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American Journal of  
Geospatial Technology (AJGT)

The Effect of  Micrite Content on the Geophysical Properties of  Reservoir Carbonate Rocks
Najeeb S. Aladwani1*, Laurence J.2, Ismael Himar Falcon-Suarez2, Angus I. Best2

Volume 4 Issue 1, Year 2025
ISSN: 2833-8006 (Online)

DOI: https://doi.org/10.54536/ajgt.v4i1.2472
https://journals.e-palli.com/home/index.php/ajgt

Article Information ABSTRACT

Received: April 01, 2024

Accepted: April 29, 2024

Published: February 14, 2025

Carbonate rocks have a significant economic value as water and hydrocarbon reservoirs and 
are essential elements for concrete and buildings. This study selected a total of  36 carbonate 
rock samples, which were tested in the lab for electrical resistivity at 80 Hz, primary and 
secondary ultrasonic wave velocity, and attenuation. Therefore, to understand the role of  mi-
crocrystalline calcite (micrite) on the geophysical properties, this study has proposed a new 
systematic method by using image analysis techniques to quantify micrite and microstruc-
tural parameters such as macro porosity, solid grains, microporous micrite (porosity within 
micrite), and calcite crystals (sparry calcite). The study findings suggest that porosity and 
permeability have U-shape trends as a function of  micrite content due to leaching processes. 
Thus, higher micrite content causes the elastic wave velocities to increase, making the rock 
stiffer. When a dual porosity effect is present, the attenuation exhibits two peaks, and is at its 
highest with micrite content. With minimal values between 10% and 30% of  micrite content 
due to maximal porosity, the apparent formation factor exhibits a U-shape trend with micrite 
content; as a result, the electrolyte conductivity is high. We concluded that knowledge of  
micrite content and macro-porosity is of  paramount importance to interpret and model the 
geophysical parameters and to develop a rock physics model.

Keywords
Attenuation, Elastic Wave Velocity, 
Electrical Resistivity, Micrite 
Content, Reservoir Carbonate

1 Department of  Earth and Environmental Sciences, Faculty of  Science, Kuwait University, Kuwait, Safat 13060, Kuwait
2 National Oceanography Centre, University of  Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK
3 Ocean & Earth Science, University of  Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, UK
* Corresponding author’s e-mail: aladwaninajeeb@outlook.com

INTRODUCTION
Carbonate rocks are major underground reservoirs 
of  hydrocarbons and water (Chilingar et al., 1967) as 
well as the primary supply of  cement for concrete and 
buildings (Grasby & Betcher, 2002; Hawkins et al., 1996). 
Understanding the physical characteristics of  carbonates, 
particularly the impact of  microstructures, is therefore 
relevant to many different specialized domains, such 
as rock physics, hydrology, geotechnical engineering, 
and tectonics, and is critical for better characterizing 
reservoir resources. Carbonate rocks have complicated 
geochemical, textural, and petrophysical features (most 
critically for reservoir rocks, porosity and permeability), 
which derive from their geological genesis (by bi oclasts, 
ooids, and so on) and subsequent diagenetic processes 
that frequently alter their mineralogy and pore networks.
There is a growing body of  literature that recognises the 
microstructural complexity of  carbonates and the factors 
that control elastic wave velocities (both Primary, or P- 
wave, and Secondary, or S- wave) and the fluid transport 
properties, porosity and permeability. Recently, the 
researcher’s interest has increased in the contribution of  
microcrystalline calcite, i.e., micrite, to the microstructural 
configuration of  carbonate rocks (Cantrell & Hagerty, 
1999; Husseiny & Vanorio, 2015; Lambert et al., 2006), 
and its effect on elastic wave properties (Fournier & 
Borgomano, 2009; Husseiny & Vanorio, 2015; Vanorio 
& Mavko, 2011). In reservoir sandstones, the scatter 
often seen in the velocity-porosity relation is frequently 
attributed to the amount of  clay content in the rock (Han, 

1986; Kowallis et al., 1984; D. Marion, 1992); indeed, 
volumetric clay content is a second order control on 
velocity after porosity. Here, we explore the hypothesis 
that microcrystalline calcite (micrite) in carbonate rocks 
has a similar influence to clay in sandstones on elastic 
wave velocity.
Micrite content can be dependent on the energy level of  
the depositional environment and is used for petrographic 
and textural classification of  carbonates (Dunham, 
1962; Folk, 1959). The micrite is a result of  diagenesis, 
which prompts the recrystallisation of  previous rock 
constituents, calcite and aragonite mud, for instance as 
stated by (Lambert et al., 2006). The particles gathered 
with microcrystalline calcite usually have a size between 1 
and 4 μm, similar to clay. As a result, they are probably to 
blame for carbonates’ micro porosity (Cantrell & Hagerty, 
1999; Vanorio & Mavko, 2011).
The advancement of  digital image analysis techniques 
has revitalised the way that modern petrographic 
analysis is performed (Hamilton, 2010; Michael Denis 
Higgins, 2006). Textural measurements such as shape, 
size, and sorting of  crystals or grains can be quickly 
and accurately acquired on 2D rock images (Michael D 
Higgins & Roberge, 2007; Dougal A Jerram et al., 2009), 
removing ambiguity in classifications. Images are typically 
obtained from petrographic thin sections as transmitted 
light photographs or SEM backscatter images. Three-
dimensional (3D) rock textures of  natural rock samples 
images can be captured and analysed through the 
application of  serial sectioning and X-ray computed 



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tomography scan analysis (D A Jerram & Higgins, 2007; 
Dougal A Jerram et al., 2009).
ImageJ (Rasband, 1997) is a widely used image-processing 
software that was developed by the National Institutes 
of  Health (NIH) in the USA. Image analysis techniques 
have major advantages over conventional methods (e.g., 
manual point counting and visual descriptions), such 
as the potential to automate and increase the capacity 
of  standard measurements with more speed, precision 
and accuracy than traditional methods. For example, 
the measuring of  rock porosity from thin sections 
impregnated with blue epoxy resin is a common method 
in geosciences and is usually conducted by point counting 
(Grove & Jerram 2011). However, point counting of  thin 
sections requires special equipment, is laborious and very 
slow, and human error must be considered. By contrast, 
digital image analysis is far superior as it measures 
millions of  points in the sample with speed, precision and 
accuracy; therefore, it presents far superior datasets (Ceia 
et al., 2017; Haines et al., 2015; Hamilton, 2010).
The objectives of  this study were to (1) suggest and establish 
a method that allows us to extract key sedimentology-
related parameters of  interest from carbonate reservoir 
rock samples, such as the volumetric microcrystalline 
calcite (micrite) content and macro porosity and (2) 
to investigate the effect of  microcrystalline calcite 

(micrite) on important reservoir petrophysical (porosity, 
permeability) and geophysical ( elastic wave velocity and 
attenuation, and electrical resistivity)  properties.
Although a thin section is a two dimensional slice through 
a three-dimensional rock, it was shown by Chayes (1956) 
that the relative areas of  the constituents of  the sample 
are equivalent to their relative volumes (Marks, 1994).

Description of  Rocks
This study selected 36 carbonate rocks, comprising 24 
carbonate samples dataset from (North et al., 2013) et al. 
(2012). Eleven oolitic limestone samples were taken from 
three core samples of  bio clastic oolitic limestone from 
the Jurassic Purbeck formation of  southern England, and 
thirteen dolomite samples were taken from a single core 
sample of  a microbial-laminated dolo-mudstone from 
the Rotweil formation of  the Southern German basin  
(Laurence (North et al., 2013) et al., 2013).
A suite of  12 new limestone samples was chosen to 
expand the dataset of  (North et al., 2013) et al. (2012). 
Eleven samples were from wells in Hampshire’s Wealden 
Basin in southern England, comprising two samples from 
Horndean lA (SU 71541260), six samples from Homdean 
4 (SU 66301346), three samples from Humbly Grove-3 
(SU 7054 4530) and one sample from Calub 1 in the 
Ogaden Basin, south-eastern Ethiopia (Berhanu, 1994).

Figure 1: Schematic diagram of  the experimental setup, also showing the arrangement of  electrodes around the rock 
sample. Scales are approximate (sample width is 5 cm). After Falcon-Suarez et al. (2017), North et al. (2013).

The Wealden Basin, southern England, comprises 
several formations, for example, the Hesters Copse 
Formation and the Great Oolite. The Hesters Copse 
formation is divided into a wackestone unit (oolitic and 

bioclastic), a dolomite unit (calcareous dolomite) and a 
basal sandstone unit (fine-grained calcareous sandstone). 
Next, the dominant component in the Great Oolite 
formation is oolitic, skeletal and oncolytic grain stones 



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and pack stones (Sellwood, Shepherd, Evans, & James, 
1989). Overall, diagenetic processes in these rocks are 
compaction, dissolution and late cementation (Berhanu, 
1994). The limestones from the Ogaden Basin belong 
to the Hamanlei Formation. The Hamanlei Formation 
is composed of  several limestone facies like grainstones 
(peloidal, oolitic and skeletal) to mudstones, dolostones 
and evaporates (Berhanu, 1994). 
A collection of  more than one hundred rock samples which 
had previously been analyzed in a lab using an ultrasonic 
pulse-echo device were used to choose the samples 
(Berhanu, 1994b). The best samples for this investigation 
were chosen using the findings of  petrophysical studies, 
which included porosity, permeability, ultrasonic velocity, 
and attenuation. For this study, new resistivity measurements 
are essential because they enable the examination of  joint 
elastic-electrical properties. Additionally, new ultrasound 
measurements are crucial because they provide a cross-
check on the samples to determine whether they have 
changed since the initial petrographic and ultrasonic 
property measurements.

