## 1 Introduction  
With the deepening of oil and gas exploration and development, deep and ultra-deep paleo carbonate rocks have become an important global field exploration and development [1; 2; 3]. Due to multiple periods of tectonic movement and diagenetic alteration [4; 5; 6], the deep and ultra-deep ancient carbonate reservoirs type are mainly fractured-vuggy dolomite reservoirs [7; 8; 9]. Oil and gas exploration in such reservoirs, particularly those represented by the Ediacaran Dengying Formation in the Sichuan Basin, Refs. [10; 11; 12] has led to the discovery of large gas fields such as Anyue and Penglai in recent years. Thesediscoveries mark the first economically profitable Precambrian exploration targets worldwide [13; 14]. The proven reserves demonstrate a vast scale of accumulation and suggest broad exploration prospects [15; 16].  
However, Fractured-Vuggy dolomite reservoirs have the characteristics of complex fabric, strong heterogeneity, and fast spatial distribution [15; 16]. It is difficult to identify reservoir facies, which restricts the expansion of exploration scale and efficient development [17]. Reservoir facies form the foundation of Fractured-Vuggy dolomite reservoirs and represent the macro-physical variations and micro-pore characteristics of rocks [16; 17; 18]. Different configurations of pore and fracture arrangements in various reservoir facies lead to variations in the storage and permeability capacities of mound structures, resulting in high heterogeneity [19; 20]. Therefore, accurately identifying reservoir facies is crucial for subsequent research on Fractured-Vuggy dolomite reservoirs.  
The vertical accuracy of logging curves is high, and their response characteristics are the comprehensive characterization of geological and geophysical information such as rock minerals, fluid properties, and reservoir space size. Identifying complex reservoir facies based on the inherent connections between various conventional logging curves has always been a research hotspot [21; 22; 23]. However, the diverse types of reservoirs in the Dengying Formation greatly increase the difficulty of data processing and analysis. With the widespread application of intelligent algorithms in the field of geology [24; 25; 26], the Fuzzy C-means clustering (FCM) method identifies reservoir clusters based on the inherent connections of the data itself [27; 28]. Its results are not easily affected by human factors and can be used to identify reservoirs. However, the algorithm itself is prone to getting stuck in local optima, which affects the accuracy of clustering.  
Therefore, this study is based on three methods: the unsupervised intelligent clustering method of improved Fuzzy C-means clustering Algorithm Based on Density Sensitive Distance and Fuzzy Partrition (FCM-DSDFP), the fusion method of Principal Components Analysis (PCA) dimensionality reduction and noise reduction, and the principle of clustering feature analysis. By using core, thin-section, logging, and full-diameter core physical property data for practical applications, accurate identification of reservoir phases in fractured dolomite gas reservoirs is achieved in order to provide a strong basis for subsequent geological research.