## 4 Results
### Correlation Coefficient Comparison
The black curve and blue dots represent the segmented production, and the red curve and green squares represent the predicted production. From the perspective of reservoir physics, an increase in water saturation typically results in a decrease in oil and gas production. This occurrence is attributed to the limited solubility of oil and gas in water. As liquid production rises, the contents of oil and gas relatively decrease. The robust negative correlation evident in the data analysis of Well 33 serves to fully validate this theory. However, in the analysis of Well 6, this negative correlation is not statistically significant. Particularly in the case of Well 7, there is no apparent correlation between water saturation and productivity. Through comparative analysis of production reports and well-logging curves, we found that even some formations with 100% water content have high oil and gas production. This may be due to inaccuracies in well site data collection and processing. Although outliers in the data may stem from measurement errors or field data processing, this method remains viable in the absence of superior processing techniques. Regarding water saturation data, future efforts may involve data normalization to ensure that all values fall within the range of 0 to 100, thereby enhancing the consistency and accuracy of the data. Upon further examination of the gas production data from Wells 6 and 7, it was observed that the data exhibited wide dispersion and sparsity, whereas the gas production data of Well 33 were relatively concentrated. This phenomenon may be linked to operational activities within the oil and gas extraction process, such as wellbore soaking operations after well opening. The sparse and widely dispersed production data can lead to significant errors in model training. For instance, while well-log data such as sonic transit time are generally distributed in the range of 65 to 60 ft/us, gas production data vary from 0 to 1000 m\({}^{3}\)/d (represented in Figure 6), and the distribution varies notably between different wells. Using Wells 6 and 7 as an example, their gas production data range from 0 to 800 m\({}^{3}\)/d, while the data from Well 33 are concentrated between 120 and 550 m\({}^{3}\)/d. This  
Figure 9:2 Logging parameter distribution and Pearson correlation coefficient analysis of Well 6, Well 7, and Well 33.  
disparity in data might be one of the main factors impacting the accuracy of the model predictions. Additionally, there are a substantial number of data points indicating zero gas production in Well 7, which is consistent with the corresponding formation water saturation reaching 100%, indicating that the well has no gas production. In the analysis of Figure 21, it can be observed that in the data from Well 7, there does not seem to be a clear correlation between water saturation and the absolute permeability of the rock. This observation serves as a reminder that the absolute permeability of rock, as a physical property, primarily depends on the pore structure of the rock itself, rather than the type of fluid in the pores. Furthermore, the surface tension and capillary pressure of rock pores play a crucial role in determining the distribution and movement of fluid in the pore space. Therefore, in future research, investigating the distribution and flow mechanisms of fluid in the pore space under specific water saturation conditions will be a crucial research direction. This will not only aid in deepening our comprehension of petrophysical properties, but also holds great significance for the efficient development and management of oil and gas reservoirs. [PERSON]'s formula is the fundamental equation for describing reservoir saturation using resistivity logging data. It effectively integrates resistivity logging with porosity logging, providing a theoretical foundation for the quantitative assessment of reservoir saturation.