## 4 Results and Testing
### Testing Methods  
Two methods were applied to verify the reservoir facies identification results based on intelligent algorithms proposed in this study. The first method is to compare the identification results with the reservoir facies types identified by core observations (Well PT103 and PS13). As shown in Figure 6, the reservoir facies types divided by this method are highly consistent with the core observation results, and the boundary division of the types is accurate. As shown in Figure 6a,b, the boundary positions of the Fractured-Vuggy type or partially cemented Fractured-Vuggy type correspond well with the boundary positions divided by the intelligent algorithm. Figure 6: (**a**) Reservoir facies identification results for the Dengying Formation 4 th Member in the study area (Well PT103). (**b**) Reservoir facies identification results for the Dengying Formation 2 nd Member in the study area (Well PS13). The second method is to verify the recognition results of intelligent algorithms by utilizing the relationship between reservoir facies types and hydrocarbon accumulation (Figure 7). Previous studies have shown that the hydrocarbon accumulation process of the Ediacaran Dengying Formation can be divided into the following three stages (Figure 7). The first stage was the paleo-oil charge event of the Triassic, which formed the paleo-oil reservoir of the Dengying Formation [33]. During the 2 nd stage, the Dengying paleo-oil reservoir was gradually cracked into gas and bitumen during the Middle Cretaceous period [34; 35]. During the last stage, intense compression during the Himalayan period resulted in rapid uplift with the exposure of Dengying paleo-reservoirs, leaving only solid bitumen in the Dengying reservoir (Figure 8a). Thus, the content of bitumen is controlled by the charging of paleo crude oil (Figure 8b). The migration and accumulation of crude oil require a higher lower porosity limit than natural gas. [PERSON] et al. (2016) proposed that the lower limit of porosity for the formation of paleo-oil reservoirs in carbonate reservoirs is 2.6% [36]. Taking the Dengying Formation 2 nd Member as an example, the only reservoir types with porosity exceeding 2.5% are the Fractured-Vuggy type and partially cemented Fractured-Vuggy type (Figure 8c). Thus, only these two types of reservoir facies can participate in the accumulation and cracking process of paleo-oil reservoirs, thereby enriching bitumen (Figure 8d). The content of bitumen can reflect the accuracy of identifying reservoir facies types (Figure 8e,f). This study used four principal component information curves as inputs and trained a model using SGBDT (Stochastic Gradient Boosting Decision Tree) to form a model that is close to the actual situation of subsurface oil reservoirs, achieving an accurate prediction of bitumen content. According to the parameter tuning of the grid search algorithm, the decision tree depth of the SGBDT algorithm is 12, the learning rate is 0.1, the number of iterations is determined as 100 based on the expected loss calculation formula, and the subsampling random factor of SGBDT is 70%. Figure 7: Hydrocarbon accumulation process of the Dengying Formation in the study area. According to the prediction results of the Dengying Formation 2 nd Member in Well PT1, the correlation between the optimized predicted bitumen content and the experimentally measured bitumen content reached 0.87 (Figure 9), indicating that the prediction results based on the SGBDT model are reliable. As shown in Figure 10, the development positions of the Fractured-Vuggy type and partially cemented Fractured-Vuggy type are highly consistent with the high bitumen content interval, indicating a high level of reliability in reservoir facies prediction. Figure 8: Bitumen occurrence in Fractured-Vuggy type and partially cemented Fractured-Vuggy type reservoir facies for the Dengying Formation 2 nd Member in the study area.