3D Pore-network quantitative analysis in deformed carbonate grainstones

Marine and Petroleum Geology 82 (2017) 251e264

Contents lists available at ScienceDirect

Marine and Petroleum Geology journal homepage: www.elsevier.com/locate/marpetgeo

Research paper

3D Pore-network quantitative analysis in deformed carbonate grainstones Miller Zambrano a, *, 1, Emanuele Tondi a, 1, Lucia Mancini b, Fabio Arzilli b, c, Gabriele Lanzafame b, Marco Materazzi a, Stefano Torrieri d a

School of Science and Technology - Geology Division, University of Camerino, Italy Elettra-Sincrotrone Trieste S.C.P.A., Basovizza, Trieste, Italy School of Earth, Atmospheric and Environmental Sciences, University of Manchester, Manchester, UK d Shell Italia Exploration and Production, Rome, Italy b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 July 2016 Received in revised form 24 December 2016 Accepted 1 February 2017 Available online 2 February 2017

This study consists of a three-dimensional (3D) assessment of the pore network properties (i.e., porosity, pore connectivity, specific surface area) in deformed carbonate grainstones cropping out in Sicily and Abruzzo regions (Italy). Previous studies, including microphotography, mercury injection analysis, and in-situ air permeameter measurements, have reported permeability differences (in the range of two-tothree orders of magnitude) between the carbonate grainstones exposed in Sicily and Abruzzo, that cannot be explained by only considering the differences of porosity. In this study, the pore network properties of suitable rock samples were studied by quantitative analysis of X-ray micro-CT images using both synchrotron and microfocus sources. On the basis of the results, inferences about the control of pore network properties on permeability were made for both host rock and deformation bands. In the host rocks, high values of connectivity seem to be associated with high values of permeability, whereas higher values of the specific surface area seem to correspond to lower permeability. Within the deformation bands (DBs), both porosity and pore connectivity are reduced except for local solution-enlarged stylolites and fractures (slip surfaces) preferentially connected parallel to the DB. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Porosity Specific surface area Connectivity density X-ray computed tomography Deformation bands

1. Introduction In all phases of geofluid reservoir development, carbonate as well as siliciclastic, it is of primary importance to rely on a representative reservoir model. Such a model is invaluable for optimizing investments. From the petrophysical, geological and reservoir engineering perspectives, one of the most elusive aspects is obtaining relationships between porosity and permeability from core data and the resulting implications for the model. In carbonate reservoirs, the porosity vs. permeability cross plots typically show significant variability indicating that pore properties, other than porosity, significantly affect the permeability (Lucia, 2007). The permeability depends not only on the porosity but also on textural and hydraulic properties of the pore network such as

* Corresponding author. School of Science and Technology - Geology Division, University of Camerino, Via Gentile II da Varano 1, Camerino 62032, Italy. E-mail address: [email protected] (M. Zambrano). 1 Reservoir Characterization Project (www.rechproject.com). http://dx.doi.org/10.1016/j.marpetgeo.2017.02.001 0264-8172/© 2017 Elsevier Ltd. All rights reserved.

specific surface area (depending on pores size distribution and texture), pores shape and tortuosity (Carman, 1937; Dullien, 1992). In carbonates, the connectivity of the pore network may also exert control on the permeability (Tondi et al., 2016). The porosity and pore size distribution are commonly estimated using microscopic or indirect laboratory methods, such as helium porosimetry and mercury injection, respectively. Tortuosity is usually obtained by matching predicted and observed permeability (Dullien, 1992). However, to describe porosity and textural properties of the pore network without destroying or modifying the rock samples, a non-destructive three-dimensional (3D) approach is required. In this regard, the X-ray computed microtomography (micro-CT) is considered the only non-destructive technique able to provide reliable microscale information of the 3D pore-network (Wildenschild and Sheppard, 2013). The X-ray micro-CT is based on X-ray attenuation within different materials and has a close relationship with density making this data a straightforward interpretation (Ketcham and Carlson, 2001). Since the pioneer applications of rock imaging

252

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

using both laboratory and synchrotron sources (i.e. Flannery et al., 1987), the X-ray micro-CT has become an established and rapidly evolving technology for geological investigations. This technique is useful in several different research applications including fluid flow, soil science, and sedimentology, textural analysis of pores and grains, etc. (Ketcham and Carlson, 2001). The three-dimensional (3D) pore-scale imaging and analysis have recently become a frequently used tool in the hydrocarbon industry (Blunt et al., 2013). In the case of potential carbonate reservoirs rocks, the Xray micro-CT has been used for obtaining porosity and pore connectivity. Cilona et al. (2014) implemented both conventional microfocus (voxel size 17.2 mm) and synchrotron-based micro-CT (voxel size 9.0 mm) for obtaining 3D porosity of both pristine and deformed (naturally and laboratory) samples of Miocene carbonate grainstones from Bolognano Formation (Central Italy). Arzilli et al. (2015) extended the work of Cilona et al. (2014) and documented for the first time how pore connectivity evolves as a function of deformation and proposed a new methodology for estimating the pore connectivity in granular media using the “cluster multiple labeling” technique. Ji et al. (2015) obtained 3D strain localization volumes by comparing X-ray micro-CT of undeformed and deformed Cretaceous carbonate grainstones of the Orfento Formation (Central Italy). This study aims to quantitatively analyze the pore network properties (porosity, pore connectivity, specific surface area) of deformed porous carbonates through X-ray micro-CT techniques (synchrotron and microfocus-based). The studied rocks belong to deformed carbonate grainstones cropping out in Sicily, southern Italy (Favignana Island and San Vito Lo Capo Peninsula) and the Abruzzo Region, central Italy (Maiella Mt.), hereafter called San Vito Lo Capo Grainstone (SVG), Favignana Island Grainstone (FIG), and Orfento Fm. Grainstone (OFG), respectively. The SVG (Early Pleistocene in age) are poor-to-medium consolidated grainstones with grains made up of fragments of carbonates, marls and shales with diameters between 0.05 and 1.0 mm. The matrix, about 22% of the rock volume, is composed of bladed and sparry calcite cement with carbonate and marl fragments smaller than 0.05 mm (Tondi, 2007). The Early Pleistocene FIG consist of well-preserved bioclasts (e.g., Vermetus, Serpula, bivalves, echinoids, red algae and corals) ranging in size from submillimeter to centimeter (Tondi et al., 2012). The host rock is poorly cemented with the cement limited to the grain contacts, around echinoids, or within intragranular pores (Tondi et al., 2012). The OFG (Campanian to Maastrichtian in age) are composed of fragments of rudists (Mutti,