Thin-Sections Description 
The thin-section 1 was taken in plane-polarised light of  

sample RS08A, a limestone rock (grainstone according 
to Dunham classification scheme). The field of  view is 
mostly occupied by grains of  oolite, pellods and some 
shell fragments, and the cement by large calcite crystals 
with perfect rhombohedral cleavage. Most crystals show at 
least one good cleavage.  The thin-section 1 is an example 
of  a grain-supported (GS) microstructural texture 
(micrite content less than 10%) with sparry cement and 
grains forming tight arrangements of  mosaic-like texture.
 The thin-section 2 was taken in crossed-polarised light 
of  sample RS08B, a limestone rock (grainstone according 
to Dunham classification scheme). The field of  view is 
occupied mostly by grains of  oolite, pelloids and some 
shell fragments. The thin section was stained by the dye 
Alizarin Red S so both macro pores and calcite crystal 
show a yellowish colour, but in crossed-polarised light, 
the macro pores turn black, as shown in thin sections 
2 and 3. Under crossed-polarised light, the interference 
colour for calcite crystals in a lower photograph in Figure 
1, Thin-section 2, can be seen to be of  very high order 
birefringence (the thin-section might be a slightly less 
than normal 0.03 mm in thickness since in sections of  
standard thickness the interference colour produced is a 
high-order white (Mackenzie et al., 1980).

Figure 2: Thin selections optical images under the microscope. 1) The thin-section taken in plane-polarized light and 
it is RS08A, limestone rock (grainstone according to Dunham classification scheme). (2) The thin-section taken in 
plane-polarized light and it is RS08B, limestone rock (grainstone according to Dunham classification scheme). 3) The 
thin-section taken in plane-polarized light and it is Base Bed 3, limestone rock (wackestone according to Dunham 
classification scheme). 4) The thin-section taken in plane-polarized light and it is S-39446, limestone rock (wackestone 
according to Dunham classification scheme).

Both the limestone and dolomite may show a twinkling 
effect, which is caused by some minerals like calcite and 
dolomite crystals showing a marked change in relief  when 
the microscope stage is rotated (Anthony E Adams et al., 
2017; Mackenzie, W. S/ Guilford, 1980). The thin-section 
2 is an example of  a fluid-supported (FS) microstructural 

texture with a slightly higher number of  macro pores 
(black colour) with sparry cement (micrite content less 
than 10%) and grains forming tight arrangements of  a 
mosaic-like texture.
Whereas, the thin-section 3 was taken in crossed-polarised 
light of  sample Base Bed 3, a limestone rock (wackestone 



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according to Dunham classification scheme). The field of  
view is occupied mostly by grains of  oolite, pelloids and 
some shell fragments and micrite matrix. The thin-section 
3 is an example of  a fluid-supported (FS) microstructural 
texture with a slightly higher number of  macro pores 
(black colour). The micrite matrix, which is mainly 
constituted of  aggregates of  rounded, micrite crystals, 
confers a stiff  and tight texture to the rock (Vanorio & 
Mavko, 2011).

Under plane-polarized light, sample S-39446-a limestone 
rock classified as wackestone under the Dunham 
categorization scheme-was examined in thin-section 4. 
The majority of  the objects in the view’s field were oolite 
grains, pelloids, micrite matrix, and a few shards of  shell. 
An illustration of  a matrix supported (MS) microstructural 
texture is thin-section 4. The micrite matrix, which is 
mostly made up of  clusters of  spherical micrite crystals, 
gives the rock a tight, hard feel (Vanorio & Mavko, 2011).

Table 1: Rock composition (from thin polished section)
Sample 
name 
(WELL-
DEPTH)

Fm. Facies Rock composition from thin section (%)
Grain Cement Matrix 

(Mic.)
Mean 
Grain dian 
(μ m)

Ooid Inel Skel. Cal. Qz. Dol Mic Non mic
Well:H1A
S-39400 Great 

Oolite
WKST 23 3 12 0 0 0 9 0 50 668

S-39415 WKST 46.4 0 3.1 0 0 0 17.9 0 15.9 480
WELL:H4
S-39433 Great 

Oolite
PKST 41.3 0 7.7 1.2 0 0 39.4 0 2.1 692

S-39437 GRST 30.4 0 4.3 0 0 0 36.5 0.3 7 423
S-39438 GRST 34.9 0 22.1 13.9 0 0 16.1 0 0.6 787
S-39440 GRST 34.1 0 18.7 0 0 0 35.3 0 0.9 205
S-39446 WKST 24.4 0 24.4 2.8 0 0 16.9 0 24.4 603
S-39448 PKST 27.4 0 21 4.6 0 0 30 0 7.1 930
well: HG3
S-39454 HC  MST 0 0 0 0 43.7 7 42 0 5.3 68
S-39456 MST 0 0 0.3 1.7 33.7 2.6 37.8 0 10.4 98
S-39457 PKST 6.2 3.8 20.9 3.4 0.3 29.9 27.1 0 7.2 940

Note: GO=Great Oolite, HC=Hester’s Copse, GRST=Grainstone, PKST=Packstone, WKST=Wackestone and MST=Mudstone).  
This table was adapted from (Berhanu, 1994)

MATERIALS AND METHODS
Experimental procedure
Every sample has been prepared using the normal 
procedures outlined in (McCann, 1992; Han, 2011b). 
The samples were cut to a length of  2 cm, having their 
two end faces ground parallel and flat to within ± 0.01 
mm. The samples were cleaned and cored as cylindrical 
plugs with a 5 cm diameter. After placing them in an 
oven set at 40 °C for three days, the samples were dried. 
After the samples had been cleaned and dried, they were 
evaluated, dimensions were recorded, and for three days 
they were submerged in 35g/l of  brine at a pressure of  7 
MPa. Subsequently, the saturated samples were placed in 
a brine-filled tank and swiftly transferred into the high-
pressure rig for electrical and ultrasonic measurements.
For carbonate rocks, the pore fluid pressure was kept at 5 
MPa. The geophysical parameters P- and S-wave velocity 
and attenuation, as well as electrical resistivity, were 
measured successively for each sample at each effective 
pressure of  50, 40, 30, 20, and 10 MPa. The pressure 

was given 30 minutes to equilibrate before measurements 
were started. The temperature and relative humidity in 
the laboratory were maintained at 19 ± 1°C and 50 ± 1%, 
respectively.

Joint Ultrasound and Electrical Resistivity 
Tomography (ERT) Rig
At frequencies ranging from 400 kHz to 800 kHz, P- 
and S-wave velocities (Vp, Vs) and attenuations (Qp-1, 
Qs-1) were measured using the ultrasonic pulse-echo 
technique (Winkler, 1982; McCann, 1992). Researchers 
employed a dual P- and S-wave transducer/receiver with 
measurement accuracy of  ± 0.3% and ± 0.2 dB/cm 
for the velocity and attenuation coefficient, respectively 
(Best, 1992). Figure 1 shows the high-pressure rig, and in 
accordance with (North et al., 2013), the sample assembly 
was modified for resistivity tomography measurements.
The sample was encircled by a rubber sleeve that held 
16 tetrapolar stainless steel electrodes spaced radially into 
two rings for the purpose of  measuring the electrical 



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resistivity tomography (Figure 1). The electrodes provided 
a resistivity measurement error under typical working 
circumstances of  < 0.1% at frequencies 1 - 500 Hz, with 
a sample electrical resistivity range of  1 - 100 Ohm m. 
The electrodes permitted current injection and boundary 
voltage probing in many permutations. 

Estimation of  Microstructural Parameters
While experimentation, the carbonate sample 
microstructure was divided into five main parameters: 
macro porosity, solid grains, microporous micrite 
(porosity within micrite), micrite content (micrite 
excluding any microporosity), and calcite crystals (sparry 
calcite). Microporous micrite has a lower density than 
solid calcite grains due to the presence of  microporosity. 
As a result, the microporosity and solid micrite grains can 
be separated according to the grey scale intensity contrast 
in petrographic images. Consequently, image processing 
techniques were also used that were suggested by Grove 
and Jerram (2011) and Vanorio and Mavko ( 2011) to 
quantify the five microstructural parameters from optical 
microphotographs of  the carbonates and dolomite 

samples. However, in practice, this was only feasible 
for dolomite samples because the intensity contrast was 
insufficient for the carbonate samples. The study used 
Fuji/ImageJ software. Additionally, the study presented 
an approach to estimate the uncertainty associated with 
the quantified parameters. 

Estimation of  Parameters From Optical Microscopy
The petrographic thin sections were blue resin-
impregnated to facilitate observation of  porosity from 
optical microscopy images, and the dolomite samples 
thin-sections were stained by the dye Alizarin Red S, 
used here to differentiate calcite and dolomite (dolomite 
shows original clear stain colour, while calcite shows a 
pink tored–brown when stained). The dyes are dissolved 
in a weak acid solution as dolomite does not react with 
cold dilute acid, whereas calcite does. During the practical 
experimentation, basically, two stages to process the 
images were used and five different textural classes. To 
illustrate the process, it was suitable to refer an example 
images in Figures 4.2 – 4.7 and the microstructural 
elements outlined in Table 4.2.

