## 4 Results and Discussion
### Elastic Inversion Results  
The first iteration of the BLI process was carried out using log data from the main wells (6608-10-02 and 6608-10-04) (see Figure 1, phases I and II). The result of this first iteration indicated that the signal-to-noise ratio (SNR) value was in the range of 0 to 10 dB, which is considerably low. To improve this value, it was decided to implement machine learning algorithms using the Pylops library. Additionally, it was decided to perform an iterative adjustment of the model hyperparameters to reach the optimal adjustment value. Next, the inversion process was applied to the velocity log data corresponding to both wells. To establish a comparison framework, two different inversion processes were applied to the data: the BLI model and the AVO model. The results are presented in Figure 9. The results of the one-dimensional inversion, that is, applied to well logs, were extrapolated to cover the entire seismic section, resulting in an inverted seismic section. This process was carried out using the BLI inversion module of the OpendTect software v6.4.5. This technique was carried out using the OpendTect program, which includes a BLI inversion module. Although Figure 9 shows an example of the inversion only for \(V_{P}\), this procedure was also applied to the other parameters (\(V_{5}\) and \(\rho\)); however, it is not displayed here. The black line on the graph represents the original data, while the red line for the AVO inversion and the blue line for the BLI inversion represent the settings received from the inversion processes. Pearson's correlation coefficient (CC) values analyzed between the original values and the values of the inversion models for primary wells No-6608-10-02 and No-6608-10-04 are also presented. For well No-6608-10-02, a coefficient of 0.8680 (86.8%) is observed for the original values with respect to the BLI inversion values and 0.8332 (83.32%) for the original values regarding the AVO inversion values. On the other hand, in well No-6608-10-04, the values of the Pearson coefficient are 0.8758 (87.58%) for the original values about the BLI inversion values and 0.8021 (80.21%) for the values about AVO inversion values. These values indicate a strong correlation between the predicted values and the original values since they are in the range of 0.9 (or 90%). This implies that the model used to make porosity predictions is quite accurate, as the predicted values align well with the original values. To achieve these values, an iterative refinement was carried out, achieving the best possible data fit in approximately seven rounds. Table 1 provides a record of the various values obtained during each iterative process for \(V_{P}\), \(V_{S}\), and \(\rho\) parameters. In Figure 9, the target area highlighted in pink, which has more uncertainty and could be home to the reservoir, shows a big difference in how well the AVO inversion fits the original data. Specifically, the AVO inversion demonstrates inferior fit, while the BLI inversion demonstrates superior fit in this region. Figure 9 provides a comparative analysis that reveals that the BLI inversion process shows a superior fit to the original data compared to the AVO inversion process. More accurate results were obtained, as seen in Figure 9.  
This is likely due in part to the changes that were made to the machine learning (ML) models and the models themselves when the well logs were first cleaned. These strategic adjustments improved the performance and accuracy of the BLI investment process, resulting in a more favorable match between the obtained results and the original data. Figure 9 shows a difference in the target area that is highlighted in red. This shows that the Bayesian Linear Inversion (BLI) method works better when used with the wavelet sparse spike to obtain the geological features in the target area accurately. This improvement is likely attributed to incorporating data uncertainty and the strategic introduction of a different wavelet into the investment process.