Journal
ACTA GEOPHYSICA
Volume 62, Issue 4, Pages 785-801Publisher
DE GRUYTER OPEN LTD
DOI: 10.2478/s11600-014-0204-y
Keywords
thermal conductivity; well logs; multiple regression; neural networks
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Funding
- National Research Centre in Poland (Narodowe Centrum Nauki) [N N525 365537]
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The aim of the presented work was to introduce a method of estimating thermal conductivity using well log data. Many petrophysical properties of rocks can be determined both by laboratory measurements and well-logs. It is thus possible to apply geophysical data to empirical models based on relationships between laboratory measured parameters and derive continuous thermal conductivity values in well profiles. Laboratory measurements were conducted on 62 core samples of Meso-Paleozoic rocks from the Carpathian Foredeep. Mathematical models were derived using multiple regression and neural network methods. Geophysical data from a set of seven well logs: density, sonic, neutron, gamma ray, spectral gamma ray, caliper and resistivity were applied to the obtained models. Continuous thermal conductivity values were derived in three well profiles. Analysis of the obtained results shows good consistence between laboratory data and values predicted from well log data.
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