Outcrop analogue study of Permocarboniferous geothermal sandstone reservoir formations (northern Upper Rhine Graben, Germany): impact of mineral content, depositional environment and diagenesis on petrophysical properties

Aretz, Achim; Bär, Kristian; Götz, Annette E.; Sass, Ingo

doi:10.1007/s00531-015-1263-2

Cited by 31 publications

(32 citation statements)

References 61 publications

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“…The investigation of regional fluid flow and heat transport has been ongoing since decades [1][2][3][4][5][6][7][8]. In Hesse, deep geothermal exploration was unsuccessful so far, despite favorable predictions beforehand [9,10]. This demonstrates that predictions of the deep geothermal potential are associated with large uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Regional Fluid Flow on Deep Temperatures (Hesse, Germany)

et al. 2019

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A successful utilization of deep geothermal resources requires accurate predictions about the distribution of reservoir temperature as well as of the hydraulic processes exerting a direct influence on the productivity of geothermal reservoirs. The aim of this study was to investigate and quantify the influence that regional thermo-hydraulic processes have on the geothermal configuration of potential reservoirs in the German Federal State of Hesse. Specifically, we have addressed the question of how the regional thermal and hydraulic configuration influence the local hydro-thermal reservoir conditions. Therefore, a 3D structural model of Hesse was used as a basis for purely hydraulic, purely thermal and coupled 3D thermo-hydraulic simulations of the deep fluid flow and heat transport. As a result of our numerical simulations, Hesse can be differentiated into sub-areas differing in terms of the dominating heat transport process. In a final attempt to quantify the robustness and reliability of the modelling results, the modelling outcomes were analyzed by comparing them to available subsurface temperature, hydraulic and hydrochemical data.

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Section: Introductionmentioning

confidence: 99%

“…The lowermost sedimentary unit consists of the continental clastic and volcanic deposits of the Rotliegend with very heterogeneous petrology [10,21,67]. Due to diagenetic processes, the Rotliegend unit has a lower hydraulic conductivity than the Buntsandstein and the Cenozoic aquifers (Table 2) but can be still considered as a regional aquifer in the model.…”

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The Effects of Regional Fluid Flow on Deep Temperatures (Hesse, Germany)

et al. 2019

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“…This formation is deeply buried (1,995 to 2,380 m b.g.s.) in the northern Upper Rhine Graben in southwestern Germany (Becker et al, 2012) and constitutes a potential target unit for hydrothermal exploitation (Aretz et al, 2015). The outcrop has been chosen in order to estimate the variability of physicochemical properties that could be expected in this formation as an uncertainty factor if the formation gets targeted in a deep geothermal project.…”

Section: Sedimentological Characterization and Rock Samplingmentioning

confidence: 99%

“…We used the previously mentioned major oxides for analyses since those can provide insight into the iron oxide and clay mineral distribution, which can impact petrophysical properties significantly. More details on the measurement devices can be found in the works of Hornung and Aigner (2002), Sass and Götz (2012), Filomena et al (2014) and Aretz et al (2015).…”

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confidence: 99%

High-Resolution Analysis of the Physicochemical Characteristics of Sandstone Media at the Lithofacies Scale

