is a grain supported oolithic limestone
consisting of ooids and shell fragments which are overgrown by sparite. During
diagenesis, some grain fragments were partially dissolved, resulting in a pore network
with well-connected pores between the grains (intergranular porosity) and secondary
porosity inside the dissolved grains (intragranular porosity or vuggy porosity), which is
connected to the rest of the pore network through micropores . A high-quality micro-
CT 2D slice through the sample with a resolution of 7.3µm is given in figure 3. The
resolution is sufficient to capture the macroporosity in the sample, but unable to capture
the microporous connections between some larger pore bodies.
The dried limestone sample was flushed with CO2 to remove the air phase in the pores.
Afterwards the sample was flushed with distilled water for a period of 2 hours to obtain a
complete water saturation. Scans before and after water flushing were used to evaluate
the water saturation degree and check for potential dissolution effects in the sample due
to the CO2 dissolution in the pore fluid. No dissolution effects were apparent in the CT
images at a resolution of 7.3µm. To investigate dispersive solute transport in the sample,
a salt solution of 10 wt.% CsCl was injected, because Cs acts as a tracer due to its high
X-ray attenuation coefficient. The change of the salt concentration in the outgoing fluid
was measured using the conductivity sensor on the in-situ flow cell, while the changes in
the distribution of the salt solution in the pore space of the carbonate sample were
continuously monitored by dynamic micro-CT imaging. The experiment was performed
twice on the same carbonate sample at volumetric injection rates of 0.25 µl/s in the first
run and 1 µl/s in the second run (with sufficient clean water flushing in between the
In order to obtain a 3D image of the sample with X-ray micro-CT, radiographs of the
sample have to be taken at different angles, which requires a rotation of the sample
relatively to the source and detector. The self-developed in-situ cell setup in the described
experiment had 2 flowlines going towards the cell (inlet and confining pressure), 1
flowline going out of the sample and 1 sensor wire coming from the sample. A more
complex setup often has even more (high pressure) flow and sensor lines connected to the
cell, which makes a rotational movements of the cell challenging. For time lapse micro-
CT imaging, where scans are acquired at a time interval of typically several hours or days
it is possible to perform a full rotation and then return to the original position, causing
less problems with fluid or sensor line tangling. Monitoring fast dynamic processes
requires a continuous rotation of the in-situ cell setup, which in turn requires very
complex in-situ cell designs with slip ring hydraulic/sensor contacts or with integration of
pumps in the in-situ cell .
The fixed sample configuration in our gantry based setup is ideal as (high pressure)
tubing or sensor lines remain immobile, thus avoiding entanglement, vibrations during
scanning and possible flow instabilities in the fluids going towards the sample.
The micro-CT systems design is optimized for fast dynamic in-situ imaging. The X-
ray source and detector are mounted on a gantry, which can be continuously rotated at a
maximum speed of 30°/s or 12 seconds for 360° rotation. Apart from the rotational
movement, the gantry can also perform a translational movement to change the distance
between the X-ray source and the detector allowing to tune the geometrical magnification
of the sample (figure 1). The X-ray source is a compact closed type transmission source
with a maximum tube voltage of 130kV and a maximum power of 39W. The tube has a
focal spot of 5 µm, which is also the highest achievable spatial resolution with the
system. The detector is a CMOS flat panel with a thick CsI scintillator and a readout
speed of 30 frames per second at a full resolution and 60 frames per second in 2 x 2
binned mode. The sample stage can be moved vertically with a travel of 0.75 m. This
offers flexibility to mount different types of in-situ equipment, allows to perform stacked
scans of elongated core samples for a more representative overview and even follow slow
moving fluid fronts through the sample.
Before performing dynamic acquisition, a high-quality 3D image of the pore structure
was obtained in a normal acquisition of 30 minutes at a voxel size of 7.3 µm, a 100 kV
tube voltage and 6W tube power (figure 3). For the dynamic scan the detector was used
in a 2 x 2 binned mode resulting in a voxel size of 14.6 µm and an increase of the signal
to noise in the micro-CT image. The tube voltage and tube power were increased to 130
kV and 16 W respectively, to increase the X-ray flux reaching the detector and therefore
decrease the noise level in the micro-CT image. The total acquisition time was 15
minutes and each 360 degree rotation took 12 seconds, resulting in a total of 45000
projection images. This data was recorded and processed with the proprietary 4D tools
(XRE, Ghent, Belgium) in the ACQUILA software (UGCT/XRE, Ghent, Belgium).
