Assessing the uncertainty in reservoir performance is a necessary step during the exploration phase. To examine the uncertainty in flow response, a large set of realizations must be processed. There are several stochastic geostatistical algorithms capable of simulating multiple equiprobable realizations. Although these can show us the possible realities highlighting the spatial uncertainty, their handling is time- and CPU-consuming during the later processes, such as flow simulations. Consequently, only a small number of realizations can be post-processed in industrial practice. The purpose of this work is to develop a method, which will reduce the huge number of realizations in a way that the remaining ones retain the spatial uncertainty of a reservoir’s flow behavior, as would be demonstrated by a larger set of realizations. To solve this problem, ranking methods can be applied. Traditional ranking techniques, such as probability selection, are highly dependent on the applied static properties. In this paper, an alternative selection method is parameterized for measuring the pairwise dissimilarity between geostatistical models, with a distance function based on the hydrodynamic properties of the hydrocarbon reservoirs. The effectivity of the method is highly dependent upon the selected criteria. Thus, the distance function refers to the flow responses and allows visualizing the space of uncertainty through multidimensional scaling. A kernel transformation of the MDS data set is required to obtain a feature space where the K-means algorithm can discover non-linear structures in the basic data set. The final step of the method is the selection of the Earth models closest to the cluster centers. This tool allows for the selection of a subset of representative realizations, containing similar properties to the larger set.
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.
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.
The Micro-Deval test method is used for testing of aggregate durability. The present paper focuses on two Hungarian andesites obtained from the quarries of Recsk (Mátra Mountains, Hungary) and of Nógrádkövesd (Cserhát Mountains, Hungary). The aim of this study is to find a simple test method based on the original Micro-Deval test method to assess the long-term durability of aggregates. An additional part of the research was to develop suitable mathematical models that can describe the behavior of the andesite aggregates under continuous abrasive impact. The relevant standard (EN 1097-1:2012) recommends 12,000 rotations to determine the Micro-Deval coefficient required for classification of the aggregates. Within the framework of this research, a modified Micro-Deval test was applied: the number of rotations was increased in several steps and the degree of abrasion was measured afterwards. Regression analyses were used to outline mathematical forms which characterize the dependence between the number of rotations and the degree of abrasion. According to the results, the long-term Micro-Deval tests significantly modify the assessed durability and thus provide information on the long-term abrasive impact. The degree of change depends on the studied material: the ratio of the long-term Micro-Deval coefficients of the two studied andesite types is larger than 3. The regression analyses of the measured Micro-Deval coefficients revealed that quadratic curves are suitable to describe these tendencies for both andesite aggregates.