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SPE-203326-MS Derivation of Permeability Logs in Carbonate Cores Using XRay CT Textural Analysis
Conference Paper · November 2020
DOI: 10.2118/203326-MS
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SPE-203326-MS
Derivation of Permeability Logs in Carbonate Cores Using X-Ray CT Textural
Analysis
Moustafa Dernaika and Shehadeh Masalmeh, Abu Dhabi National Oil Company; Safouh Koronfol, David Gonzalez,
Osama Aljallad, and Faris Mahgoub, Ingrain Halliburton
Copyright 2020, Society of Petroleum Engineers
This paper was prepared for presentation at the Abu Dhabi International Petroleum Exhibition & Conference to be held in Abu Dhabi, UAE, 9 – 12 November 2020.
Due to COVID-19 the physical event was changed to a virtual event. The official proceedings were published online on 9 November 2020.
This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents
of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect
any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written
consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may
not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
Abstract
Permeability prediction is an important step in reservoir characterization for effective modeling of waterflood performance and subsequent EOR processes. Permeability should be determined accurately versus
depth in all cored and logged wells in a field. This is normally achieved by the use of cross-plots of core
permeability versus core porosity from log-derived porosity in un-cored wells. The main aim of this research
is to use X-ray CT images of reservoir cores to identify geological textures and predict permeability based
on well-established models in carbonates.
Full-diameter reservoir cores were imaged using dual energy X-ray CT. Porosity logs were derived along
the entire cores from the dual energy data. Textural variations were identified from the appearance of the
CT images. Statistical sample selection was designed to honor the distribution of porosity and CT-textures.
Porosity-permeability correlations were established in the texture domain using plug-scale poroperm data
and thin-section photomicrograph analysis.
Plug-scale geological analysis confirmed the CT textures in the images, which allowed the derivation of
texture logs along the cores. The porosity and permeability data were fitted into unique trends that were
derived from the detailed textural analysis. This process provided the link between the poroperm trends and
the different textures in the core enabling permeability to be predicted along the entire whole core intervals.
This paper describes a novel approach of combining textures with porosity to model permeability along
the reservoir column. A unique dual energy CT technique was used to ensure that all the core property
variations were well represented in the plug-scale core analysis measurements. This analysis will open the
way for predicting permeability in un-cored wells using texture information from down-hole image logs.
In the future, machine-learning algorithms will be employed following the same workflow for effective
permeability prediction in the field.
Introduction
Permeability estimation is an important step in reservoir characterization for effective modeling of waterflood performance and subsequent Enhanced Oil Recovery (EOR) processes. The ultimate goal is to obtain
permeability description as a function of reservoir space. This would require accurate determination of
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permeability versus depth in all logged wells in a field. This is normally achieved by the use of cross-plots of
core permeability versus core porosity from log-derived porosity in cored and uncored wells. In carbonate
reservoirs, however, the cores have complex and multimodal pore systems, which result in large variation
in permeability at a single porosity range. The permeability variation can be as high as several orders of
magnitude, which leads to poor porosity-permeability relationships (Masalmeh et al., 2012 and Buiting and
Clerke, 2013) and hence imposes a challenge in permeability prediction.
In our earlier research, the porosity-permeability relationships were resolved by additional information
about the pore system, which was mainly related to the rock texture and diagenesis (Dernaika and Sinclair,
2017a and Dernaika et al., 2019). The research was focused on seven different cretaceous carbonate
reservoirs in the Middle East. The texture information was obtained from thin-section photomicrographs
and was shown to have significant pore geometrical properties that largely affected flow in porous media,
and thus absolute permeability. Figure 1 shows the porosity-permeability trends from the analyzed cores
(Dernaika, Masalmeh et al., 2019). The data present conventional 1.5" plug measurements of helium
porosity and gas permeability at ambient conditions. The porosity and permeability data for each plug were
associated with the sample texture that was identified from the corresponding thin-section photomicrograph.
