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See detailData-driven selection of the minimum-gradient support parameter in time-lapse focused electrical imaging
Nguyen, Frédéric ULg; Kemna, Andreas; Robert, Tanguy et al

in Geophysics (2016), 81(1), 1-5

We have considered the problem of the choice of the minimum-gradient support (MGS) parameter in focused inversion for time-lapse (TL) electric resistivity tomography. Most existing approaches have relied ... [more ▼]

We have considered the problem of the choice of the minimum-gradient support (MGS) parameter in focused inversion for time-lapse (TL) electric resistivity tomography. Most existing approaches have relied either on an arbitrary choice of this parameter or one based on the prior information, such as the expected contrast in the TL image. We have decided to select the MGS parameter using a line search based on the value of the TL data root-mean-square misfit at the first iteration of the nonlinear inversion procedure. The latter was based on a Gauss-Newton scheme minimizing a regularized objective function in which the regularization functional was defined by the MGS functional. The regularization parameter was optimized to achieve a certain target level, following the Occam principles. We have validated our approach on a synthetic benchmark using a complex and heterogeneous model and determined its effectiveness on electric tomography TL data collected during a salt tracer experiment in fractured limestone. Our results have determined that the approach was successful in retrieving the focused anomaly and did not rely on prior information. [less ▲]

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See detailEvaluating the performance of short-term heat storage in alluvial aquifer with 4D electrical resistivity tomography and hidrological monitoring
Robert, Tanguy; Paulus, Claire; Bolly, Pierre-Yves et al

Poster (2015, December 14)

In the context of energy demand side management (DSM), energy storage solutions are needed to stock energy during high production periods and recover energy during high demand periods. Among currently ... [more ▼]

In the context of energy demand side management (DSM), energy storage solutions are needed to stock energy during high production periods and recover energy during high demand periods. Among currently studied solutions, storing energy in the subsurface through heat pumps and/or exchangers (thermal energy storage) is relatively simple with low investment costs. However, the design and functioning of such systems have strong interconnections with the geology of the site which may be complex and heterogeneous, making predictions difficult. In this context, local temperature measurements are necessary but not sufficient to model heat flow and transport in the subsurface. Electrical resistivity tomography (ERT) provides spatially distributed information on the temperature distribution in the subsurface. In this study, we monitored, with 4D ERT combined with multiple hydrological measurements in available wells, a short-term heat storage experiment in a confined alluvial aquifer. We injected heated water (ΔT=30K) during 6 hours with a rate of 3 m³/h, stored during 3 days, and then we pumped it back to estimate the energy balance. We collected ERT data sets using 9 parallel profiles of 21 electrodes and cross-lines measurements. Inversion results clearly show the ability of ERT to delimit the thermal plume growth during injection, the diffusion and decrease of temperature during storage, and the decrease in size after pumping. Quantitative interpretation of ERT is difficult at this stage due to strong spatial variations of the total dissolved solid content in the aquifer, due to historical chloride contamination of the site. Energy balance shows that up to 75% of the energy can be easily recovered with an adapted strategy in the context of DSM. Short-term heat storage in alluvial aquifer is efficient and ERT is a valuable tool to image and estimate the temperature distribution in the subsurface. [less ▲]

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See detailTime-lapse inversion of ERT monitoring data using variogram-based regularization
Hermans, Thomas ULg; Dumont, Gaël ULg; Kemna, Andreas et al

Conference (2015, November 24)

Hydrogeophysics has become a major field of research in the past two decades and time-lapse electrical resistivity tomography (ERT) is one of the most popular techniques to monitor passive and active ... [more ▼]

Hydrogeophysics has become a major field of research in the past two decades and time-lapse electrical resistivity tomography (ERT) is one of the most popular techniques to monitor passive and active processes in subsurface reservoirs. Time-lapse inversion schemes have been developed to refine inversion results; but, in contrast with static inversion, they mostly still rely on the spatial regularization procedure based on the standard smoothness constraint. In this contribution, we propose to apply a variogram-based regularization operator in the time-lapse ERT inverse problem, using the model difference covariance matrix to replace the standard smoothing operator. The variogram of resistivity variations can be computed through independent borehole data, such as electromagnetic logs or hydrogeological monitoring, which is often available during monitoring experiments. We first illustrate the method for surface ERT with a synthetic case and compare the results with the standard smoothness constraint solution. This example shows that the variogram-based constraint images better the assumed anomaly both in terms of shape and amplitude. The improvement is largely higher than the one obtained with more classical anisotropic smoothness constraint. This synthetic example also shows that an error made in the range of the variogram has a limited impact on the resulting image, which still remains better than the smoothness constraint result. Anomalies located in various part of the tomograms were tested. Although more crucial in low-sensitivity zones, improvements are observed everywhere in the tomograms. The method is then applied to cross-borehole ERT field data from a heat tracing experiment, where the comparison with direct temperature measurements shows a strong improvement of the breakthrough curves retrieved from ERT. Using the variogram-based regularization, it is possible to reduce the smoothing of resistivity variations in low sensitivity zones and therefore to avoid overestimation of temperatures. The proposed method could be extended to the time dimension which would allow the use of variogram-based constraints in 4D inversion schemes. [less ▲]

