Estimating temperature distributions in geothermal areas using a neuronet approach |
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| Affiliation: | 1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of Environment and Resources, Jilin University, Changchun 130021, China;2. CSIRO Land and Water, Locked Bag 2, Glen Osmond, SA 5064, Australia;1. Laboratory of Soil Mechanics, Swiss Federal Institute of Technology in Lausanne, EPFL, Lausanne, Switzerland;2. Mechanics and Energy Laboratory, Department of Civil and Environmental Engineering, Northwestern University, Evanston, USA;1. College of Construction Engineering, Jilin University, Changchun 130026, China;2. Key Laboratory of Groundwater Resource and Environment, Ministry of Education, Jilin University, Changchun 130026, China;3. Key Lab of Geo-Environment Qing Hai Province, Xining 810007, China;4. Environmental Geological Prospecting Bureau of Qinghai Province;5. Qingdao Geotechnical Investigation and Surveying Research Institute, Qingdao 266032, China |
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| Abstract: | A method 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. |
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