The textures of the samples were classified as grainy, mixed and muddy (Mousavi et al., 2013). All the
textures revealed large porosity ranges, which was the result of different degrees of leaching, cementation
and compaction (i.e. diagenesis). The different textures had clear effects on the porosity-permeability
relationships. Each texture was fitted with FZI trends (Amaefule et al., 1993): grainy samples showed an
average FZI value of 3.5, Mixed 0.9 and Muddy 0.35 micron.
Figure 1—Texture-Diagenesis based Poro-Perm trends in the seven carbonate reservoirs in the Middle East region. Each
texture was fitted with FZI trend. Grainy samples showed an average FZI value of 3.5, Mixed 0.9 and Muddy 0.35 micron.
In this study, we attempted to trace the geological textures in the whole core CT images along the entire
core lengths. The CT-textures were used to populate the permeability along the core using dual-energy CT
porosity and the texture-based porosity-permeability relationships (i.e. figure 1). The predicted permeability
logs showed acceptable match with the plug measurements. The identification of the different CT textures
in the core were made by experienced geologists based on the appearance of the CT images. Such analysis
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is cumbersome; however, the results of this research should set the basis for more advanced and effective
applications using artificial intelligence algorithms such as image pattern recognition and machine learning.
X-ray CT Textures
X-ray CT imaging is a powerful non-destructive tool used in the oil industry to evaluate the internal textures
(or structures) of reservoir cores. When the core is imaged by the Dual Energy technique, it provides two
distinct 3D images by using a high- and a low- energy setting. The high-energy images are more sensitive
to bulk density (Compton Scattering effect) and the low-energy images are more sensitive to mineralogy
(Photoelectric Absorption effect). The bulk density (RHOB) and effective atomic number (Zeff) values (or
Pe) are computed independently for each CT slice along the core length (Wellington and Vinegar, 1987).
This technique has been applied in unconventional reservoirs for accurate evaluation of sweet spot locations
(Walls and Armbruster, 2012 and Almarzooq et al., 2014), and in conventional (carbonates/sandstones)
reservoirs for mineralogical variations, heterogeneity and sample selection (Al-Owihan et al., 2014 and
Dernaika, et al., 2015&2018). It has also proven to be essential in evaluating downhole logs such as density
logs and Pe logs (Dernaika et al., 2017).
Normally, X-ray CT images come in shades of gray, which are directly influenced by the effective atomic
number of the material and its density. Dense material appears white while pores appear black. Unresolved
pores would appear dark gray. Such gray-scale images may not always show all the detailed internal features
of the rock but they can certainly be used to characterize different textures based on the distribution of the
constituents of the reservoir core. Figure 2 shows an example about the link that can be established between
thin-section photomicrograh and gray-scale X-ray CT image of a carbonate reservoir core sample. A oneinch plug was imaged at a voxel resolution of 40 microns. Different textures can easily be seen in the plug
CT image. Two subsamples were taken from the plug to represent the different textures, and were imaged
at high resolutions to resolve the pore system. The micro-CT images confirm the different textures, which
can be described from the corresponding thin-section photomicrograph. The images show that the plug is
made of grainy and muddy textures.
Figure 2—From left to right: 1" plug CT image at 25microns/voxel. Two micro-CT images on two different microsamples representing different textures in the plug; 1micron/voxel (lower image) and 0.05micron/voxel (upper
image). Corresponding thin-section photomicorgrah showing the different geological textures in the plug.
Figure 3 depicts an example of a full-diameter four-inch carbonate whole core. The core was imaged
by the dual energy technique at 500microns/voxel. Porosity log was derived from the dual energy bulk
density log with a good comparison to the plug data. Permeability was predicted from the porosity log and
from the porosity-permeability trends for the different textures in the core. The continuous whole core CT
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image clearly distinguishes between the two geological textures identified in the corresponding thin-section
photomicrographs. This example clearly shows that whole core CT images can infer different features in
the core that can be linked to the geology, which in turn can be used to model permeability along the core
length. Moreover, core CT images can give valuable information about the amount and distribution of each
geological texture that is used as an essential input for reservoir modeling and well planning (Pranter et
al., 2005).
