###### Abstract  
The aim of this study was to confirm if predictive regression algorithms can provide reliable results in missing geophysical logging data in the western and eastern parts of the Drava Super Basin, especially Gola Field, and to apply unsupervised machine learning methods for a better understanding of lithological subsurface relations. Numerous regression models have been used for the estimation of prediction accuracy, along with some clustering algorithms to support the estimation of lithology distribution estimations in well log datasets, consisting of 20 wells in total. Tree-based algorithms and the boosting algorithm have been optimized and proven valuable in predicting well log data when they are not measured or are unavailable at all depth intervals. For blind datasets, predictions become much less reliable. For this purpose, neural networks with at least one Long Short-Term Memory (LSTM) layer have significantly improved the accuracy and reliability of predictions, not in terms of absolute values but in the aspect of the trends in values that change with the depth and other well features, as well as in terms of the magnitudes. Trendlines can further be used for pattern recognition or as a newly engineered feature. Unsupervised learning has confirmed reliability in lithology recognition on validation sets and has proven to be a great asset in distinguishing variabilities in the petrophysical properties of sediments. well log predictions; machine learning; unsupervised lithology predictions 2024202024  
shape, dimension, and dynamic intertwining with impermeable rocks. When the well data are sparse in usability, other methods are helpful for the further analysis and processing of the available data. In this case, machine learning methods are relatively inexpensive methods, even though they charge in terms of time. The application of machine learning (ML) methods to well log analysis and petrophysics has grown rapidly from the 2010s to the present day [4, 5]. Statistical techniques, along with supervised and unsupervised machine learning algorithms, have been successfully applied to well logs for lithofacies identification [6, 7, 8] and reservoir characterization [9, 10, 11]. A machine learning model for lithology prediction depends on the quality and representativeness of the input data, and the suitability of the chosen algorithm. The main object of this research was the application of machine learning algorithms within the Neogene-Quaternary infill in the Drava Basin. The focus of this research was the Gola Field locality (Figure 1), as the data from production wells contained a large geophysical dataset suitable for machine learning training. The goal was to find alternative ways for well data preparation and interpretation to come to an uplifted lithological characterization and a better understanding of the spatial position of the sandstone bodies. From the available well data, a prediction of missing log data, especially the acoustic log, has been made. Based on the relationship between various well logs, such as resistivity logs and gamma-ray logs, spontaneous potential, calipers, and missing logging intervals of interest were recreated. Since the Gola Field dataset in western Drava contains a large amount of information, this location was the focal point of this study. Recreating and analyzing data there helped with extrapolating the methodology on the wider area of eastern Drava. Besides recreating data for an increase in interpretation materials, clustering analyses were used for the recognition of lithology patterns in well logs. Exploratory Data Analysis (EDA), as well as regression and clustering models, were carried out using the Python programming language. Figure 1: (**a**) Location map of the multiple Eastern Drava fields, Gola Field in the western Drava Basin (Croatia), and a profile line on a section map of Europe with (**b**) the wells in Gola Field.