## 3 Materials and Methods  
The dataset consisted of well log data from ten wells within Gola Field, from the western part of the Drava Basin, and ten wells from the eastern part of the Drava Basin. Although a large number of logging measurements have been performed, it is important for machine learning purposes to distinguish which data can be used in regard to the quality and completeness of the dataset (Link in Supplementary Materials: Exploratory Data Analysis). Density logs, acoustic and neutron logs, calipers, spontaneous potential, gamma rays, and various shallow and deep resistivity log measurements were available in most of the wells, with a sampling interval of 0.1 m.  
Machine learning (ML) is a method of data analysis that uses algorithms to recognize patterns present in the data. It assumes that systems can learn from data and the learning process is improved without having to be explicitly programmed [4; 32]. The drawback of the method is the data preparation process for ML algorithm applications, as it is time-consuming. Even though the structure of logging data remains the same no matter the contractor, log names and acronyms may vary. In addition, there are very often duplicate measurements for particular logs that need to be thoroughly compared and the reasoning behind them needs to be validated. Along with raw measurements, there are oftentimes corrections and additional analyses being generated from raw data that need to be examined particularly carefully, in terms of understanding the background behind their making [5; 33]. Even after the exploratory data analysis has been performed, the model building based on the cleared data can shed a different light on the input data being used based on the type of algorithm that is being used and its hyperparameters [34]. To obtain the best results from  
Figure 2: Schematic presentation of a lithost stratigraphic profile (Figure 1) from the western part to the eastern part of the Drava Basin (the distribution depths are according to [22]).  
the learning process, analysis of outliers could be very helpful, as well as normalization and standardization [35]. However, once the machine learning model is built, it can be fed with input data and used for its intended purposes. In the current research, multiple well-known regression models have been used to find the best models for predicting our data, especially acoustic well logs (AC). They belong to supervised learning models, which means that the test data consist of different input variables and the target variable represents the desired output of the learning process [9; 14; 34; 36; 37] (Links in Supplementary Materials: Supervised learning). Unsupervised machine learning algorithms have also been used to gain a better understanding of the spatial distribution of the Neogene sediments in the researched area [8; 38].  
In this study, machine learning techniques were applied for predicting well logs to see if the models can be reused on different parts of the Drava Basin and in which cases (Figure 3). The measured values of well logs have been further analyzed alongside the predicted values to recognize similar patterns in the whole dataset that would reflect the lithology distribution. Since the lithology in this case was treated as an unknown variable, unsupervised learning was applied, more specifically clustering algorithms.  
All of the data analyses and processing have been performed using the Python programming language, and the exact packages are disclosed further in the subsections.