Figure 3—From left to right: Dual energy CT porosity log in comparison with selected plug porosity measurements.
Predicted permeability log in comparison with plug measurements. Continuous CT image at 500 micron/
voxel with two distinct textures, which were described from corresponding thin-section photomicrographs.
The following sections will demonstrate the applied workflow to predict permeability along reservoir
columns using core CT images and the well-established porosity-permeability relationships in figure 1.
Experimental Workflow and Main Results
Dual Energy CT Scanning and CT Textures
From a heterogeneous limestone reservoir in Abu Dhabi, we scanned a 350-foot long core by the Dual
Energy (DE) X-ray CT imaging technique at a resolution of 0.468×0.468×0.500 (x, y, z) mm per voxel.
We produced continuous whole core scans at 0.5 mm spacing, and we derived bulk density (BD), and
Photoelectric (Pe) logs along the entire core interval. The DE core density varied between 2 to 2.7 g/cc,
whereas the Pe profile was almost flat at around a value of 5, indicating single calcite mineralogy (Siddiqui
et al., 2010). The XCT images were examined by an experienced geologist to identify distinct whole core
CT textures based on the gray-scale variations in the CT images. The CT textures were important in the
sample selection process and were key input parameter for predicting permeability at the whole core level.
Texture-Based Porosity-Permeability Relationships
1.5-inch core plug samples were selected in the core to represent the density changes as well as the CT
textural variations. Failure to consider the CT textures in the plug selection process can cause important
permeability intervals as well as key rock types to be unrepresented in the final set of the selected samples.
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This is especially true in carbonates that normally show large permeability variations over a narrow
porosity range. Such permeability variations are mainly attributed to textural changes that can be defined
and described at the micro-level using thin-section photomicrographs. The selected plugs were cleaned
by conventional Soxhlet extractors using hot solvent reflux and then underwent helium porosity and gas
permeability measurements at ambient conditions. The plugs were also X-ray CT scanned to examine the
internal features of the samples and remove any plug with induced fractures or other anomalies that may
affect the porosity-permeability distribution. Representative trims were cut from the plugs for Mercury
Injection Capillary Pressure (MICP) analysis and thin-section photomicrography.
The above analysis would allow us to obtain the following key information that would lead to predicting
permeability along the core depth,
•
•
•
Core Density Log
CT Texture Log
Porosity-Permeability Trends based on Textures
Figure 4 presents the dual energy (DE) core density profile in comparison with the wireline (WL) density
log. The figure also presents the continuous CT images and the derived CT texture log. Representative thinsection photomicrographs are shown in the figure to define the geological textures in the core. The core
density compares very well with the WL density log. However, the DE density gave slightly lower density
values than the downhole measurement due to fluid loss upon core retrieval. The difference between the
two measurements becomes larger at lower density intervals indicating higher fluid loss in higher porosity
regions. The CT images show consistent gray-scale variation with density such that brighter images are
indications of higher densities, and hence lower porosities. The analysis of the CT images revealed four
distinct CT textures that are given in different colors along the core depth. The textures were confirmed and
described in the corresponding thin-section photomicrographs.
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SPE-203326-MS
Figure 4—Dual energy core density is given along the core length in comparison with the Wireline density log. Continuous
CT image log is also given together with CT Texture log. Four CT textures are color coded along the core depth, which
are defined in representative thin-section photomicrographs. The textures are Grainy, Mixed, Muddy and Tight Muddy.
Figure 5 presents multi-scale textures from whole CT image, plug CT image and from thin-section
photomicrograph. The plugs were taken from the core representing distinct whole core CT textures
and were confirmed and described by corresponding thin-section photomicrographs. Four main textures
were identified in the core based on the rock content as grainy (grain-dominated), mixed, muddy (muddominated) and tight muddy. Such a classification had been used in previous researches and had been shown
to provide essential information for predicting fluid flow in reservoir cores (Jennings and Lucia, 2003,
SPE-203326-MS
7
Mousavi et al., 2013, Dernaika and Sinclair, 2017a, Dernaika et al., 2019). Figure 5 serves as a confirmation
to the link that can be established between geological textures and CT textures.