Table 4.2: Relative image intensity levels of  each microstructural element, and symbols used in text
Microstructural element Symbol Image intensity Grey levels Min/Max
Macro porosity 𝜙macro Black 0-40 (± 20)

Ooids, pelloids and shell fragments F bio Dark grey 40-90 (± 20)
Micrite Micrite Grey 90-160 (± 20)
Calcite, sparite F calcite Light grey 160-255 (± 20)
Microporosity in dolomite 𝜙micro White 120-225 (± 20)

However, figure 2 shows a flowchart for the key 
operations used to extract the relevant measurement. 
Digital image acquisition was achieved using a NIKON 
D5600 DSLR Camera attached to a microscope to 
acquire a digital image directly from the thin section. The 
image was acquired with low magnification (5X zoom)  
in an optical microscope to capture a large area so that 

it will be representative of  the rock  (Croizé et al., 2010; 
Fournier & Borgomano, 2009; Lambert et al., 2006; 
Saxena, Hofmann, et al., 2017). Grove & Jerram (2011) 
pointed out that some of  the images they acquired at 
low magnification had distortion toward the edges of  the 
frame (darker than the rest of  the frame). Still, the author 
did not observe this in the low-magnification images.
A total of  five different locations were captured from the 
thin section, and each optical microscopy image was used 
to estimate the microstructural parameters of  interest. 
It was determined that the overall microstructural 
parameters for the rock by averaging the values obtained 
from all five images. Figure 3 shows the workflow that 
was used to estimate the microstructural parameters. The 
first stage was colour thresholding the images to indicate 
the macro porosity, which has a blue colour for blue 
resin-impregnated thin sections, as shown in Figures 3A 
& B, and appears yellow to black for thin sections stained 
by Alizarin Red S dye in Figure 3C. For example, fenestral 
porosity in a carbonate rock is pore space larger than the 
normal grain-supported spaces, and it can be a different 
size or shape depending on its origin (see Figure 3 C).



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Figure 3: Workflow using stage1, colour thresholding within ImageJ to convert the blue colour to black. Screenshots 
are shown for key operations. A)  Segmentation of  a thin section optical image (from sample S-39400, an oolitic 
limestone) under the transmitted light microscope. B) Segmentation of  a thin section optical image (from sample 
RS01-2, a sparry calcite) under the transmitted light microscope. C)  Segmentation of  a thin section optical image 
(from sample GRN 2 a sparry calcite) under the transmitted light microscope.



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Fenestrae with an elongated shape might be due to 
entrapment of  fluid in a sediment during desiccation 
(A E Adams et al., 1984). Using ImageJ, 24-bit colour 
images are thresholded using an 8-bit colour conversion 
tool (image>Type>8-Bit colour) set to 256 colours. 
Then, converted the blue/yellow colour to black. One 
of  the drawbacks of  using grey-intensity thresholding 
is that the macro porosity in blue resin-impregnated and 

Alizarin Red S dye-stained thin sections changes colour 
to grey, giving a low contrast between macro porosity 
and micrite aggregates, making it difficult to segment. 
This can be avoided by changing the colour of  the macro 
porosity from blue /yellow to black, so it can be easily 
distinguished during the subsequent grey-scale intensity 
thresholding. The image was then converted to a grey-
scale, as shown in Figure 4A.

Figure 4: Workflow using stage2, greyscale thresholding within ImageJ. Screenshots are shown for key operations. A) 
image convert to 8-bit grey intensity. B) Shows greyscale thresholding the image and thresholder slide used to choose 
the greys intensity for microstructural parameters and the results

The second stage of  the workflow was to use grey-scale 
thresholding to segment the images. Table 4.2 shows the 
different grey-scale intensities for the variety of  allochem 
type clasts; for example, bioclasts and coated grains 
(ooids), calcite (sparite), micrite, micro porosity and 
macro porosity. This diversity in the intensity response 
allowed us to segment the picture and obtain five different 
distinct classes. It was judiciously selected a value for 
thresholding based on a grey-scale intensity and geology 
knowledge to segment the image. After that, it can be 
calculated the fraction of  the classes associated with the 
grey-scale intensities below the threshold. A two-step 
segmentation scheme (i.e., two threshold values) allowed 
to obtain the microstructural parameters as follows: (A) 
the first threshold separates the macro porosity (i.e., black 
regions in optical microscope image) from the rest of  the 
image, (f1), is given by
f1 = ∅macro,                (1)
Where f1 is the first threshold and ∅macro is macroporosity. 
Thereby calculating the macroporosity (𝜙macro) as 
illustrated in Figures 5, (A), Figure 6 (A) and 4.7 (A). The 
second threshold (f2) has a higher grey-intensity value 

than the first threshold and separates the fbio and macro 
porosity from the rest of  the image, as shown in Figure 
5 B f2 is given by
f2 =∅macro+ fbio,                           (2)
Where f2 is the second threshold and fbio is a fraction 
of  ooids, pelloids and shell fragments. The fraction of  
calcite crystals (fcalcite) has a light grey colour, so to 
calculate the sparry calcite only in ImageJ software, it 
simply reverses the threshold to start from white to black, 
and the results are added to solid grains (fgrains), see 
Figure 5C and Figure 6C. fcalcite   is given by
fcalcite =f3,                  (3)
where fcalcite is the fraction of  calcite crystals (sparite), and 
f3 is the third threshold.
The fraction fbio is given by
fbio =f2- ∅macro.                 (4)
The fraction of  solid grains  fgrains  is given by
fgrains =fbio + f3 ( fcalcite)                 (5)
The fraction of  micrite aggregate, i.e., solid micrite 
crystals (fmicrite) + micro porosity (𝜙micro), is given by
fmicrite aggregate = 100 - (fgrtains+ ∅macro )               (6)
The micro-porosity cannot be estimated directly from 



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optical microscope images due to the low magnification 
used in the image acquisition. In a low magnification 
image, the contrast in a grey intensity for the solid micrite 
crystals and microporosity is too low to be thresholded. 
Therefore, the micro-porosity was estimated indirectly 
by subtracting the macro-porosity from the measured 
helium porosity (𝜙); ∅micro  is given by
∅micro = ∅ - ∅macro, (7)
where ∅micro is microporosity, ∅ is measured helium 
porosity and ∅macro is macroporosity. Finally, the micrite 
content fmicrite is given by
fmicrite =f  micrite aggregate - ∅micro               (8)
The overall fraction of  microstructural parameters for 

each rock sample was estimated in the five different 
images and then averaged.
Figures 5 to 7 show examples of  the Stage 2 workflow: 
grey-scale intensity thresholding of  the segmentation 
output separating solid grains’ from macro porosity and 
calcite crystals. Figure 5 is rock sample S-39400, an oolitic 
limestone (Wackestone).
Figure 5A shows the final segmented image with macro 
porosity in black. Figure 5B, shows macro porosity and 
solid grains, which can be recognised in two forms: 
allochems type clasts such as bioclasts and coated grains 
(ooids) highlighted in red. Figure 5 C shows the calcite in 
red colour.

Figure 5: Segmentation of  a thin section optical image (from sample S-39400, an oolitic limestone, Wackestone) 
under the transmitted light microscope. A) The macroporosity is highlighted in black. B) Shows macroporosity and 
bioclasts and coated grains (ooids) highlighted in red. C) Shows calcite in red colour

Figure 6 shows a sequence of  digital images of  a thin 
section microphotograph of  sample RS01-2 (pack stone 
according to Dunham classification scheme). The macro 
porosity is shown in black in Figure 6 A. This sample 
comprises entirely sparry calcite without any ooids, so 
some adjustment has been made for the calculation of  
microstructural parameters. The first threshold was 
the macro porosity. The second threshold was macro 
porosity and micrite aggregate. The micrite aggregate 
can be calculated by subtracting f1 from f2. The third 

threshold was calcite. The micro-porosity was calculated 
using Equation 7.
Figure 7 shows sample ALG1.1, a dolomite rock 
(microbial-laminated dolo-mudstone according to 
Dunham classification scheme). Figure 7 A shows the first 
threshold macro porosity. The second threshold was macro 
porosity and micrite aggregate. The micrite aggregate can 
be calculated by subtracting f2 from f1, as shown in Figure 
7 B. The third threshold was the microporosity within 
micrite aggregate, as shown in Figure 7 C.



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Figure 6: Demonstrating of  the first stage segmentation of  thin section optical image (Packstone; sample No. RS01-2 
under the microscope .A) The macroporosity is highlighted in black. B) Shows macroporosity and micrite highlighted 
in red. C) Shows calcite in red colour

Figure 7: Demonstrating of  the segmentation of  thin section optical image under the microscope. The sample 
ALG 1.1 a, dolomite rock (microbially- laminated dolo-mudstone according to Dunham classification scheme). (A) 
The macroporosity was highlighted with red colour. (B) The micrite aggregate was highlighted in red colour. (d) The 
micro-porosity was highlighted with red colou



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Generally, the workflow is able to distinguish (visually) 
quite well the different components of  calcite grains, 
macroporosity and micrite aggregate (including micro-
porosity). However, it is not possible to resolve the actual 
micro-porosity associated with the micrite aggregates 
in limestone samples as micrite consists of  clay-size 
particles (1 - 4 μm); we would need a higher magnification 
to capture the micro-porosity.

Quantification of  Uncertainty Associated with 
Parameters Estimation
Quantifying the uncertainty related with an estimation 
of  microstructural parameters is important to indicate 
the reliability of  rock-type classifications for theoretical 
modelling. Table 4-3 shows an example of  the uncertainty 
associated with the picking threshold when pushing the 
limit of  what would be the correct thresholding to see 
the sensitivity of  each parameter. Table 4-3 shows four 
examples, one from each facies: mudstone, wackestone, 
packstone and grainstone.
A semi-quantitative estimate of  confidence, as a measure 
of  uncertainty, was determined by calculating the 
estimated percentages of  the parameters of  interest 
(macroporosity, oolitic grains, calcite and micrite) for the 
chosen threshold grey scale value, and for this threshold 
value plus or minus ten grey scale units. The results in 
Table 4-3 show the variation in all parameters is less than 
6% for all rock types. Thus, the uncertainty associated 
with the picking threshold is within an acceptable range, 
and the results still show similar trends for micrite content 
as a function of  porosity, permeability and compressional 
velocity as the results reported in the literature (Vanorio, 
2011; El Husseiny, 2015).