1995). The maximum burial depth experienced by the OFG was broadly constrained between 0.5 and 3 km (Ori et al., 1986; Graham et al., 2003; Rustichelli et al., 2016). Whereas burial depth for both SVG and FIG was approximated as 30 m (Tondi et al., 2012; Antonellini et al., 2014). The studied rocks are affected by compactive shear bands (single or clustered) or well-developed faults with discrete slip surfaces and cataclastic material. Deformation Bands (DBs) are common structures in deformed porous rocks (siliciclastics and carbonates) where strain localization (Aydin, 1978; Antonellini et al., 1994; Fossen et al., 2007; Cilona et al., 2012) and chemical processes such as pressure solution and cementation (Tondi et al., 2006; Tondi, 2007) may take place. For DBs hosted in porous carbonates grainstones, Tondi et al. (2006) defined three diagenetic/structural tabular regions (Zones I, II, III; Fig. 1). Both Zone I and II are defined as the fault core of the deformation band (Tondi, 2007). Zone I, located at the inner part of the DB, includes the slip surfaces and a well-developed continuous volume of grain size and porosity reduction. Zone II, which limits the Zone I, is a compacted grain region characterized by pressure dissolution at the grain contacts. Zone III surrounds the fault core and is characterized by porosity reduction due to precipitation of calcite cement. Within DBs, the porosity and permeability are reduced considerably representing buffer zones or barriers during migration of geofluids (Fossen and Bale, 2007; Tondi, 2007; Antonellini et al., 2014; Tondi et al., 2016). The porosity of the SVG and FIG was previously investigated using thin section digital image analysis (Tondi, 2007) and helium porosimetry (Tondi et al., 2012). These authors reported porosity values between 25% and 30% for host rock (lithofacies III, Tondi et al., 2012) and between 1% and 7% for DBs (Tondi, 2007). For the host rock of OFG, different ranges of porosity have been reported: i) 15e28% using redrawn microphotographs (Tondi et al., 2006), ii) 30e32% using triple weight method (Baud et al., 2009; Zhu et al., 2010; Cilona et al., 2012), and iii) 31e32% using a helium porosimeter (Tondi et al., 2016). In addition, Ji et al. (2015) reported for the host rock macroporosity and microporosity values of 11.4% and 19.6%, respectively, using X-ray micro-CT images (microfocal source, voxel size ¼ 4.0 mm). According to the microphotograph analysis, the porosity within the DBs is between 1 and 4% (Tondi et al.,2006). In situ permeability measurements in both host rock and DBs (Zones I, II and III) were carried out by Antonellini et al. (2014) and Tondi et al. (2016) using a TinyPerm II Portable Air Permeameter (mini-permeameter with a reliable permeability range of 101 to

Fig. 1. Single deformation band from SVG characterized by tabular zones (Zones I, II, and III) with different textures (see text for description) enclosed by the host rock; (a) microphotographs and (b) interpretation (after Tondi, 2007).

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

103 mD). The previously reported permeability data are summarized by the arithmetic mean and standard error, respectively (Fig. 2). For the SVG, Antonellini et al. (2014) reported permeability values of 1.5  104 ± 2.5  103 mD for poorly cemented host rock; 286.0 ± 59.6 mD for highly cemented zones around the fault core of deformation bands (Zone III); and 1.1  103 ± 295.8 mD for the fault core of DBs (Zone I and II). For the FIG, Tondi et al. (2016) documented mean permeability values of 6.9  104 ± 4.1  103 mD for host rocks, 510.0 ± 170.0 mD for the Zone I, and 1.9  104 ± 1.7  103 mD for the Zones II and III. Tondi et al. (2016) also reported for the OFG permeability in the host rock nearly 450.0 ± 33.0 mD, whereas in the DBs, the mean permeability is about 8 mD for the Zone I and 160.0 ± 30.0 mD for the Zone II. This work provides a methodology for obtaining the primary textural and hydraulic properties of deformed porous carbonates using X-ray micro-CT. In addition, it stresses the importance of defining a representative elementary volume (REV) for proper parameter estimation within individual zones (i.e., host rock, Zone I, II, and III). Based on the quantitative analysis results, this study attempts to explain the permeability differences (in the range of two-to-three orders of magnitude) reported for the studied host rocks besides their apparent similar porosity (Fig. 2). Since the connected pores were considered as the only contributing to fluid flow, pores space was divided into connected and isolated pores and studied separately. Isolated pores were also evaluated in order to understand the effects of diagenesis and deformation within DBs. 2. Materials and methods 2.1. Sample preparation To perform the X-ray microtomography experiments, parallelepiped-shaped samples (Fig. 3) were prepared. For synchrotron-based X-ray microtomography analysis samples are about 4 mm  4 mm  30 mm, which corresponds to the maximum thickness allowed by the experimental setup in the synchrotron beamline. Larger samples (15 mm  15 mm  80 mm) for microfocus X-ray micro-CT analysis were prepared with the intention of covering wider volumes of the host rock that present wider pore

Fig. 2. Scatter plot of porosity and permeability of the studied carbonate grainstones including host rock (HR) and the Zones I, II and III within the DBs. Data of porosity for the SVG (triangles) from Tondi, 2007, for FIG (circles) from Tondi et al. (2012), and for OFG (squares) from Tondi et al. (2016). Data of permeability for the SVG Antonellini et al. (2014), and for both FIG and OFG from Tondi et al. (2016). Error bars indicate the standard error of the mean.

253

diameters (SVG and FIG). Samples were labeled according to their location and their chosen X-ray CT laboratory (S: synchrotron or M: microfocus). From the SVG, two samples were selected (SVG-S1 and SVG-M1) that contain a compactive shear band composed of three different zones (I, II, III; sensu Tondi et al., 2006) surrounded by undeformed host rock. From the FIG, three samples were collected; two of the host rock (FIG-S2 and FIG-M2) and one belonging to a fault core of 10s of centimeters in thickness. From the OFG, two samples were selected, one is only host rock (OFG-S4) and the other includes a single DB (OFG-S5). 2.2. X-ray computed microtomography (X-ray micro-CT) measurements The hard X-ray imaging measurements were performed by Xray micro-CT at the Elettra-Sincrotrone Trieste laboratory in Basovizza (Trieste, Italy). Two different X-ray sources were used: a synchrotron source at the SYRMEP (SYnchrotron Radiation for MEdical Physics) beamline and a laboratory microfocus system (socalled TomoLab). At the SYRMEP beamline, a bending magnet source is located at about 23 m from the source allowing to obtain a nearly-parallel geometry and a high spatial coherence of the X-ray beam (Abrami et al., 2005; Tromba et al., 2010). This configuration allows the phase contrast effects to enhance the visibility of objects with similar linear attenuation coefficients (Cloetens et al., 1996). For this beam geometry, the spatial resolution is mainly limited by the detector optics. By using a double-Si (111) crystal monochromator system, the X-ray energy can be the tuned between 8.3 and 38 KeV permitting the optimal energy for the sample under investigation, and reduce scattering. Regarding the monochromatic beam configuration, the sampleto-detector distance was set at 180 mm (phase-contrast mode) and an X-ray energy of 34 keV was selected. The sample was placed on a high-resolution rotation stage, which rotated in 0.1 increments over a total angular range of 180 during the CT scan. A series of 1800 radiographic projections with an exposure time/projection of 3.5 s was recorded by the detector for each sample. Projections were acquired by a water-cooled, 12-bit, 4008  2672 pixels CCD camera (VHR, Photonic Science) with an effective pixel size of 4.5 mm. The camera chip is coupled to a Gadox scintillator screen through a fiber optics taper to convert the X-ray into visible light. Applying a 2  2 binning an output isotropic voxel size of 9 mm was used for slice reconstruction.

