## 5 Discussion
The Crocker and Temburong Formation samples all showed poor sorting, while most of the Belait Formation samples showed a moderate sorting, reflecting the nature of the deposition of the deep marine and fluvio-marine depositional environments. The graphic skewness values showed different values between the symmetry and the positive and negative skewness (Table 4). The samples from the same outcrops of the Belait Formation showed some coherence, like L4 and L6, while the samples from the Crocker and Temburong Formation outcrops did not show the same coherence, which may lead to less diagenetic effects on the Belait Formation. The narrow range of the grain size and the poor sorting of most of the examined samples explains the domination of microporosity, as the pores may be found in the areas between the grains, and since the grains ranged from fine to very fine grain sizes, the in-between spaces tended to be in micro-scale, thus forming more micropores. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **STDV** & **Skewness** & **Kurtosis** & **Sorting** & **Skewness** \\ \hline L101 & 0.729545 & 0.10 & 4.10 & Moderately sorted & Near symmetrical \\ L301 & 1.221212 & \(-\)0.03 & 2.40 & Poorly sorted & Near symmetrical \\ L302 & 1.427273 & 0.16 & 2.46 & Poorly sorted & Positive skewed (coarse) \\ L303 & 1.055303 & \(-\)0.07 & 2.87 & Poorly sorted & Near symmetrical \\ L401 & 0.5575756 & 0.37 & 2.32 & Moderately & Positive skewed (coarse) \\ L402 & 0.855303 & 0.24 & 3.59 & Moderately sorted & Positive skewed (coarse) \\ L501 & 1.24697 & 0.04 & 3.77 & Poorly sorted & Near symmetrical \\ L601 & 0.749242 & \(-\)0.07 & 3.83 & Moderately sorted & Near symmetrical \\ L602 & 0.768939 & \(-\)0.06 & 2.66 & Moderately sorted & Near symmetrical \\ L802 & 1.070455 & \(-\)0.22 & 3.69 & Poorly sorted & Negative skewed (fine) \\ L803 & 1.110606 & 0.12 & 1.68 & Poorly sorted & Positive skewed (coarse) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Folk & Ward grain size statistical parameters measured from samples frequency curves [19]. Figure 22: Grain size cumulative frequency curves showing the range of grain size distribution of the studied samples. The data from the grain size analysis were integrated with the microporosity data from the visual image analysis to understand the effect of the different grain sizes on the micropores. It was evident from the results of this study that the focus was concentrated on the finer fraction of the elastic rocks. The figures below illustrate the relationship between the microporosity in the sample and the mud grain size. The calculated correlation coefficient in Figure 23 between the permeability and porosity is 0.7. In Figure 24, the scatter plot shows a strong relationship between the presence of microporosity in elastic rocks and the mud percentage with R\({}^{2}\) = 0.703. This relationship can describe the role of the silt and clay grain sizes on microporovosity. Figure 23: Scatter plot of permeability and porosity. Figure 24: Scatter plot of visual microporosity and mud size. The Th/K cross-plot values were combined with the permeability at different levels to understand the effect of the different clay minerals on the permeability. The cross-plot between the permeability qualities is classified into four levels, from poor to fair to very good permeability. The clay minerals content obtained from SGR explained the role of each clay type on the permeability (Figure 25). From the Th/K cross-plot, it is evident that chlorite and montmorillonite have preserved permeability against porosity, reducing diagenetic processes such as pore filling, quartz cement, and the overgrowth of quartz. However, kaolinite and mixed-layer clay can be responsible for reducing the permeability. Integrating the sieve analysis with the air permeability and spectral gamma-ray results has shown the different effects of mud size percentage, including silt and clay sizes, on the permeability and Th/K.