Estimated Parameters and Associated Uncertainty
Figure 8 below shows a comparison between micrite 
aggregate estimated using digital photomicrographs of  
thin sections by an optical microscope and that estimated 
from the point counting method for samples with 
available data (only 10 samples). The results show that 
the two techniques, in general, are comparable. Moreover, 
figure 8 also shows micrite aggregate estimated by two 

different techniques: by image analysis using ImageJ 
software and by point counting. The most interesting 
aspect of  this graph is that the two techniques, in general, 
are comparable for the samples characterised by very 
low or high microcrystalline calcite with micro-porosity. 
In contrast, there is a mismatch for the samples with 
intermediate micrite aggregate (30% - 50%). The vertical 
bar corresponds to the standard deviation associated 
with average reported micrite aggregate from optical 
microscope image technique.

Figure 8: Plot of  micrite aggregate (i.e., solid micrite 
crystals (fmicrite) + micro porosity (𝜙micro) estimated 
from the optical image under the microscope versus that 
obtained from point counting

In general, the mismatch might be due to the larger 
number of  data points used in ImageJ image analysis than 
used in point counting. Equally important, the parameter 
standard deviations for these samples are relatively 
high, indicating the samples are more heterogeneous. 
Therefore, the two techniques are less likely to match. 
Furthermore, in the next sections, the study also explored 
the relations between the parameters measured from 
image analysis, and in particular, the effect of  micrite 
content on reservoir and geophysical properties.

Table 4-3: Semi-quantitative estimate of  confidence when using threshold and ± 10 is grey scale values
Samples Facies Macroporosity 

(%)
Oolitic grains (%) Calcite (%) Micrite (%)

-10

T
hr

es
ho

ld +10 -10

T
hr

es
ho

ld +10 -10

T
hr

es
ho

ld +10 -10

T
hr

es
ho

ld +10

ALG1.3 Mudstone 5.09 8.13 12.72 0 0 0 0 0 0 87.28 91.87 94.91
Base Bed 3 Wackstone 0.41 3.31 8.35 53.74 58.63 64.21 4.71 5.81 7.53 28.24 32.25 38.11
S-39457 Packstone 0 1.37 6.88 23.39 25.37 27.92 32.87 42.15 50.21 28.45 31.11 34.72
RS08B Grainstone 0.33 1.05 5.68 58.2 61.48 64.57 27.01 30.11 33.42 4.26 7.36 10.67



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RESULTS AND DISCUSSION
The Effect of  Micrite Content on Reservoir Porosity, 
Permeability and Geophysical Properties 
Micrite and Porosity Relationships
Figure 9 shows a cross-plot of  the microcrystalline 
calcite (micrite) and porosity on a log-linear scale for 
all 21 limestone samples and 15 dolomite samples. For 
limestones and dolomites in Figure 9, a U-shape was 
observed where micrite content ranged from 0% to 
10%, showing no relationship between micrite content 
and porosity as a different range of  porosity was found 
between 0% and 10% micrite content.

Figure 9, Sample 1, represents a grain supported (GS) 
microstructural texture (micrite content less than 10%) 
with sparry cement and grains forming tight arrangments 
of  mosaic-like texture. Thin section 2 in Figure 1 
corresponds to sample number 2 in Figure 9, which is 
RS08B (a grain stone according to Dunham classification 
scheme). Sample 2 represented fluid supported (FS) rock 
which had a secondary porosity, like vugs, fenestral and 
moldic porosity.
For limestones and dolomites in Figure 9, porosity 
starts to increase for micrite content lower than 10%. 
Thus, a possible mechanism for this behaviour could be 
diagenesis; for example, as the grains and sparry cement of  
sample 1 in Figure 9 become corroded and etched around 
their boundaries, this could result in the textural features 
seen in, for example, sample 2. The carbonate diagenesis 
dissolution and leaching process may transform sample 
1 grain stone supported texture to evolve toward a fluid-
supported texture, as seen in sample 2 in Figure 1.
Thin section 4 in Figure 1 corresponds to sample number 
4 in Figure 9, which is S-39446 (wackestone according to 
Dunham classification scheme). This sample has a matrix-
supported (MS) rock microstructure predominantly 
constituted of  aggregates of  rounded, microcrystalline 
calcite (micrite), conferring a stiff  and tight texture to the 
rock. Hence, a significant portion of  the total porosity 
is formed from small, rounded micro-pores of  micrite. 
Thin section 3 in Figure 1 corresponds to sample number 
3 in Figure 9, which is Base Bed 3 (wackestone according 
to Dunham classification scheme). Thin-section 3 shows 
significant numbers of  macropores.
For limestones and dolomites in Figure 9, porosity 
decreases from the maximum around 10 – 30% micrite 
as micrite content increases to 70%. The majority of  
dolomite samples that have micrite content above 30 
% were wackestone and pack stone and the dolomite 
samples were mudstone. A possible mechanism for 
this behaviour could be diagenesis, illustrated by, for 
example, as sample 4 textures alter to textures seen in 
sample 3 in Figure 9; it can be seen that some micrite 
has been washed out from the intergranular space. The 
carbonate diagenesis dissolution and leaching process 
leads to micro-pores becoming more connected, and the 
development of  molds, large vugs and channels, as seen 
in sample 4 in Figure 1. That is, a matrix supported rock 
evolves toward a fluid supported rock.  
Figure 10 shows the cross-property relation between 
macro porosity and micrite content. Note that micrite 
content is inversely proportional to the amount of  macro 
porosity (see equation 1). The micro-porosity in Figure 
11 increases as micrite content increases and these data 
agree with the literature (Baechle et al., 2004; Brigaud et 
al., 2010; Cantrell & Hagerty, 1999; Eberli et al., 2003; 
Fournier & Borgomano, 2009; Kazatchenko et al., 2006; 
Norbisrath et al., 2015; Regnet et al., 2015; Vanorio & 
Mavko, 2011; Weger et al., 2009).

Figure 9: Shows the variation of  porosity as a function 
of  micrite content. Red data are the limestone samples, 
and blue are the dolomite samples. The numbers in 
black refer to values measured on the samples whose 
microstructure is shown in Figure 1.

Vanorio and Mavko (2011) showed a similar trend to this 
study of  porosity as a function of  micrite content. On 
the other hand, (Husseiny & Vanorio, 2015; Dominique 
Marion, 1990) reported an opposite trend of  porosity as a 
function of  micrite content. The trend was from synthetic 
micrite samples that lack macroporosity. Whereas, in 
our study, and in that of  (Vanorio & Mavko, 2011), the 
samples were from natural carbonate rocks which are 
inherently populated with macro porosity and, secondary 
porosity. The presence of  secondary porosity can be 
derived from different diagenetic evolution of  porosity 
in carbonates rocks.
The numbers in black in Figure 9 refer to values measured 
on the samples whose microstructure is shown in Figure 
1. A visual check of  the thin sections 1 to 4 in Figure 1 
for limestone samples (dolomite samples showing similar 
microstructures) shows a possible mechanism for the 
trends in Figure 9 porosity with micrite content.
Thin section 1 in Figure 1 corresponds to sample 
number 1 in Figure 9, which is sample RS08A (a grain 
stone according to Dunham classification scheme). 



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Figure 10: Shows the variation of  macroporosity as a 
function of  micrite content. Red data are the limestone 
samples, and blue are the dolomite samples

Figure 11: Shows the variation of  macroporosity as a 
function of  micrite content. Red data are the limestone 
samples, and blue are the dolomite samples

Figure 1 supports the conceptual model of  Vanorio and 
Mavko (2011) that describes the evolution of  the calcite 
grain-micrite mixture in carbonates based on grain-
micrite-pore fraction and the above explanation of  the 
variation of  porosity with respect to micrite fraction (see 
section 1.2.3).

Micrite and Permeability Relationships
Figure 12 shows the cross-property relations between 
permeability and micrite content on a log-linear scale. 
For limestones and dolomites in Figure 12 , the micrite 
content ranging from 0% to 10%, the permeability shows 
no correlation with micrite fraction as permeability exists 
in all ranges. The permeability shows a peak when micrite 
content is around 10% and 30% and these samples 
were limestone wackestone. Then, permeability starts to 
decrease drastically for micrite content higher than 30% 
and the facies straddle between limestone ( wackestone 
and packstone) and the dolomite was the only mudstone. 

Vanorio (2011) reported the variation of  permeability 
with micrite did not appear to be large (permeability from 
4.9 to 213.3 mD), and also noted a peak at 10% to 20% 
micrite content. We see a much larger dependence of  
permeability on micrite in our rock samples (permeability 
from 0.01 to 824 mD).
The numbers in black in Figure 12 refer to values measured 
on the samples whose microstructure is shown in Figure 
1. A visual check of  thin sections 1 to 4 in Figure 1 for 
limestone samples (dolomite samples showing similar 
microstructures) suggests a possible mechanism for the 
permeability-micrite trends seen in Figure 12.
Thin-sections 1 and 2 are grain-supported with sparry 
cement. For limestones and dolomites in Figure 12, 
permeability starts to increase with increasing micrite 
content approximately up to 10%. Thus, a possible 
mechanism for this behaviour could be due to diagenesis 
as sample 1 goes to sample 2 in Figure 12, the grains and 
sparry cement might become corroded and etched around 
their boundaries. The carbonate diagenesis dissolution 
and leaching process may transform sample 1 in Figure 1 
grain stone supported to evolve toward fluid a supported 
rock, for example, sample 2 in Figure 1.
For limestones and dolomites in Figure 12, permeability 
starts to increase as micrite content deacreas from 70%  
up to around 30%. Thus, a possible mechanism for 
this behaviour could be diagenesis, as sample 4 goes to 
sample 3 in Figure 12, which shows that some micrite is 
washed out from the intergranular space. The carbonate 
diagenesis dissolution and leaching process leads micro-
pores to become connected, with molds, large vugs and 
channel development leading to sample 4 in Figure 1, i.e. 
matrix supported evolves towards a fluid-supported rock, 
for example, sample 3 in Figure 1.