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See detailERT monitoring of water infiltration process through a landfill cover layer
Dumont, Gaël ULg; Pilawski, Tamara ULg; Robert, Tanguy et al

in Berichte der Geologischen Bundesanstalt, 112 (2015, November)

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See detailAssessing electrical resistivity tomography for hydrofacies detection using a sensitivity dependent probabilistic methodology
Hermans, Thomas ULg; Irving, James

in EarthDoc - Near Surface Geosciences 2015 (2015, September 08)

Alluvial aquifers are generally composed of several facies with complex architectures and interconnections depending on the fluvial system. In this context, electrical resistivity tomography (ERT) may ... [more ▼]

Alluvial aquifers are generally composed of several facies with complex architectures and interconnections depending on the fluvial system. In this context, electrical resistivity tomography (ERT) may provide important information on the spatial distribution of hydrogeological parameters. However, ERT inversion introduces some bias in the resulting resistivity distribution due to regularization and resolution issues. In this study, we refine ERT inversions by incorporating prior information in order to improve the identification of facies through a probabilistic relationship derived from collocated measurements. We then analyze with synthetic cases the effect of spatially varying sensitivity on the probabilistic relationship. As expected, when sensitivity decreases, the distributions of resistivity for the different facies tend to be superimposed. A mean distribution thus overestimates the ability of surface ERT to discriminate hydrofacies in depth. [less ▲]

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See detailUse and utility of combined solute and heat tracer tests for characterizing hydrogeothermal properties of an alluvial aquifer
Klepikova, Maria; Wildemeersch, Samuel; Jamin, Pierre ULg et al

Conference (2015, June 05)

Using heat as a tracer together with a solute tracer is interesting for characterizing hydrogeothermal properties of the underground. These properties are particularly needed to dimension any low ... [more ▼]

Using heat as a tracer together with a solute tracer is interesting for characterizing hydrogeothermal properties of the underground. These properties are particularly needed to dimension any low temperature geothermal project using an open doublet system (pumping-reinjection) in a shallow aquifer. The tracing experiment, conducted in the alluvial aquifer of the River Meuse (Hermalle near Liège), consisted in injecting simultaneously heated water at 40°C and a dye tracer in a piezometer and monitoring the evolution of temperature and tracer concentration in the recovery well and in nine monitoring piezometers located in three transects with regards to the main groundwater flow direction. The breakthrough curves measured in the recovery well showed that heat transfer in the alluvial aquifer is slower. All measured results show also that the heat diffusivity is larger than the solute dispersion. These contrasted behaviours are stressed in the lower permeability zones of the aquifer. Inverse modelling is applied for calibrating the numerical simulation of the groundwater flow, heat and solute transport. First results are presented showing that the density effect must be taken into account and that, as expected, the most important parameter to be calibrated accurately is the hydraulic conductivity. [less ▲]

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See detailUncertainty in Training-Image Based Inversion of Hydraulic Head Data Constrained to ERT Data : Workflow and Case Study
Hermans, Thomas ULg; Nguyen, Frédéric ULg; Caers, Jef

Scientific conference (2015, May 06)

In inverse problems, investigating the relationship between data and prior models and the uncertainty related to the posterior distribution of model parameters are as important as matching the data. In ... [more ▼]

In inverse problems, investigating the relationship between data and prior models and the uncertainty related to the posterior distribution of model parameters are as important as matching the data. In recent years, many efforts have been done to assess the posterior distribution of a given problem with reasonable computational costs through inversion techniques such as McMC. The derived posterior distribution is always dependent on the prior distribution. However, most of the studies ignore modeling the prior with realistic uncertainty. In this paper, we propose a workflow to assess the uncertainty of inversion of hydraulic heads data through the addition of electrical resistivity tomography (ERT) constraining data. The workflow is divided in three successive steps: 1) Construction of prior: we generate multiple alternative geological scenarios from literature data (architecture of facies) as well as site specific data (proportions of facies). Spatial uncertainty within each scenario is integrated hierarchically through geostatistics (multiple-point statistics simulation of facies constrained by ERT data as soft data). 2) Validation of prior scenarios: we transform prior facies scenarios into resistivity distribution scenarios through forward and inverse modeling. The scenarios are validated by comparison with field ERT data. The comparison is made through distance calculation and projection into a low dimensional space to calculate the probability of each scenario given field ERT data. 3) Matching dynamical data: we use the probability perturbation method, within each scenario, to integrate hydraulic heads to our models. We account for scenario probabilities, calculated in 2, in determining how many models per scenario we have to consider for building a reliable posterior distribution. The method is first applied on synthetic cases where the "true" model is known. Then, it is apllied a field case study in an alluvial aquifer (Belgium) where we consider prior uncertainty related to the type of elements (gravel channels or bars) and to their size. This study shows the importance of considering the uncertainty of the prior in inverse problems as it has a strong influence on model predictions and decision-making problems. [less ▲]

