## 4 Results and Testing  
### Reservoir Facies Identification Results  
This study collected logging and core data from a total of 26 wells in Dengying Formation 4 th Member (10 wells) and Dengying Formation 2 nd Member (16 wells). Two cored wells were randomly selected for clustering effect testing, and the remaining well data were used for model clustering training. Among them, logging data are used as input for identifying reservoir facies, including GR, AC, CNL, DEN, RT, RXO, K, Th, U, and PE (Table 1). The relevant data are used for the qualitative classification of clusters, giving them geological significance, including core, thin-section, and full-diameter core physical property testing data. When using two cored wells as inspection standards, the differences in reservoir facies between the Dengying Formation 4 th Member and Dengying Formation 2 nd Member were taken into consideration. Based on the PCA fusion method, dimensionality reduction, and noise reduction were performed on the logging curves of the Dengying Formation 4 th Member and Dengying Formation 2 nd Member, with a cumulative information value greater than 0.9 to cover the complete information standard. Four principal component information curves were obtained for the Dengying Formation 4 th Member and Dengying Formation 2 nd Member, that is, PC1, PC2, PC3, and PC4 (Table 1). In the principal component information parameters of Dengying Formation 4 th Member, PC1 has a high negative correlation with RT and RXO, with values of \(-0.65\) and \(-0.65\), respectively. PC2 has a high correlation with DEN and AC, with a positive correlation with DEN and a negative correlation with AC of \(0.56\) and \(-0.61\), respectively. PC3 has a high correlation with DEN and AC, with a negative correlation with DEN and a positive correlation with AC, with values of \(-0.76\) and \(0.62\), respectively. PC4 has a high correlation with CNL and AC, with a positive correlation with CNL and a negative correlation with AC of \(0.76\) and \(-0.49\), respectively. In the principal component information parameters of Dengying Formation 2 nd Member, PC1 has a high negative correlation with RT and RXO, with values of \(-0.69\) and \(-0.70\), respectively. PC2 has a high correlation with DEN and CNL, with a positive correlation with CNL and a negative correlation with DEN of \(0.69\) and \(-0.72\), respectively. The positive correlation between PC3 and AC is high, at \(0.95\). PC4 has a high negative correlation with DEN and CNL, with values of \(-0.69\) and \(-0.70\), respectively. Using the unsupervised intelligent method of FCM-DSDFP, cluster the principal component information parameters obtained from Dengying Formation 4 th Member and Dengying Formation 2 nd Member separately, with an iteration count of \(10\). Scientifically analyze the number of clusters and the clustering effect using the principle of clustering feature analysis. Among them, the distortion degree of Dengying Formation 4 th Member and Dengying Formation 2 nd Member shows a significant mutation at position \(7\), and the silhouette coefficients at position \(7\) are \(0.84\) and \(0.77\), respectively, which are closest to \(1\) (Figure 2a,b). Based on the two indicator coefficients, it is suggested that the Dengying Formation 4 th Member and Dengying Formation 2 nd Member are most suitable to be divided into seven categories, and the clustering effect is the best.