Title: |
Estimation of Static Temperature Distribution by Means of Audio-Magnetotelluric Data |
Authors: |
Maryadi MARYADI and Hideki MIZUNAGA |
Key Words: |
temperature estimation, audio-magnetotelluric, artificial neural network, geothermometry |
Conference: |
Stanford Geothermal Workshop |
Year: |
2018 |
Session: |
Geophysics |
Language: |
English |
Paper Number: |
Maryadi |
File Size: |
1607 KB |
View File: |
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Knowledge about the temperature distribution within the geothermal system is considered significant in geothermal exploration. The study is mainly done by the costly well drilling, and/or a number of indirect geothermometers which only provide limited information. In this work, the static geothermal heat distribution was analyzed based on the proven relation between resistivity and temperature. The temperature was estimated after the application of indirect electromagnetic geothermometer which employs the calibrated artificial neural network. A validation test of this method was satisfying as the predicted temperature profiles were diverse to the measured ones with around 10% of relative error. Following the results, a full assessment of subsurface temperature was performed, approximated from a three-dimensional audio-magnetotelluric inversion result. The varying geothermal gradients within the area and the resistivity anomalies provide an idea about the existing geothermal structure and the fluid-heating system under the field examined in this study. As a conclusion, forecasting the temperature from the resistivity value can be considered as a quick and reliable method to massively explore the geothermal resource potential.
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