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See detailRegularized focusing inversion of time-lapse electrical resistivity data: an approach to parametrize the minimum gradient support functional
Nguyen, Frédéric ULg; Hermans, Thomas ULg

Poster (2015, April 15)

Inversion of time-lapse resistivity data allows obtaining ‘snapshots’ of changes occurring in monitored systems for applications such as aquifer storage, geothermal heat exchange, site remediation or ... [more ▼]

Inversion of time-lapse resistivity data allows obtaining ‘snapshots’ of changes occurring in monitored systems for applications such as aquifer storage, geothermal heat exchange, site remediation or tracer tests. Based on these snapshots, one can infer qualitative information on the location and morphology of changes occurring in the subsurface but also quantitative estimates on the degree of changes in certain property such as temperature or total dissolved solid content. Analysis of these changes can provide direct insight into flow and transport and associated processes and controlling parameters. However, the reliability of the analysis is dependent on survey geometry, measurement schemes, data error, and regularization. Survey design parameters may be optimized prior to the monitoring survey. Regularization, on the other hand, may be chosen depending on available information collected during the monitoring. Common approaches consider smoothing model changes both in space and time but it is often needed to obtain a sharp temporal anomaly, for example in fractured aquifers. We here propose to use the alternative regularization approach based on minimum gradient support (MGS) (Zhdanov, 2002) for time-lapse surveys which will focus the changes in tomograms snapshots. MGS will limit the occurrences of changes in electrical resistivity but will also restrict the variations of these changes inside the different zones. A common difficulty encountered by practitioners in this type of regularization is the choice of an additional parameter, the so-called , required to define the MGS functional. To the best of our knowledge, there is no commonly accepted or standard methodology to optimize the MGS parameter . The inversion algorithm used in this study is CRTomo (Kemna 2000). It uses a Gauss-Newton scheme to iteratively minimize an objective function which consists of a data misfit functional and a model constraint functional. A univariate line search is performed at each Gauss-Newton iteration step to find the optimum value of the regularization parameter  which minimizes the data misfit as a function of  while the data misfit is above the desired value and yields the desired target misfit (root-mean square value of error-weighted data misfit equal to 1) at the last iteration for a maximum value of . We propose here to optimize the  of the MGS functional by considering a univariate line search at the first iteration to find the  that minimizes the data misfit. The parameter is then kept constant during the Gauss-Newton iterative scheme. In this contribution, we validate our approach on a numerical benchmark and apply it successfully on a case study in the context of salt tracers in fractured aquifers. Zhdanov M.S. 2002. Geophysical Inverse Theory and Regularization Problems. Elsevier, Amsterdam, 628 p. Kemna A. 2000. Tomographic Inversion of Complex Resistivity - Theory and Application. PhD Thesis, Ruhr University Bochum. [less ▲]

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See detailVariogram-based inversion of time-lapse electrical resistivity data: development and application to a thermal tracing experiment
Hermans, Thomas ULg; Nguyen, Frédéric ULg

Poster (2015, April 15)

Electrical resistivity tomography (ERT) has become a popular imaging methodology in a broad range of applications given its large sensitivity to subsurface parameters and its relative simplicity to ... [more ▼]