Fig. 3. Studied samples belonging to deformed porous carbonate grainstones. Samples were labeled according to their location (SVG, FIG, or OFG) and their chosen analytical facility (S: synchrotron or M: microfocus).

254

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

A white beam configuration mode was used to image samples belonging to DBs or fault rock (Baker et al., 2012) at higher spatial resolution, filtering the X-ray beam with 1.5 mm Si þ 0.025 mm of Mo. The sample-to-detector distance was set at 150 mm. For each sample, 1800 projections were acquired over a total scan angle of 180 with an exposure time/projection of 2 s. The detector consisted of a 16 bit, air-cooled, sCMOS camera (Hamamatsu C11440e22C) with a 2048  2048 pixel chip. The effective pixel size of the detector was set at 2.4 mm  2.4 mm, yielding a maximum field of view of about 5.0 mm  5.0 mm. Some samples (SVG-M1 and FIG-M2; Fig. 3) were imaged at the TomoLab laboratory (http://www.elettra.trieste.it/lightsources/ labs-and-services/tomolab/tomolab.html). This microfocus CT system equipped with a sealed X-ray source (L9181, Hamamatsu) delivers a polychromatic beam in a Voltage range of 40e130 kV with a maximum current of 300 mA and a minimum focal spot size of 5 mm. In this case, the X-ray beam has a cone-beam geometry and the spatial resolution is limited by the focal spot size of the source. A water-cooled, 12-bit, 4008  2672 pixels CCD camera (VHR, Photonic Science) with an effective pixel size of 12.5 mm was used as a detector. The TomoLab instrument is complementary to the SYRMEP beamline for both the energy range and the X-ray beam size (Zandomeneghi et al., 2010). Indeed, it is possible to investigate samples characterized by a higher X-ray attenuation, but the resulting images will be characterized by a lower spatial and contrast resolution and more artifacts. The tomographic scans were performed with a Voltage of 130 kV and a tube current of 61 mA, using a 1.5 mm Al filter. The source-to-sample distance was 220 mm, while the sample-to-detector distance was 320 mm. A series of 2400 radiographic projections for each sample were recorded by the detector over a total angular range of 360 and an exposure time/projection of 4.9 s. A 2  2 binning was applied to the pixels giving an isotropic voxel size of 17.2 mm for slice reconstruction. 2.3. Image reconstruction The tomographic slice reconstruction of the synchrotron X-ray microCT images was performed using the SYRMEP Tomo Project software developed at Elettra (Brun et al., 2015) and powered by ASTRA tomography toolbox (Palenstijn et al., 2011) and TomoPy (Gürsoy et al., 2014). To improve the reliability of quantitative

morphological analysis and enhance the contrast between solid and porous phase, a single-distance phase-retrieval algorithm was applied to the white beam projections (Fig. 4) using the Paganin's algorithm (Paganin et al., 2002) based on the Transport of Intensity Equation (TIE). For the microfocus X-ray microCT, the slice reconstruction was performed using the commercial software COBRA (Exxim) and the Feldkamp algorithm (Feldkamp et al., 1984). The 3D visualization of the output images was facilitated by a volume rendering procedure using the commercial software VGStudio MAX 2.0 (Volume Graphics). 2.4. Segmentation and post-processing The aim of segmentation is to differentiate the pores from the other elements, such as calcite grains, calcite cement, and silica grains. These objects may be characterized in the X-ray images by a different shade of gray color (Fig. 5a). The darkest color is related to pores and brightest color to both calcite grains or cement. The intermediate gray color is due to two sources i) microporous areas (as is indicated in SEM images Fig. 5c and d) and ii) grains composed of silica (determined by optical microscopy and X-ray diffraction analysis). Considering this multiphase composition of the X-ray images, the selected segmentation method was the automatic multiphase k-means clustering (Hartigan, 1975; Hartigan and Wong, 1979) with 3e4 classes, depending on the sample. Segmentation results are binary images composed of voids and grains (Fig. 5b). Usually, filtering is required in pre- and postsegmentation steps to remove background noise from the reconstructed tomographic images (e.g., Zandomeneghi et al., 2010; Voltolini et al., 2011). Concerning the monochromatic beam synchrotron (9 mm voxel size) and the microfocus X-ray microCT images, a bilateral filter (Tomasi and Manduchi, 1998) was applied for smoothing the images and preserving edges. For white beam synchrotron images (2.4 mm voxel size), filtering was unnecessary due to the high image quality (high contrast and signal-to-noise ratio). Using the tool ‘Find Connected Structures’ of FIJI (Schindelin et al., 2012), the extracted porous phase was lately divided into two components i) connected pores and ii) isolated pores. 2.5. Skeletonization The skeletonization is a thinning procedure where the

Fig. 4. Comparison between reconstructed images without phase retrieval (a) and applying the Paganin's phase retrieval algorithm (b) with a d/b ratio ¼ 40, where b is the imaginary part and d is the decrement from unity of the complex X-ray refractive index n ¼ 1 - d þ i b. The starting values of d and b were taken from a publicly available database and the ratio was then tuned to maximize the removal of phase-contrast ‘artifacts’ and minimize the image blurring.

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

processed 3D digital object is reduced to a simpler 1D version which preserves the geometric and morphological features of the original object (Linquist and Venkatarangan, 1999). The skeleton of a 3D porous medium is intuitively the “backbone” of the object running along its geometric middle, which consists of a graph of nodes and branches (Linquist and Venkatarangan, 1999; Brun et al., 2010). In the selected VOIs, the skeletonization of the connected pore network was performed by applying the LKC algorithm (Lee et al., 1994). Results of skeletonization include both numerical data and 3D volumes.

