Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

Samaei, Masoud; Massalow, Timur; Abdolhosseinzadeh, Ali; Yağız, Saffet; Sabri, Mohanad Muayad Sabri

doi:10.3390/app12189187

Cited by 3 publications

(1 citation statement)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to the rise in energy costs and energy need, the energy management and organization must play a significant role in human lives and energy national strategies of most world countries. Heat as a form of energy which could be transferred more quickly in solid medium than in gas and liquid [1] is suitable target for exploration. Consequently, the understanding of the solid materials potential to transfer heat can aid in sustaining energy more efficiently.…”

Section: Introductionmentioning

confidence: 99%

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

El Sayed,

El Sayed

2024

View full text Add to dashboard Cite

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).

show abstract

Section: Introductionmentioning

confidence: 99%

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

El Sayed,

El Sayed

2024

View full text Add to dashboard Cite

show abstract

Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach

Samaei,

Alinejad Omran,

Keramati

et al. 2024

Earth Sci Inform

View full text Add to dashboard Cite

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

Jiang

Fuchs

et al. 2023

Geophysical Journal International

View full text Add to dashboard Cite

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.

show abstract

Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks

Cited by 3 publications

References 67 publications

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

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

Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach

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

Contact Info

Product

Resources

About