Figure 12: Shows the variation of  permeability as a 
function of  micrite content. Red data are the limestone 
samples, and blue are the dolomite samples

Porosity and Permeability Relationships
Figure 13 shows the cross-property relations between 
porosity and permeability for all 21 limestone samples 
and 15 dolomite samples. In general, the permeability 
increases as porosity increases, as expected, according 



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to the literature for similar rocks. (Archilha et al., 2016; 
Costa, 2006; Dvorkin, 2009; El Husseiny & Vanorio, 
2017; Fabricius et al., 2010; R. Han et al., 2010; Lima 
Neto et al., 2014; Mavko & Nur, 1997; Saxena, Mavko, 
et al., 2017; Verwer et al., 2011; Vialle et al., 2013; Weger 
et al., 2009) found that Kozeny-Carmen relation fitted 
their observations using constant parameters B = 5, 0.5, 
0.05, 0.005 overlain on Figure 13. This shows that our 
data are in reasonable agreement with the literature for 
similar oolitic limestone, sparry calcites and hydrothermal 
dolomites. The Kozeny-Carmen relation is
k=B  ( ∅- ∅c)

2 )/(1+ ∅c- ∅)2 d2,              (9)
where k is permeability, B constant, d = 150 μm is 
diameter, ∅c =36 % is critical porosity and ∅ is porosity.
Also, the data points are colour-coded for micrite content 
and symbols for grain-supported dolomite (sparry calcite 
cement), matrix-supported dolomite (micrite matrix), 
grain-supported limestone (sparry calcite cement), 
matrix-supported limestone (oolitic-micrite matrix) and 
fluid-supported limestone (oolitic-micrite matrix with 
large vugs, moldic, and fenestral porosity).
Limestone samples in figure 13 shows that fluid-supported 
samples which was a result of  diagenesis dissolution have 
a higher porosity-permeability than matrix-supported 
samples and these samples have ooids and peloids for 
grains. The grain-supported limestone has less than 10% 
micrite content and follows matrix-supported samples 
in porosity-permeability space. Dolomite samples show 
that adding micrite content seems to reduce porosity 
and permeability. In general, the sample with high macro 
porosity also has high permeability due to pore space being 
connected, as shown in Figure 13, with fluid-supported 
samples (diamond shape), and the main processing was 
diagenesis leaching and dissolution. However, samples 
with critical porosity in which the pore space is filled 
with micrite (matrix-supported) or sparry calcite (grain-
supported limestone) in Figure 13 has implications for 
overall results as critical porosity samples have a high 
porosity but low permeability as the pore space is not 

connected. The critical porosity samples consider to be 
poor reservoir as they have lower permeability which 
makes the migration of  hydrocarbon low.
El Husseiny & Vanorio (2017) investigated the effect 
of  micrite content and macro porosity on the porosity-
permeability relationships using analog samples created 
in a laboratory in a dual porosity system. They showed 
that adding micrite to grain-supported samples reduces 
porosity and permeability drastically, up to approximately 
30% micrite content. At higher micrite contents, the 
sample became micrite-supported and adding more 
micrite only affects porosity, not permeability.

The Effect of  Micrite on Elastic Wave Velocity
The variation of  P-wave velocity as a function of  micrite 
content is shown in Figure 4-15. For limestones, the 
velocity decrease with increasing micrite content, where 
a minimum in velocity is observed around 10% and 30% 
micrite content while the dolomites samples between 0 % 
and 10 % show no relationship between micrite content 
and velocity. Then, the velocity starts to increase with 
increasing micrite content above 30% (S-wave velocity 
shows similar).
Regarding the effect of  micrite between 0 and 30% on 
elastic properties, we notice from a visual check of  thin 
section 1 in Figure 1 and thin section 2 in Figure 1 that 
the dominant process was leaching and dissolution. The 
optical microscopy shows a carbonate microstructural 
texture mainly represented by a grain-supported 
framework with relatively low amounts of  lime-mud 
interstitial material in Figure 1 and presence of  tight 
spar cement and microcrystalline calcite forming crystal-
mosaic textures and interlocked arrangements.
Figure 12 reveals that carbonate rocks characterised 
by micrite contents between 10% and 30% have a 
higher fraction of  grains to micrite matrix. These 
carbonate samples were characterised by higher macro 
porosity in Figure 10, due to leaching and dispersing 
of  microcrystalline calcite matrix that filling the grains’ 
interstices (Vanorio & Mavko, 2011). Thus, leading to 
increasing of  total porosity in Figure 9, and low velocity 
in this region.
For limestones and dolomites in Figure 15, velocity starts 
to decrease for micrite content lower than 10%. Thus, 
could be due to diagenesis as sample 1 goes to sample 2 
in Figure 4.9, the grains and sparry cement might become 
corroded and etched around their boundaries and the 
facies of  limestone were grain stone and wackestone. The 
carbonate diagenesis dissolution and leaching process 
may transform sample 1 grain stone supported to sample 
2 fluid supported rock.
Figure 15 shows velocity to decrease with decreasing 
micrite content from 70% up to 30% and the dolomite 
were only from one facies, and it was mudstone 
while limestone was pack stone and wackestone. The 
sample is matrix supported (MS) rock microstructure 
predominantly constituted of  aggregates of  rounded, 
microcrystalline calcite (micrite) crystals conferring a stiff  

Figure 13: Shows the variation of  permeability as a 
function of  porosity. Red data are the limestone samples, 
and blue are the dolomite samples



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Figure 15: Shows the variation of  (a) P-wave and (b) S-wave velocities as a function of  micrite content

Figure 16: Pressure sensitivity of  (a) P-wave and (b) S-wave velocities as function of  micrite content. Data are coded 
as a function of  pressure

and tight texture to the rock. Hence, a significant portion 
of  the total porosity is from small, rounded micro-pores 
of  micrite (Vanorio & Mavko, 2011). Figure 15, Sample 3, 
represent fluid supported (FS).
Thin sections 3 and 4 in Figure 1 suggest some micrite 
was washed out from the intergranular space and this 
factor leads total porosity to increase. Also, the removal 
of  micrite might reduce the effective rock stiffness as 
micrite (an aggregate of  rounded micro-crystals) is a stiff  
microstructural component, therefore, both factors might 
reduce the velocity. Thus, the main process that leads 
velocity to decrease with micrite content from 70 % up to 
30% is carbonate diagenesis dissolution and leaching.

Figure 16 shows the effect of  pressure on velocity and 
micrite content. The limestone samples with micrite 
content of  less than 10 % show very high-pressure 
sensitivity. Then samples between 10 and 30 % micrite 
content shows that the sensitivity of  velocity to pressure 
was low. After that, the sensitivity of  velocity to pressure 
start to slightly increase but it’s less than samples with less 
than 10 % micrite content. The dolomite samples were 
very tight and was independent of  pressure. Red data are 
the limestone samples, and blue are the dolomite samples. 
The numbers in black refer to values measured on the 
samples whose microstructure is shown in Figure 1.

This phenomenon was due to micrite-rich samples being 
stiffer than grain-supported samples even though micrite-
rich samples had higher velocities. This suggests that the 
micrite-rich samples were tight, with few compliant pores, 
whereas grain-supported samples had more compliant 
pore structures. Red colour is limestone samples and blue 
colour is dolomite samples. The differential pressure is 
50MPa and 10MPa in plus sign.

 These results agree with (Husseiny & Vanorio, 2015), for 
the relation between velocity and micrite content as they 
made a synthetic carbonate rock of  two sets of  data, one 
without cement and the other sets of  data with cement. 
Husseiny & Vanorio et al., ( 2015) show that micrite-rich 
samples characterised by larger acoustic velocities for both 
the uncemented and cemented samples which suggests 
that the presence of  cement was not the main controlling 



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factor. Husseiny & Vanorio et al., ( 2015) explained this 
phenomenon by velocity sensitivity to pressure as the 
sensitivity of  velocity to pressure increases as the micrite 
content decreases. In the contrary, while the sensitivity 
of  velocity to pressure increases, the micrite content 
decreases which indicate that grain-supported samples 
have a more compliant structure.

The Effect of  Micrite on Attenuation
Seismic attenuation is a great attribute that can be utilised 
as an indicator of  fracture, lithology, clay, fluid content 

Figure 14: Shows the variation of  permeability as a function of  a) microporosity b) macroporosity. Red data are the 
limestone samples, and blue are the dolomite samples

Figure 17: Shows the variation of  a) P-wave b) S-wave quality factors as a function of  micrite content. Red data are 
the limestone samples, and blue are the dolomite samples

and pore structure in a reservoir rock (Parra, 2006). The 
cross-property relations between micrite content and 
Qp-1 and Qs-1 are shown in Figure 14 (a, b), respectively. 
The data shows a non-linear relationship. Also, it is 
sparse and scattered. However, the quality factors show 
two peaks: the first peak was around 10% micrite content 
and, the dominant facies were grainstone, and the second 
peak was around 40% micrite content. The facies were 
straddled between mudstone, packstone and wackstone, 
forming a bell-shaped correlation between Qp-1 and 
micrite (Qs-1 show similar results).