255

 Connected porosity [%]: the volume of connected pores divided by the total rock volume.  Isolated porosity [%]: the volume of isolated pores divided by the total rock volume.  Specific surface area [mm1]: the surface of pores (connected or isolated) divided by the total pores volume.  Connectivity Density [mm3]: a scalar value derived from the skeleton of the connected pore network representing the number of redundant connections normalized to the total rock volume.

2.6. Quantitative image analysis

2.7. Volumes of interest and REV determination

The quantitative 3D analysis of the porous network was performed by using the Pore3D software library custom-developed at Elettra (Brun et al., 2010; Zandomeneghi et al., 2010). The core of the Pore3D software has been optimized for quantitative examination of X-ray micro-CT images of porous media and multiphase systems and includes several modules, such as filtering, morphological, anisotropy and skeleton analysis. For analyzing the basic textural properties, distribution, organization and connectivity of the pores the following parameters were obtained:

Suitable volumes of interests (VOIs) were selected to assess the pore network properties of each zone (i.e., host rock, zone III, II and I). In order to evaluate the significance of the VOIs, Representative Elementary Volumes (REVs) were determined. The REV is defined as the volume in which the variability of a property (e.g., porosity) tends to decay significantly, enclosing a representative amount of the sample heterogeneity (Bear, 1988; Zandomeneghi et al., 2010). To determine the REV, porosity, specific surface area and connectivity were measured for multiple concentric cubic volumes of different size (e.g. Al-Raoush and Papadopoulos, 2010; Costanza-

Fig. 5. Segmentation of gray scale images. a) X-ray microCT image (gray scale) of FIG, darkest gray corresponds to pores, brightest gray to both calcite grains and cement, and intermediate gray are related to (i) microporous areas or (ii) or siliciclastic grains, b) segmented X-ray microCT image using the K-means method with four classes, c) and d) SEM images of the same rocks, which show pores below the X-ray microCT resolution.

256

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

Robinson et al., 2011; Mostaghimi et al., 2013). To obtain results independent of the position, the center of the sample window was located in different arbitrary location. As a dimensionless measure of variability, the coefficient of variation (Cv), defined as the ratio of the arithmetic mean divided by the sample standard deviation, was calculated. The criterion proposed by Zhang et al. (2000) was considered for defining the REV of each property, which is defined as the size of a volume beyond which the mean becomes approximately constant and variability of a property is insignificant (Cv < 0.2). 3. Results The graphical results of the segmentation of pores and the skeletonization are displayed in figs. 6e8. The deformation bands are recognizable from the adjacent host rocks by the dominance of isolated pores (in red) over the connected ones (in yellow). Moreover, the skeletonized pore network of VOIs clearly exposes the differences between the connected and the poorly connected zones which correspond to host rocks and deformed rocks, respectively. In the sample SVG-S1 (Fig. 6a), the pores of fault core (Zone I and II) and the surrounding cemented zone (Zone III) are mostly isolated. The host rock is characterized by a well-connected pore network as it is observed in the skeletonized volume (Fig. 6b). Zone III is only formed by isolated intraparticle pores (fossil chambers). In the innermost portion of the fault core (Zone I), slip surfaces form connected channelized pore-networks parallel to the DB (Fig. 6d). In the fault core sample from FIG (Fig. 7a), regions characterized by connected pores, interpreted as solution-enlarged stylolites (Zone II), alternate with cemented-dominated ones (Zone III). The host rock (Fig. 7b) presents a more homogenous distribution of connected pores, as it is shown in the skeletonized pore network. The sample with a DB from OFG (Fig. 8a) shows the dominance of isolated porosity within the band. The pores within the band are isolated blobs, while in the host rock pores are more connected. The quantitative analysis of the previously exposed images gives more detailed information about the porosity and the textural properties of the pore network. The results are indicated by the arithmetic mean, the standard error and the coefficient of variation (Cv) of the evaluated property (Table 1). 3.1. Porosity The total porosity of the analyzed rocks is given by the sum of connected pores and isolated pores. In the host rock, the connected pores are interparticle voids. In the deformed zones, the main connected porosity is related to solution-enlarged stylolites and slip surfaces. The analyzed samples present a relatively high connected porosity in the host rocks, although the porosity is mostly composed of isolated pores in the deformed/cemented rock volumes (Fig. 9). For the host rock of SVG-S1, the connected porosity is 19.1 ± 0.5%. The isolated porosity, composed mainly by intraparticle porosity, is 1.3 ± 0.06%. For the same host rock, the images of SVGM1 show a lower value of connected porosity (14.8%) and a higher value of isolated porosity (1.4%). In the cemented zones limiting the DB (Zone III), the connected porosity is absent, and the isolated porosity is between 4.8 and 5.5%. The whole fault core, composed of compaction and cataclastic volumes (Zones I and II, respectively), has a connected porosity of 2.0%. The pore space consists of channelized solution-enlarged stylolites oriented mostly parallel to the fault surface alternated with bands of null connected porosity. The isolated porosity within the Zone I is in average 5.8 ± 0.6%. Regarding the sample FIG-S2, the host rock exhibits values of

