New approaches for the relationship between compressional wave velocity and thermal conductivity

Gegenhuber, Nina; Schoen, Juergen H.

doi:10.1016/j.jappgeo.2011.10.005

Cited by 41 publications

(20 citation statements)

References 22 publications

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“…A large number of empirical models for λ b prediction of different rock types exists, based on other physical properties, such as porosity (Φ) (e.g., Sugawara and Yoshizawa 1961;Maqsood et al 2004), bulk density (Anand et al 1973), P-wave velocity (v p ) (e.g., Goss et al 1975;Hartmann et al 2005;Gegenhuber and Schön 2012), or mineralogical composition (e.g., Birch and Clark 1940;Horai and Baldridge 1972). The theoretical geometric mean model (Woodside and Messmer 1961a, b) often is applied to convert λ b values measured of air-saturated sandstone samples to λ b values considering water-saturated conditions (e.g., Aurangzeb and Maqsood 2007;Fuchs et al 2013) as it is the only mixing law model which provides reliable results for porous rocks (e.g., Pribnow 1994;Fuchs et al 2013).…”

Section: Existing and New Empirical Models For Bulk Thermal Conductivmentioning

confidence: 99%

Pore-fluid-dependent controls of matrix and bulk thermal conductivity of mineralogically heterogeneous sandstones

Kämmlein

Stollhofen

2019

Geotherm Energy

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For a variety of geothermal engineering applications, the only indirectly determinable matrix thermal conductivity (λ m) is frequently used to convert the measured bulk rock thermal conductivity (λ b) of air-saturated sandstones to water-saturated conditions. However, the necessary assumption that the absolute value of λ m remains constant irrespective of the pore fluid present turns out to be not valid in practice and the explicit control factors on λ m have not been demonstrated yet for different pore fluids. A pore fluid-controlled change in the λ m value also questions the transferability of empirical proxy models for the estimation of water-saturated λ b when they were calibrated on air-saturated samples. This study applies a multiple regression analysis to quantitative mineralogical composition data and porosity (Φ) to identify the controls of the λ m for different types of pore fluids (water-vs. air-saturation). In addition, the differences in the calculated λ m values resulting from different calculation methods or input data (theoretical geometric mean model or mineralogical composition data) are examined. We further test the suitability of different sandstone properties as potential proxies for the estimation of the λ b , with respect to the pore fluid type. Differences in the absolute value of λ m of sandstones from different measurement conditions (air-or water-saturated) are most probably related to the formation of authigenic kaolinite in the pore space, originating from the alteration of alkali feldspar. In addition, the thermal properties of the rock matrix are mainly controlled by the volume fractions of the high thermally conductive mineral fractions quartz and dolomite. Empirical models that have solely Φ or P-wave velocity (v p) as variables are not suitable for the prediction of water-saturated λ b of sandstones. Instead, quantitative mineralogical data of high thermally conductive mineral phases such as quartz and dolomite have to be included. The easily measureable v p is proposed as a promising proxy for both pore fluid types tested: combined with the total quartz volume/porosity ratio for air-saturated sandstones, and combined with the quartz plus dolomite volume fractions for water-saturated sandstones.

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Section: Existing and New Empirical Models For Bulk Thermal Conductivmentioning

confidence: 99%

Pore-fluid-dependent controls of matrix and bulk thermal conductivity of mineralogically heterogeneous sandstones

Kämmlein

Stollhofen

2019

Geotherm Energy

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“…Empirical approaches can, on the other hand, directly relate a well log signal to thermal conductivity (Pribnow et al 1993;Kukkonen and Peltoniemi 1998;Popov et al 2003;Hartmann et al 2005;Goutorbe et al 2007;Sundberg et al 2009;Gegenhuber and Schoen 2012;Fuchs and Förster 2013;Gasior and Przelaskowska 2014). Limited well logs signals can be used for that purpose.…”

Section: Geophysical Well Logmentioning

confidence: 99%

Colloquium 2016: Assessment of subsurface thermal conductivity for geothermal applications

Raymond

2018

Can. Geotech. J.

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The construction of green buildings using geothermal energy requires knowledge of the ground thermal conductivity, assessed when designing the heating and cooling system of commercial buildings with ground-coupled heat pumps. The most commonly used method for active field assessment is the thermal response test (TRT), which consists of circulating heated water in a pilot ground heat exchanger (GHE) where temperature and flow rate are monitored. The transient thermal perturbation is analyzed to evaluate the subsurface thermal conductivity. Heat injection can also be performed with a heating cable in the GHE to conduct a TRT without water circulation, which can be affected by surface temperature variations. Mots-clés : géothermie, géothermique, géophysique thermique, pompe à chaleur, conductivité thermique, test de réponse thermique, échangeur de chaleur au sol.

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“…Jako wielkość wektorowa, zależy również od geometrii przestrzeni porowej, od takich czynników jak: kształt i wielkość porów oraz dystrybucja poszczególnych minerałów w skale. Do oceny przewodności cieplnej stosowane są różnego rodzaju modele oparte na pewnym uproszczeniu wewnętrznej geometrii (struktury) skał [8,9,16], umożliwiające wyliczenie przewodności cieplnej skały na podstawie własności jej składników. Modele te wykorzystywane są zarówno do przeliczania wartości przewodności cieplnej próbki suchej na nasyconą roztworami porowymi [4,16,18], jak i do charakterystyki przewodności cieplnej skały na podstawie przewodności cieplnej budujących ją minerałów [5,7,9,10,13,16,17].…”

Section: Podstawy Teoretyczneunclassified

Szacowanie wartości współczynnika przewodności cieplnej piaskowców fliszowych na podstawie składu mineralnego

Przelaskowska¹,

Badawczy²

2018

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W ramach przedstawionej pracy przeprowadzono analizę modeli matematycznych umożliwiających ocenę wartości współczynnika przewodności cieplnej skały na podstawie składu mineralnego i porowatości. Zastosowano różnego rodzaju modele, od najprostszych, zakładających warstwową budowę skały, do bardziej skomplikowanych modeli inkluzji sferycznych. Wartości obliczone porównano z danymi laboratoryjnymi. Uzyskane wyniki umożliwiły dobór optymalnych modeli służących do obliczenia przewodności cieplnej piaskowców fliszowych. Słowa kluczowe: przewodność cieplna, modele matematyczne, skład mineralny. Estimating the thermal conductivity value of the Carpathian flysch sandstones on the basis of their mineral composition Mathematical models for the estimation of the thermal conductivity of rocks on the basis of their mineral composition and porosity were analyzed in the presented work. Different types of models from the simplest, layer models to more complex spherical inclusion models were introduced. Calculated values were compared with the laboratory data. The obtained results enabled the selection of the most effective models for the calculation of the thermal conductivity of flysch sandstones.

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New approaches for the relationship between compressional wave velocity and thermal conductivity

Cited by 41 publications

References 22 publications

Pore-fluid-dependent controls of matrix and bulk thermal conductivity of mineralogically heterogeneous sandstones

Pore-fluid-dependent controls of matrix and bulk thermal conductivity of mineralogically heterogeneous sandstones

Colloquium 2016: Assessment of subsurface thermal conductivity for geothermal applications

Szacowanie wartości współczynnika przewodności cieplnej piaskowców fliszowych na podstawie składu mineralnego

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