## 4 Results
Higher values of the coefficient of determination show that the middle-eastern Drava shares similar petrophysical subsurface properties to the western Drava Neogene deposits (Figure 3c). The low or negative values of the coefficient of determination represent wells in the parts of eastern Drava where petrophysically different sandstones are expected, or in variable abundances alongside their lithological similarity (Figure 2). While the wells positionally furthest from the research area in focus (western Drava) were expected to show the highest correlation between the measured and predicted well logs, this was not the case here (Figure 3a). The results contribute to the fact that the Gola Field area has a lot of \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \cline{2-13}  & **ET** & **RF** & **XGB** & **DT** & **KNN** & **LGB** & **GB** & **SVR** & **AB** & **LSTM** & **RIDGE** & **PLS** & **ELNT** \\ \hline
**RMSE** & 1.57 & 1.93 & 1.69 & 2.71 & 2.29 & 2.52 & 1.66 & 3.84 & 5.90 & 3.16 & 7.88 & 7.88 & 7.88 \\
**R\({}^{2}\)** & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 & 1.00 & 0.98 & 0.96 & 0.94 & 0.94 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selection of regression models that have been optimized for well log predictions, including an LSTM neural network that is specialized in sequential data (the lowest scoring models were not further tuned). variations in properties of seemingly simple lithology (mostly sandstones and shales). The LSTM model was built using the TensorFlow platform [47]. The variations in properties are indicative of the structure of the input data for the model needed. The more geologically complex the region, the more well data is needed to establish the relations of petrophysical and lithological properties, which is why the trained LSTM model competently handles missing values in the wells that have been part of the learning process (Figure 4). Primarily, the training dataset consisted of the measured intervals where all the input variables were present. Therefore, even if there were ten wells with measured data for training to begin with, in the end, all the prediction models had to be built on five partial wells, which is why the results of predicting the missing values of acoustic logs (AC), density logs (DEN), and compensated neutron logs (CN) show higher RMS errors for wells with less available data (Figure 4). However, the predicted values are highly correlated to the measured values, and with R\({}^{2}\) values between 0.52 and 0.98, tuned LSTM architecture has been proven to be able to efficiently predict the missing intervals of AC, DEN, and CN well logs in Gola Field test data (Figure 4). This is why it is a great tool for predicting well logs, specifically AC, DEN, and CN, in wells that do not have these measurements (Figure 4). When it comes to clustering methods, only well WH contained applicable data throughout the whole well, which is why it was used for presenting the results. The interval of interest consisted of Neogene sediments that were easily distinguished by using the statistically optimal number of clusters. The optimal number was defined with the silhouette method which resulted in five clusters. One of the five clusters (golden-brown) was correctly assigned to Pliocene deposits and the remaining four were assigned to values corresponding to sandstones and shales (Figure 5). Based on the a priori interpreted sandstone bodies, it is visible that all the values assigned to sandstone clusters (yellow and red) are correctly assigned. However, some of the sandstone bodies were still wider in range than interpreted. The thicker sandstone body patterns were easily recognized by algorithms, including in the shallower interval (2000-2200 m), while the deepest sandstone body was not fully recognized (Figure 5). For this reason, additional clusters have been added to deepen the interpretation.