## 4 Results
### Property Estimation of Lithuanian Samples  
Once we established the effectiveness and robustness of our workflow on analogous formation samples, we proceeded to apply this methodology to actual reservoir samples, thereby providing a well-validated foundation for our research into CO\({}_{2}\) storage capabilities. The core samples from the Syderai and Vaskai structures were utilized to estimate their petrophysical properties using their digital volumes obtained at two different resolutions  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Sub-Volumes** & **Permeability Using LBM (mD)** & **Average Permeability (mD)** & **Laboratory (mD)** & **Error (\%)** \\ \hline \multirow{4}{*}{BB} & 1 & 282 & & & \\  & 2 & 307 & 317 & 275 & 15 \\  & 3 & 360 & & & \\  & 4 & 320 & & & \\  & 1 & 83 & & & \\  & 2 & 105 & 91 & 62 & 47 \\  & 3 & 100 & & & \\  & 4 & 77 & & & \\  & 1 & 445 & & & \\  & 2 & 334 & & & \\ L & 3 & 370 & & 327 & 13 \\  & 4 & 330 & & & \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of permeability obtained for each sub-volume (size: 201 \(\times\) 201 \(\times\) 201 voxels) using LBM with laboratory-measured values.  
(22 \(\upmu\)m and 8 \(\upmu\)m). The obtained porosity values are shown in Table 6 for both the samples at different resolutions.  
The porosity values obtained from the k-means algorithm were compared with the laboratory values. It was observed that the samples scanned at a higher resolution (8 \(\upmu\)m) resulted in higher porosity values as compared to those of the lower-resolution samples (22 \(\upmu\)m). This difference was attributed to the presence of smaller pores that are not adequately captured at a lower resolution (22 \(\upmu\)m). The finer features, including the smaller pores, are revealed at a higher resolution.  
The segmented volumes of S3 and V4 were then used to extract different sub-volumes, each measuring 251 \(\times\) 251 \(\times\) 251 voxels in size. From each of the two resolutions, a total of 12 sub-volumes were extracted from each sample, resulting in a grand total of 24 sub-volumes from each sample. The objective of this sub-volume extraction was to understand the distribution of the pores within the digital volume comprehensively and to identify a sub-volume that most accurately represents the core (Figure 10). The average permeability value was computed to serve as a representative value for the core, shown in Table 7.  
The average permeability value was subsequently compared with the laboratory-measured permeability value. An analysis of the results in Table 7 revealed that the error in permeability values was notably lower in the 8 \(\upmu\)m-resolution volume when compared to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Sample Type** & **Porosity Using K-Means Algorithm (\%)** & **Laboratory-Measured Porosity (\%)** & **Porosity Error (\%)** \\ \hline S3 & Core (22 \(\upmu\)m) & 14.38 & 16 & 10 \\  & Cube (8 \(\upmu\)m) & 15.17 & & 5 \\ V4 & Core (22 \(\upmu\)m) & 19.74 & & 10 \\  & Cube (8 \(\upmu\)m) & 23 & 22 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the obtained porosity values obtained from k-means with laboratory-measured values (sample size: 1020 \(\times\) 1020 \(\times\) 1014 voxels).  
Figure 10: Segmented 3D digital sub-volume (**left**) and corresponding simulated flow volume (**right**) for sample S3. In the segmented volume (**left**), the solid matrix is represented in a dark (red) color, the pore space is depicted in a light (white) color, and the pink color shows the different mineral phases. In the simulated volume (**right**), the flow of fluid through the pore space is indicated by blue lines.  
the 22 \(\upmu\)m-resolution samples. This reduction in error can be attributed to the enhanced level of detail achieved when scanning at a higher resolution (8 \(\upmu\)m).