26.7 ± 1.3% and 0.74 ± 0.12% for connected and isolated porosity, respectively. The sample FIG-M2 indicates values of connected porosity of 25.7 ± 0.9% and isolated porosity of 0.24 ± 0.03%. The sample FIG-S3, from the fault core, contains alternating regions dominated by dissolution or cementation. In the cementationdominated zones, the connected porosity is negligible (<2.0%) and the isolated porosity is on average 6.4 ± 0.4%. In the dissolution-dominated zones, the connected porosity reaches values in the range of 5.5e14.5%. The isolated intraparticle porosity is 2.23 ± 0.4% on average. In the host rock sample OFG-S4, the porosity is dominated by intraparticle pores. The connected porosity is between 14.6% and 14.9%, while the isolated porosity is 0.6%. The sample OFG-S5 is more heterogeneous, presenting well-cemented areas where connected porosity is null but isolated porosity, mostly composed of intraparticle pores, is about 2.1%. In the rest of the sample, the connected porosity varies between 2.9% and 11.6% depending on the degree of cementation. The isolated porosity is 1.3 ± 0.2% average, represented by intraparticle porosity. 3.2. Connectivity density The degree of connectivity of the pore network is expressed in terms of connectivity density, derived from the skeletonization of the connected pore network. The host rocks present the higher values of connectivity followed by partially dissolved zones (FIGS3), and the zones affected by dissolution and cataclasis (Fig. 10). For some VOIs within the highly-cemented or cataclastic zones, the connectivity is null because they lack a connected pore network. The host rock of sample SVG-S1 indicates an average connectivity density of 381 ± 32.8 mm3. Surrounding the fault core, the cemented zone (Zone III) presents an isolated pore network, while the entire fault core (Zone I and II) has a value of connectivity density of 7.9 mm3. However, a detailed examination of the cataclastic zone (Zone I) indicates that the pore network is isolated. As a consequence, the connectivity referred to the fault core is due to solution-enlarged stylolites and slip surfaces. The sample SVGM1, with a minor resolution to the synchrotron images, presents a minor value of connectivity density of 23.3 mm3 for the host rock. The sample FIG-S2, host rock, presents an average connectivity density of 501.9 ± 40.7 mm3. Conversely, the analysis of the host rock sample FIG-M2 showed a lower average connectivity density of 17.9 mm3 ± 1.0 mm3. With respect to the fault core, the sample FIG-S3 indicates more variable results for connectivity with an average of 158.2 ± 56.4 mm3 and a coefficient of variation equal to 1.0 (heterogeneous medium). In the partially cemented areas, the connectivity density takes a maximum value of 40 mm3. However, zones with null connectivity are present due to the cement. In the zones affected by dissolution, the connectivity density is higher (299.1 ± 38.8 mm3). In the sample OFG-S4, composed of host rock, connectivity density has values in the range of 296.6 ± 10.44 mm3. In the case of sample OFG-S5, the connectivity density is in average 67.9 ± 18.3 mm3. The whole sample presents a heterogeneous connectivity density (Cv ¼ 0.7), with values ranging from 0 to 166.2 mm3 for totally and partly cemented areas, respectively. 3.3. Specific surface area The specific surface area was measured in both the connected pore network and the isolated pore network. In general, the specific surface area of the connected pores is lower than in the isolated pores. In the studied host rocks, the specific surface area of the connected pores is higher for the OFG-S4 samples

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264 Fig. 6. Volume renderings belonging to SVG sample. a) sample SVG-S1 and VOIs of b) host rock, c) cemented zone (Zone III), and d) fault core (Zone I and II). Renderings include i) X-ray tomographic images, ii) segmented pores (connected and isolated pores in yellow and red, respectively), and iii) skeletonized volume (pores in yellow, throats in blue, node-to-node branches in green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

257

258 M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264 Fig. 7. Volume renderings belonging to FIG samples. a) sample FIG-S3 and VOIs of b) host rock from FIG-S2, c) fault core affected by dissolution, and d) fault core partially-cemented. Renderings include i) X-ray tomographic images, ii) segmented pores (connected and isolated pores in yellow and red, respectively), and iii) skeletonized volume (pores in yellow, throats in blue, node-to-node branches in green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264 Fig. 8. Volume renderings belonging to OFG samples. a) sample OFG-S5 (with a deformation band), b) sample OFG-S4 (host rock), c) and d) cemented volumes of the deformation band. Three different rendered volumes are presented per each VOI i) the x-ray tomographic images, ii) segmented pores (connected and isolated pores in yellow and red, respectively), and iii) skeletonized volume (pores in yellow, throats in blue, node-to-node branches in green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

259

260

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

Table 1 Results of quantitative image analysis of both connected and isolated pore network performed using the Pore3D software. Images were acquired using a synchrotron (SYRMEP), at different resolution, and a conventional microfocus X-ray (TomoLab) source. Sample

SVG-S1

VOIs

Host Rock

Zone III

SVG-M1 SVG-S1

Fault core Host Rock Zone I

FIG-S2

Host Rock

FIG-M2

Host Rock

FIG-S3

Fault core

OFG-S4

Host Rock

OFG-S5

Fault core

Connected Pores

AM SE Cv AM SE Cv e e AM SE Cv AM SE Cv AM SE Cv AM SE Cv AM SE Cv AM SE Cv

-2

Isolated Pores -3

F [%]

S [mm ]

CD [mm ]

F [%]

S [mm-2]

19.06 0.53 0.06 e e e 2.00 14.80 e e e 26.72 1.31 0.11 27.08 0.28 0.03 4.55 1.69 1.05 14.75 0.15 0.01 8.33 1.80 0.38

52.9 1.8 0.1 e e e 45.7 18.7 e e e 38.1 3.1 0.2 18.1 0.5 0.1 76.9 3.3 0.1 79.61 1.06 0.02 56.6 2.6 0.1

381.0 32.8 0.2 e e e 10.6 23.3 e e e 501.9 40.7 0.2 17.9 1.0 0.1 158.2 56.4 1.0 296.60 10.44 0.05 65.2 19.0 0.7

1.28 0.06 0.10 5.15 0.35 0.10 2.10 1.40 5.88 0.60 0.20 0.74 0.05 0.13 0.20 0.00 0.00 4.33 0.83 0.54 0.60 0.00 0.00 1.28 0.19 0.33

104.31 2.71 0.06 74.33 0.96 0.02 148.10 32.29 252.22 7.20 0.06 121.44 7.47 0.12 42.72 1.32 0.08 81.17 8.34 0.29 149.33 2.33 0.02 121.31 6.01 0.11

Note: Results include the porosity (F), specific surface area (S), and connectivity density (CD) of the pore network. For each property, the arithmetic mean (AM), standard of the mean (SE) and the coefficient of variation (Cv) are indicated. Samples were labeled according to their location (SVG, FIG or OFG) and their chosen analytical facility (S: synchrotron or M: microfocus).

(79.6 ± 1.1 mm1) with respect to the SVG-S1 (52.9 ± 1.8 mm1) and FIG-S2 (38.1 ± 3.1 mm1). This may be an indicator of the pore size, which is lower in the OFG than in the FIG and SVG (Fig. 11). The specific surface area of the connected pores is also high for the deformed samples OFG-S5 (56.6 ± 2.6 mm1), SVG-S1 (45.7 mm1), and FIG-S3 (79.9 ± 3.3 mm1).

Fig. 9. Scatter plot of connected and isolated porosity. VOIs from SVG are represented with triangles, FIG with circles, OFG with squares. VOIs correspond to host rock (HR) and the Zones I, II and III within the DBs. Error bars correspond to the standard error of the mean.