Figure 17 shows the sensitivity of  the attenuation to 
pressure as a function of  micrite content. We notice 
that the two peaks were more pressure sensitive, which 
indicates the closure of  cracks and microcracks in these 
samples from 10 MPa to 50 MPa. The possible mechanism 
to explain the attenuation behaviour with respect to micrite 
was Biot mechanism (Global fluid flow). Further, in the 
two peaks, the closure of  cracks and the microcracks were 
very high as shown in Figure 18 so another mechanism 
is possible here and it is the Squirt-flow mechanism. The 

squirt-flow mechanism focuses on the loss resulting from 
the ‘local’ flow of  viscous fluid into and out of  microcracks 
during the passage of  acoustic waves. Assefa, McCann and 
Sothcott, (1999) studied the effect of  attenuation on facies 
such as mudstone, wackestone, grainstone and packstone. 
They concluded that when the attenuation reaches the 
maximum values, the dual porosity system is developed, 
indicating that the squirt-flow mechanism, which has 
previously been shown to occur in shaley sandstones, also 
operates in the limestones.



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The Effect of  Micrite on Electrical Resistivity
Electrical resistivity is affected by key characteristic which 
is pore structure as the connectivity and size of  the pores 
control the overall pore network connection, and the 
pore throats will be affected by capillary forces. Many 
controlling factors affect Electrical resistivity such as (1) 
size of  the pore throats (Abousrafa et al., 2009), (2) on the 
amount of  separate-vugs porosity (Lucia & Conti, 1987), 
and (3) tortuosity (Saner et al., 1996).
Apparent electrical formation factor F* (defined as ρ0/
ρw, where ρw =0.213 Ωm for 35 g/l brine at a temperature 
of  19°C) measured at 80 Hz versus microcrystalline 
calcite for all 21 samples of  limestone and 15 samples 
dolomite are shown in Figure 19 on a log-linear scale. 
Apparent formation factor F* shows, in general, a 
U-shape trend with a general decreasing trend with 
increasing microcrystalline calcite for samples below 
15% microcrystalline calcite margin. Apparent electrical 
formation factor F* at the lowest value between 10 
and 30 % microcrystalline calcite. Figure 19 shows an 
increasing trend of  apparent formation factor F* with 
microcrystalline calcite for the limestone samples above 
30% microcrystalline calcite, and a steep decrease in F* 
with micrite for dolomites. These data show the opposite 
trend to the permeability data in Fig 4.12, as would be 
expected, as F* is an analogous transport property. 
Figure 19 shows apparent formation factors against 
micrite content. The apparent formation factors start 
to decrease when the micrite content below 10%. A 
visual check of  the thin sections 1 and 2 in figure 19, 
corresponds to sample number 1 and 2 in Figure 1, grain 
stone and fluid supported respectively. 
Thus, could be due to diagenesis as, sample 1 goes to 
sample 2 in Figure 1, the grains and sparry cement might 
become corroded and etched around their boundaries. 
The carbonate diagenesis dissolution and leaching 
process may transform sample 1 grain stone supported 
to sample 2 fluid supported rock. This will lead porosity 

to increase as shown in Figure 9 and the macro porosity 
will be connected and form secondary porosity such as, 
vugs, moldic and fenestral as shown in Figure 10. 
The apparent formation factors have the lowest value 
between micrite content 20-30 % due to the presence of  
both, macro porosity (vugs) and micro porosity within 
micrite content so the conductivity is high. Figure 19 
shows the apparent formation factors increase as micrite 
content increase above 30% micrite content with two 
trends parallel to each other and oolitic limestone samples 
higher than dolomite samples.
A visual check of  the thin sections 3 and 4 in Figure 1, 
corresponds to sample 3 and 4 in Figure 19, which both 
are oolitic limestone (wackestone) as shown in red colour 
in Figure 19. Sample 3, represent fluid supported (FS) 
and sample 4 represent matrix supported (MS). The red 
colour samples above 30% micrite in Figure 19 shows 
first trend and these samples are limestone characterised 
by large grains (oolite) and fine grains (micrite). The 
blue colour samples above 30% micrite in Figure 19 
are dolomite samples with majority of  rocks are fine 
grains (micrite) and they are matrix supported (MS) rock 
microstructure predominantly constituted of  aggregates 
of  rounded, microcrystalline calcite (micrite) crystals 
conferring a stiff  and tight texture to the rock (Figure 7 
A). Hence, a significant portion of  the total porosity were 
from small, rounded micropores of  micrite (Vanorio & 
Mavko, 2011). 
For limestones and dolomites in Figure 19, the carbonate 
samples above 30% micrite have higher apparent 
formation factors as micrite seems to reduce the mean 
pore size and the overall porosity and block the electrolyte 
conduction due to a reduction in connectivity between 
the pores.
The apparent formation factors start to decrease as 
micrite content decreases from 70%  up to around 30 %. 
The thin sections 3 and 4 in Figure 1 suggest a possible 
mechanism for this behaviour, and it could be due to 

Figure 18: Shows the pressure sensitivity of  a) P-wave b) S-wave quality factors as a function of  micrite content. Red 
data are the limestone samples, and blue are the dolomite samples. The differential pressure is 50MPa and 10MPa in 
plus sign



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diagenesis as, sample 4 goes to sample 3 in Figure 19, 
which shows that some of  micrite is washed out from the 
intergranular space. 
The carbonate diagenesis dissolution and leaching 
process lead micropores to become connected, molds, 
large vugs, and channel might develop, leading sample 
4 in Figure 1 matrix supported to evolve toward fluid-
supported rock, for example, sample 3 in Figure 1.  The 
apparent formation factor data show the opposite trend 
to the permeability data in Fig 12, and porosity data 
in Fig 9 as would be expected, as F* is an analogous 
transport property. Thus, the main factor that affect F* 
in carbonate rocks seems to be the porosity and micrite 
content, due to porosity and micrite content were highly 
negatively correlated for the majority of  our samples. 
The main process that leads apparent formation factors 
to decreases with micrite content from 70 % up to 30% is 
carbonate diagenesis dissolution and leaching.

macro porosity. The study also quantified the uncertainty 
associated with the estimated parameters by using 
standard deviation. The amount of  micrite aggregate 
obtained was compared by two techniques: the point 
counting method and image analysis using ImageJ 
software. The error seems to be larger when micrite 
aggregate is around (30% - 50%). The research findings 
suggested that Carbonate samples with higher micrite 
content and lower macroporosity have lower permeability 
at any given porosity. Moreover, the study also quantified 
the uncertainty associated with the estimated parameters 
and found that optical microscope images can reproduce 
microstructural parameters with an average root mean 
square error of  6%.

REFERENCES 
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Figure 19: Shows the variation of  apparent formation 
factors as a function of  micrite content. Red data are the 
limestone samples, and blue are the dolomite samples

The matrix-supported samples having most of  the 
porosity in the type of  microporosity and the grain-
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CONCLUSION
The study presented a methodological approach to 
estimate microstructural parameters such as micrite 
content and macro porosity in a carbonate rock based 
on image analysis obtained from the optical microscope. 
The image analysis technique was an effective, accurate, 
and easy method of  measuring Micrite aggregate and 



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APPENDIX I
Petrophysical and Facies properties of  the 36 carbonate 
samples

Sample ID Measured physical 
properties

Microstructural parameters estimated from optical 
microscopy images

Facies

Limestone Porosity 
(%)

Permeability 
(mD)

Φ macro (%) Φ micro (%) f  grains f  micrite Facies

Base Bed 2 21.747 458.000 12.310 9.437 55.773 22.480 WKST
Base Bed 3 21.410 569.000 13.190 8.220 57.600 20.990 WKST
Base Bed 4 22.646 824.000 12.250 10.396 55.024 22.330 WKST
BWB 2 21.615 452.000 11.800 9.815 56.655 21.730 WKST



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Sample ID Measured physical properties Microstructural parameters estimated from 
optical microscopy images

Dolomite Porosity (%) Permeability (mD) Φ macro (%) Φ micro (%) f  grains f  micrite Facies
Alg 1.1 23.788 12.860 2.310 21.478 25.962 50.250 MST
Alg 1.3 25.527 30.700 3.310 22.217 27.843 46.630 MST
Bio 1.1 19.631 3.980 2.350 17.281 29.249 51.120 MST
Bio 2.2 19.524 54.660 4.760 14.764 29.996 50.480 MST
BM 1 6.088 0.090 1.540 4.548 30.112 63.800 MST
BM 2 7.273 0.120 1.680 5.593 26.987 65.740 MST
BM 3 4.747 0.080 2.060 2.687 27.223 68.030 MST
BM 4 5.092 2.470 1.650 3.442 26.858 68.050 MST
BV 2 17.373 7.500 1.610 15.763 23.997 58.630 MST
GRN 1 3.332 7.300 3.100 0.232 92.818 3.850 GRST 
GRN 2 12.349 161.000 9.119 3.230 81.191 6.460 GRST 
GRN 3 12.454 155.000 10.044 2.410 82.726 4.820 GRST 
GRN 4 16.440 250.200 13.000 3.440 76.680 6.880 GRST 
S-39456 15.860 1.620 10.660 5.200 73.740 10.400 GRST 
S-39457 14.860 1.600 1.020 13.840 26.230 58.910 PKST

