Communications – Page 3

Presentation: Potential Impact of Groundbased Gravity Gradiometer for Subsurface Reservoir Monotoring

Event: EGU General Assembly, Vienna (Austria) 2019
Presentation by Anne-Karin Cooke, Cédric Champollion, Pierre Vermeulen, Nicolas Le Moigne and Sébastien Merlet

Abstract

Ground-based gravity measurements can provide accurate constrains on the water storage dynamics of subsurface reservoirs. At the scale of the measurements time-lapse gravity experiments allow to monitor the water mass balance taking into account both the saturated and the unsaturated zone.

One major characteristic of the gravity measurement is the integration of all water masses across scales: gravity variations can be the effect of continentalscale soil humidity or aquifer (such as seen by GRACE measurements) and of local effects (such as the umbrellaeffect of a building or reservoir heterogeneities). The vertical gravity gradient is similar to gravity while showing a higher sensibility to local masses. The interest of the gradient for subsurface features such as caves is well known.

The objective of the presentation is the specific potential of gravity vertical gradient monitoring for water reservoir studies. The study is first based on existing measurements of gravity gradients time series (with a relative spring gravimeter): can significant hydro-logical signals be detected? Then based on numerical simulations, the potential of future ground-based for reservoir monitoring is investigated.

The capability of the gravity gradient method to detect heterogeneities (contrary to theclassical gravity) is evident: for example the classical Bouguer plate approximation often used to convert gravityinto equivalent water height (even in heterogeneous area such as karst) has no effect on the gravity gradient, hence new models need to be applied that combine gravity and gravity gradient measurements.

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Publication: A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity

in Advances in Water Resources Vol 133 (November 2019)
by Jorge Lopez-Alvis, Thomas Hermans, Frédéric Nguyen
https://doi.org/10.1016/j.advwatres.2019.103427

Abstract

Spatial heterogeneity is a critical issue in the management of water resources. However, most studies do not consider uncertainty at different levels in the conceptualization of the subsurface patterns, for example using one single geological scenario to generate an ensemble of realizations.

In this paper, we represent the spatial uncertainty by the use of hierarchical models in which higher-level parameters control the structure. Reduction of uncertainty in such higher-level structural parameters with observation data may be done by updating the complete hierarchical model, but this is, in general, computationally challenging.

To address this, methods have been proposed that directly update these structural parameters by means of extracting lower dimensional representations of data called data features that are informative and applying a statistical estimation technique using these features.

The difficulty of such methods, however, lies in the choice and design of data features, i.e. their extraction function and their dimensionality, which have been shown to be case-dependent. Therefore, we propose a cross-validation framework to properly assess the robustness of each designed feature and make the choice of the best feature more objective. Such framework aids also in choosing the values for the parameters of the statistical estimation technique, such as the bandwidth for kernel density estimation.

We demonstrate the approach on a synthetic case with cross-hole ground penetrating radar traveltime data and two higher-level structural parameters: discrete geological scenarios and the continuous preferential orientation of channels.

With the best performing features selected according to the cross-validation score, we successfully reduce the uncertainty for these structural parameters in a computationally efficient way. While doing so, we also provide guidelines to design features accounting for the level of knowledge of the studied system.

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Publication: Application of Stable Isotopes of Water to Study Coupled Submarine Groundwater Discharge and Nutrient Delivery

in Water 11:9 (September 2019)
by Carlos Duque, Soren Jessen, Joel Tirado-Conde, Sachin Karan and Peter Engesgaard
https://doi.org/10.3390/w11091842

Abstract

Submarine groundwater discharge (SGD)—including terrestrial freshwater, density-driven flow at the saltwater–freshwater interface, and benthic exchange—can deliver nutrients to coastal areas, generating a negative effect in the quality of marine water bodies. It is recognized that water stable isotopes (¹⁸O and ²H) can be helpful tracers to identify different flow paths and origins of water. Here, we show that they can be also applied when assessing sources of nutrients to coastal areas.

A field site near a lagoon (Ringkøbing Fjord, Denmark) has been monitored at a metric scale to test if stable isotopes of water can be used to achieve a better understanding of the hydrochemical processes taking place in coastal aquifers, where there is a transition from freshwater to saltwater.

