Stochastic analysis of field-scale heat  advection in heterogeneous aquifers

Chang, C. M.; Yeh, Hund‐Der

doi:10.5194/hess-16-641-2012

Cited by 11 publications

(13 citation statements)

References 54 publications

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“…In a field setting, the values of the parameters that are used to calculate R th will vary spatially, although they are expected to vary over a smaller range with respect to the range of K (e.g., Waples and Waples 2004;Anderson 2005;Chang and Yeh 2012). In a field setting, the values of the parameters that are used to calculate R th will vary spatially, although they are expected to vary over a smaller range with respect to the range of K (e.g., Waples and Waples 2004;Anderson 2005;Chang and Yeh 2012).…”

Section: Tracer Interpretationmentioning

confidence: 99%

“…The value t heat is the time ( v th relative to v because heat is lost to the porous media. In a field setting, the values of the parameters that are used to calculate R th will vary spatially, although they are expected to vary over a smaller range with respect to the range of K (e.g., Waples and Waples 2004;Anderson 2005;Chang and Yeh 2012). As the ranges of thermal parameter values are many orders of magnitude lower than variations in K , variations in θ , ρ s , and c s have not been included in this analysis.…”

Section: Tracer Interpretationmentioning

confidence: 99%

“…For example, Ferguson (2007) studied the influence of aquifer heterogeneity on the recovery of heat in the process of aquifer thermal energy storage and recovery. Chang and Yeh (2012) used stochastic analysis of thermal transport to develop a thermal transport equation with effective parameters. Hidalgo et al (2009) investigated the influence of aquifer heterogeneity on steady state heat transport with a point source of heat.…”

Section: Introductionmentioning

confidence: 99%

“…Research into the influence of heterogeneity on the transport of heat in groundwater on larger scales has been conducted by Ferguson (2007), Hidalgo et al (2009), and Chang and Yeh (2012). While these researchers did not explicitly study the interpretation of temperature measurements to determine rates of groundwater flow, they still provide insight into the effect of aquifer heterogeneity on the transport of heat in groundwater.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the lateral spreading of temperature from a point source is proportional to the variance in the ln(K ) field and the mean flow rate. Chang and Yeh (2012) used stochastic analysis of thermal transport to develop a thermal transport equation with effective parameters. They found a linear relationship between the longitudinal and transverse macrodispersivities and variance in ln(K ), suggesting that the dispersive flux of heat (i.e., flow of heat due to temperature gradients) at the field scale increases linearly with heterogeneity in hydraulic conductivity of the medium.…”

Section: Introductionmentioning

confidence: 99%

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Heat and Solute Tracers: How Do They Compare in Heterogeneous Aquifers?

et al. 2013

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A comparison of groundwater velocity in heterogeneous aquifers estimated from hydraulic methods, heat and solute tracers was made using numerical simulations. Aquifer heterogeneity was described by geostatistical properties of the Borden, Cape Cod, North Bay, and MADE aquifers. Both heat and solute tracers displayed little systematic under‐ or over‐estimation in velocity relative to a hydraulic control. The worst cases were under‐estimates of 6.63% for solute and 2.13% for the heat tracer. Both under‐ and over‐estimation of velocity from the heat tracer relative to the solute tracer occurred. Differences between the estimates from the tracer methods increased as the mean velocity decreased, owing to differences in rates of molecular diffusion and thermal conduction. The variance in estimated velocity using all methods increased as the variance in log‐hydraulic conductivity (K) and correlation length scales increased. The variance in velocity for each scenario was remarkably small when compared to σ2ln(K) for all methods tested. The largest variability identified was for the solute tracer where 95% of velocity estimates ranged by a factor of 19 in simulations where 95% of the K values varied by almost four orders of magnitude. For the same K‐fields, this range was a factor of 11 for the heat tracer. The variance in estimated velocity was always lowest when using heat as a tracer. The study results suggest that a solute tracer will provide more understanding about the variance in velocity caused by aquifer heterogeneity and a heat tracer provides a better approximation of the mean velocity.

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Section: Tracer Interpretationmentioning

confidence: 99%

Section: Tracer Interpretationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Heat and Solute Tracers: How Do They Compare in Heterogeneous Aquifers?

et al. 2013

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The impact of aquifer heterogeneity on the performance of aquifer thermal energy storage

et al. 2013

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[1] Heterogeneity in hydraulic properties of the subsurface is not accounted for in current design calculations of aquifer thermal energy storage (ATES). However, the subsurface is heterogeneous and thus affects the heat distribution around ATES wells. In this paper, the influence of heterogeneity on the performance of a doublet well system is quantified using stochastic heat transport modeling. The results show that on average, thermal recovery decreases with increasing heterogeneity, expressed as the lognormal standard deviation of the hydraulic conductivity field around the doublet. Furthermore, heterogeneity at the scale of a doublet ATES system introduces an uncertainty in the amount of expected thermal interference between the warm and cold storage. This results in an uncertainty in thermal recovery that also increases with heterogeneity and decreases with increasing distance between ATES wells. The uncertainty in thermal balance due to heterogeneity can reach values near 50 percent points in case of regional groundwater flow in excess of 200 m/yr. To account for heterogeneity whilst using homogeneous models, an attempt was made to express the effect of heterogeneity by an apparent macrodispersivity. As expected, apparent macrodispersivity increases with increasing heterogeneity. However, it also depends on well-to-well distance and regional groundwater velocity. Again, the uncertainty in thermal recovery is reflected in a range in the apparent macrodispersivity values. Considering the increasing density of ATES systems, we conclude that thermal interference limits the number of ATES systems that can be implemented in a specific area, and the uncertainty in the hydraulic conductivity field related to heterogeneity should be accounted for when optimizing well-to-well distances.Citation: Sommer, W., J. Valstar, P. van Gaans, T. Grotenhuis, and H. Rijnaarts (2013), The impact of aquifer heterogeneity on the performance of aquifer thermal energy storage, Water Resour. Res., 49,[8128][8129][8130][8131][8132][8133][8134][8135][8136][8137][8138]

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