Evaluate best-mixing model for estimating thermal conductivity for granitoids from mineralogy: A case study for the granitoids of the Bundelkhand craton, central India

Calculation of Thermal Conductivity of Low‐Porous, Isotropic Plutonic Rocks of the Crust at Ambient Conditions From Modal Mineralogy and Porosity: A Viable Alternative for Direct Measurement?

“…With respect to the mixing model and the closeness of agreement, the outcome of our study is in full harmony with those of Chopra et al (), who assessed 21 granites and granitic gneisses (SiO 2 > 67 wt.%) of abnormally low porosity (<0.3%) from the Bundelkhand craton in central India, and Ray et al (), who surveyed 26 samples representing low‐porosity (<1.5%) high‐grade orthometamorphic rocks of amphibolite to granulite facies from the Indian Southern Granulite Province. The few other studies tackling this problem in crystalline rocks (Horai & Baldridge, ; Pribnow & Umsonst, ; Zhao et al, ) considered a smaller number of samples and came out with a poorer degree of agreement of the applied mean models, which most probably is related to the fact that the HM model was not taken into account.…”

“…Comparison of relative (left) and absolute (right) errors resulting from the application of the HM model for computing λ b for igneous rocks (this study, Chopra et al, /Cho18) and metamorphic rocks (Ray et al, /Ray15). Numbers denote to mean ± 2σ standard deviation.…”

“…Those approaches infer λ from (I) data on mineralogical composition, porosity, and composition of saturation fluids deploying numerical mixing models or from (II) correlations of λ with other physical properties. Studies pertaining to group‐I have addressed low‐porous granitic and metamorphic rocks (Chopra et al, ; Drury & Jessop, ; Pribnow & Umsonst, ; Ray et al, ; Zhao et al, ). Applications to sedimentary rocks have focused on models to determine bulk (or effective) thermal conductivity (λ b ) from matrix conductivity (λ m ) and varying porosity (e.g., Albert et al, ; Fuchs et al, ; Merkle et al, 1976).…”

The PetroPhysical Property Database (P&lt;sup&gt;3&lt;/sup&gt;) – a global compilation of lab-measured rock properties

“…This will extend the opportunity to also use the database to derive phenomenological constitutive models for petrophysical rock properties from their chemical or mineralogical composition (Chopra et al, 2018;Gard et al, 2019) or from their microstructure (Pimienta et al, 2014) or to develop empirical correlations between distinct properties of certain rock types (Gegenhuber and Schön, 2012;Esteban et al, 2015;Mielke et al, 2017). Altherr et al (2000), Aretz (2015), Ashwal et al (1987), Aswathanarayana (1986), Atal et al (1978), Attoh (2000), Babaie et al (2001), Bär (2008Bär ( , 2012, Baker et al (1997), Balakrishna and Ramana (1968), Ballard et al (1987), Barrett andAumento (1970), Bauluz et al (2000), Bea and Montero (1999), Bemmlott (2014), Best and Christiansen (2002), Betten (2015), Bhatia and Crook (1986), Biewer (2014, Birch and Clark (1940), Birch (1942), Blackwell and Richards (2004), Brady et al (2006), Brandt et al (2004), Brehme et al (2016a, b), Brettreich (2016), Bridgman (1924), Brigaud et al (1992), Brown et al (1981), Bullard (1939), Bultit...…”

P&lt;sup&gt;3&lt;/sup&gt; – PetroPhysical Property Database – a global compilation of lab measured rock properties

“…This will extend the opportunity to also use the database to derive phenomenological constitutive models for petrophysical rock properties from their chemical or mineralogical composition (Chopra et al, 2018, Gard et al, 2019 or from their microstructure (Pimienta et al, 2014) or to develop empirical correlations between distinct properties of certain rock types (Gegenhuber and Schön, 2012, Esteban et al, 2015, Mielke et al, 2017.…”