Linsel¹,

Wiesler²,

Hornung³

et al. 2020

Preprint

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Abstract. The prediction of physicochemical rock properties in subsurface models regularly suffers from uncertainty observed at the sub-meter scale. Although at this scale – which is commonly termed the lithofacies scale – the physicochemical variability plays a critical role for various types of subsurface utilization, its dependence on syn- and post-depositional processes is still subject to investigation. The impact of syn- and post-depositional geological processes, including depositional dynamics, diagenetical compaction and chemical mass transfer, onto the spatial distribution of physicochemical properties in siliciclastic media at the lithofacies scale is investigated in this study. We propose a new workflow using two cubic rock samples where eight representative geochemical, thermophysical, elastic and hydraulic properties are measured on the cubes' faces and on samples taken from the inside. The scalar fields of the properties are then constructed by means of spatial interpolation. The rock cubes represent the structurally most homogeneous and most heterogeneous lithofacies types observed in a Permian lacustrine delta formation that deposited in an intermontane basin. The spatio-temporal controlling factors are identified by exploratory data analysis and geostatistical modeling in combination with thin section and environmental scanning electron microscopy analyses. Sedimentary structures are well preserved in the spatial patterns of the negatively correlated permeability and mass fraction of Fe2O3. The Fe-rich mud fraction, which builds large amounts of the intergranular rock matrix and of the pseudomatrix, has a degrading effect onto the hydraulic properties. This relationship is underlined by a zonal anisotropy that is connected to the observed stratification. Feldspar alteration produced secondary pore space that is filled with authigenic products including illite, kaolinite and opaque phases. The local enrichment of clay minerals implies a non-pervasive alteration process that is expressed by network-like spatial patterns of the positively correlated mass fractions of Al2O3 and K2O. Those patterns are spatially decoupled from primary sedimentary structures. The elastic properties, namely p- and s-wave velocity, indicate a weak anisotropy that is not inevitably oriented perpendicularly to the sedimentary structures. The multifarious patterns observed in this study emphasize the importance of high-resolution sampling in order to properly model the variability present in a lithofacies-scale system. In fact, the physicochemical variability observed at the lithofacies scale might nearly cover the global variability in a formation. Hence, if the local variability is not considered in full-field projects – where the sampling density is usually low – statistical correlations and thus conclusions about causal relationships among physicochemical properties might be feigned inadvertently.

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“…Thin-section analysis using a polarized microscope revealed andesite has a porphyritic texture with large plagioclase crystals (up to 3 mm in diameter), pyroxene in a cryptocrystalline matrix and isolated vesicles up to 6 mm in diameter (Chauhan et al, 2016). Rotliegend sandstone had different grain size (between 0.5 and 5 mm) of fine sand and gravel, with 26 % monocrystalline quartz, up to 35 % polycrystalline quartz, 8 % feldspar, 9 % sedimentary volcanic lithoclast grains and 13 % cement (Aretz et al, 2016). Andrä et al (2013) confirm that the porosity of the Berea sandstone (total porosity 19.97 %; TM Petroleum Cores, Ohio, USA) was performed using an Helium Pycnometer 1330 (Micrometritics Instrument Corp., Belgium) and a mercury porosimetry using a Pascal 140+440 Mercury Porosimeter (Thermo Electron Corporation, Germany).…”

Section: Experimental Approachmentioning

confidence: 99%

Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

Chauhan

Rühaak

Anbergen³

et al. 2016

Solid Earth

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Abstract. Performance and accuracy of machine learning techniques to segment rock grains, matrix and pore voxels from a 3-D volume of X-ray tomographic (XCT) grayscale rock images was evaluated. The segmentation and classification capability of unsupervised (k-means, fuzzy c-means, self-organized maps), supervised (artificial neural networks, least-squares support vector machines) and ensemble classifiers (bragging and boosting) were tested using XCT images of andesite volcanic rock, Berea sandstone, Rotliegend sandstone and a synthetic sample. The averaged porosity obtained for andesite (15.8 ± 2.5 %), Berea sandstone (16.3 ± 2.6 %), Rotliegend sandstone (13.4 ± 7.4 %) and the synthetic sample (48.3 ± 13.3 %) is in very good agreement with the respective laboratory measurement data and varies by a factor of 0.2. The k-means algorithm is the fastest of all machine learning algorithms, whereas a least-squares support vector machine is the most computationally expensive. Metrics entropy, purity, mean square root error, receiver operational characteristic curve and 10 K-fold cross-validation were used to determine the accuracy of unsupervised, supervised and ensemble classifier techniques. In general, the accuracy was found to be largely affected by the feature vector selection scheme. As it is always a trade-off between performance and accuracy, it is difficult to isolate one particular machine learning algorithm which is best suited for the complex phase segmentation problem. Therefore, our investigation provides parameters that can help in selecting the appropriate machine learning techniques for phase segmentation.

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Outcrop analogue study of Permocarboniferous geothermal sandstone reservoir formations (northern Upper Rhine Graben, Germany): impact of mineral content, depositional environment and diagenesis on petrophysical properties

Cited by 31 publications

References 61 publications

The Effects of Regional Fluid Flow on Deep Temperatures (Hesse, Germany)

The Effects of Regional Fluid Flow on Deep Temperatures (Hesse, Germany)

High-Resolution Analysis of the Physicochemical Characteristics of Sandstone Media at the Lithofacies Scale

Phase segmentation of X-ray computer tomography rock images using machine learning techniques: an accuracy and performance study

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