## 3 Results and Analysis
### Comprehensive Impact Analysis  
The previous research results indicate that both the number of fracturing clusters and the number of natural fractures significantly affect the propagation pattern of artificial fractures in reservoirs [26, 27]. Among them, the number of natural fractures is usually the internal condition of the reservoir, which is difficult to change through construction methods, while the number of fracturing clusters is a construction method that can be  
Figure 7: The results of the artificial fracture morphology in reservoirs under the influence of different natural fracture numbers ((**a**) is a 3D view and (**b**) is a top view) (the numbers (20, 30, 40, 50) represent the quantity of natural fractures in various simulation models). directly adjusted during on-site construction. If different cluster fracturing is used for areas with different natural fractures, it may further enhance the effectiveness of reservoir transformation. Therefore, to further discuss the comprehensive impact of multi-cluster fracturing construction and natural fractures, this section comprehensively compares and simulates the expansion of multi-cluster fracturing fractures in target reservoir areas with different numbers of natural fractures. In Figure 8, there is a phenomenon of incomplete calculation in the fracturing results of six clusters. Therefore, the analysis is only based on the fracturing results of three and nine clusters. From the above figure, when the fracture density is 20, multi-cluster fracturing does not necessarily increase the area of reservoir fractures but has a significant impact on the volume of reservoir fractures. This phenomenon refers to the different behavior of artificial fractures within the reservoir caused by the presence of natural fractures. Notably, the presence of natural fractures often leads to a further increase in the stimulated reservoir volume (SRV). This observation highlights the importance of considering the interplay between natural fractures and hydraulic fracturing operations. It can be significantly observed from Figure 9 that in this simulation model, there were three main fracture extensions in three clusters of fracturing, six main fracture extensions in six clusters of fracturing, and two main fractures and local complex fracture network extensions in nine clusters of fracturing. This result suggests that with an increasing number of fracturing clusters, complex fracture networks are more likely to form in the reservoir. Meanwhile, it is not difficult to observe from the results in the above figure that the two-dimensional view of the nine-cluster fracturing results is not significant, with many low-opening fractures. When considering fracture opening, the fracturing results of cluster 6 are significantly better than those of cluster 3 and cluster 9. Figure 8: Results of conventional quantitative metrics under different cluster configurations when there are 20 natural fractures in region ((**a**) number of damaged interface elements versus time; (**b**) fracture area versus time; (**c**) fracture aperture at injection point versus time; (**d**) maximum fracture aperture versus time; (**e**) fracture volume versus time; and (**f**) ratio of tensile failure versus time) (C3, C6, and C9 correspond to fracturing scenarios involving 3 clusters, 6 clusters, and 9 clusters, respectively). Figure 10 presents comparative results of traditional quantitative metrics across various cluster configurations in the presence of 30 significant natural fractures within the region of interest. Compared to the simulation results with a fracture density of 20, the simulations of 1, 3, 6, 9, and 12 clusters were all fully calculated with a fracture density of 30. Therefore, the results for different clusters with a fracture density of 30 were further extracted. From the above figure, as the number of fracturing clusters increases, the artificial fracture area, fracture opening, and shear failure ratio of the reservoir all significantly increase.