Electrical resistivity tomography (ERT) has become a popular imaging methodology in a broad range of applications given its large sensitivity to subsurface parameters and its relative simplicity to implement. More particularly, time-lapse ERT is now increasingly used for monitoring purposes in many contexts such as water content, permafrost, landslide, seawater intrusion, solute transport or heat transport experiments. Specific inversion schemes have been developed for time-lapse data sets. However, in contrast with static inversions for which many techniques including geostatistical, minimum support or structural inversion are commonly applied, most of the methodologies for time-lapse inversion still rely on non-physically based spatial and/or temporal smoothing of the parameters or parameter changes. In this work, we propose a time-lapse ERT inversion scheme based on the difference inversion scheme. We replace the standard smoothness-constraint regularization operator by the parameter change covariance matrix. This operator takes into account the correlation between changes in resistivity at different locations through a variogram computed using independent data (e.g., electromagnetic logs). It may vary for subsequent time-steps if the correlation length is time-dependent. The methodology is first validated and compared to the standard smoothness-constraint inversion using a synthetic benchmark simulating the injection of a conductive tracer into a homogeneous aquifer inducing changes in resistivity values of known correlation length. We analyze the influence of the assumed correlation length on inversion results. Globally, the method yields better results than the traditional smoothness constraint inversion. Even if a wrong correlation length is assumed, the method performs as well as the smoothness constraint since the regularization operator balances the weight given to the model constraint functional in the objective function. Then the methodology is successfully applied to a heat injection and pumping experiment in an alluvial aquifer. The comparison with direct measurements in boreholes (temperature loggers and distributed temperature sensing optic fibres) shows that ERT-derived temperatures and breakthrough curves image reliably the heat plume through time (increasing part of the curve, maximum and tail are correctly retrieved) and space (lateral variations of temperature are observed) with less spatial smoothing than standard methods. The development of new regularization operators for time-lapse inversion of ERT data is necessary given the broad range of applications where ERT monitoring is used. In many studies, independent data are available to derive geostatistical parameters that can be subsequently used to regularize geophysical inversions. In the future, the integration of spatio-temporal variograms into existing 4D inversion schemes should further improve ERT time-lapse imaging. [less ▲]

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See detailUncertainty in training image-based inversion of hydraulic head data constrained to ERT data: workflow and case study
Hermans, Thomas ULg; Nguyen, Frédéric ULg; Caers, Jef

in Water Resources Research (2015), 51

In inverse problems, investigating uncertainty in the posterior distribution of model parameters is as important as matching data. In recent years, most efforts have focused on techniques to sample the ... [more ▼]

In inverse problems, investigating uncertainty in the posterior distribution of model parameters is as important as matching data. In recent years, most efforts have focused on techniques to sample the posterior distribution with reasonable computational costs. Within a Bayesian context, this posterior depends on the prior distribution. However, most of the studies ignore modeling the prior with realistic geological uncertainty. In this paper, we propose a workflow inspired by a Popper-Bayes philosophy, that data should first be used to falsify models, then only be considered for matching. We propose a workflow consisting of three steps: (1) in defining the prior, we interpret multiple alternative geological scenarios from literature (architecture of facies) and site specific data (proportions of facies). Prior spatial uncertainty is modeled using multiple-point geostatistics, where each scenario is defined using a training image. (2) We validate these prior geological scenarios by simulating electrical resistivity tomography (ERT) data on realizations of each scenario and comparing them to field ERT in a lower dimensional space. In this second step, the idea is to probabilistically falsify scenarios with ERT, meaning that scenarios which are incompatible receive an updated probability of zero while compatible scenarios receive a non-zero updated belief. (3) We constrain the hydrogeological model with hydraulic head and ERT using a stochastic search method. The workflow is applied to a synthetic and a field case studies in an alluvial aquifer. This study highlights the importance of considering and estimate prior uncertainty (without data) through a process of probabilistic falsification. [less ▲]

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See detailQuantitative temperature monitoring of a heat tracing experiment using cross-borehole ERT
Hermans, Thomas ULg; Wildemeersch, Samuel ULg; Jamin, Pierre ULg et al

in Geothermics (2015), 53

The growing demand for renewable energy leads to an increase in the development of geothermal energy projects and heat has become a common tracer in hydrology and hydrogeology. Designing geothermal ... [more ▼]

The growing demand for renewable energy leads to an increase in the development of geothermal energy projects and heat has become a common tracer in hydrology and hydrogeology. Designing geothermal systems requires a multidisciplinary approach including geological and hydrogeological aspects. In this context, electrical resistivity tomography (ERT) can bring relevant, qualitative and quantitative information on the temperature distribution in operating shallow geothermal systems or during heat tracing experiments. We followed a heat tracing experiment in an alluvial aquifer using cross-borehole time-lapse ERT. Heated water was injected in a well while water of the aquifer was extracted at another well. An ERT section was set up across the main flow direction. The results of ERT were transformed into temperature using calibrated petrophysical relationships. These ERT-derived temperatures were then compared to direct temperature measurements in control piezometers collected with distributed temperature sensing (DTS) and groundwater temperature loggers. Spatially, it enabled to map the horizontal and vertical extent of the heated water plume, as well as the zones where maximum temperatures occurred. Quantitatively, the temperatures and breakthrough curves estimated from ERT were in good agreement with the ones observed directly during the rise and maximum of the curve. An overestimation, likely related to 3D effects, was observed for the tail of the heat breakthrough curve. The error made on temperature can be estimated to be between 10 to 20 %, which is a fair value for indirect measurements. From our data, we estimated a quantification threshold for temperature variation of 1.2°C. These results suggest that ERT should be considered when designing heat tracing experiments or geothermal systems. It could help also to assess the geometrical complexity of the concerned reservoirs. It also appears that ERT could be a useful tool to monitor and control geothermal systems once they are in operation. [less ▲]

