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Earth and environmental sciences cover all planetary and Earth science aspects, including solid Earth processes, development of Earth, environmental issues, ecology, marine and freshwater systems, as well as the human interaction with these systems.
Earth and Environmental Sciences
In this study, the joint shear strength of low-strength Hungarian sandstones of different grain size and surface roughness was investigated. The direct shear tests along discontinuities were performed under constant normal load. Previously, the direct shear test basic rock mechanic parameters of the investigated intact rocks were determined, such as the UCS value. The goal of the investigation is to determine the effect of the surface properties, such as surface roughness, grain size, and surface quality, on the joint shear strength of Hungarian sandstones. The failure curves derived from the experimental results of direct shear tests under laboratory conditions, and the empirical results according to Barton and Choubey (1977) were compared.
Depth of reservoirs of Hungarian oil fields and related oil density data were collected from the database of the Hungarian Mineral Resource Inventory. The purpose of the investigation was to point out the correlation between oil density and reservoir depth in some of the Hungarian hydrocarbon productive regions. Oil density related to reservoir depth in a particular area is generally linked to the migration mechanism. Zala Basin trends show a different migration process regionally and locally; tertiary migration by overflow mechanism can be supposed for the latter case. In the case of the Szeged–Kiskunság region, locally and regionally, migration along carrier beds through semipermeable sediments is present, with faults playing a significant role. In the Nagykunság region, the migration processes are similar to those in Zala, but the presence of faults seems more important. At depths below 2,000 m, the Bihar region trends are similar to those of the Szeged–Kiskunság region. In the shallower zone, hydrodynamic effects are recognizable. In two studied regions, the Battonya–Pusztaföldvár High and the Hungarian Paleogene Basin, the density of crude oil data does not show any significant variability and trend. Biodegradation and water washing were recognizable in the depth sections shallower than 2,000 m below surface. In karstic reservoirs of the Zala Basin (Nagylengyel, Sávoly), alteration is presumed at greater depths due to the karst water flow. The presented results show several trends of oil migration in the explored areas, which can be used for future estimation of the hydrocarbon potential in the Hungarian part of the Pannonian Basin.
The aim of our research was to better understand the spectral characteristics of precipitation variability, because through infiltration, this is the most important source of groundwater recharge. To better understand the periodicity of the rainfalls, we used monthly and annual rainfall data. We examined precipitation time records over a 110-year period from two different cities in the Carpathian Basin, obtained from the Hungarian Meteorological Service. With discrete Fourier-transformation (DFT) and wavelet time series analysis, we defined local cycles and developed a forecast for the Debrecen area.
Using DFT, we calculated the time-period distributions (spectra) of monthly and annual rainfall data. Spectra from the annual rainfall data showed 16 dominant periods in Debrecen and 17 in Pécs. At the two stations, the most dominant cycles were 3.6 and 5 years, respectively; there were several other cycles locally present in the data sets. From the monthly data sets, several other periodic components were calculated locally and countrywide as well.
Using wavelet analysis, the time dependence of the cycles was determined in the 110-year data set for two Hungarian cities, Debrecen and Pécs.
This paper deals with a question: how many stochastic realizations of sequential Gaussian and indicator simulations should be generated to obtain a fairly stable description of the studied spatial process? The grids of E-type estimations and conditional variances were calculated from pooled sets of 100 realizations (the cardinality of the subsets increases by one in the consecutive steps). At each pooling step, a grid average was derived from the corresponding E-type grid, and the variance (calculated for all the simulated values of the pooling set) was decomposed into within-group variance (WGV) and between-group variance (BGV). The former was used as a measurement of numerical uncertainty at grid points, while the between-group variance was regarded as a tool to characterize the geologic heterogeneity between grid nodes. By plotting these three values (grid average, WGV, and BGV) against the number of pooling steps, three equidistant series could be defined. The ergodic fluctuations of the stochastic realizations may result in some “outliers” in these series. From a particular lag, beyond which no “outlier” occurs, the series can be regarded as being fully controlled by a background statistical process. The number of pooled realizations belonging to this step/lag can be regarded as the sufficient number of realizations to generate. In this paper, autoregressive integrated moving average processes were used to describe the statistical process control. The paper also studies how the sufficient number of realizations depends on grid resolutions. The method is illustrated on a computed tomography slice of a sandstone core sample.
Upon completion, the National Radioactive Waste Repository in Bátaapáti will provide safe storage for low- and medium-level radioactive waste. The emplacement chambers were excavated in a fractured, blocky, granitic rock mass approximately 240 m below surface. One of the tasks related to the repository development is the feasibility demonstration of the permanent repository closure, including long-term rock mass associated issues. The required lifetime exceeds the usual one of an engineering structure. The long-term behavior of the repository needs to be extrapolated from observation over a shorter time period, or from analogous natural caverns. Numerical methods are the most promising techniques to carry out the extrapolation. It is commonly understood that there are significant uncertainties in long-term predictions. Uncertainties can be mitigated by utilizing independent methods to assess long-term behavior and by improving the prediction capability of the calculation model in the short term. The aim of the paper is to: (1) create a numerical model to effectively capture a wide range of the observed behavior of the rock mass, including tunnel-excavation-induced stress change and stress-dependent permeability and (2) identify the possible cause of long-term creep and show that the long-term creep can be captured by the selected calculation method. The moderately fractured rock mass is modeled using the Universal Distinct Element Code, released by Itasca. The joints in the rock mass are explicitly modeled; the blocky nature of the rock mass is captured. The model is verified with actual field observations and monitoring results. Based on the predicted stress state of the rock mass, the potential cause of long-term creep is identified. By fulfilling the two aims explained above, it is concluded that the model can be used to extrapolate in time and serve as a possible estimation method for the long-term behavior of the repository.