Figure 5—Texture representation from whole CT, plug CT (gray-scale and color-scale) and from thinsection photomicrograph. Circles on the WC CT shows the plug locations. Example textures are depicted
in the core from grainy (grain-dominated), mixed, muddy (mud-dominated) and tight muddy (Color-scale
plug CT is missing for texture 4). Figure 6 presents the poroperm and MICP properties of these textures.
Figure 6 gives the mercury-derived pore-throat size distribution curves and the capillary pressure curves
from the four textures depicted in figure 5. At the same porosity, the different textures gave different
permeability values that are consistent with the samples' pore-throat sizes and capillary pressure properties.
This shows that rock textures are fundamental for permeability predictions and rock typing.
Figure 6—Mercury-derived pore-throat size distribution curves and capillary pressure curves from the four textures
in figure 5. At the same porosity, the different textures show different petrophysical properties and pore sizes.
Figure 7 shows the porosity-permeability data from the selected plugs. Large permeability difference
(~three orders of magnitude) is seen at the same porosity. The data was plotted in the texture domain and
was fitted into four distinct trends. It is not straightforward to obtain porosity-permeability trends based on
8
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geological textures. The process involves analysis of the plug CT images and the thin-sections. Plugs with
induced fractures and other unrepresentative features should be removed from the porosity-permeability
plot. Examples of such analyses can be found in Dernaika et al., 2017c and Dernaika et al., 2019b. The
identified textures in this core are as follows:
•
•
•
•
Texture 1 (Grainy texture) – grainstone-to-rudstone with macro porosity and enhanced leaching.
Texture 2 (Mixed texture) – partially leached lime packstone, graded locally to grainstone. The
grains are supported by leached micritic matrix and minor skeletal components.
Texture 3 (Muddy texture) – bioturbated lime wackstone graded locally to packstone. The sample
is mainly made of micritic matrix and minor fragments of skeletal components.
Texture 4 (Tight Muddy texture) – tight lime mudstone mainly composed of micritic matrix and
rare fragments of skeletal components. The micritic matrix suffered from recrystallization and very
low degree of dissolution.
Figure 7—To the left is the porosity-permeability data from the conventional core plugs. To the
right is the same plug data plotted in the texture domain. The data were fitted into four distinct
trends based on the geological textures from the corresponding thin-section photomicrographs.
The grainy texture in this core is rare and comprises around 1% of the total core footage. It occurs around
the middle of the core. The Mixed texture is more abundant and comprises 16% of the core volume, and
is also seen around the middle of the core. The mud-dominated textures (both the muddy and the tight
muddy) are the most abundant textures in the core and they equally comprise 43% of the core volume.
The tight muddy texture is mainly distributed towards the upper part of the core whereas the better quality
muddy texture is more abundant towards the lower part of the core. The understanding of the percentage and
distribution of the different textures (and hence rock types) in reservoir cores enhances our understanding of
the depositional setting and provide a tool towards predicting texture within the field (see Al-Owihan et al.,
2014).This will eventually allow us to obtain permeability description as a function of the reservoir space.
Samples with grainy textures tend to show high permeability values compared to the mixed and muddy
textures. This is because mixed textures contain micrite matrix with fine grain particles and intercrystalline
micro porosity, which hinder the flow and reduce the overall permeability. The muddy textures should
always give the least permeability values due to predominant micro porosity. Although the rock texture
appears to be the main control of flow, we should not ignore the effects of the porosity type. For instance,
a muddy carbonate may have ‘touching vug' porosity with higher permeability than expected. Such rock
types should be identified and understood within the texture analysis, if present. In earlier researches (e.g.
Dernaika and Sinclair, 2017a, Dernaika et al., 2019), no major effect was seen from the porosity type that
would disturb the established porosity-permeability trends as depicted in figure 1.
