## 4 Discussion  
The absolute porosity distribution was normal with negative skewness, and thus relatively symmetrical, whereas the effective porosity distribution exhibited a gamma distribution, indicating that the data tend to cluster in the lower distribution range and are positively skewed. Therefore, the effective porosity values are smaller than the absolute porosity. Not all pores in the rock can be used for fluid flow, as some pores may be isolated or too small to function effectively. This is important because, by definition, effective porosity is the result of subtracting ineffective porosity from absolute porosity, so the effective porosity value will always be smaller than absolute porosity. In other words, absolute porosity includes all pore spaces in the rock, including those that do not contribute to fluid flow, while effective porosity includes only those pores that function in fluid transport. Variations in effective and absolute porosity can reflect different geological processes such as sedimentation, diagenesis, and recrystallization [32]. For example, rocks that have undergone recrystallization, such as Castlegate and Leopard, have high absolute porosity but low effective porosity because many pores are isolated by newly formed crystals [33]. The tortuosity results for all rock samples were within the lower range because the range of tortuosity commonly found in soils and sedimentary rocks is between 1.2 and 3. In  
Figure 14: Distribution of permeability empirical calculation results using the Kozeny–Carman model. Figure 13: Distribution of permeability simulation results using the Darcy model LBM. reality, it is challenging to find tortuosity values above 2, as this would affect low porosity. Extremely low porosity in some rocks causes the lack of pathways for fluid flow, making tortuosity values impossible to calculate. There was a clear negative correlation between tortuosity and effective porosity, whereby the tortuosity decreased as the effective porosity increased, indicating an inverse relationship. The high \(R^{2}\) value (0.81) indicates that the Kozeny-Carman Equation provides a good representation of the experimental data. The correlation can be considered representative because higher porosity usually means more direct and less tortuous flow paths, thus reducing tortuosity. There was a positive correlation between effective porosity and permeability, whereby the permeability increased as the effective porosity increased. Curve fitting using the Kozeny-Carman Equation model shows that the data tend to follow the research data well, despite some significant data scatter. The high \(R^{2}\) value (0.70) indicates that the regression model represents the data relatively well. There was also a negative correlation between tortuosity and permeability, whereby the permeability decreased as the tortuosity increased. The curve fitting demonstrated the data trend but the lower correlation coefficient (0.50) indicates that this model does not represent the data, so other factors may affect permeability besides tortuosity. Overall, these results are representative because the higher the tortuosity is, the longer the fluid path is, resulting in lower permeability. There was a positive correlation between throat size and effective porosity, with the effective porosity increasing as the throat size increased. The \(R^{2}\) value of 0.28 indicates that about 28% of the variation in effective porosity can be explained by variations in average throat size. This is consistent with the basic theory that larger throat sizes allow for more effective pore space within the rock. There was a weak positive correlation between average throat size and permeability. Throat size is the radius of the cavity within the rock, so the larger the value is, the higher the permeability is. In contrast, there was a negative correlation between average throat size and tortuosity, with decreasing tortuosity as the throat size increased.