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See detailUncertainty in Training-Image Based Inversion of Hydraulic Head Data Constrained to ERT Data: Workflow and Case Study
Hermans, Thomas ULg; Nguyen, Frédéric ULg; Caers, Jef

Conference (2014, December 18)

In inverse problems, investigating the relationship between data and prior models and the uncertainty related to the posterior distribution of model parameters are as important as matching the data. In ... [more ▼]

In inverse problems, investigating the relationship between data and prior models and the uncertainty related to the posterior distribution of model parameters are as important as matching the data. In recent years, many efforts have been done to assess the posterior distribution of a given problem with reasonable computational costs through inversion techniques such as McMC. The derived posterior distribution is always dependent on the prior distribution. However, most of the studies ignore modeling the prior with realistic uncertainty. In this paper, we propose a workflow to assess the uncertainty of inversion of hydraulic heads data through the addition of electrical resistivity tomography (ERT) constraining data. The workflow is divided in three successive steps: 1) Construction of prior: we generate multiple alternative geological scenarios from literature data (architecture of facies) as well as site specific data (proportions of facies). Spatial uncertainty within each scenario is integrated hierarchically through geostatistics (multiple-point statistics simulation of facies constrained by ERT data as soft data). 2) Validation of prior scenarios: we transform prior facies scenarios into resistivity distribution scenarios through forward and inverse modeling. The scenarios are validated by comparison with field ERT data. The comparison is made through distance calculation and projection into a low dimensional space to calculate the probability of each scenario given field ERT data. 3) Matching dynamical data: we use the probability perturbation method, within each scenario, to integrate hydraulic heads to our models. We account for scenario probabilities, calculated in 2, in determining how many models per scenario we have to consider for building a reliable posterior distribution. As an illustration, the method is applied on a field case study in an alluvial aquifer (Belgium) where we consider prior uncertainty related to the type of elements (gravel channels or bars) and to their size. This study shows the importance of considering the uncertainty of the prior in inverse problems as it has a strong influence on model predictions and decision-making problems. [less ▲]

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See detailProspection géophysique de la zone faillée de Hockai dans la région de Malmedy: Rapport des tomographies de résistivité électrique
Hermans, Thomas ULg; Nguyen, Frédéric ULg

Report (2014)

Ce rapport consiste en la présentation des résultats des prospections géophysiques par tomographie de résistivité électrique (ERT) menées sur la zone faillée de Hockai dans la région de Malmedy. Le but ... [more ▼]

Ce rapport consiste en la présentation des résultats des prospections géophysiques par tomographie de résistivité électrique (ERT) menées sur la zone faillée de Hockai dans la région de Malmedy. Le but principal de ces investigations est de juger de la fracturation de la roche dans et en dehors de la Zone de Faille de Hockai (ZFH) et de mettre en évidence les structures liées à cette zone de failles. [less ▲]

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See detailA heat and dye tracer test for characterizing and modelling heat transfer in an alluvial aquifer
Klepikova, Maria ULg; Wildemeersch, Samuel; Jamin, Pierre ULg et al

Poster (2014, September 22)

Using heat as an active tracer is a topic of increasing interest with regards to characterizing shallow aquifers for ATES (Aquifer Thermal Energy Storage) systems. In this study, we investigate the ... [more ▼]

Using heat as an active tracer is a topic of increasing interest with regards to characterizing shallow aquifers for ATES (Aquifer Thermal Energy Storage) systems. In this study, we investigate the potential interest of coupling simultaneous heat and dye tracer injection tests for characterization of an alluvial aquifer. The study site is located near Liege in the alluvial aquifer of the Meuse River, Belgium. The tracing experiment consisted in simultaneously injecting heated water and a dye tracer in a piezometer and monitoring the evolution of groundwater temperature and tracer concentration in the recovery well and in nine monitoring wells located according to three transects with regards to the main groundwater flow direction. The breakthrough curves measured in the recovery well showed that heat transfer in the alluvial aquifer is slower and more dispersive than solute transport. Recovery is very low for heat while in the same time it is measured as relatively high for the solute tracer. This shows how heat diffusion is larger than molecular diffusion, implying that exchange between groundwater and the porous medium matrix is far more significant for heat than for solute tracers. In a first step, temperature and concentrations in the recovery well are used for estimating the specific heat capacity with an energy balance calculation and the estimated value is found to be consistent with those found in the literature. Then, the measured temperature breakthrough curves in the piezometers are used for constraining the heat transport model. They are highly contrasted with what would be expected in an ideal layered aquifer. They reveal strongly unequal lateral and vertical components of the transport mechanisms. A preliminary interpretation of these temperature breakthrough curves is provided with first results from the model. Then it will allow for estimating the entire set of heat transfer parameters and their spatial distribution by inverse modelling. The developed concepts and tests may lead to real projects of various extents that can be now optimized by the use of a rigorous and efficient methodology at the field scale. [less ▲]