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Permeability Log
Our general approach for predicting permeability along reservoir core depths is depicted in the conceptual
model in Figure 8. The texture log is derived from the continuous whole core CT images. It can alternatively
be derived from downhole image logs or from core descriptions. The porosity log is derived from dual
energy CT or can alternatively be obtained from downhole logs. The texture-based porosity-permeability
trends are normally established from plug analysis in the same core. The trends can also be used from the
well-developed FZI trends, which were established from the earlier research as shown in figure 1. Therefore,
permeability can be calculated from the porosity log using the porosity-permeability relationships for each
texture along the reservoir depth.
Figure 8—Conceptual model for permeability prediction. The texture log is derived from CT images. The porosity
log is derived from dual energy CT. The texture-based porosity-permeability trends are derived from plug analysis.
Permeability is calculated from the porosity-permeability relationship at the log porosity for each identified texture.
Figure 9 plots the plug dry bulk density versus the DE core bulk density. This data was used as a
calibration tool to convert the DE core density log to a dry density log (see figure 10). Figure 9 suggests an
approximate linear corrolation between the DE whole core density and the corresponding plug bulk density
at a cleaned and dry state. This is a necessary calculation to derive accurate porosity log along the core depth.
The dry porosity was calculated with an accuracy of +/− 2 porosity unit. We used a measured grain density
of 2.71g/cc (from plug analysis), which is also confirmed by the calcitic (limestone) nature of the core at
a Pe value of around 5 (Siddiqui et al., 2010). We used the following equation to derive the porosity log;
porosity = 1 – BD/GD, where BD is the dry bulk density log and GD is 2.7 g/cc (for limestone reservoirs).
If the core contains different mineralogies then different GD values can be employed based on the Pe (or
Zeff) response. This application was indeed used in Dernaika et al., 2015.
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Figure 9—Plug dry bulk density versus DE core bulk density. This data was used to convert the core density log to dry
density log and hence to porosity log. The dry porosity was calculated with an accuracy factor of +/− 2porosity unit.
Figure 10—From left to right: (1) depth, (2) WL density log, DE density log & dry density log; (3) continuous CT image log;
(4) CT Texture log; (5) CT porosity log & plug porosity (data points); (6) permeability logs & plug permeability (data points).
SPE-203326-MS
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Figure 10 presents the full suite of the data generated from the applied workflow. From left to right are
(1) core depth (2) WL density log, DE density log & dry density log; (3) continuous CT image log; (4) CT
Texture log; (5) CT porosity log and plug porosity (data points); (6) permeability log and plug permeability
(data points). The porosity and permeability logs compare reasonably well with the plug measurements.
The permeability was first calculated using the same core trends in figure 7 and was calculated again using
the general FZI trends in figure 1. We see very good comparison between the two trends, which highlights
the useful application of this technique in other carbonate reservoirs.
Predictions from Other Middle East Reservoirs
We applied the same workflow to two more carbonate cores from different limestone reservoirs in the Middle
East region. The results are shown in figure 11 and figure 12. Figure 11 shows the DE and the dry core
density logs. Plug sampling was based on density and CT textural variations. The porosity-permeability data
was fitted into grainy, mixed and muddy trends. Both porosity and permeability logs showed reasonable
match with the plug-measured data. The samples were analyzed in details and five different textures (or
rock types) were identified as follows:
•
•
•
•
•
Rudstone-to-Boundstone texture with the highest permeability trend (Grainy Texture)
Rudstone-Boundstone-to- Floatstone with the lower permeability trend (Grainy Texture)
Floatstone-to-Boundstone (Mixed Texture)
Floatstone-to-Packstone with high porosity along the muddy trend (Muddy Texture)
Cemented Packstone-to-Wackstone with low porosity along the muddy trend (Muddy Texture)
Figure 11—DE and dry core density logs along with CT images and texture log. Porosity log derived from
dry density and permeability calculated from the porosity-permeability trends. Good match is seen between
the poroperm logs and measured plug data. Three main textures are given underneath the poroperm
plot. Two comparable permeability logs are derived: one from the same core data and one from figure 1.
