2020
DOI: 10.1093/gji/ggaa209
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Robust well-log based determination of rock thermal conductivity through machine learning

Abstract: SUMMARY Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there a… Show more

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Cited by 25 publications
(4 citation statements)
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“…Heat transfer simulation inside the PI-based composites [27] . 速高效地预测材料的热导率和界面热阻 [42,43] , 用来 预测各种单链聚合物的热导率 [26] , 预测就地取材 的物质的热导率, 比如估计岩石、混凝土、土壤的 导热性能 [44−46] . 机器学习技术需要通过大量的数 发新型导热材料 [17] .…”
Section: 导热模型对深入理解影响材料导热性能的因unclassified
“…Heat transfer simulation inside the PI-based composites [27] . 速高效地预测材料的热导率和界面热阻 [42,43] , 用来 预测各种单链聚合物的热导率 [26] , 预测就地取材 的物质的热导率, 比如估计岩石、混凝土、土壤的 导热性能 [44−46] . 机器学习技术需要通过大量的数 发新型导热材料 [17] .…”
Section: 导热模型对深入理解影响材料导热性能的因unclassified
“…Molnar and Hodge, 1982;Blackwell and Steele, 1989;Hartmann et al, 2005;Fuchs and Förster, 2014). The second approach is to apply machine learning techniques to estimate the thermal properties from well logs (e.g Meshalkin et al, 2020;Shakirov et al, 2021). The regressionbased empirical equations are typically limited to the rocks on the basis of which they were established (e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Measurements on rock samples from outcrops and data from well logs and core samples may significantly narrow the ranges of inferred petrophysical properties. Whenever possible, the techniques for thermal conductivity determination should be used (see, e.g., [22,23]).…”
mentioning
confidence: 99%