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See detailCase studies of incorporation of prior information in electrical resistivity tomography: comparison of different approaches
Caterina, David ULg; Hermans, Thomas ULg; Nguyen, Frédéric ULg

in Near Surface Geophysics (2014), 12(4), 451-465

Many geophysical inverse problems are ill-posed and their solution non-unique. It is thus important to reduce the amount of mathematical solutions to more geologically plausible models by regularizing the ... [more ▼]

Many geophysical inverse problems are ill-posed and their solution non-unique. It is thus important to reduce the amount of mathematical solutions to more geologically plausible models by regularizing the inverse problem and incorporating all available prior information in the inversion process. We compare three different ways to incorporate prior information for electrical resistivity tomography (ERT): using a simple reference model or adding structural constraints to Occam's inversion and using geostatistical constraints. We made the comparison on four real cases representing different field applications in terms of scales of investigation and level of heterogeneities. In those cases, when electromagnetic logging data are available in boreholes to control the solution, it appears that incorporating prior information clearly improves the correspondence with logging data compared to the standard smoothness constrain. However, the way to incorporate it may have a major impact on the solution. A reference model can often be used to constrain the inversion; however, it can lead to misinterpretation if its weight is too strong or the resistivity values inappropriate. When the computation of the vertical and/or horizontal correlation length is possible, the geostatistical inversion gives reliable results everywhere in the section. However, adding geostatistical constraints can be difficult when there is not enough data to compute correlation lengths. When a known limit between two layers exists, the use of structural constrain seems to be more indicated particularly when the limit is located in zones of low sensitivity for ERT. This work should help interpreters to include their prior information directly into the inversion process through an appropriate way. [less ▲]

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See detailInverting Hydraulic Heads In An Alluvial Aquifer Constrained With Electrical Resistivity Tomography Data Through Multiple-Point Statistics And Probability Perturbation Method: A Case Study
Hermans, Thomas ULg; Scheidt, Celine; Caers, Jef et al

Conference (2014, July)

Solving spatial inverse problems in the Earth Sciences remains a considerable challenge given the large number of parameters to invert for, the non-linearity of forward models and as a result the ill ... [more ▼]

Solving spatial inverse problems in the Earth Sciences remains a considerable challenge given the large number of parameters to invert for, the non-linearity of forward models and as a result the ill-posedness of the problem. Geostatistics is therefore needed to specify prior models, more particularly, information to control the spatial features of the inverse solutions. We used multiple-point statistics (MPS) to build models of pre-defined hydrofacies: clay, sand and gravel facies constrained to geological data (hard data) and geophysical data (soft data). The electrical resistivity tomography method was chosen to bring relevant spatially distributed information on the presence of the facies, given its sensitivity to variations in lithology and porosity. The comparison of the geophysical signature of the deposits with direct observations in boreholes enables to derive the conditional probability of observing a facies given its electrical resistivity. This is used to produce probability maps for each facies and constrain stochastic simulations of the alluvial aquifer. Then, the probability perturbation method (PPM) is used to integrate hydraulic heads data, using MPS to generate models. This process enables us to obtain calibrated models of the aquifer. The PPM algorithm will automatically seek solutions fitting both hydrogeological data and training-image based geostatistical constraints. Only geometrical features of the model are affected by the perturbation, i.e. we do not attempt to directly find the optimal value of hydrogeological parameters (chosen a priori), but the optimal spatial distribution of facies whose prior distribution is quantified in a training image. The methodology is first tested with a synthetic benchmark. The tests performed show that the choice of the training image is a major source of uncertainty. Therefore, one first needs to select those training images consistent with the geophysical data (and hence reject the inconsistent ones). Then, we proceed with them to hydrogeological inversions. Geophysical data (soft constraints) acts as an accelerator of convergence by reducing prior uncertainty. The hydraulic conductivity of each facies is a sensitive parameter, but it can be easily optimized prior to the PPM process. The stochastic method is then successfully applied within the context of an alluvial aquifer submitted to a pumping experiment. We show how the integration of various sources of data (borehole logs, geophysics, hydraulic heads) aids in calibrating hydrogeological models, locating high hydraulic conductivity zones and reducing uncertainty. The developed methodology proposes a common framework (multiple-point statistics) to integrate various information sources with variable resolutions relevant for hydrogeology: geological, geophysical and hydrogeological data. The method can be extended to integrate tracer tests to enable the calibration of transport parameters as well. The originality of the method is to use geophysical data both to refine the choice of the training image and to constrain the inversion of hydrogeological models. [less ▲]