BWB 3 21.111 393.000 11.990 9.121 63.469 15.420 WKST
BWB 4 22.004 723.000 11.900 10.104 59.566 18.430 WKST
Pond Free Stone 13.962 0.400 12.862 1.100 84.318 1.720 GRST 
RS08A 17.200 0.250 15.110 2.090 79.230 3.570 GRST 
RS08B 20.850 1.050 17.040 3.810 69.780 9.370 GRST 
RS08C 15.910 0.330 12.310 3.600 77.530 6.560 GRST 
WB 2 19.736 209.000 12.276 7.460 61.054 19.210 WKST
S-39400 3.200 0.090 2.690 0.510 37.800 59.000 WKST
S-39415 16.700 0.280 1.830 14.870 49.500 33.800 WKST
S-39433 5.400 0.950 2.710 2.690 53.100 41.500 PKST
S-39437 14.900 0.110 9.200 5.700 78.100 7.000 GRST 
S-39438 17.300 0.210 14.020 3.280 76.500 6.200 GRST 
S-39440 11.400 0.420 8.550 2.850 82.900 5.700 GRST 
S-39446 6.600 0.100 1.620 4.980 52.100 41.300 WKST
S-39448 14.800 0.680 2.570 12.230 48.100 37.100 PKST
S-39454 15.860 785.000 1.890 13.970 36.840 47.300 WKST
S-39464 10.900 2.340 1.900 9.000 56.800 32.300 PKST

APPENDIX II: Joint Elastic-Electrical Measurement Results on the 36 Carbonate Samples Studied in This Paper
Sample 60 MPa 50 MPa

Vp Qp Vs Qs ρ F Vp Qp Vs Qs ρ F
Base Bed 2

41
84

61
.7

68

22
14

43
.8

73

3.
86

1

18
.1

25

41
79

70
.0

54

22
09

39
.9

58

3.
82

4

17
.9

54

Base Bed 3

41
64

64
.2

31

22
17

49
.2

44

3.
84

1

18
.0

34

41
54

67
.3

22

22
06

39
.8

34

3.
74

2

17
.5

67

Base Bed 4

41
49

27
.3

03

22
18

36
.2

77

3.
39

9

15
.9

57

41
44

28
.2

57

22
10

33
.2

82

3.
38

3

15
.8

82



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Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

BWB 2

41
64

42
.7

15

22
02

29
.0

15

3.
91

9

18
.3

98

41
56

42
.7

33

21
97

28
.8

06

3.
89

5

18
.2

87

BWB 3

42
06

38
.2

99

22
46

31
.1

56

4.
27

5

20
.0

71

41
95

39
.6

66

22
41

32
.9

22

4.
24

7

19
.9

37

BWB 4

41
92

28
.6

05

22
50

26
.3

19

3.
83

8

18
.0

20

41
91

29
.8

54

22
49

23
.3

29

3.
78

8

17
.7

84

Pond Free 
Stone

45
26

78
.0

47

24
54

11
4.

89
2

8.
86

0

41
.5

98

45
02

83
.9

82

24
41

10
2.

66
6

8.
81

2

41
.3

72

RS08A

42
18

27
.8

95

22
34

30
.0

72

7.
79

1

36
.5

79

41
70

25
.8

69

22
09

26
.9

55

7.
69

8

36
.1

41

RS08B

38
59

19
.6

26

18
73

17
.3

26

6.
48

4

30
.4

39

38
07

18
.9

39

18
33

17
.3

57

6.
43

0

30
.1

89

RS08C

47
86

12
.4

05

25
08

13
.9

95

13
.9

32

65
.4

08

47
37

11
.8

74

24
69

13
.1

68

13
.7

89

64
.7

37

WB 2

42
36

29
.5

12

23
69

36
.9

09

5.
03

1

23
.6

18

42
10

30
.4

22

23
65

34
.7

16

5.
00

1

23
.4

79

S-39400

      53
61

11
7.

09
0

27
15

20
.3

65

22
5.

22
1

10
57

.3
76

S-39415

      37
99

20
.1

37

24
35

17
.0

90

25
.6

38

12
0.

36
5

S-39433

      52
79

54
.4

59

29
00

32
.4

00

71
.2

96

33
4.

72
3

S-39437

      43
94

13
.4

47

23
35

27
.9

73

23
.6

75

11
1.

15
0

S-39438

      41
56

30
.8

36

25
12

32
.2

08

11
.9

98

56
.3

26

S-39440

      45
13

54
.2

88

24
03

58
.3

92

18
.0

03

84
.5

21

S-39446

      50
96

27
.0

10

26
90

21
.1

82

42
.2

04

19
8.

14
1

S-39448

      44
59

10
.4

11

23
86

10
.7

02

30
.6

85

14
4.

06
3

S-39454

      50
91

28
.7

87

26
85

43
.7

36

49
.0

68

23
0.

36
8

S-39464

      47
73

45
.6

12

24
49

28
.5

16

32
.3

01

15
1.

64
6



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Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

Alg 1.1

46
86

16
.8

32

26
15

16
.1

71

7.
27

5

34
.1

55

46
70

16
.9

52

25
95

13
.9

77

7.
09

5

33
.3

12

Alg 1.3

42
33

8.
98

4

23
78

10
.8

47

5.
78

8

27
.1

72

42
22

8.
98

9

23
37

10
.9

22

5.
64

1

26
.4

86

Bio 1.1

48
28

29
.3

51

26
35

32
.0

53

91
.5

64

42
9.

87
9

48
13

27
.9

46

26
29

28
.2

89

86
.9

15

40
8.

05
1

Bio 2.2

54
38

6.
12

2

25
92

13
.4

37

10
.1

11

47
.4

71

54
23

5.
91

7

25
98

12
.7

16

9.
95

3

46
.7

26

BM 1

59
55

38
.3

69

32
58

24
.2

06

54
.3

18

25
5.

01
4

59
42

37
.0

74

32
43

17
.5

62

52
.7

23

24
7.

52
6

BM 2

58
58

22
.7

44

32
16

12
.4

06

38
.2

92

17
9.

77
4

58
42

22
.9

05

32
14

12
.8

64

36
.3

61

17
0.

71
0

BM 3

62
84

67
.3

13

34
99

16
4.

73
1

85
.9

63

40
3.

58
2

62
62

72
.2

54

34
80

64
.4

58

83
.1

74

39
0.

48
8

BM 4

63
58

23
.9

27

35
35

32
.6

42

85
.8

11

40
2.

86
9

63
43

24
.5

34

35
21

29
.6

26

82
.7

45

38
8.

47
4

BV 2

49
69

23
.2

76

28
24

19
.1

22

13
.3

19

62
.5

32

49
58

24
.5

66

28
15

19
.9

20

12
.8

20

60
.1

88

GRN 1

66
59

96
.4

99

36
29

10
0.

42
9

23
1.

77
0

10
88

.1
22

66
39

10
0.

55
7

36
20

95
.8

41

21
1.

94
0

99
5.

02
3

GRN 2

55
66

11
1.

91
2

31
18

46
.6

62

24
.5

06

11
5.

05
2

55
56

11
8.

54
0

31
10

46
.7

86

23
.8

67

11
2.

05
2

GRN 3

54
78

54
.4

73

29
99

30
.2

08

19
.9

40

93
.6

15

54
64

56
.8

86

29
91

29
.6

70

19
.6

87

92
.4

27

GRN 4

49
01

18
.5

29

27
26

16
.9

89

8.
82

7

41
.4

42

48
78

17
.5

67

27
21

18
.9

99

8.
69

0

40
.7

96

S-39456

      59
63

12
3.

53
4

33
89

10
8.

92
6

19
.1

05

89
.6

92

S-39457

      62
85

73
.0

51

34
53

59
.0

61

88
.1

46

41
3.

83
1

Sample 40 MPa 30 MPa
Vp Qp Vs Qs ρ F Vp Qp Vs Qs ρ F

Base Bed 2

41
57

72
.1

56

21
94

37
.5

82

3.
78

6

17
.7

75

41
23

68
.2

74

21
74

34
.0

90

0.
00

0

0.
00

0

Base Bed 3

41
34

67
.4

19

21
95

47
.6

77

3.
68

7

17
.3

10

41
03

62
.9

31

21
74

32
.5

77

3.
67

4

17
.2

49

Base Bed 4

41
23

28
.5

53

22
00

33
.0

59

3.
37

4

15
.8

41

40
93

28
.2

55

21
81

27
.4

40

3.
36

8

15
.8

11



Pa
ge

 
44

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

BWB 2

41
28

40
.0

87

21
77

27
.8

49

3.
86

6

18
.1

49

40
94

38
.6

97

21
56

24
.3

93

3.
83

5

18
.0

05

BWB 3

41
77

40
.3

68

22
31

30
.3

61

4.
20

6

19
.7

45

41
39

36
.4

28

22
05

28
.0

87

4.
17

8

19
.6

15

BWB 4

41
85

31
.8

51

22
42

25
.9

97

3.
74

2

17
.5

68

41
82

35
.7

95

22
47

29
.3

08

3.
71

8

17
.4

54

Pond Free 
Stone

44
77

91
.8

72

24
26

89
.9

46

8.
77

1

41
.1

78

44
28

89
.0

89

24
02

71
.4

26

8.
71

8

40
.9

31

RS08A

40
80

22
.2

29

21
49

19
.2

07

7.
46

6

35
.0

51

39
50

19
.2

54

20
42

16
.1

08

7.
59

2

35
.6

42

RS08B

37
34

17
.2

31

17
75

14
.9

29

6.
40

6

30
.0

77

36
26

15
.1

22

17
17

10
.0

60

6.
46

5

30
.3

53

RS08C

46
56

10
.8

62

24
31

12
.5

77

13
.6

93

64
.2

86

45
46

9.
61

9

23
81

9.
84

0

13
.5

25

63
.4

98

WB 2

41
79

29
.1

75

23
52

32
.1

62

4.
97

5

23
.3

55

41
38

28
.3

52

23
40

29
.9

91

4.
94

8

23
.2

30

S-39400

53
36

10
3.

27
1

27
09

17
.4

90

24
3.

59
6

11
43

.6
43

52
97

71
.9

27

26
94

16
.1

06

27
8.