The specific surface area of isolated pores (Fig.12a) is high for the host rock of the samples OFG-S4 (149.3 ± 2.3 mm1), FIG-S2 (121.4 ± 7.5 mm1), and SVG-S1 (104.3 ± 2.7 mm1). Whereas the DBs present very high values: SVG-S1 (148.1 mm1), FIG-S3 (81.2 ± 8.3 mm1), and OFG-S5 (121.31 ± 6.01 mm1). A detailed assessment in the cataclastic zone of SVG-S1 indicated even higher values of specific surface area. 252.2 ± 7.2 mm1. In the highlycemented areas around the DB, the specific surface area of isolated pores is lower, SVG-S1 (74.3 ± 1.0 mm1). Similarly, within the sample FIG-S3 the most cemented zones of the fault core present a lower specific surface area for the isolated pores (60.1 ± 0.58 mm1). 4. Discussion This study documents important differences of porosity, specific surface area and connectivity density among the host rocks and deformation bands (DBs) of three porous carbonate rocks: i) San Vito Lo Capo Grainstone (SVG), ii) Favignana Island Grainstone (FIG), and iii) Orfento Fm. Grainstone (OFG). In the DBs, the cemented zone (Zone III) encompassing the fault core (Zone I and II) is dominated by cementation (Tondi, 2007). As a result, the connected porosity is often null. Isolated pores within Zone III have a lower specific surface area than the host rock (Fig. 12b), which may be caused by the smoothing of the pore surface due to the cement. The fault cores composed of Zone I and II, are more connected than Zone III due to the presence of stylolites likely enlarged by dissolution. In comparison with the host rock, the fault cores are 3e6 times less porous. The host rock from OFG is characterized by lower values of connected porosity (14.8%) and connectivity density (296.6 mm3) in comparison to the SVG (Fc ¼ 19.1%; CD ¼ 381.0 mm-3) and the FIG (Fc ¼ 26.7%; CD ¼ 501.9 mm-3). Whereas the isolated porosity takes values of 1.3%, 0.8% and 0.6% for the host rocks of SVG, FIG and OFG, respectively. The specific surface of the area of the host rock of FIG (38.1 mm-1) is lower in comparison to the SVG (52.9 mm-1) and OFG (79.6 mm-1). For the OFG, higher values of specific surface area measurements agree with the dominance of micro-porosity (Fig. 11a) previously documented by Zhu et al. (2010) and Cilona et al. (2012). In

Fig. 10. Scatter plot of connected porosity and connectivity density. VOIs from SVG are represented with triangles, FIG with circles, OFG with squares. VOIs correspond to host rock (HR) and the Zones I, II and III within the DBs. Error bars correspond to the standard error of the mean.

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

261

Fig. 11. Reconstructed slice for comparing the pores and grain size of the host rocks a) OFG, and b) the SVG. The pore sizes of the OFG are significantly smaller than the SVG increasing the specific surface area.

contrast, host rocks of FIG and SVG are characterized by larger grains and pores (Fig. 12b and c). The control of the pore network properties (i.e., porosity, pore connectivity, specific surface area) on permeability is inferred considering the results of permeability published by Antonellini et al. (2014) and Tondi et al. (2016) for the same formations. According to Tondi et al. (2016), the carbonate grainstones investigated in this study have similar porosity but they differ by up to two orders of magnitude in permeability. These authors suggested that additional characteristics of the pore network (i.e. pore types, dimensions, distribution, and connectivity) could control the permeability in the studied host rocks. The connected porosity, which exerts a primary control on the permeability, showed significant differences among the studied rocks. Moreover, the higher values of the specific surface area, due to smaller pores, may put a larger surface per volume in contact with hypothetical fluids. Which may exert a higher friction on fluid flow and therefore lower permeability of the rock. The connectivity of the pore network may also play an important role, which is not included for the deterministic estimation of permeability (e.g., Carman, 1937). In summary, the differences of the connected porosity and connectivity and specific surface area may explain the lower permeability values reported by Tondi et al. (2016) between the OFG and the FIG. However, a study for measuring permeability on the same imaged samples is required to provide more accurate conclusions. Deformation bands may display different textural zones (I, II, III; sensu Tondi, 2007). The outer zone (Zone III) is characterized only by cementation without any sign of compaction (Tondi et al., 2006). In the studied samples, pores within the Zone III are totally isolated and may not contribute to permeability. Depending on the extension and continuity of the cemented Zone III, this may act as a local barrier to fluid flow. On the contrary the compacted region (Zone II) is characterized by the presence of connected channel-shaped pores likely related to solution-enlarged stylolites, which follow the orientation of the DB. The cataclastic region (Zone I), characterized by isolated pores, may act as a buffer zone for fluid flow. Wellconnected pore space within the slip surfaces, due to asperity superposition, may permit the fluid flow parallel to the DB. These characteristics allows us to speculate a high permeability anisotropy within the DB. With respect to the significance of the result, one of the most problematic steps during the X-ray quantitative image analysis is

the definition of representative elementary volume (REV). Due to the heterogeneous nature of carbonates, their REVs usually exceeded the sample size used in X-ray micro-CT analysis (e.g., Mostaghimi et al., 2013). In this work, we found statistically meaningful REVs (Zhang et al., 2000) for the host rocks, which are characterized by mean porosity, specific surface area and connectivity density with low values of coefficient of variation (Cv) lower than 0.2. Similar to Mostaghimi et al. (2013), we found that specific surface area typically has a higher variability (Cv about 1.5 times higher) than the porosity lower the in the host rock. Moreover, we documented that the connectivity density has CV more than three times higher. These results indicate the importance of following a multiple property approach for assessing the REV. For the deformation bands (DBs), the definition of REV is still an open problem. In essence, deformation bands exhibit a higher heterogeneity of the pore space properties in comparison to the host rock. Even though in this work REVs were defined for individual regions, the results should not be directly extrapolated to higher scales. 5. Conclusions In this study, the pore-network of deformed carbonate grainstones exposed in San Vito Lo Capo Peninsula (SVG) and Favignana Island (FIG), and Maiella Mountain (Orfento Formation; OFG), has been investigated by means of a three-dimensional (3D) quantitative analysis using X-ray microtomography images. The investigated pore network properties correspond to porosity (connected and isolated), specific surface area and pore connectivity. The following conclusions are made:  In the host rocks, the fraction of pores, their connectivity and their texture depend on the morphology and size distribution of grains. In deformation bands (DBs), these properties change due to cementation, dissolution, compaction and cataclasis processes. In the studied samples, the connected porosity is reduced by cementation and cataclasis. Isolated pores in the host rocks and purely cemented zones, Zone III, are mostly intraparticle. Within the fault core, pressure solution increases the connectivity of the pores.  The Zone III, often enclosing the fault core, is characterized by a high cementation and zero connected porosity. The source of the cement material may be from the fault core where pressure solution and grain size reduction take place.

262

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

Fig. 12. Scatter plots of the specific surface area and a) the connected porosity, and b) the isolated porosity. VOIs from SVG are represented with triangles, FIG with circles, OFG with squares. VOIs correspond to host rock (HR) and the Zones I, II and III within the DBs. Error bars correspond to the standard error of the mean.