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See detailThermal tracer tests for characterizing a shallow alluvial aquifer
Wildemeersch, Samuel ULg; Klepikova, Maria ULg; Jamin, Pierre ULg et al

in Geophysical Research Abstracts (2014, April 28)

Using heat as an active tracer in different types of aquifers is a topic of increasing interest [e.g. Vandenbohede et al.; 2008, Wagner et al., 2013; Read et al., 2013]. In this study, we investigate the ... [more ▼]

Using heat as an active tracer in different types of aquifers is a topic of increasing interest [e.g. Vandenbohede et al.; 2008, Wagner et al., 2013; Read et al., 2013]. In this study, we investigate the potential interest of coupling heat and solute tracer tests for characterization of a shallow alluvial aquifer. A thermal tracer test was conducted in the alluvial aquifer of the Meuse River, Belgium. The tracing experiment consisted in simultaneously injecting heated water and a dye tracer in a piezometer and monitoring the evolution of groundwater temperature and tracer concentration in the recovery well and in nine monitoring wells located according to three transects with regards to the main groundwater flow direction. The breakthrough curves measured in the recovery well showed that heat transfer in the alluvial aquifer is slower and more dispersive than solute transport. Recovery is very low for heat while in the same time it is measured as relatively high for the solute tracer. This is due to the fact that heat diffusion is larger than molecular diffusion, implying that exchange between groundwater and the porous medium matrix is far more significant for heat than for solute tracers. Temperature and concentrations in the recovery well are then used for estimating the specific heat capacity with the energy balance approach and the estimated value is found to be consistent with those found in the literature. Temperature breakthrough curves in other piezometers are contrasted with what would be expected in an ideal layered aquifer. They reveal strongly unequal lateral and vertical components of the transport mechanisms. By means of a numerical heat transport model, we provide a preliminary interpretation of these temperature breakthrough curves. Furthermore, these data could be included in the calibration of a complex heat transfer model for estimating the entire set of heat transfer parameters and their spatial distribution by inverse modeling. [less ▲]

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See detailIntegration of near-surface geophysical, geological and hydrogeological data with multiple-point geostatistics in alluvial aquifers
Hermans, Thomas ULg

Doctoral thesis (2014)

Alluvial plains constitute essential geological bodies for environmental studies such as contaminated sites remediation, low-enthalpy geothermal energy or groundwater resources. The heterogeneity of these ... [more ▼]

