Rock thermal properties from well-logging data accounting for thermal anisotropy

Shakirov, A.; Чехонин, E.; Popov, Yu. A.; Popov, Evgeny; Spasennykh, М.; Zagranovskaya, D. E.; Serkin, Maksim

doi:10.1016/j.geothermics.2021.102059

Cited by 18 publications

(6 citation statements)

References 38 publications

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“…In this research, we applied a recently developed, novel method for determining the thermal conductivity and volumetric heat capacity of rock within not-cored intervals from well-logging data [28]. This method allows the determination of principal components of the thermal conductivity and the volumetric heat capacity of the rock.…”

Section: Well-log Based Determination Of Rock Thermal Conductivitymentioning

confidence: 99%

“…A detailed workflow of the developed method is given in [28]. The new method for the well-log based determination of thermal conductivity and volumetric heat capacity consists of five main steps:…”

Section: Well-log Based Determination Of Rock Thermal Conductivitymentioning

confidence: 99%

“…Determining rock thermal properties from well-logging data within intervals that were not cored [28].…”

mentioning

confidence: 99%

“…The developed method for the well-log-based determination of the thermal properties of rock includes the possibility to apply the advanced theoretical modeling of thermal conductivity and volumetric heat capacity; e.g., the application of the effective medium theory [35]. The testing of the developed method for oil shale deposits [28] and heavy oil field rocks [36] revealed that the prediction uncertainty for thermal conductivity (accounting for thermal anisotropy) and volumetric heat capacity did not exceed 10% and 6%, respectively (at the confidence level of 0.95).…”

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confidence: 99%

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Advanced Determination of Heat Flow Density on an Example of a West Russian Oil Field

et al. 2021

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Reliable geothermal data are required for basin and petroleum system modeling. The essential shortcomings of the methods and results of previous geothermal investigations lead to a necessity to reappraise the data on the thermal properties and heat flow. A new, advanced experimental basis was used to provide reliable data on vertical variations in the thermal properties of formation and heat flow for the area surrounding a prospecting borehole drilled through an unconventional hydrocarbon reservoir of the Domanik Formation in the Orenburg region (Russia). Temperature logging was conducted 12.5 months after well drilling. The thermal properties of the rocks were measured with continuous thermal core profiling on all 1699 recovered core samples. Within non-cored intervals, the thermal conductivity of the rocks was determined from well-logging data. The influence of core aging, multiscale heterogeneity and anisotropy, in situ pressure and temperature on the thermal properties of rock was accounted for. The terrestrial heat flow was determined to be 72.6 ± 2.2 mW·m−2—~114% larger than the published average data for the studied area. The experiment presents the first experience of supporting basin modeling in unconventional plays with advanced experimental geothermal investigations.

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Section: Well-log Based Determination Of Rock Thermal Conductivitymentioning

confidence: 99%

Section: Well-log Based Determination Of Rock Thermal Conductivitymentioning

confidence: 99%

“…Determining rock thermal properties from well-logging data within intervals that were not cored [28].…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Advanced Determination of Heat Flow Density on an Example of a West Russian Oil Field

et al. 2021

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“…It plays a fundamental role in heat transfer in the Earth's interior for determining temperature gradients in the interior of Earth. [3] stated that Information on thermal rock properties is important for many applications ranging from the production of geothermal energy or hydrocarbons (e.g., heavy oils), over subsurface management (e.g., nuclear waste repositories) to civil engineering (e.g., heat transfer or insulation in buildings).Thermal property determination can be classified into two [4] main categories: (1) regression model-based [5][6][7][8][9][10] and (2) theoretical model-based [11][12][13]. This is due to the limitations of the existing techniques for in situ rock thermal property measurements in boreholes.…”

Section: Introductionmentioning

confidence: 99%

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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Applications of artificial intelligence in geothermal resource exploration: A review

AlGaiar,

Hossain,

Petrovski

et al. 2024

Deep Underground Science and Engineering

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Artificial intelligence (AI) has become increasingly important in geothermal exploration, significantly improving the efficiency of resource identification. This review examines current AI applications, focusing on the algorithms used, the challenges addressed, and the opportunities created. In addition, the review highlights the growth of machine learning applications in geothermal exploration over the past decade, demonstrating how AI has improved the analysis of subsurface data to identify potential resources. AI techniques such as neural networks, support vector machines, and decision trees are used to estimate subsurface temperatures, predict rock and fluid properties, and identify optimal drilling locations. In particular, neural networks are the most widely used technique, further contributing to improved exploration efficiency. However, the widespread adoption of AI in geothermal exploration is hindered by challenges, such as data accessibility, data quality, and the need for tailored data science training for industry professionals. Furthermore, the review emphasizes the importance of data engineering methodologies, data scaling, and standardization to enable the development of accurate and generalizable AI models for geothermal exploration. It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources. By effectively addressing key challenges and leveraging AI technologies, the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

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Rock thermal properties from well-logging data accounting for thermal anisotropy

Cited by 18 publications

References 38 publications

Advanced Determination of Heat Flow Density on an Example of a West Russian Oil Field

Advanced Determination of Heat Flow Density on an Example of a West Russian Oil Field

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

Applications of artificial intelligence in geothermal resource exploration: A review

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