Thermal Diffusivity, Heat Capacity, and Thermal Conductivity of Oil Reservoir Rock at High Temperatures

Abdulagatov, Ilmutdin M.; Abdulagatova, Zumrud Z.; Grigor'ev, B. A.; Kallaev, S. N.; Omarov, Z. M.; Bakmaev, A. G.; Ramazanova, A. E.; Rabadanov, K. M.

doi:10.1007/s10765-021-02878-x

Cited by 11 publications

(6 citation statements)

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“…The thermal conductivity of water (0.6 W m −1 K −1 ) and the thermal conductivity of air (0.02 W m −1 K −1 ). Thermal diffusivity (Ҡ t) of some sandstone samples is calculated according to the laboratory measurements using LFA technique and introduced by [15,. By using these data [15], the regression line equation is obtained as:…”

Section: Methodsmentioning

confidence: 99%

“…Thermal diffusivity (Ҡ t) of some sandstone samples is calculated according to the laboratory measurements using LFA technique and introduced by [15,. By using these data [15], the regression line equation is obtained as:…”

Section: Methodsmentioning

confidence: 99%

“…The thermal conductivity (λ t) is assessed using the above listed equation (3) approved by [15]. Subsequently, the relationship between λ t and Ҡ t, is made to calculate the regression line relation controlling them (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The use of constant values of the thermophysical parameters (i.e., λ, CP, Ҡ, ρ =constant) instead of their temperature dependencies, λ(T), CP(T), Ҡ(T), in the heat transfer process leads to inaccuracy of the natural reservoir temperature profile, T (x, t) which in turn affecting on the real thermodynamic conditions in the reservoir and the process of oil or geothermal energy recovery. The pressure dependence of the thermal diffusivity, heat capacity and thermal conductivity are negligibly small and can be ignored (Abdulagatov, et al,2021). The experimental thermal conductivity data can be correctly described high temperature behaviour is introduced by [15] as:…”

Section: Introductionmentioning

confidence: 99%

“…The pressure dependence of the thermal diffusivity, heat capacity and thermal conductivity are negligibly small and can be ignored (Abdulagatov, et al,2021). The experimental thermal conductivity data can be correctly described high temperature behaviour is introduced by [15] as:…”

Section: Introductionmentioning

confidence: 99%

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Przewidywanie dyfuzyjności termicznej na podstawie prędkości P-fal i oceny porowitości dla zbiorników piaskowca

El Sayed,

El Sayed

2024

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Petrophysical heterogeneities of sandstone reservoirs which are generated by rock internal variability resounded to the magnitude of the rock thermal diffusivity. This is expected mostly to variation of rock density, porosity, reservoir temperature and its thermal conductivity. New methodology for calculating thermal diffusivity in a sandstone rock formation is intended and effectively employed some laboratory thermophysical measurements for sandstone reservoirs. The proposed petrophysical model establishes thermal diffusivity if both the effective porosity and acoustic (compressional) wave velocity of the rock are known. Some reliable petrophysical models (El Sayed, 2011 and Ahmed, 2019) concerned to both the Baharyia (Egypt) and Szolnok (Hungary) sandstone formations are used with only some modifications to build an innovative nomography. It permitted precise quantification and determination of the thermal diffusivity for both dry and saturated sandstone samples normalized to reservoir temperature (300K-1060 K). Verification of the proposed model is achieved with applying study cases of laboratory measured thermophysical properties (i.e., porosity, thermal diffusivity/or conductivity and longitudinal wave velocity) for different sandstone types, geological ages and geographic locations. A regression analysis of thermal diffusivity between laboratory measured and predicted data for dry (Ҡ-dry) rock samples yield a plausible coefficient of correlations as (R =0.73; 0.86 and 0.98) for three different sandstones obtained from Permo-Carboniferous in Germany (Aretz, 2016) and of dissimilar geologic age in Switzerland (Pimienta, 2018) respectively while, the average standard error equals 0.011. Then again, the laboratory measured and predicted thermal diffusivity (Ҡ-sat) of saturated samples display an appropriate coefficient of correlation (R = 0.76) and average standard error (0.0089).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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