Abstract :
Amethod is proposed for predicting the distribution of temperatures in geothermal areas using the neuronet
approach and, in particular, downhole temperature logs. The method was tested against the results of an
analytical model, showing that the errors in neuronet temperature estimates based on well log data derive
from: (a) the neuronet “education level” (which depends on the amount and structure of information used
for teaching) and (b) the distance of the point at which the estimate is made from the area for which data are
available. These conclusions were confirmed when estimating temperatures in eight actual wells, using 50
downhole temperature logs from other wells in the geothermal area. It was found that, for this particular case,
neuronet teaching utilizing 30 well logs results in an average forecast error of 20%. As the number of training
logs increases (up to 50), the error slightly decreases (down to 16.9%). The effects of the teaching data pattern
(conductive-type versus convective-type of temperature profiles) were also studied, and an optimal strategy
was developed for the neuronet training, based on the information available.
© 2006 CNR. Published by Elsevier Ltd. All rights reserved
