Pernille Aabye Marker

Hydrogeochemical and mineralogical effects of sustained CO2 contamination in a shallow sandy aquifer: A field‐scale controlled release experiment

A shallow aquifer CO2 contamination experiment was performed to investigate evolution of water chemistry and sediment alteration following leakage from geological storage by physically simulating a leak from a hypothetical storage site. In a carbonate-free aquifer, in western Denmark, a total of 1600 kg of gas phase CO2 was injected at 5 and 10 m depth over 72 days through four inclined injection wells into aeolian and glacial sands. Water chemistry was monitored for pH, EC, and dissolved element evolution through an extensive network of multilevel sampling points over 305 days. Sediment cores were taken pre and postinjection and analyzed to search for effects on mineralogy and sediment properties. Results showed the simulated leak to evolve in two distinct phases; an advective elevated ion pulse followed by increasing persistent acidification. Spatial and temporal differences in evolution of phases suggest separate chemical mechanisms and geochemical signatures. Dissolved element concentrations developed exhibiting four behaviors: (1) advective pulse (Ca, Mg, Na, Si, Ba, and Sr), (2) pH sensitive abundance dependent (Al and Zn), (3) decreasing (Mn and Fe), and (4) unaffected (K). Concentration behaviors were characterized by: (1) a maximal front moving with advective flow, (2) continual increase in close proximity to the injection plane, (3) removal from solution, and (4) no significant change. Only Al was observed to exceed WHO guidelines, however significantly so (10-fold excess). The data indicate that pH is controlled by equilibrium with gibbsite which is again coupled to cation exchange processes. Pre and postinjection sediment analysis indicated alteration of sediment composition and properties including depletion of reactive mineral species.

Automatic Generation of Groundwater Model Hydrostratigraphy from AEM Resistivity and Boreholes

Regional hydrological models are important tools in water resources management. Model prediction uncertainty is primarily due to structural (geological) non-uniqueness which makes sampling of the structural model space necessary to estimate prediction uncertainties. Geological structures and heterogeneity, which spatially scarce borehole lithology data may overlook, are well resolved in AEM surveys. This study presents a semi-automatic sequential hydrogeophysical inversion method for the integration of AEM and borehole data into regional groundwater models in sedimentary areas, where sand/clay distribution govern groundwater flow. The coupling between hydrological and geophysical parameters is managed using a translator function with spatially variable parameters followed by a 3D zonation. The translator function translates geophysical resistivities into clay fractions and is calibrated with observed lithological data. Principal components are computed for the translated clay fractions and geophysical resistivities. Zonation is carried out by k-means clustering on the principal components. The hydraulic parameters of the zones are determined in a hydrological model calibration using head and discharge observations. The method was applied to field data collected at a Danish field site. Our results show that a competitive hydrological model can be constructed from the AEM dataset using the automatic procedure outlined above.

Addressing Current Challenges on Groundwater Model Structure through Effective Use of Geophysical Data

We wish to present a method for effective generation of structural models for groundwater flow simulations. The methodology is presented for two cases. A regional scale test, where geophysical data and borehole data is used for generating the regional scale hydrostratigraphy, and a local detailed case, where the same methodology is used to address the question of structural uncertainty.

An efficient and automatic procedure for integrating resistivity and borehole information for large scale groundwater modelling

We present an automatic method for parameterization of a 3D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of creating a direct input to groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay-units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3D clay fraction model, which holds information from the resistivity dataset and the borehole dataset in one variable. Finally, we use k-means clustering to generate a 3D model of the subsurface structures, which we then use as direct input in a groundwater model. We apply the concept to the Norsminde survey in Denmark integrating approximately 700 boreholes and more than 100,000 resistivity models from an airborne survey in the parameterization of the 3D model covering 156 km2. The final five-cluster 3D model is input to a groundwater model and it performs equally well or slightly better than traditional groundwater models from the area.