Results show that ¹⁸O and ²H differentiate the coastal aquifer into three zones: Freshwater, shallow, and deep saline zones, which corresponded well with zones having distinct concentrations of inorganic phosphorous. The explanation is associated with three mechanisms: (1) Differences in sediment composition, (2) chemical reactions triggered by mixing of different type of fluxes, and (3) biochemical and diffusive processes in the lagoon bed.

The different behaviors of nutrients in Ringkøbing Fjord need to be considered in water quality management. PO₄ underneath the lagoon exceeds the groundwater concentration inland, thus demonstrating an intra-lagoon origin, while NO₃, higher inland due to anthropogenic activity, is denitrified in the study area before reaching the lagoon.

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Publication: Evaluation of Temperature Profiling and Seepage Meter Methods for Quantifying Submarine Groundwater Discharge to Coastal Lagoons: Impacts of Saltwater Intrusion and the Associated Thermal Regime

in Water 11:8 (August 2019)
by Joel Tirado-Conde, Peter Engesgaard, Sachin Karan, Sascha Müller and Carlos Duque
https://doi.org/10.3390/w11081648

Abstract

Surface water-groundwater interactions were studied in a coastal lagoon performing 180 seepage meter measurements and using heat as a tracer in 30 locations along a lagoon inlet. The direct seepage meter measurements were compared with the results from analytical solutions for the 1D heat transport equation in three different scenarios: (1) Homogeneous bulk thermal conductivity (K_e); (2) horizontal heterogeneity in K_e; and (3) horizontal and vertical heterogeneity in K_e.

The proportion of fresh groundwater and saline recirculated lagoon water collected from the seepage experiment was used to infer the location of the saline wedge and its effect on both the seepage meter results and the thermal regime in the lagoon bed, conditioning the use of the thermal methods.

The different scenarios provided the basis for a better understanding of the underlying processes in a coastal groundwater-discharging area, a key factor to apply the best-suited method to characterize such processes. The thermal methods were more reliable in areas with high fresh groundwater discharge than in areas with high recirculation of saline lagoon water.

The seepage meter experiments highlighted the importance of geochemical water sampling to estimate the origin of the exchanged water through the lagoon bed.

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Publication: Heterogeneity and Prior Uncertainty Investigation Using a Joint Heat and Solute Tracer Experiment in Alluvial Sediments

in frontiers in Earth Sciences (May 2019)
by Richard Hoffmann, Alain Dassargues, Pascal Goderniaux, Thomas Hermans
https://doi.org/10.3389/feart.2019.00108

Abstract

In heterogeneous aquifers, imaging preferential flow paths, and non-Gaussian effects is critical to reduce uncertainties in transport predictions. Common deterministic approaches relying on a single model for transport prediction show limitations in capturing these processes and tend to smooth parameter distributions. Monte-Carlo simulations give one possible way to explore the uncertainty range of parameter value distributions needed for realistic predictions. Joint heat and solute tracer tests provide an innovative option for transport characterization using complementary tracer behaviors. Heat tracing adds the effect of heat advection-conduction to solute advection-dispersion.

In this contribution, a joint interpretation of heat and solute tracer data sets is proposed for the alluvial aquifer of the Meuse River at the Hermalle-sous-Argenteau test site (Belgium). First, a density-viscosity dependent flow-transport model is developed and induce, due to the water viscosity changes, up to 25% change in simulated heat tracer peak times. Second, stochastic simulations with hydraulic conductivity (K) random fields are used for a global sensitivity analysis. The latter highlights the influence of spatial parameter uncertainty on the resulting breakthrough curves, stressing the need for a more realistic uncertainty quantification.

This global sensitivity analysis in conjunction with principal component analysis assists to investigate the link between the prior distribution of parameters and the complexity of the measured data set. It allows to detect approximations done by using classical inversion approaches and the need to consider realistic K-distributions.

Furthermore, heat tracer transport is shown as significantly less sensitive to porosity compared to solute transport. Most proposed models are, nevertheless, not able to simultaneously simulate the complementary heat-solute tracers.

Therefore, constraining the model using different observed tracer behaviors necessarily comes with the requirement to use more-advanced parameterization and more realistic spatial distribution of hydrogeological parameters. The added value of data from both tracer signals is highlighted, and their complementary behavior in conjunction with advanced model/prediction approaches shows a strong uncertainty reduction potential.

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Datasets of this study are to be found on the H+ database