## 4 Results  
Baseline regression models for each algorithm were tested on the blind dataset that has never been in a training set. Even though an unseen dataset comes from a well from the same exploitation field, its position is the furthest from most of the training wells. After the base model estimations, regression models were trained by optimizing the model parameters (Links in Supplementary Materials: Hyperparameters tuning), i.e., the parameters within the model were searched for those that yielded the best coefficients of  
Figure 6: Well log data presented in the form of clusters, according to different clustering algorithms; the number of clusters is increasing to the right and the exact boundaries of sandstone bodies become more prominent, with most validated bodies being a part of the blue cluster. determination and the smallest root mean squared errors. By achieving this, the probability of obtaining a prediction with minimal deviation from the real data is the highest. GridSearchCV and Bayesian optimization were used for further hyperparameter tuning, as previously performed in [34] (Table 2). It can be observed that optimization has been the most beneficial for underachieving algorithms; however, overall, the highest-scoring algorithms before the procedure performed better even without hyperparameter tuning. Even with the great prediction of sonic logs on the Test Dataset (R\({}^{2}\) over 95%), the most successful algorithms were struggling with the unseen data and even the five best algorithms had issues with predicting stable results. However, the only model that showed improvement with unseen data was the artificial neural network. With two hidden layers of 150 and 50 neurons, the tanh activation function, and the lowest learning rate of 0.00001, it was possible to achieve a 0.73 coefficient of determination (Figure 3). With negative coefficient values for all the other algorithms, the neural network was the only one that was able to recognize the data trend on unseen data. The best neural networks for reliable trend predictions on the well data were the ones with the added Long Short-Term Memory (LSTM) layer, which has also been explained in previous papers [46]. It was interesting to observe variability in the ability of the LSTM model to reliably predict well log values across the wells in both the western and eastern parts of the Drava Basin. Even though the model itself was trained and optimized solely on the data available from the western Drava Basin, and even more locally on data from Gola Field, it showed some of the highest results of prediction (coefficient of determination, R\({}^{2}\)) on the wells in the middle of the eastern Drava Basin (ED-2) (Figure 3). On the other hand, wells that are the furthest East (ED-1), almost on the border with the Republic of Serbia, showcased the worst results of prediction in the entire blind dataset (Figure 3). Since the well logs available from the eastern part of the Drava Basin were not included in the training dataset for model building, the result shows a step forward into a more generalized machine learning model that would be applicable in a much wider, lithologically complex area of the Drava Basin. The wells from eastern Drava were purposefully excluded from the training dataset as the scarcity of some of the log curves that were determined to be the best for model building was the higher of the two sets. Furthermore, the year of well completion made a large contribution to the decision for selecting data, as a large portion of the wells were drilled over three decades ago. This means that most of them were vectorized throughout the years and the log values have a higher chance of being contaminated by an error. However, based on the varying values of prediction results, a model trained in different parts of the basin can provide a lot of information about the similarity of the subsurface in eastern and western areas.