Monitoring artificially stimulated fluid movement in the Cretaceous Dakota aquifer, western Kansas

Macfarlane, Allen P.; Förster, Andrea; Merriam, Daniel F.; Schrötter, Jörg; Healey, John

doi:10.1007/s10040-002-0223-7

Cited by 27 publications

(24 citation statements)

References 14 publications

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“…This technique has been employed in groundwater applications to a limited degree (e.g., borehole monitoring [Henninges et al, 2003] and aquifer characterization [MacFarlane et al, 2002]) and to a much greater degree in civil infrastructure [e.g., Measures, 2001]. The spatial resolution of such time domain measurements is limited by the ability to resolve a signal in time (instrument performance), and the dispersion of signals within the fiber (sensor performance).…”

Section: Principles Pros and Pitfallsmentioning

confidence: 99%

Distributed fiber‐optic temperature sensing for hydrologic systems

Selker

Thévenaz

Huwald

et al. 2006

Water Resources Research

533

485

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[1] Instruments for distributed fiber-optic measurement of temperature are now available with temperature resolution of 0.01°C and spatial resolution of 1 m with temporal resolution of fractions of a minute along standard fiber-optic cables used for communication with lengths of up to 30,000 m. We discuss the spectrum of fiber-optic tools that may be employed to make these measurements, illuminating the potential and limitations of these methods in hydrologic science. There are trade-offs between precision in temperature, temporal resolution, and spatial resolution, following the square root of the number of measurements made; thus brief, short measurements are less precise than measurements taken over longer spans in time and space. Five illustrative applications demonstrate configurations where the distributed temperature sensing (DTS) approach could be used: (1) lake bottom temperatures using existing communication cables, (2) temperature profile with depth in a 1400 m deep decommissioned mine shaft, (3) air-snow interface temperature profile above a snow-covered glacier, (4) air-water interfacial temperature in a lake, and (5) temperature distribution along a first-order stream. In examples 3 and 4 it is shown that by winding the fiber around a cylinder, vertical spatial resolution of millimeters can be achieved. These tools may be of exceptional utility in observing a broad range of hydrologic processes, including evaporation, infiltration, limnology, and the local and overall energy budget spanning scales from 0.003 to 30,000 m. This range of scales corresponds well with many of the areas of greatest opportunity for discovery in hydrologic science.Citation: Selker, J.

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Section: Principles Pros and Pitfallsmentioning

confidence: 99%

Distributed fiber‐optic temperature sensing for hydrologic systems

Selker

Thévenaz

Huwald

et al. 2006

Water Resources Research

533

485

View full text Add to dashboard Cite

show abstract

“…With temperature profiling, a fracture was detected at the position of a sharp decrease in temperature. Macfarlane et al [128] evaluated aquifer properties by heating fluid and stimulating flow (forced gradient) between wells.…”

Section: Using Dts To Measure Temperaturementioning

confidence: 99%

Geophysical Methods for Monitoring Temperature Changes in Shallow Low Enthalpy Geothermal Systems

Hermans

Nguyen

Robert³

et al. 2014

Energies

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Low enthalpy geothermal systems exploited with ground source heat pumps or groundwater heat pumps present many advantages within the context of sustainable energy use. Designing, monitoring and controlling such systems requires the measurement of spatially distributed temperature fields and the knowledge of the parameters governing groundwater flow (permeability and specific storage) and heat transport (thermal conductivity and volumetric thermal capacity). Such data are often scarce or not available. In recent years, the ability of electrical resistivity tomography (ERT), self-potential method (SP) and distributed temperature sensing (DTS) to monitor spatially and temporally temperature changes in the subsurface has been investigated. We review the recent advances in using these three methods for this type of shallow applications. A special focus is made regarding the petrophysical relationships and on underlying assumptions generally needed for a quantitative interpretation of these geophysical data. We show that those geophysical methods are mature to be used within the context of temperature monitoring and that a combination of them may be the best choice regarding control and validation issues. OPEN ACCESSEnergies 2014, 7 5084

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“…In practice, to improve the reliability of models, in‐situ tests such as push/pull tests (Klepikova et al, ; Park et al, ; Vandenbohede et al, ), heat storage experiments (Palmer et al, , Vandenbohede et al, ), heat tracer tests (Wagner et al, , Wildemeersch et al, ; Macfarlane et al, ) or other specific tests (e.g., Kuo & Liao, ) can be performed to gain knowledge on subsurface parameters. However, these conventional approaches generally lack the spatial coverage required to characterize the heterogeneity of the subsurface.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Medium‐Term Heat Storage From Short‐Term Geophysical Experiments Using Bayesian Evidential Learning

Hermans

Nguyen

Klepikova

et al. 2018

Water Resources Research

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In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat stored in the aquifer during summer to increase the energy efficiency of the system. In practice, the energy efficiency is often lower than expected from simulations due to spatial heterogeneity of hydraulic properties or non‐favorable hydrogeological conditions. A proper design of ATES systems should therefore consider the uncertainty of the prediction related to those parameters. We use a novel framework called Bayesian Evidential Learning (BEL) to estimate the heat storage capacity of an alluvial aquifer using a heat tracing experiment. BEL is based on two main stages: pre‐ and postfield data acquisition. Before data acquisition, Monte Carlo simulations and global sensitivity analysis are used to assess the information content of the data to reduce the uncertainty of the prediction. After data acquisition, prior falsification and machine learning based on the same Monte Carlo are used to directly assess uncertainty on key prediction variables from observations. The result is a full quantification of the posterior distribution of the prediction conditioned to observed data, without any explicit full model inversion. We demonstrate the methodology in field conditions and validate the framework using independent measurements.

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Monitoring artificially stimulated fluid movement in the Cretaceous Dakota aquifer, western Kansas

Cited by 27 publications

References 14 publications

Distributed fiber‐optic temperature sensing for hydrologic systems

Distributed fiber‐optic temperature sensing for hydrologic systems

Geophysical Methods for Monitoring Temperature Changes in Shallow Low Enthalpy Geothermal Systems

Uncertainty Quantification of Medium‐Term Heat Storage From Short‐Term Geophysical Experiments Using Bayesian Evidential Learning

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