Incomplete well logging datasets measured in boreholes are frequently encountered especially in old wells. The main goal of the research, described in this paper, is to refill a data matrix with synthetic data that is missing for some reason and how the imputation procedure was to determine well the measured and estimated values fit in the case of data gaps of different sizes, and how the clay content and porosity change as a result. There can be many reasons for the lack of data, such as not measuring the desired parameters in a given depth zone. To fill this data gap, correlation-based imputation method was used which was applied in MATLAB software development system. A case study involving a Hungarian thermal water well is shown to demonstrate the reliability of the multi-linear correlation based method, which can be fruitfully applied in other wells and investigation areas.

Seismic and well-logging data are useful for comparison and then integratation for a comprehensive geophysical interpretation. In the course of this work, seismic results were compared with well-logging geophysical data and profiles for more reliable evaluation of groundwater formations in the Tokaj region, north-east Hungary. The seismic results and profiles were obtained by the Common Reflection Surface (CRS) stacking technique. This is an advantageous stacking technique that is expected to greatly improve seismic profiles. In connection with this, we also examined some well-logging geophysical profiles so that we can improve later results. By comparing and examining the two types of sections together, we provide a basis for various method developments that improve the sections. Thus, the aim is to fill in the missing data, so that the well-logging and seismic sections can be examined together as accurately as possible in order to gain a more accurate picture of the subsurface formations. It is important to determine the lithology and petrophysical characteristics using well logs. The section replacement the improvement of the units is done using machine learning-based and inversion methods such as factor analysis or cluster analysis. An important outcome in the application of geophysical inversion methods is that the results of different methods can be jointly interpreted and this can significantly increase the reliability of the results. The application of these methods and their development is expected to reduce uncertainty and ambiguity and to increase the accuracy of the sections.