# Innovative Hydrogeophysical Approaches as Aids to Assess Hungarian Groundwater Bodies
## 3 Results
### Well Logging in Porous Water Bodies  
The well logging approach proposed in Section 2.3.2 was tested in Baktalorathaza-1 well. Surface geoelectric methods concluded that the upper 100 m part of the well was a Pleistocene formation dominated by sands, in which only to the variation of grain sizes the traditional surface DC geoelectric methods were sensitive. The underlying rock of this sandy sequence is mainly shale. Between the interval of 100 and 160 m, sands were deposited, followed by a shaly formation, and thin (5-15 m thick) coarse-grained layers could be deposited, too. The boundary of the Pleistocene and Pannonian ages was detected at a depth of around 240 m. The Pannonian shaly complex mainly consists of clayey sand, gravel, clayey silt, clayey marl and bituminous clay (Figure 10). In Baktalorathaza-1 well, clastic formations along the interval of 100-450 m were investigated, where the pore spaces were fully saturated with water. The following types of open-hole wireline logs were processed: natural gamma-ray intensity (GR), borehole caliper (CAL), spontaneous potential (SP), gamma-gamma (GG) and neutron-neutron (NN) and shallow resistivity (RS). The last track of Figure 10 shows the fractional volume of pore spaces, including free- and bounded water (POR), clay volume calculated by the IRFA method using (8) (VCL), and matrix volume dominated by quartz (VSD). (The laboratory measurements revealed some carbonates here and there as cementing material.) The above volumetric parameters and hydraulic conductivity had been previously estimated by the Csokas method using (4) and (7) [63]. Grain-sizes were derived by laboratory measurements made on 220 core samples. The values of \(d_{10}\) and \(d_{60}\) were given from grain-size distribution curves, while the dominant grain size was calculated using (3). The statistical distributions of characteristic grain sizes can be found in Figure 11, which show the domination of fine-grained components from impermeable (clayey and silty) formations and also refer to some bigger grain sizes associated with low and medium conductivity (sand and gravel) beds. The wide range of the shape parameter (Figure 11d) defined as the ratio of \(d_{60}\) and \(d_{10}\) also shows the heterogeneity of the studied formation. Figure 10: Observed wireline logs (tracks 1-6) and the estimated petrophysical parameters of porous groundwater formations (track 7) in Baktaloráantháza-1 well, northeast Hungary. Regression analysis was applied to find a connection between the rock physical properties and measured well logs. Clay volume from core measurements shows a strong linear connection to the shallow resistivity log (Figure 12a). The Pearson's correlation coefficient is \(-0.85\), which shows the strong inverse (linear) relationship between the observed variables. The standard deviations of slope and intercept with 95% confidence bounds are 0.002 and 0.04, respectively. A similar strong correlation was found for the hydraulic conductivity with the same well log (Figure 12b). The former was extracted by the combination of (2) and (3) using the data of sieve analysis. The correlation coefficient is 0.75, which refers to the strong direct (nonlinear) relationship between the hydraulic property and the resistivity log. The evaluation of porous groundwater formations begins with reliable rock typing. Simultaneous cluster analysis of all available logs was applied for separating lithological units. To assure a robust solution, the similarity between the multidimensional vectors of well logging data using a distance metric was defined based on the L\({}_{1}\)-norm. The city-block (or Manhattan) distance calculated as the sum of the absolute values of deviations of the components of data vectors is well-known to give outlier-free solutions in geosciences applications. The sum of squared error calculated at different numbers of cluster numbers yielded an optimum at three (elbow method). Based on this simple manner, one can separate the aquitards and aquifers using the well logs.