Thermal response test for shallow geothermal applications: a probabilistic analysis approach

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• 作者：Francesco Tinti ; Roberto Bruno ; Sara Focaccia
• 关键词：Geostatistics ; Thermal response test ; Borehole heat exchanger ; Ground thermal conductivity ; Estimation variance ; Borehole thermal resistance ; Conditional probability distribution function
• 刊名：Geothermal Energy
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：3
• 期：1
• 全文大小：2,398 KB
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• 刊物主题：Geotechnical Engineering & Applied Earth Sciences; Renewable and Green Energy; Geoecology/Natural Processes;
• 出版者：Springer Berlin Heidelberg
• ISSN：2195-9706

文摘

Background Thermal Response Test (TRT) is an onsite test used to characterize the thermal properties of shallow underground, when used as heat storage volume for shallow geothermal application. It is applied by injecting/extracting heat into geothermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of thermal properties through different models, specific for each closed loop and test conditions. Methods A typical TRT record is a graph joining a series of experimental temperatures of the thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization- one of the possible results; the random nature of the test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior. Results In this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source-(ILS) methodology, which is the most commonly used for BHE. Conclusions The probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t 0 and the final time t f) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of thermal parameter calculation (ground conductivity λ g, ground capacity c g, borehole resistance R b). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.