Estimating Thermal Response Test Coefficients: Choosing Coordinate Space of The Random Function

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• 作者：Roberto Bruno ; Francesco Tinti ; Sara Focaccia
• 关键词：Thermal response test ; Random function ; Geothermal energy ; Pseudo ; variogram models ; Non ; linear transformation
• 刊名：Mathematical Geosciences
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：48
• 期：1
• 页码：3-23
• 全文大小：3,199 KB
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• 作者单位：Roberto Bruno (1)
  Francesco Tinti (1)
  Sara Focaccia (1) (2)
  
  1. Department of Civil, Chemical, Environmental and Materials Engineering, University of Bologna, via Terracini 28, 40131, Bologna, Italy
  2. CERENA, Instituto Superior Técnico de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Mathematical Applications in Geosciences
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Geotechnical Engineering
  Hydrogeology
  
• 出版者：Springer Berlin Heidelberg
• ISSN：1874-8953

文摘

In shallow geothermal systems, the main equivalent underground thermal properties are commonly calculated with a thermal response test (TRT). This is a borehole heat exchanger production test where the temperature of a heat transfer fluid is recorded over time at constant power heat injection/extraction. The equivalent thermal parameters (thermal conductivity, heat capacity) are simply deduced from temperature data regression analysis that theoretically is a logarithmic function in the time domain, or else a linear function in the log-time domain. By interpreting the recorded temperatures as a regionalized variable whose drift is the regression function, in both cases the formal problem is a linear estimation of the mean. If the autocorrelation function (variogram, covariance) of residuals is known, coefficient variance can be directly deduced. Coefficient estimates are independent of the drift form adopted, and the residuals are the same in the same points. The random function is different in the time domain, however, and in the log-time domain. In fact, residual variograms are different due to the transformation of the coordinate space. This paper uses a TRT case study to examine the consequences of coordinate space transformation for a random function, namely its variogram. The specific question addressed is the choice of coordinate space and variogram.