Modeling and analysis of the thermal conductivities of air saturated sandstone, quartz and limestone using computational intelligence

Gitifar, Vahid; Abbasi, Alireza; Setoodeh, Payam; Poursadegh, M.; Sahebnazar, Zahra; Alamdari, Abdolmohammad

doi:10.1016/j.ijthermalsci.2014.04.015

Cited by 16 publications

(4 citation statements)

References 41 publications

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“…The TC can be obtained by the following principal methods: (i) from empirical relationships based on laboratory measurements, which relate thermal conductivity and other petrophysical properties measurements of rocks (Özkahraman et al, 2004;El Sayed, 2011;Duchkov et al, 2014), (ii) from wells log data, by searching the major minerals composition of the rock, then derive indirectly the thermal conductivity from them (Demongodin et al, 1991;Hartmann et al, 2005Hartmann et al, , 2008 and (iii) recently some authors have used artificial intelligence to predict thermal conductivity for sandstone (Goutorbe et al, 2006;Singh et al, 2007;Vaferi et al, 2014;Gitifar et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Zerrouki

Géraud

Diraison

et al. 2019

Journal of African Earth Sciences

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In geothermic studies, the thermal conductivity (TC) is an essential parameter needed to calculate heat flow in rocks. It is obtained generally with high accuracy from laboratory measurements. The methodology proposed in this paper is to estimate the TC, which depends on many parameters; such as mineralogy, porosity, shape of voids and nature of contact between grains, based on linear and nonlinear relationships. In order to predict the thermal conductivity from other petrophysical parameters in the Hamra Quartzites reservoir, we have measured porosity, density and permeability, for dry samples taken from core wells. The correlation coefficients (R) were calculated between thermal conductivity and other petrophysical parameters for all samples. The results show that, the correlation coefficient is moderate between TC and the porosity, weak between TC, density and permeability. To improve these correlations, samples were classified into cemented and uncemented sets. A minor improvement on the correlation coefficients is noted between TC, density and porosity in uncemented samples, with values equal 0.51 and 0.73, respectively. The application of Radial Basis Function (RBF) neural networks, using density, porosity and permeability as inputs and thermal conductivity as output, permit us to predict the thermal conductivity with high precision. The correlation coefficient between TC estimated by the RBF neural network is the same as that measured in laboratory equaling 0.983.

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Section: Introductionmentioning

confidence: 99%

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Zerrouki

Géraud

Diraison

et al. 2019

Journal of African Earth Sciences

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“…1-3) are lower than those in dry tuff rocks, whereas the Vp velocities in andesite, basalt, diabase, granodiorite and travertine (samples no. [4][5][6][7][8][9][10][11][12] are greater in the water-saturated state. The V p velocities did not change in the dry and water-saturated states in the limestone and concrete samples (samples no.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Physical Properties of Rocks and Their Relationships to the Thermal Conductivity Coefficient

ELÇİ,

Şalk,

Sarı

2024

Preprint

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The thermal conductivity coefficient is an important physical feature of rocks that determines the rate at which heat is transported through a specific rock type. It is a measure of a rock's ability to conduct heat and is determined by a variety of physical characteristics. The relationship between a rock's physical qualities and its thermal conductivity coefficient is complex since it varies based on rock type, mineral composition, porosity, moisture content, and temperature Because ultrasonic techniques are nondestructive and simple to use, VP-wave velocity measurements are utilized both in situ and in the laboratory to explain the dynamic characteristics of rocks. Several factors influence the seismic properties of rock types, including density, grain size and shape, porosity, direction dependency, pore water, clay concentration, compression pressure, and temperature. A rock's acoustic VP-wave velocity is closely related to its velocity in its natural environment, which represents the intact rock's characteristics, structure, and texture. Research has demonstrated that the thermal conductivity of porous rock is primarily determined by its porosity, mineral composition, the presence of fluids filling the pores, and the temperature and pressure of the surrounding environment. Porosity and thermal conductivity impact fluid-rock interactions and define building materials. Physical qualities such as porosity and thermal conductivity coefficient are essential factors in determining the quality of building blocks. In this work, the thermal conductivity coefficient, VP wave velocity, porosity, density, pressure resistance, and other properties of rocks were assessed using laboratory measurements of the thermal conductivity coefficient and acoustic VP wave velocity. The correlations among physical properties were investigated.

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“…Because artificial neural networks have been able to estimate the thermal properties of different systems [19] to [21], it was proposed to use them to estimate the thermal conductivity and the heat capacity of an experimental micro-alloyed steel as a function of temperature, and apply them to the thermal analysis of the HAZ. It was decided to design two ANNs, one to estimate the thermal conductivity and the other to estimate the heat capacity.…”

Section: Artificial Neural Network To Estimate the Thermal Propertiementioning

confidence: 99%

Artificial Neural Networks to Estimate the Thermal Properties of an Experimental Micro-Alloyed Steel and Their Application to the Welding Thermal Analysis

López–Martínez

Vergara–Hernández

Serna

et al. 2015

SV-JME

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Modeling and analysis of the thermal conductivities of air saturated sandstone, quartz and limestone using computational intelligence

Cited by 16 publications

References 41 publications

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

A Preliminary study of relationships between thermal conductivity and petrophysical parameters in Hamra Quartzites reservoir, Hassi Messaoud field (Algeria)

Physical Properties of Rocks and Their Relationships to the Thermal Conductivity Coefficient

Artificial Neural Networks to Estimate the Thermal Properties of an Experimental Micro-Alloyed Steel and Their Application to the Welding Thermal Analysis

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