Lithofacies definition in the subsurface is an important factor in modeling, regardless of the scale being at reservoir or basin level. In areas with low exploration level, modeling of lithofacies distribution presents a complicated task as very few inputs are available. For this purpose, a case study in the Požega Valley was selected with only one existing well and several seismic sections within an area covering roughly 850 km2. For the task of expanding the input data set for lithofacies modeling, neural network analysis was performed that incorporated interpreted lithofacies (sandstone, siltite, marl, and breccia-conglomerate) in a single well and attribute data gathered from a seismic section. Three types of different neural networks were used for the analysis: multilayer perceptron, radial-basis function, and probabilistic neural network. As a result, three lithofacies models were built alongside a seismic section based upon predictions acquired from the neural networks. Three lithofacies were successfully predicted on the section while the breccia-conglomerate was either missing or underpredicted and mostly positioned in a geologically invalid interval. Results obtained by single networks differed from one another, which indicated that a result from a single network should not be treated as representative; thus, the facies distribution for modeling should be acquired from either an ensemble of neural networks or several neural networks. Analysis showed the initial potential of the usability of neural networks and seismic attribute analysis on vintage seismic sections with possible drawbacks of the applications being pointed out.
In the present explorative study, different time-series analysis methods, such as moving average, deterministic methods (linear trend with seasonality), and non-parametric Mann–Kendall trend test, were applied to monthly precipitation data from January 1871 to December 2014, with the aim of comparing the results of these methods and detecting the signs of climate change. The data set was provided by the University of Pannonia, and it contains monthly precipitation data of 144 years of measurements (1,728 data points) from the Keszthely Meteorological Station. This data set is special because few stations in Hungary can provide such long and continuous measurements with detailed historical background. The results of the research can provide insight into the signs of climate change in the past for the region of West Balaton. Parametric methods (linear trend and t-test for slope) for analyzing time series are the simplest ones to obtain insight into the changes in a variable over time. These methods have a requirement for normal distribution of the residuals that can be a limitation for their application. Non-parametric methods are distribution-free and investigators can get a more sophisticated view of the variable tendencies in time series.
Modelling the flow and transport of fluids (water and non-aqueous phase liquids or NAPLs) in porous systems or soils requires the accurate and reliable determination of basic input modelling parameters, such as liquid retention (Pc–S) and conductivity (Ksat, Kh). Methods for the determination (measurement and estimation) of water retention and conductivity have improved enormously over the last 60 years (Table 1). Promising results verified the applicability of pedotransfer functions (PTF) and their incorporated versions into software and submodels. However, the development of models was only aimed at improving methods with which these hydrological parameters could be determined for water, while calculations for NAPLs can still only be made indirectly. Several studies (e.g. in the petroleum industry, and research for environmental or hydrological purposes) revealed differences in the relationship between the hydraulic properties and pore system of the porous solid phase. Interactions (swelling-shrinking, desaggregation, etc.) between the phases may be significantly different in water/soil and NAPL/soil systems, affecting the efficiency of modelling. However, relatively few well-documented results have been published on the measurement of these hydraulic properties for NAPL-type fluids using a sufficient number of real, especially undisturbed soils. The establishment of databases of this sort might provide a basis for creating and developing PTF-type estimation methods for predicting NAPL retention and conductivity. Furthermore, it might improve our knowledge on interactions specific to the solid and fluid phases of pore systems, and also on the soil properties influencing the pore size distribution of soils (e.g. soil structure, the size distribution, morphology or stability of aggregates) and their relationship with the hydrophysical properties of the soil.
In worst-case leakage scenarios of CO2 geological storage, CO2 or brine may contaminate shallower drinking water aquifers. This work applies an advanced geochemical modeling methodology to predict and understand the effects of the aforementioned contamination scenarios. Several possibilities, such as equilibrium batch, kinetic batch, and 1D kinetic reactive transport simulations, were tested. These have all been implemented in the widely applied PHREEQC code. The production of figures and animations has been automated by R programming. The different modeling levels provide complementary information to each other. Both scenarios (CO2 or brine leakage) indicate the increase of ion concentrations in the freshwater, which might exceed drinking water limit values. The dissolution of CO2 changes the pH and induces mineral dissolution and precipitation in the aquifer and therefore changes in solution composition. Brine replacement of freshwater due to the pressure increase in the geological system induces mineral reactions as well.
