A software tool for geostatistical analysis of thermal response test data: GA-TRT

Focaccia, Sara; Tinti, Francesco; Rossion, Bruno

doi:10.1016/j.cageo.2013.06.003

Cited by 12 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A very smooth test is considered in the following image, but showing anyway some small fluctuations of average fluid temperature (Figure 2). In the following figures, the paper is presenting the temperature data set of a TRT carried out by ZAE Bayern (Germany) on a 195-m double-U heat exchanger, subjected to a 9.5-kW constant power injection for a period of 3.5 days, previously selected as standard TRT in the IEA-ECES Annex 21 TRT Final Report and related publications (IEA ECES Annex 21 Thermal Response Test 2013; Focaccia et al 2013).…”

Section: Methodsmentioning

confidence: 99%

“…It has been shown that the precision of estimates of the slope b by the drift method is always higher than the precision given by the classic method (Focaccia et al 2013). This comparison is possible thanks to the estimation variance computation by Equation 9.…”

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

“…The proposed method does not change the general logic of the approach, but the way of estimating b. As the application of the drift is in time-log space, with data density progressively increasing along the t-axis, a declustering selection technique is needed prior to the drift calculation; it consists of a random selection method, which allows to obtain a final data set, with all points regularly spaced from each other (Focaccia et al 2013). In principle, the 'drift method' has a couple of advantages over the classical method:…”

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

“…The drift is a line passing through the origin; therefore, the regression weights are simply proportional to the independent variable, i.e., the time-log lag (Focaccia et al 2013).…”

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

“…During the years, the practice has shown that the approach can be applied to many aspects of TRT analysis and measurements, increasing the degree of confidence of the results (Bruno et al 2011;Mercuri et al 2013;Focaccia et al 2013).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

2015

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

“…The drift is a line passing through the origin; therefore, the regression weights are simply proportional to the independent variable, i.e., the time-log lag (Focaccia et al 2013).…”

Section: Estimating Ground Thermal Conductivity Through Driftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

2015

Self Cite

View full text Add to dashboard Cite

show abstract

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

Shariatinik,

Gloaguen,

Raymond

et al. 2024

Near Surface Geophysics

View full text Add to dashboard Cite

Geothermal Energy Systems such as heat pump relying on aquifers uses renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, heat tracing experiment is a typical field test that can help with characterization of the subsurface. During a heat tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help to characterize the hydrogeological properties. Previous monitoring tools have shortcomings such as low‐resolution data and over‐smoothing, thus they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the ground water is extracted, and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modeling (Resipy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to a experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduce since the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move toward K values inferred with slug tests.This article is protected by copyright. All rights reserved

show abstract

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

2015

Self Cite

View full text Add to dashboard Cite

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 The work was presented to GeoEnv 2014 Conference on Geostatistics for Environmental Applications, held in Paris, 9-11/07/2014.

show abstract

A software tool for geostatistical analysis of thermal response test data: GA-TRT

Cited by 12 publications

References 5 publications

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

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

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

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

Contact Info

Product

Resources

About