Dynamic acquisitions generate a massive amount of data and require dedicated smart
reconstruction and analysis tools to the desired and useful information of the pore scale
process under investigation. The acquired projections were automatically analysed and
differences in radiographies were used to pinpoint changes in the pore space of the
sample. Data from external sources like sensor data can also be incorporated and synced
with the continuous stream of X-ray projections to augment the data and avoid redundant
data from being reconstructed. Tomographic reconstruction was performed with the FDK
algorithm, implemented on the GPU.
Because the acquisition was continuous, projections acquired during any full rotation of
the system could be reconstructed regardless of the starting angle. This is very useful
when discrete events like fracture formation or sudden pore filling events like Haines
jumps are visualized, as a reconstruction can be done just before and just after the event.
This avoids image blurring and motion artefacts. In these experiments the pore scale
process is a continuous process without discrete events. Therefore the angle between
every consecutive reconstruction equalled 360 degrees, resulting in a full 3D image every
RESULTS AND DISCUSSION
The effluent salt concentration was calculated based on the electrical conductivity
measurements, resulting in breakthrough curves for both experiments. The two curves for
the 0.25 µl/s and the 1 µl/s experiments are shown in figure 4. In the breakthrough curve
of the 1 µl/s flow experiment, we can see an almost instantaneous increase in salt
concentration of the effluent fluid, indicating that the dispersion in the system is mainly
controlled by advective processes.. We used a non-linear least squares analysis
implemented in STANMOD to roughly estimate the dispersion coefficient based on the
breakthrough curves. This yielded an effective dispersion coefficient of 3.89E-3 mm2/s
for the 1 µl/s experiment. Assuming a tortuosity of 24.4 and a porosity of 26% in
Savonnières , the effective diffusion coefficient of Cs in this rock is estimated at
1.83E-5 mm2/s. This confirms that the behavior in the well-connected macropores is
In the 0.25 µl/s constant flow experiment the diffusive processes play a larger role in the
dispersion coefficient, but generally it is also an advective dominated system (estimated
dispersion coefficient 1.44E-3 mm2/s). From this curve it is however also clear that the
solute transport process takes longer than the scanned timeframe of 15 minutes, so only a
part of the process was captured with in-situ dynamic imaging.
Vertical slices trough the reconstructed volumes of the dynamic acquisition are given in
figure 5 and figure 6. In the raw vertical slices the Cs-concentration rise within the pore
space can be clearly seen. In the 1 µl/s experiment the fluid front reaches the bottom of
the sample after about 1 minute and reaches the top about 1 minute later. The
heterogeneity of the advective flow field is clearly visible: the Cs-concentration in some
well-connected macropores lags behind. After about 3 minutes however, the well-
connected pore space is fully saturated with the salt. Some pores in the system behave
rather differently and show a significantly slower concentration increase. Most of these
pores are ooids that were dissolved during diagenesis of the carbonate and which are only
connected to the rest of the pore network through micropores. In these microporous
connections, the advective flow of the solute is limited, and the Cs-transport to these
pores is thus likely dominated by diffusion. In figure 6 at 168s, the indicated ooid pore is
still filled with distilled water, while the rest of the pore space already contains solute. It
takes more then 10 minutes for its Cs-concentration starts to rise. It should be noted that
in the 1 µl/s experiment, some pores contain trapped air. During the experiment, this can
be considered as an immobile phase that does not interact with the solute transport.
In the 0.25 µl/s experiment, the Cs-concentration rise is much slower and moves more
gradually towards the top of the sample. Contrary to the 1 µl/s experiment, the well-
connected macropores have not yet reached a constant Cs-saturation before the grey
values of the ooid pores start increasing. This could indicate that diffusive transport may
start to play a role in some parts of the pore space with lower advection rates (other than
the ooid pores). Advective transport is however still dominant for the solute distribution
at 0.25 µl/s.