89
6

13
09

.3
71

S-39415

37
65

17
.3

57

24
12

16
.0

28

24
.1

83

11
3.

53
7

37
08

14
.1

96

23
77

12
.6

02

25
.4

07

11
9.

28
1

S-39433

52
47

48
.8

76

28
56

24
.0

79

53
.6

39

25
1.

82
8

51
96

38
.9

17

27
58

20
.3

37

63
.5

16

29
8.

19
5

S-39437

43
42

12
.3

19

23
21

22
.6

31

23
.7

77

11
1.

62
9

42
47

10
.4

19

22
79

22
.1

69

22
.0

65

10
3.

59
0

S-39438

41
14

28
.7

66

24
79

18
.6

72

11
.9

05

55
.8

91

40
50

22
.7

25

23
67

17
.3

98

12
.0

45

56
.5

50

S-39440

44
82

46
.4

32

23
91

54
.7

39

17
.8

13

83
.6

27

44
22

32
.8

96

23
46

41
.7

62

17
.7

90

83
.5

22

S-39446

50
60

26
.4

35

26
48

18
.9

32

42
.0

46

19
7.

40
0

49
88

22
.5

59

25
84

15
.8

47

41
.7

99

19
6.

23
8

S-39448

44
09

10
.0

94

23
72

9.
80

6

31
.2

73

14
6.

82
2

43
31

9.
17

3

23
45

9.
54

6

30
.8

88

14
5.

01
5

S-39454

50
64

27
.5

04

26
75

40
.7

02

49
.7

57

23
3.

59
9

50
14

23
.7

49

26
25

28
.6

90

50
.0

67

23
5.

05
7

S-39464

47
36

41
.4

51

24
21

19
.3

84

32
.5

25

15
2.

70
0

46
69

32
.6

49

23
53

17
.0

42

30
.9

57

14
5.

33
8

Alg 1.1

46
38

17
.0

70

25
75

13
.7

80

6.
81

6

31
.9

98

45
97

17
.2

28

25
60

15
.5

13

6.
56

9

30
.8

41



Pa
ge

 
45

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

Alg 1.3

42
03

8.
67

4

23
19

11
.4

89

5.
33

2

25
.0

32

41
68

8.
20

8

23
33

11
.7

08

5.
25

8

24
.6

86

Bio 1.1

48
06

36
.4

00

26
15

25
.9

88

81
.5

73

38
2.

97
2

47
90

31
.0

00

26
01

25
.7

09

73
.0

73

34
3.

06
8

Bio 2.2

54
08

5.
80

5

25
89

9.
81

5

9.
67

8

45
.4

38

53
60

5.
37

2

25
52

12
.0

99

8.
97

4

42
.1

30

BM 1

59
16

34
.9

93

32
06

18
.5

02

51
.1

65

24
0.

21
1

58
87

31
.2

20

32
05

20
.2

27

49
.5

56

23
2.

65
7

BM 2

58
13

23
.0

05

31
95

14
.3

34

34
.9

71

16
4.

18
1

57
68

21
.9

96

31
88

11
.9

00

33
.8

59

15
8.

96
3

BM 3

62
39

65
.4

64

34
59

74
.9

56

79
.8

76

37
5.

00
5

62
14

54
.3

09

34
55

84
.3

84

75
.8

30

35
6.

00
9

BM 4

63
19

23
.9

33

35
11

29
.6

65

79
.6

26

37
3.

83
1

62
93

21
.1

13

34
96

25
.9

37

74
.9

46

35
1.

85
9

BV 2

49
38

25
.1

37

28
09

17
.1

55

12
.7

84

60
.0

17

49
19

25
.2

54

27
75

17
.8

03

11
.7

28

55
.0

62

GRN 1

66
13

86
.0

26

36
06

79
.1

80

19
4.

14
0

91
1.

45
5

65
82

69
.1

44

35
81

72
.3

44

17
4.

93
0

82
1.

26
8

GRN 2

55
37

11
5.

10
7

31
04

43
.3

31

23
.5

90

11
0.

75
1

55
11

10
1.

02
8

30
87

35
.7

99

22
.7

94

10
7.

01
4

GRN 3

54
49

51
.7

69

29
81

28
.5

55

19
.1

87

90
.0

80

54
24

48
.9

53

29
69

27
.7

62

17
.2

38

80
.9

30
GRN 4

48
57

16
.9

34

27
07

18
.3

64

8.
61

1

40
.4

25

48
34

16
.2

43

26
90

18
.6

41

8.
52

3

40
.0

14

S-39456

59
24

73
.8

73

33
52

66
.2

37

20
.4

54

96
.0

28

58
62

41
.3

90

32
95

41
.6

88

17
.8

05

83
.5

90

S-39457

62
61

69
.9

57

34
29

51
.6

71

85
.8

85

40
3.

21
5

62
14

52
.0

24

33
93

48
.5

22

87
.0

54

40
8.

70
6

Sample 20 MPa 10 MPa
Vp Qp Vs Qs ρ F Vp Qp Vs Qs ρ F

Base Bed 2

40
81

60
.0

98

21
46

28
.7

61

3.
71

6

17
.4

46

39
89

39
.3

34

20
81

20
.0

07

3.
67

6

17
.2

57

Base Bed 3

40
63

53
.6

71

21
50

34
.8

89

3.
60

9

16
.9

45

40
11

39
.2

24

21
03

21
.7

60

3.
75

3

17
.6

20

Base Bed 4

40
53

27
.5

21

21
57

24
.8

10

3.
36

4

15
.7

91

39
82

21
.9

01

21
10

19
.9

58

3.
36

6

15
.8

03

BWB 2

40
47

35
.5

54

21
20

20
.3

20

3.
79

2

17
.8

00

39
46

26
.2

69

20
43

16
.5

82

3.
75

5

17
.6

31



Pa
ge

 
46

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

BWB 3

40
90

33
.8

03

21
82

23
.8

46

4.
13

4

19
.4

07

39
93

23
.8

08

21
10

16
.6

51

3.
95

7

18
.5

77

BWB 4

41
76

41
.1

99

22
48

22
.5

10

3.
66

9

17
.2

26

41
60

42
.8

62

22
35

25
.3

96

3.
58

7

16
.8

42

Pond Free 
Stone

43
64

74
.4

34

23
69

54
.1

62

8.
65

1

40
.6

16

42
23

42
.9

02

22
89

24
.9

24

8.
60

6

40
.4

04

RS08A

37
83

16
.6

62

19
41

14
.9

35

7.
28

9

34
.2

18

35
76

9.
44

4

17
90

9.
04

6

6.
92

6

32
.5

14

RS08B

34
59

12
.1

53

16
20

11
.0

41

6.
37

4

29
.9

24

32
61

9.
94

7

14
89

10
.2

06

6.
12

1

28
.7

36

RS08C

43
89

8.
22

4

23
78

9.
12

2

13
.0

98

61
.4

93

42
26

7.
27

9

21
46

9.
17

2

12
.3

65

58
.0

52

WB 2

40
78

26
.2

66

23
18

25
.5

41

4.
89

9

23
.0

00

39
98

22
.0

95

22
88

20
.2

92

4.
84

0

22
.7

21

S-39400

52
48

45
.7

85

26
49

15
.6

45

18
4.

34
2

86
5.

45
5

51
76

26
.0

17

25
98

10
.4

80

15
4.

89
4

72
7.

20
2

S-39415

36
24

12
.0

52

23
34

10
.4

62

24
.5

37

11
5.

19
8

34
35

7.
79

3

21
96

9.
66

9

25
.3

76

11
9.

13
7

S-39433

51
13

27
.4

43

26
57

18
.5

60

39
.5

82

18
5.

83
2

49
42

16
.5

32

26
29

15
.1

13

43
.5

80

20
4.

60
3

S-39437

41
24

7.
38

7

21
82

21
.5

21

22
.5

91

10
6.

06
3

39
64

5.
56

6

20
96

19
.0

92

21
.3

91

10
0.

42
9

S-39438

39
61

18
.4

07

23
47

17
.3

08

11
.7

50

55
.1

62

37
79

12
.1

42

20
23

15
.3

83

10
.6

47

49
.9

86
S-39440

43
19

19
.7

53

22
81

33
.0

76

17
.4

14

81
.7

57

39
18

7.
27

3

21
37

13
.8

24

15
.3

79

72
.2

00

S-39446

48
62

16
.6

49

24
86

12
.3

00

39
.5

08

18
5.

48
4

46
25

10
.8

55

23
73

7.
73

5

34
.3

54

16
1.

28
6

S-39448

42
25

7.
95

9

23
32

9.
31

2

28
.4

49

13
3.

56
4

40
21

5.
43

4

22
42

9.
08

9

25
.0

78

11
7.

73
7

S-39454

49
39

18
.2

63

25
47

23
.9

92

47
.4

80

22
2.

90
8

47
67

11
.0

24

23
79

16
.6

09

39
.3

93

18
4.

94
4

S-39464

45
64

23
.3

09

23
07

16
.2

84

30
.2

86

14
2.

18
7

43
51

13
.3

27

22
34

12
.2

18

27
.4

66

12
8.

94
7

Alg 1.1

45
31

16
.1

33

25
49

13
.4

71

6.
24

9

29
.3

36

43
95

12
.4

45

24
28

13
.1

29

5.
87

5

27
.5

80

Alg 1.3

41
32

7.
60

4

23
66

10
.6

01

5.
33

0

25
.0

25

40
56

7.
44

8

23
60

8.
72

1

5.
20

4

24
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31



Pa
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47

https://journals.e-palli.com/home/index.php/ajgt

Am. J. Geo Spat. Technol. 4(1) 22-48, 2025

Bio 1.1

47
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04
1


