## 5 Results and Discussion  
### Statistical Analysis  
For each conventional log, 206 were taken, and the essential statistical properties were determined using descriptive statistics to shed light on the physical significance of the data (Table 1). The data were analyzed using the Pearson correlation coefficient and principal component analysis, identifying the factors that significantly impact the illite wt.% to choose the most relevant input parameters. According to the Pearson correlation matrix, the parameters affecting the illite wt.% in ascending order were K, Resist., Depth, CGR, GR, B.D., SGR, and U (Table 2). Dimensional reduction evaluation PCA is considered one of the oldest and most widely used  
Figure 4: Visualizations of artificial neural networks (ANNs) showing the architecture following [48]. algorithms. Its basic premise is to minimize a dataset's dimensionality while retaining as much \"variability\" (i.e., statistical information) as possible. As a descriptive tool, PCA requires no distributional assumptions and, as such, is a flexible exploratory method that may be used for a wide range of numerical data [49]. PCA results classify the parameters into groups (components) and identify the variables with the most significant influence on the illite wt.%. (Table 3). The extracted components show that illite is highly affected by component 1, which contains (B.D., SGR, GR, and U), followed by factor 3, which has (Resist. ), while the weakest influence was by factor 2, which contains (Depth, K, and CGR) (Table 3). These results are in line with the Pearson correlation coefficient. Based on the statistical analysis outcomes and the graphical depiction of the log responses in relation to the illite wt.% (Figure 5), the inputs for the ANN comprised the log responses of K, Resist., Depth, CGR, GR, B.D., SGR, and U. These variables correspond to the depth at which the XRD samples were collected. The sole output of the ANN was the illite wt.%. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Depth** & **B.D.** & **G.R.** & **Resist.** & **DT-C** & **DT-SH** & **SGR** & **U** & **Th** & **K** & **CGR** & **Illite** \\ \hline
**Depth** & \(1\) & & & & & & & & & & \\ \hline
**B.D.** & \(-0.12\) & \(1\) & & & & & & & & & \\ \hline
**GR** & \(0.12\)** & \(-0.42\)** & \(1\) & & & & & & & & \\ \hline
**Resist.** & \(0.08\) & \(0.01\) & \(0.31\)** & \(1\) & & & & & & & \\ \hline
**DT-C** & \(-0.45\)** & \(-0.11\) & \(0.04\) & \(-0.22\)** & \(1\) & & & & & & \\ \hline
**DT-SH** & \(0.19\)** & \(-0.27\)** & \(0.03\) & \(-0.18\)** & \(-0.59\)** & \(1\) & & & & & \\ \hline
**SCR** & \(-0.03\) & \(-0.37\)** & \(0.71\)** & \(0.19\)** & \(0.02\) & \(0.16\)* & \(1\) & & & & \\ \hline
**U** & \(0.24\)** & \(-0.41\)** & \(0.77\)** & \(0.22\)** & \(-0.03\) & \(0.16\)* & \(0.79\)** & \(1\) & & & \\ \hline
**Th** & \(0.05\) & \(-0.07\) & \(0.09\) & \(0.10\) & \(0.11\) & \(-0.1\) & \(0.17\)* & \(0.06\) & \(1\) & & \\ \hline
**K** & \(0.25\)** & \(-0.11\) & \(0.08\) & \(-0.02\) & \(0.001\) & \(-0.06\) & \(0.16\)* & \(0.08\) & \(0.87\)** & \(1\) & \\ \hline
**CGR** & \(0.26\)** & \(-0.09\) & \(0.11\) & \(0.04\) & \(0.07\) & \(-0.12\) & \(0.28\)** & \(0.20\)** & \(0.87\)** & \(0.91\)** & \(1\) \\ \hline
**Illite** & \(-0.16\)** & \(0.26\)** & \(-0.26\)** & \(-0.13\) & \(-0.03\) & \(0.016\) & \(-0.29\)** & \(-0.49\)** & \(-0.07\) & \(-0.10\) & \(-0.21\)** & \(1\) \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).  
\end{table} 
Table 2: The correlation matrix shows the strength of the relationship between the log parameters and illite.