It is possible to obtain a good idea about the general type of transport in the carbonate
based on the fast scans. The noise level in these images makes it however difficult to
obtain reliable quantitative information from the fast scans. Especially segmenting the
pore space is a challenging endeavour due to the noise in the data and the changes inside
the pores. One of the options to improve the fast micro-CT data is to apply image
filtering. For example, 4D filtering seems to be a promising method to deal with the
higher noise levels associated with fast-acquired, dynamic data [2,16]. A second method
uses high quality, pre-acquired data on the sample to augment the 4D data. For example,
in the present case the pore space can be segmented from a high-quality micro-CT scan,
which can then be used as a mask to analyze the dynamic data (figure 3). By analyzing
the different phases on the high quality data and combining this information with the fast
scans, a dynamic map of the changing pore space can be obtained. Research into the
extraction of the velocity field from these experiments is ongoing.
Given the fact that at short ranges in the pore space, the Cs-concentration can be assumed
to vary little, we applied a median filter to average the greyscale values in the pore space.
The resulting 3D distribution map of the CsCl concentration in the sample in function of
time is shown in figure 7 for the 0.25 µl/s experiment. This concentration map shows
pores where the CsCl concentration change more slowly in time compared to the
surrounding pores. These pores remain blue through time and are the more isolated vuggy
ooid porosity, in which the concentration is controlled by mainly diffusive processes. The
concentration in the surrounding pores increases more rapidly which is illustrated by the
more rapid change from blue to orange-red. These pores are well connected and are the
preferred pathways along which the solute is transported.
The possibilities of laboratory based in-situ fast dynamic imaging are illustrated by
visualizing solute transport inside a porous carbonate rock. By maximizing the X-ray
transparency in-situ flow cell; optimizing the X-ray flux from the source; choosing the
right detector with a maximum efficiency and high read out speed; optimizing dynamic
reconstruction and integrating other micro-CT data to augment the dynamic scans, it is
possible to obtain temporal resolutions of 12 seconds. Thus allowing to continuously
monitor and quantify dynamic processes pore scale processes through time. In the applied
example a 3D map of the solute concentration in function of time could be generated,
which allows to visualize advection controlled preferential flow paths and more isolated
pore bodies controlled by diffusive transport.
These results provide a direct insight in fluid transport in complex porous media and
provide vital information to predict slower processes like reactive fluid flow and provide
input and validation for pore scale modeling.
The Research Foundation – Flanders (FWO) is acknowledged for the FWO research
grants 1521815N and 3G004115. Tom Bultreys is funded by the Flemish agency for
Innovation by Science and Technology.
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Figure 1. Scanner setup with arrows illustrating the magnification and rotational movement of source and
detector (left). Detail of the flow cell (right).
Figure 2. (adapted from ) The temporal and spatial resolutions attained at different synchrotron beamlines and lab set-ups.
Open symbols denote synchrotron sources, while filled ones represent laboratory sources, squares denote polychromatic
(“white”) beam and circles denote monochromatic scanners. The reported time is the time needed to gather 1000 projections
Figure 3. Vertical slice through a high quality scan of the Savonnières carbonate sample showing the inter- and
intragranular porosity in the sample. Scan time 30 minutes and resolution 7.3 µm.
Figure 4. Breakthrough curve of the salt solution determined by conductivity measurement in the flow cell
Figure 5. Raw vertical slices through selected reconstructed volume of the continuous acquisition.
Experiment at a constant flow speed of 0.25 µl/s. Dotted circle indicating more isolated, diffusion
controlled vuggy porosity.
Figure 6. Raw vertical slices through selected reconstructed volume of the continuous acquisition.
Experiment at a constant flow speed of 1 µl/s. Dotted circle indicating more isolated, diffusion controlled
Figure 7. 3D rendering of the CsCl concentration map at different points in time. The concentration in the
majority of the pores increases rapidly (rapid change from blue to red), indicating that these pores are well
connected and are a part of the preferential flow path of the solute. Other pores remain blue and these are
isolated pores controlled by diffusion. Image at 0 seconds showing a rendering of the rock sample without