Pa ge 1 Pa ge 22 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 Pa ge 23 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 24 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 25 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 26 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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). Pa ge 27 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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. Pa ge 28 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 29 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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. Pa ge 30 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 31 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 32 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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). Pa ge 33 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 34 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 35 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 36 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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. Pa ge 37 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 38 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Abousrafa, E. M., Somerville, J. M., Hamilton, S. A., Olden, P. W. H., Smart, B. D. G., & Ford, J. (2009). Pore geometrical model for the resistivity of brine saturated rocks. Journal of Petroleum Science and Engineering, 65(3–4), 113–122. https://doi. org/10.1016/j.petrol.2008.12.009 Adams, A E, Guilford, C., & MacKenzie, W. S. (1984). Atlas of sedimentary rocks under the microscope. 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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- supported samples having the most of the porosity as microporous and vugs, might suggest that microporosity, pore network connectivity, pore size of macro porosity might a have a significant influence in F* rather than pore size. Verwer et al. (2011) suggest that for carbonate rocks, the significant controlling factor for resistivity were both pore structure and the absolute number of pores, regardless of the size of the pore throats based on seventy-one plugs from boreholes and digital image analysis. 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. 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AAPG Bulletin, 93(10), 1297–1317. https://doi.org/10.1306/05270909001 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 Pa ge 41 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 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 Pa ge 42 https://journals.e-palli.com/home/index.php/ajgt Am. 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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 Pa ge 43 https://journals.e-palli.com/home/index.php/ajgt Am. 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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. 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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. 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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. 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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 .4 31 Pa ge 47 https://journals.e-palli.com/home/index.php/ajgt Am. J. Geo Spat. Technol. 4(1) 22-48, 2025 Bio 1.1 47 32 23 .0 71 25 67 19 .4 42 66 .4 10 31 1. 78 5 46 53 18 .0 91 25 30 17 .5 98 64 .0 52 30 0. 71 2 Bio 2.2 53 56 4. 98 1 25 57 12 .0 28 8. 70 8 40 .8 84 53 54 5. 91 7 23 40 8. 50 7 8. 44 8 39 .6 64 BM 1 58 43 26 .7 30 32 05 14 .6 48 46 .9 27 22 0. 31 5 57 61 21 .2 76 31 50 14 .3 21 43 .7 15 20 5. 23 5 BM 2 57 17 19 .5 52 31 73 8. 25 6 32 .0 70 15 0. 56 2 56 02 14 .8 12 29 86 10 .6 86 29 .5 90 13 8. 92 2 BM 3 61 82 45 .5 52 34 33 36 .0 36 70 .8 67 33 2. 70 9 61 18 36 .8 00 33 88 40 .6 02 65 .1 48 30 5. 85 9 BM 4 62 65 18 .8 22 34 72 23 .5 38 69 .2 65 32 5. 18 8 62 03 16 .4 31 34 48 21 .1 01 61 .8 81 29 0. 52 1 BV 2 48 69 23 .9 92 27 64 18 .5 78 11 .7 22 55 .0 31 47 87 21 .0 56 26 99 19 .0 12 10 .8 08 50 .7 40 GRN 1 65 43 56 .5 93 35 55 67 .4 22 15 0. 99 0 70 8. 87 3 64 66 43 .9 06 35 00 41 .3 45 12 5. 06 0 58 7. 13 6 GRN 2 54 78 80 .8 62 30 62 33 .8 84 21 .8 47 10 2. 56 8 53 92 50 .3 79 30 06 23 .2 49 20 .8 28 97 .7 84 GRN 3 53 81 41 .7 36 29 43 22 .4 43 17 .8 58 83 .8 40 52 95 29 .4 73 29 01 19 .3 04 17 .4 49 81 .9 20 GRN 4 47 90 16 .2 64 26 72 16 .9 54 8. 32 2 39 .0 71 47 03 13 .7 43 26 21 18 .4 88 8. 09 2 37 .9 89 S-39456 57 50 19 .5 90 32 15 21 .1 33 18 .5 05 86 .8 78 54 27 7. 43 0 31 63 17 .9 13 15 .8 63 74 .4 76 S-39457 61 47 35 .4 27 33 66 36 .8 83 80 .1 37 37 6. 22 8 60 53 24 .2 06 32 97 16 .1 78 61 .5 66 28 9. 04 1