1887
Volume 57, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We study the feasibility of the application of an indirect EM geothermometer, developed recently, to the temperature extrapolation in depth using magnetotelluric data collected in the seismically active northern Tien Shan faulted area (Bishkek Geodynamic Test Site, Kyrgyzstan) and Hengill geothermal zone (Iceland). The approach used is based on the artificial neural network technique, which does not imply the prior knowledge of the electrical conductivity mechanisms on the one hand and provides temperature estimates based on the analysis of the implicit conductivity‐temperature relations, on the other.

The samples for neuronet teaching consisted in the well temperature records and electrical conductivity values determined for the same depths from the magnetotelluric data measured in the vicinities of eight boreholes in each testing area. The testing of the taught neuronets was carried out using the temperature records not involved in the teaching process. The results indicate that the temperature extrapolation accuracy essentially depends on the ratio between the well length and the extrapolation depth. In particular, in extrapolation to a depth twice as large as the well depth the relative error is 5–6% and in case of threefold excess, the error is around 20%. This result makes it possible to increase significantly the depth of indirect temperature estimation in the Earth's interior (in particular, for geothermal exploration) based on the available temperature logs.

The practical application of an indirect electromagnetic geothermometer could provide the following facilities: 1) more exact temperature estimation in the extrapolation mode; 2) remote temperature estimates in the boreholes in areas characterized by extreme conditions for conventional geothermometers.

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