Estimation of thermal conductivity of plutonic drill cuttings from their mineralogy: A case study for the FORGE Well 58–32, Milford, Utah

Yu, Ruyang; Jiang, Shu; Wang, Hu; Du, Fengshuang; Zhang, Luchuan; Wen, Yiran; Luo, Cai; Zhang, Ren

doi:10.1016/j.geothermics.2022.102407

Cited by 4 publications

(5 citation statements)

References 77 publications

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“…7a; Table 3). This result is in agreement with previous studies carried out on igneous and metamorphic rocks (Förster et al 2015;Ray et al 2015;Chopra et al 2018;Fuchs et al 2018;Yu et al 2022). The harmonic mean model is based on a banding model, where the mineral phases are layered perpendicularly to the heat flow (Clauser 2011;Jia et al 2019).…”

Section: Thermal Conductivity Calculated From the Mineralogical Compo...supporting

confidence: 91%

“…In the literature, there are few studies where the TC of crystalline rocks was predicted from their mineralogical composition and porosity and using different mixing models (Horai & Baldridge 1972;Pribnow & Umsonst 1993;Förster et al 2015;Ray et al 2015;Zhao et al 2016;Chopra et al 2018;Fuchs et al 2018;Yu et al 2022), and no studies at all for ultramafic rocks. Horai & Baldridge (1972) calculated the TC of nineteen igneous rocks samples from the mineralogical and chemical compositions using the Hashin-Shtrikman mean model.…”

Section: Thermal Conductivity Calculated From the Mineralogical Compo...mentioning

confidence: 99%

“…(i) The heat transport through isotropic rock is considerably delayed like a flow perpendicular to stratification in an anisotropic medium due to the countless heat-refraction events caused by the chaotic arrangement of the mineral grains (Fuchs et al 2018;Yu et al 2022). Also, the foliated structure of the serpentine minerals causes heat to be transported in a manner and quantity that are thought to make it perpendicular to the planar boundaries in an anisotropic medium.…”

Section: Thermal Conductivity Calculated From the Mineralogical Compo...mentioning

confidence: 99%

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Petrophysical characterization and thermal conductivity prediction of serpentinized peridotites

Chibati

Géraud

Essa

2022

Geophysical Journal International

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Summary We investigated 22 different serpentinization level peridotite samples from the North Pyrenean Zone (NPZ) and along the North Pyrenean Fault (NPF) to characterize their mineralogical composition and petrophysical properties (bulk and grain density, porosity, compressional and shear wave velocities and thermal conductivity and diffusivity). Peridotites have a percentage of serpentine which varies between 5% and 97% for fresh samples and totally altered ones, respectively. The mineralogical changes by serpentinization induced a wide variety of petrophysical properties. The key indicators of serpentinization from petrophysical properties in serpentinized peridotite include an increase of porosity of up to 10% and the associated decrease in the solid density ranging from 3.29 g cm–1 for fresh peridotites to 2.5 g cm–3 for the totally serpentinized peridotites. Thermo-physical properties were found to follow the same deceasing trend as the serpentinization increases up to 3000 m.s–1 and 1.85 W m–1 K–1 for P-wave velocity and thermal conductivity (TC), respectively. For TC estimation, empirical relationships were produced using multiple linear regression (MLR) and a mineralogy model. For the mineralogy model, we used ten mixing models commonly used in indirect TC estimation based on mineral composition. For the mean TC of individual mineral phases, the TC estimation shows that in case of non-availability of the proper samples for direct measurement, the TC of peridotite can be inferred with an acceptable level of error from the geometric mean model and harmonic mean models for the dry and saturated conditions respectively. Based on the results of MLR, the study also confirms that the TC of dry rocks can be predicted through acoustic velocity, density and porosity with a prediction accuracy of ± 0.24 W m−1 K−1 and confidence of > 80%.

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Section: Thermal Conductivity Calculated From the Mineralogical Compo...supporting

confidence: 91%

Section: Thermal Conductivity Calculated From the Mineralogical Compo...mentioning

confidence: 99%

Section: Thermal Conductivity Calculated From the Mineralogical Compo...mentioning

confidence: 99%

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Petrophysical characterization and thermal conductivity prediction of serpentinized peridotites

Chibati

Géraud

Essa

2022

Geophysical Journal International

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“…Earlier studies have shown that it is possible to predict thermal conductivity with an acceptable RA for low-porosity granitic and metamorphic rocks, the predictability of thermal conductivity using mixing models based on mineralogical composition (Pribnow & Umsonst 1993 ;Fuchs et al 2018 ;Yu et al 2022 ). In this study, we compared the performance of ML-based models, JS model and traditional mixing models based on a data set of 462 plutonic rocks (subset-A) compiled by Jennings et al ( 2019 ), that includes the CIPW normative mineral composition, major oxides and observed -c) and higher R 2 (Fig.…”

Section: Machine Learning Algorithms Versus Traditional Mixing Modelsmentioning

confidence: 99%

“…Ho wever , there is no consensus on the thermal conducti vity of indi vidual minerals. For example, the minimum and maximum thermal conductivity values reported for biotite are 1.70 and 3.14 Wm −1 K −1 (Yu et al 2022 ), respecti vel y. So far, onl y a few studies, such as those by Horai & Baldridge ( 1972b ) and Jennings et al ( 2019 ) have attempted to estimate rock thermal conducti vity directl y from major oxide compositions.…”

Section: Introductionmentioning

confidence: 99%

Estimating the thermal conductivity of plutonic rocks from major oxide composition using machine learning

Jiang

Fuchs

et al. 2023

Geophysical Journal International

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Summary The accurate estimation of temperature distribution in the earth's crust and modeling of heat-related processes in geodynamics requires knowledge of the thermal conductivity of plutonic rocks. This study compiled an extensive dataset of 530 representative plutonic rock samples, including thermal conductivity, major oxide composition and (for two subsets of data) modal mineralogy. For the first time, three machine learning algorithms (ML; i.e. support vector regression, random forest and extreme gradient boosting) were employed to estimate the thermal conductivity of plutonic rocks using the major oxide composition feature as input variables. The performance of these ML-based models was evaluated against a geochemically-compositional model and eight mineral-driven physically-based empirical mixing models. Results show that the means of predicted thermal conductivity by the ML-based models and the geochemically-compositional model are not significantly different from the measured thermal conductivity at a significance level of 5 per cent. However, the ML-based models outperformed the best-performing non-ML model, the geochemically-compositional model. The highest prediction accuracy was achieved by extreme gradient boosting, which reduced the mean absolute percentage error and root mean square error by more than 50 per cent. Furthermore, SiO2 is confirmed as the most important independent variable, followed by Al2O3, TiO2, CaO, and K2O. The turning point observed in the thermal conductivity trend with SiO2 wt- per cent may be primarily attributed to variations in mineral composition within the subgroup of igneous rock types classified based on SiO2 wt- per cent. The present study explores, for the first time, the use of ML algorithms to estimate the thermal conductivity of plutonic rocks from their major oxide composition.

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Influencing factors of rock thermal conductivity and applicability evaluation of its mixing law predictive models

et al. 2023

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Estimation of thermal conductivity of plutonic drill cuttings from their mineralogy: A case study for the FORGE Well 58–32, Milford, Utah

Cited by 4 publications

References 77 publications

Petrophysical characterization and thermal conductivity prediction of serpentinized peridotites

Petrophysical characterization and thermal conductivity prediction of serpentinized peridotites

Estimating the thermal conductivity of plutonic rocks from major oxide composition using machine learning

Influencing factors of rock thermal conductivity and applicability evaluation of its mixing law predictive models

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