## 5 Conclusions  
This study presents a machine learning (ML) approach using Bayesian linear inversion (BLI) to optimize initial data for porosity prediction using seismic and well-log data along with a support vector regression (SVR) model and an empirical model. The BLI inversion functions as an optimal model prediction in our methodology, mitigating the intrinsic uncertainty of the data before implementing the empirical and SVR formulations. We were able to verify this by using the Pearson coefficient, which in both results showed that the value was in the range of 0.9 (90%), which demonstrates that by effectively integrating the BLI model, the performance of the empirical models and SVR improves significantly. Thus, also the use of a procedure such as BLI inversion, which takes into account the uncertainty in the initial data, can be a valuable asset in the evaluation and interpretation of reservoirs within the context of predictive modeling, as This study demonstrates. Seismic and well-log data from the Norne field in the North Sea will be used for the workflow application. By using iterative analysis, the best fit for the accuracy and hyperparameter values of the predictive investment models was achieved. This is verified thanks to the implementation of different evaluation metrics. In particular, the Pearson correlation coefficient (CC) of the BLI model experienced a substantial improvement, increasing from 0.7865 to 0.8680. Likewise, the coefficient \(R^{2}\) reached a value of 0.784, indicating a greater proportion of variance explained by the model. The root mean square error (MSE) was reduced to 0.020, suggesting a decrease in the discrepancy between the predicted values and the observed values, while the root mean square error (RMSE) was set at 0.141. These results show a notable improvement in the performance of the BLI model, which highlights the effectiveness of the hyperparameter tuning process. The incorporation of machine learning algorithms (in several steps of the work) was mainly responsible for improving the accuracy and quality of the predictions produced by the models. Because of this, our ability to identify complicated patterns and complex correlations in the dataset improved significantly, and as a result, selecting optimal hyperparameter values for inversion and prediction models became easier. However, it is worth noting that although this approach was carried out in the Norne field, the methodology described in a previous workflow is still relevant for other domains and environments that depend on data accessibility. Also, it was shown that the prediction of seismic porosity (\(\phi\)) using Support Vector Regression (SVR) outperformed the forecast made using [PERSON]'s empirical equation, which used a variety of wavelets (Ricker, SS4, and SS2). To acquire the \(\phi\) attribute, it was also necessary to use the well-log curves that showed the results of Bayesian Linearized Inversion (BLI) and prediction models for \(V_{P}\), \(V_{S}\), and \(\rho\). Notably, the inversion prediction based on \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**C.C.**} & \multirow{2}{*}{**INITIAL**} & \multicolumn{2}{c}{\(\phi\)**—SVR**} & \multicolumn{2}{c}{\(\phi\)**—EMP**} \\  & & **ITER.1** & **FINAL** & **INITIAL** & **ITER.1** & **FINAL** \\ \hline Ricker & 0.698 & 0.782 & 0.870 & 0.605 & 0.755 & 0.870 \\ \hline SS4 & 0.731 & 0.805 & 0.895 & 0.619 & 0.795 & 0.909 \\ \hline SS2 & 0.668 & 0.795 & 0.895 & 0.589 & 0.706 & 0.865 \\ \hline
**M.S.E** & **INITIAL** & **ITER.1** & **FINAL** & **INITIAL** & **ITER.1** & **FINAL** \\ \hline Ricker & 0.650 & 0.590 & 0.420 & 0.630 & 0.600 & 0.436 \\ \hline SS4 & 0.680 & 0.560 & 0.370 & 0.585 & 0.567 & 0.407 \\ \hline SS2 & 0.700 & 0.570 & 0.477 & 0.620 & 0.595 & 0.460 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The prediction model’s fit was evaluated using the Pearson correlation coefficient (C.C.) and Mean Squared Error (MSE) values from three iterations, as shown in Figure 14 (referring to well No-6608-10-04).