 The Zone II is characterized by the presence of well-connected channel pores likely due to stylolites enlarged by dissolution mostly oriented parallel to the DB.  In the innermost part of the fault core, Zone I, the combination of both asperity superposition and dissolution along slip surfaces create highly connected channelized porenetworks.

 The morphology and distribution of connected pores may have an impact on the permeability may explain variability on their permeability reported for the studied rocks. In the OFG, permeability values of two or three orders of magnitude lower than the SVG and FIG are related to the influence of three factors: i) a lower connected porosity, ii) a lower pore connectivity, and iii) a higher specific surface area.

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

 In the fault zones, both connected porosity and pore connectivity decrease drastically due to the collapse or occlusion of pores by compaction, cataclasis or cementation. Therefore, the cemented and the cataclastic zones could be considered as local buffer zones or barriers to fluid flow. On the contrary, the connected pore-network, made of solution-enlarged stylolites and slip surfaces, is preferably connected and distributed parallel to the fault. Therefore, this configuration of conduits and local barriers may cause an anisotropy in the permeability within the fault zone. Acknowledgements This research was supported by the FAR Project 2014 “Characterization and modeling of natural reservoirs of geofluids in fractured carbonate rocks”, funded by the University of Camerino (Principal Investigator Emanuele Tondi), and the Reservoir Characterization Project (www.rechproject.com). We acknowledge the EXTREMA COST Action MP 1207 for networking support. We acknowledge two anonymous reviewers for providing useful comments and corrections of the manuscript. We are grateful to Francesco Brun (Synchrotron of Trieste) for useful advice and help with the Pore3D software. We acknowledge Antonino Cilona who kindly provided SEM images of Orfento Fm. and Favignana grainstones. Special thanks to Gaetano Dinolfo (University of Palermo), Nijiati Aibibula (University of Camerino), and Yilidan Aierkan (University of Camerino) for productive and interesting discussions. We also appreciated the help of Gabriele Giuli and Marco Bello of the laboratory of mineralogy of the University of Camerino for helping in the X-ray diffractometry experiments. We are grateful to Alan Pitts and Hannah Riegel (University of Camerino) for kindly revising the grammar and orthography of the manuscript. References Abrami, A., et al., 2005. Medical applications of synchrotron radiation at the SYRMEP beamline of ELETTRA. Nucl. Instrum. Methods Phys. Res. 548, 221e227. Al-Raoush, R., Papadopoulos, A., 2010. Representative elementary volume analysis of porous media using X-ray computed tomography. Powder Technol. 200, 69e77. Antonellini, M.A., Aydin, A., Pollard, D.D., 1994. Microstructures of deformation bands in porous sandstones at Arches national park. Utah. J. Struct. Geol. 16, 941e959. Antonellini, M., Cilona, A., Tondi, E., Zambrano, M., Agosta, F., 2014. Fluid flow numerical experiments of faulted porous carbonates, Northwest Sicily (Italy). Mar. Petroleum Geol. 55, 185e201. http://dx.doi.org/10.1016/j.marpetgeo. 2013.12.003. Arzilli, F., Cilona, A., Mancini, L., Tondi, E., 2015. Using synchrotron X-ray microtomography to characterize the pore network of reservoir rocks: a case study on carbonates. Adv. Water Resour. http://dx.doi.org/10.1016/j.advwatres. 2015.07.016. Aydin, A., 1978. Small faults formed as deformation bands in sandstone. Pure Appl. Geophys. 116, 913e930. Baker, D.R., Mancini, L., Polacci, M., Higgins, M.D., Gualda, G.A.R., Hill, R.J., Rivers, M.L., 2012. An introduction to the application of X-ray microtomography to the three-dimensional study of igneous rocks. Lithos 148, 262e276. Baud, P., Vinciguerra, S., David, C., Cavallo, A., Walker, E., Reuschle, T., 2009. Compaction and failure in high porosity carbonates: mechanical data and microstructural observations. Pure Appl. Geophys. 166, 869e898. http:// dx.doi.org/10.1007/s00024-009-0493-2. Bear, J., 1988. Dynamics of Fluids in Porous Media. Elsevier, New York. Blunt, M.J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S., Mostaghimi, P., Paluszny, A., Pentland, C., 2013. Pore-scale imaging and modelling. Adv. Water Resour. 51, 197e216. Brun, F., Mancini, L., Kasae, P., Favretto, S., Dreossi, D., Tromba, G., 2010. Pore3D: a software library for quantitative analysis of porous media. Nucl. Instrum. Meth A 615, 326e332. http://dx.doi.org/10.1016/j.nima.2010.02.063. , Accardo, A., 2015. Enhanced and flexible software tools for x-ray Brun, F., Pacile computed tomography at the Italian synchrotron radiation facility elettra. Fundam. Inf. 141 http://dx.doi.org/10.3233/FI-2015-1280, 1e11 1. Carman, P.C., 1937. Fluid flow through granular beds. Trans. Inst. Chem. Eng. 15, 150. Cilona, A., Baud, P., Tondi, E., Agosta, F., Vinciguerra, S., Rustichelli, A., Spiers, C.J., 2012. Deformation bands in porous carbonate grainstones: field and laboratory observations. J. Struct. Geol. 45, 137e157.