Alluvial plains constitute essential geological bodies for environmental studies such as contaminated sites remediation, low-enthalpy geothermal energy or groundwater resources. The heterogeneity of these deposits governs flow processes and needs to be quantified. A proper description of such complex deposits requires an integrated approach combining geological, geophysical and hydrogeological data. Solving such spatial inverse problems in the Earth Sciences remains a considerable challenge given the large number of parameters to invert for, the non-linearity of forward models and, as a result, the ill-posedness of the problem. Geostatistics is therefore needed to specify prior models, more particularly, information to control the spatial features of the inverse solutions. Two-point geostatistical approaches have been developed to describe the heterogeneity of one geological formation but fail to reproduce the heterogeneity of fluvial deposits with multiple facies. Multiple-point statistics (MPS) introduced the training image (TI) concept to replace the variogram within an extended sequential simulation framework. The use of geophysics to constrain such simulations has been studied in the petroleum industry with wave-based methods (seismic reflection), but little research has been done to assess the use of near-surface potential methods to condition MPS in environmental studies. In this work, we propose to integrate geological (borehole logs), geophysical (electrical resistivity tomography (ERT) profiles) and hydrogeological (hydraulic heads) data within MPS models on the alluvial plain of the Meuse River, Belgium. Potential-based geophysical methods being integrative, they suffer from a relatively poor resolution. We first study how we can improve the informative content of geophysical inversion by including prior information in the ERT inverse problem. Three methods are tested and compared in several field cases, namely the reference model inversion, the structural inversion and the regularized geostatistical inversion. If every method has advantages and drawbacks, the best suited method for the considered problem is the regularized geostatistical method. Electromagnetic borehole logs enable to derive the vertical correlation length of electrical resistivity in the deposits and to subsequently use it to constrain the inversion. In addition to the knowledge of the bedrock position, it enables to retrieve an electrical resistivity distribution of the deposits close to direct observations. This ensures that geophysical models will be informative to constrain MPS simulations. Given the lack of geological and sedimentological data to build accurate TIs, a data base of TIs is built using several different parameters and scenarios. They are all based on a three facies description: clay/loam, sand and gravel corresponding to low, intermediate and high hydraulic conductivity. Then, we develop a methodology to verify the consistency of independently-built TIs with geophysical data. Our methodology starts by creating subsurface models with each TI. From these models we create synthetic geophysical data and from this synthetic data, synthetic inverted models. These models are now compared with a single inverted model obtained from the field survey, allowing for our definition of what is ``consistent''. To that extent, we calculate the Euclidean distance between any two inverted models as well as field data and visualize the results in a 2D or 3D space using multidimensional scaling (MDS). With this technique, it is possible to verify if field cases fall in the distribution represented by synthetic cases, and thus are consistent with them. In a second step, we present a cluster analysis on the MDS-map to highlight which parameters are the most sensitive for the construction of TI. Based on this analysis, a probability of each geological scenario is computed through kernel smoothing of the densities in reduced projected metric space. The integration of hydrogeological data is made through a stochastic inversion method: the probability perturbation method (PPM), using MPS constrained with geophysical data to generate models. The PPM algorithm automatically seeks solutions fitting both hydrogeological data and training-image based geostatistical constraints. Only geometrical features of the model are affected by the perturbation, i.e. we do not attempt to directly find the optimal value of hydrogeological parameters (chosen a priori), but the optimal spatial distribution of facies whose prior distribution is quantified in a training image. Tracing experiments may be used to further constrain hydrogeological models. ERT has proven its ability to monitor salt tracer tests, but few studies have investigated its performances in thermal tracing experiments. In this study, we demonstrate the ability of surface and crosshole ERT to image quantitatively temperature changes during heat injection experiments. Such resistivity data provides important information to improve hydrogeological models. Our study proves that ERT, especially crosshole ERT, is a reliable tool to follow thermal tracing experiments. It also confirms that ERT should be included to in situ techniques to characterize heat transfer in the subsurface and to monitor geothermal resources exploitation. [less ▲]

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See detailUtilisation de la géostatistique multi-points pour l'intégration de données de tomographie de résistivité électrique aux modèles hydrogéologiques
Hermans, Thomas ULg; Scheidt, Céline; Caers, Jef et al

Scientific conference (2014, January 15)

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Full Text
Peer Reviewed
See detailCoupling heat and chemical tracer experiments for estimating heat transfer parameters in shallow alluvial aquifers
Wildemeersch, Samuel ULg; Jamin, Pierre ULg; Orban, Philippe ULg et al

in Journal of Contaminant Hydrology (2014), 169

Geothermal energy systems, closed or open, are increasingly considered for heating and/or cooling buildings. The efficiency of such systems depends on the thermal properties of the subsurface. Therefore ... [more ▼]

Geothermal energy systems, closed or open, are increasingly considered for heating and/or cooling buildings. The efficiency of such systems depends on the thermal properties of the subsurface. Therefore, feasibility and impact studies performed prior to their installation should include a field characterization of thermal properties and a heat transfer model using parameter values measured in situ. However, there is a lack of in situ experiments and methodology for performing such a field characterization, especially for open systems. This study presents an in situ experiment designed for estimating heat transfer parameters in shallow alluvial aquifers with focus on the specific heat capacity. This experiment consists in simultaneously injecting hot water and a chemical tracer into the aquifer and monitoring the evolution of groundwater temperature and concentration in the recovery well (and possibly in other piezometers located down gradient). Temperature and concentrations are then used for estimating the specific heat capacity. The first method for estimating this parameter is based on a modeling in series of the chemical tracer and temperature breakthrough curves at the recovery well. The second method is based on an energy balance. The values of specific heat capacity estimated for both methods (2.30 and 2.54 MJ/m3/K) for the experimental site in the alluvial aquifer of the Meuse River (Belgium) are almost identical and consistent with values found in the literature. Temperature breakthrough curves in other piezometers are not required for estimating the specific heat capacity. However, they highlight that heat transfer in the alluvial aquifer of the Meuse River is complex and contrasted with different dominant process depending on the depth leading to significant vertical heat exchange between upper and lower part of the aquifer. Furthermore, these temperature breakthrough curves could be included in the calibration of a complex heat transfer model for estimating the entire set of heat transfer parameters and their spatial distribution by inverse modeling. [less ▲]

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