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Figure 12—DE, dry core density logs in comparison with WL along with CT images and texture log. Porosity
log derived from dry density and permeability calculated from the porosity-permeability trends. Good match
is seen between the poroperm logs and measured plug data. Two main textures are given underneath the
poroperm plot. Two comparable permeability logs are derived: one from the same core data and one from figure 1.
The five geological textures were grouped into four porosity-permeability trends, which were used to
predict permeability at the identified CT textures along the core. This analysis yielded good permeability
log in comparison with the plug data. We then predicted the permeability by using the general FZI trends
in figure 1, and we saw good match with the plug measurements as well (see permeability log track in
figure 11). This confirms that the general trends in figure 1 can be used with good confidence to predict
permeability without the need of establishing porosity-permeability correlations from the core. The Grainy,
Mixed and Muddy classification seems to be sufficient in predicting permeability once the porosity and
texture logs are available. It had already been demosntarted that permeability can be predicted with a factor
of +/− 2 using the FZI model parameters presented in figure 1 (Dernaika et al., 2019)
The results of the other core are presented in figure 12. The figure shows the DE and dry core density
logs in comparison with the wireline density. Two main textures were identified in this core. The porositypermeability data was fitted into grainy and mixed trends. Both porosity and permeability logs showed
reasonable match with the plug-measured data. The main textures in the core are as follows:
•
•
Grainstone texture made mainly of micritized ooidal and peloidal grains (Grainy Texture)
Packstone texture made mainly of peloidal grains (Mixed Texture)
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In the permeability log track, we show comparable permeability profiles from the poroperm trends from
the same core and from figure 1. This is another confirmation that the general trends in figure 1 can be used
to predict permeability if the porosity and texture logs are available.
Figure 13 and figure 14 present example whole core CT images from the cores described in figure 11 and
figure 12, respectively. The images demonstrate that it is possible to identify different geological textures
at the core level using X-ray CT images.
Figure 13—Example whole CT images depicted in the core from figure 11. It is shown that
different geological textures can be identified at the core level using X-ray CT scanning.
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Figure 14—Example whole CT images depicted in the core from figure 12. It is shown that
different geological textures can be identified at the core level using X-ray CT scanning.
Summary and Conclusions
We scanned full-diameter reservoir whole cores to derive high-resolution porosity and texture logs. Porositypermeability correlations were established in the texture domain, which were used to predict permeability
along the entire core length. The workflow was successfully applied in several reservoirs across the Middle
East region. This analysis will open the way for predicting permeability in un-cored wells using texture
information from down-hole image logs. Machine-learning algorithms should be employed following the
same workflow for effective permeability prediction in the field. The following can be concluded from this
study,
1.
2.
3.
4.
5.
Dual Energy CT scanning provided representative density and porosity logs at the core level.
It was possible to identify geological textures at the core level using X-ray CT images.
Plug selection was based on the CT textures and porosity profiles.
The porosity and permeability data were grouped into unique trends based on textures.
The different poroperm trends represented the different CT textures in the core enabling the
prediction of permeability along the core depth.
SPE-203326-MS
6.
15
Permeability can be predicted from proposed FZI parameters (i.e. figure 1) if porosity and texture
logs are available.
Acknowledgments
The authors wish to acknowledge the support from Abu Dhabi National Oil Company (ADNOC) and Ingrain
- Halliburton.
Nomenclature
Abbreviations
BD
CT
DE
FZI
GD
MICP
ROHB
WL
XCT
3D
Symbols
g/cc
Pe
Zeff
%
References
Bulk density
Computed tomography
Dual energy
Flow Zone Indicator
Grain density
Mercury injection capillary pressure
Bulk density
Wireline
X-ray computed tomography
Three-dimension
grams per cubic centimeter
Photo electric factor
Effective atomic number
Percent
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