263

Cilona, A., Faulkner, D.R., Tondi, E., Agosta, F., Mancini, L., Rustichelli, A., Baud, P., Vinciguerra, S., 2014. The effects of rock heterogeneity on compaction localization in porous carbonates. J. Struct. Geol. 67, 75e93. http://dx.doi.org/10.1016/ j.jsg.2014.07.008. Cloetens, P., Barrett, R., Baruchel, J., Guigay, J.P., Schlenker, M., 1996. Phase objects in synchrotron radiation hard X-ray imaging. J. Phys. D. Appl. Phys. 29, 133e146. http://dx.doi.org/10.1088/0022-3727/29/1/023. Costanza-Robinson, M.S., Estabrook, B.D., Fouhey, D.F., 2011. Representative elementary volume estimation for porosity, moisture saturation, and air-water interfacial areas in unsaturated porous media: data quality implications. Water Resour. Res. 47 (7). http://dx.doi.org/10.1029/2010WR009655. Dullien, F.A.L., 1992. Porous Media: Fluid Transport and Pore Structure, 574. Academic, San Diego, Calif. Feldkamp, L.A., Davis, L.C., Kress, J.W., 1984. Practical cone-beam algorithm. J. Opt. Am. Soc. A1, 612e619. Flannery, B.P., Deckman, H.W., Roberge, W.G., D'Amico, K.L., 1987. Three-dimensional X-ray microtomography. Science 237, 1439e1444. Fossen, H., Bale, A., 2007. Deformation bands and their influence on fluid flow. AAPG Bull. 91, 1685e1700. Fossen, H., Schultz, R.A., Shipton, Z.K., Mair, K., 2007. Deformation bands in sandstone: a review. J. Geol. Soc. 164, 755e769. http://dx.doi.org/10.1144/001676492006-036. Graham, B., Antonellini, M., Aydin, A., 2003. Formation and growth of normal faults in carbonates within a compressive environment. Geology 31, 11e14. Gürsoy, D., De Carlo, F., Xiao, X., Jacobsen, C., 2014. TomoPy: a framework for the analysis of synchrotron tomographic data. J. Synchrotron Radiat. 21 (5), 1188e1193. Hartigan, J.A., 1975. Clustering Algorithms. Wiley, New York. Hartigan, J.A., Wong, M.A., 1979. A K-means clustering algorithm. Appl. Stat. 28, 100e108. Ji, Y., Hall, S.A., Baud, P., Wong, T.F., 2015. Characterization of pore structure and strain localization in Majella limestone by X-ray computed tomography and digital image correlation. Geophys. J. Int. 200 (2), 701e719. http://dx.doi.org/ 10.1093/gji/ggu414. Ketcham, R.A., Carlson, W.D., 2001. Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences. Comput. Geosci. 27, 381e400. Lee, T.C., Kashyap, R.L., Chu, C.N., 1994. Building Skeleton Models via 3D Medial Surface axis Thinning Algorithms. CVGIP: Graphical Models and Image Proceedings 56 (6), 462e478. http://dx.doi.org/10.1006/cgip.1994.1042. Linquist, W.B., Venkatarangan, A., 1999. Investigating 3D geometry of porous media from high resolution images. Phys. Chem. Earth, Part A: Solid Earth Geodesy 24, 593e599. http://dx.doi.org/10.1016/S1464-1895(99)00085-X. Lucia, F.J., 2007. Carbonate Reservoir Characterization: an Integrated Approach. Springer Science & Business Media. Mostaghimi, P., Blunt, M.J., Bijeljic, B., 2013. Computations of absolute permeability on micro-CT images. Math. Geosci. 45 (1), 103e125. Mutti, M., 1995. Porosity Development and Diagenesis in the Orfento Supersequence and its Bounding Unconformities (Upper Cretaceous, Montagna Della Majella, Italy). American Association of Petroleum Geologists Special Publications, pp. 141e158. Ori, G.G., Roveri, M., Vannoni, F., 1986. Plio-pleistocene sedimentation in the apennineeadriatic foredeep (central Adriatic sea, Italy). In: Allen, P.A., Homewood, P. (Eds.), Foreland Basins. Blackwell, Oxford, pp. 183e198. Paganin, D., Mayo, S.C., Gureyev, T.E., Miller, P.R., Wilkins, S.W., 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J. Microsc. 206, 33e40. http://dx.doi.org/10.1046/j.13652818.2002.01010.x. Palenstijn, W., Batenburg, K., Sijbers, J., 2011. Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs). J. Struct. Biol. 176 (2), 250e253. Rustichelli, A., Torrieri, S., Tondi, E., Laurita, S., Strauss, C., Agosta, F., Balsamo, F., 2016. Fracture characteristics in cretaceous platform and overlaying ramp carbonates: an outcrop study from Maiella mountain (central Italy). Mar. Petroleum Geol. 76, 68e87. Schindelin, J., Arganda-Carreras, I., Frise, E., et al., 2012. Fiji: an open-source platform for biological-image analysis. Nat. methods 9 (7), 676e682. Tomasi, C., Manduchi, R., 1998. Bilateral filtering for gray and color images. Sixth International Conference on Computer Vision, New Delhi, India, 839e846. Tondi, E., 2007. Nucleation, development and petrophysical properties of faults in carbonate grainstones: evidence from the San Vito Lo Capo peninsula (Sicily, Italy). J. Struct. Geol. 29, 614e628. http://dx.doi.org/10.1016/j.jsg.2006.11.006. Tondi, E., Antonellini, M., Aydin, A., Marchegiani, L., Cello, G., 2006. The role of deformation bands, stylolites and sheared stylolites in fault development in carbonate grainstones of Maiella mountain, Italy. J. Struct. Geol. 28, 376e391. http://dx.doi.org/10.1016/j.jsg.2005.12.001. Tondi, E., Cilona, A., Agosta, F., Aydin, A., Rustichelli, A., Renda, P., Giunta, G., 2012. Growth processes, dimensional parameters and scaling relationships of two conjugate sets of compactive shear bands in porous carbonate grainstones, Favignana Island, Italy. J. Struct. Geol. 37, 53e64. http://dx.doi.org/10.1016/ j.jsg.2012.02.003. Tondi, E., Rustichelli, A., Cilona, Balsamo, F., Storti, F., Napoli, G., Napoli, G., Renda, P., Giorgioni, M., 2016. Hydraulic properties of fault zones in porous carbonates, examples from central and southern Italy. Italian J. Geosciences 135, 68e79. http://dx.doi.org/10.3301/IJG.2015.08 f.1.

264

M. Zambrano et al. / Marine and Petroleum Geology 82 (2017) 251e264

Tromba, G., et al., 2010. The SYRMEP beamline of elettra: clinical mammography and bio-medical applications. In: AIP Conference Proceedings, 1266, pp. 18e23. Voltolini, M., Zandomeneghi, D., Mancini, L., Polacci, M., 2011. Texture analysis of volcanic rock samples: quantitative study of crystals and vesicles shape preferred orientation from X-ray microtomography data. J. Volcanol. Geoth Res. 202, 83e95. http://dx.doi.org/10.1016/j.jvolgeores.2011.02.003. Wildenschild, D., Sheppard, A.P., 2013. X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems. Adv. water Resour. 51, 217e246.

Zandomeneghi, D., Voltolini, M., Mancini, L., Brun, F., Dreossi, D., Polacci, M., 2010. Quantitative analysis of X-ray microtomography images of geomaterials: application to volcanic rocks. Geosphere 6, 793e804. http://dx.doi.org/10.1130/ GES00561.1. Zhang, D., Zhang, R., Chen, S., Soll, W.E., 2000. Pore scale study of flow in porous media: scale dependency, REV, statistical REV. Geophys Res. Lett. 27, 1195e1198. Zhu, W., Baud, P., Wong, T.F., 2010. Micromechanics of cataclastic pore collapse in limestone. J. Geophys. Res. 115, B04405.