## 3 Results
### Petrophysical Calculations from Micro-Tomography Images  
Calculations were conducted on micro-tomography images to obtain petrophysical parameters. Two porosities were calculated from the XRMT images, as mentioned in Section 2.3.2. As a reminder, calculations were made on the phase where only macro-void porosity is taken into account. Calculations from the XRMT give values of permeability \(k_{XRMT}\) and hydraulic tortuosity \(\tau_{h}\) (see Section 2.3.2). From tortuosity, formation factor \(F_{XRMT}\) and cementation index \(m_{XRMT}\) can be deduced (Equations (5) and (6)). All these parameters, as well as the ratio of pore surface over pore volume \(\frac{\xi}{V}\) and the proportion of the percolating volume \(PV\) are displayed in Table 2. Permeability values present fluctuating results due to high sensibility calculations. Changing the segmentation threshold by only one point considerably affects the sample connectivity. Indeed, if, for a given segmentation, a pixel cloggs a path while for another segmentation, this pixel is a pore, the pores organization and the sample connectivity will change, inducing an increase in permeability. This is mainly why permeability values present such variability, especially for rocks with a majority of small pores such as Normandie. Nevertheless, different segmentations were conducted and porosity values were not strongly affected by a segmentation threshold of one pixel. From Table 2, Lexos samples display specificity. Regarding L03 and L07 on the imaging software, they do not present percolation path due to the large proportion of pores smaller than 12 \(\upmu\)m. Therefore, the connectivity is not visualized on the imaging software. Most of the properties calculated with the software, such as macro-void porosity, permeability, electrical factors and the percolating volume \(PV\), cannot be obtained for these two samples. Tomography analysis has therefore only been conducted on three samples: L01, L04 and L08. Even if macro-phase porosities \(\phi_{macro}\) are largely underestimated, Normandie presents the highest porosity while Euville and Lexos have lower and similar values. As written above, permeability varies but Normandie displays the highest and Lexos the lowest, with Euville in between. Concerning electrical measurements, Normandie presents the lowest ones, consistently with its high porosity. It also has the lowest variability, except for N04 which has very low values compared to the other samples of the set. It should be  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{\(\mathbf{\phi}_{macro}\) **(\%)**} & \multirow{2}{*}{\(\mathbf{\phi}_{total}\) **(\%)**} & \multirow{2}{*}{\(\mathbf{k_{XRMT}}\) **(mD)**} & & & \(\frac{\xi}{V}\) & \(\mathbf{PV}\) \\  & & & & **(\%)** & & **(mD)** & & **(m\({  
highlighted that N04 is the sample imaged with another scanner. Euville has the highest electrical factors and Lexos is comprised between the two others. Pore surface over pore volume \(\lx@sectionsign\) is higher for Normandie and Lexos than for Euville. A small \(\lx@sectionsign\) is consistent with a low proportion of small pores. The pore size distribution is displayed in Figure 5 with the two calculation methods. It must be noticed that sphere diameters distribution is less relevant for elongated pores than for spherical pores, compared to that of chord lengths. The poral anisotropy can be quantified by chord lengths distributions in X, Y and Z directions, as it can be seen in Figures 6-8. X and Y are width directions, while Z direction is longitudinal to the cylinder core direction. Then, in Figure 5, Euville samples always display a low proportion of small pores, while the majority of pores diameters is higher than 120 \(\upmu\)m, which is consistent with small values of \(\lx@sectionsign\). Finally, Euville, with a proportion of percolating volume higher than 98%, has a very good connectivity in the poral phase.