## 5 Discussion and Conclusions  
The prediction of well logs, specifically an acoustic log with regression machine learning algorithms, has shown great results on the test datasets. All 17 of the tested algorithms have predicted acoustic log values with over 80% correlation between the originally measured and predicted data. The highest scoring algorithms are tree-based structured algorithms (DT, RF, and ET), boosting algorithms (GB, XGB, and LGB), and neural networks (MLP and LSTM), with over 90% correlation between the predicted and original values. The results of the predictions of acoustic logs on blind data have been  
Figure 7: Clusters defined by a majority of the clustering algorithms used in the selected well WH. lower, with measured and predicted target variables showing up to 75% correlation at best but with distinguishable trendlines to the actual measured data. By using regression learning models, it is possible to precisely determine individual logging curves if the training dataset is large enough to represent parts of lithologically similar data. This can be useful in predicting missing parts of well logs for which there are other well logs available. The accuracy of correctly predicting well log values depends on the curve being predicted and its correlation to the feature values, as well as on the spatial location of the well itself. The correlation between measured acoustic logs and predicted logs has been astonishingly high for the tested data and validation data. The values can be predicted with a correlation of up to over 95%. When it comes to blind wells, or unseen well data, a neural network has proven to be the best scoring algorithm and has been able to achieve correlations between the measured and predicted well data of up to 45% for acoustic values, but it depends on the curve being predicted. Nevertheless, the neural network, especially the Long Short-Time Memory (LSTM) layered network, has shown great predictions of measured trendlines. The trendlines could provide great insight into subsurface patterns and relations. Furthermore, some of the predictions with the highest correlation in blind wells are proven to be the ones from the eastern part of the Drava Basin. Despite the complexity of the lithological distribution in the subsurface, the LSTM model has given excellent results in predicting missing well log data in wells that have been trained on and can provide a good starting point for the interpretation of regional subsurface relations in the area. The starting point would mean that petrophysical similarity can be estimated between the deposits of different parts of the Drava Basin based on the reliability of the prediction models. When it comes to lithology prediction using unsupervised clustering algorithms, these were tested for five to twelve clusters. The lowest number of clusters has been defined as optimal by different statistical methods, and the higher number of clusters has been recognized to be more accurate in representing lithological patterns. The optimal number of clusters has been calculated to be five and was sufficient to enable recognition of the interval of interest, i.e., the Neogene sediments, as well as the permeable and impermeable deposits, sandstone, and shale. By adding new clusters, more variations in these deposits could be noticed. For this purpose, ten clusters were the optimal number for the validation of sandstone bodies. There was one specific cluster, categorized in blue in this case, that was identified inside all the verified sandstone bodies and could be correlated to gas-bearing reservoirs. All of the used algorithms have given similar results of grouped data that can be correlated to lithological properties. The Gaussian Mixture Method and Spectral Clustering have more effectively captured the variations of clusters in a dataset. Depth intervals with fewer data were still prone to be assigned to a less accurate lithology representative or a thicker interval.