A shallow aquifer CO 2 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 CO 2 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.

Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into 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 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use kmeans clustering to generate a 3-D model of the subsurface structures. We apply the procedure 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 3-D model covering 156 km 2 . The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.

Abstract. Large-scale hydrological models are important decision support tools in water resources management. The largest source of uncertainty in such models is the hydrostratigraphic model. Geometry and configuration of hydrogeological units are often poorly determined from hydrogeological data alone. Due to sparse sampling in space, lithological borehole logs may overlook structures that are important for groundwater flow at larger scales. Good spatial coverage along with high spatial resolution makes airborne electromagnetic (AEM) data valuable for the structural input to large-scale groundwater models. We present a novel method to automatically integrate large AEM data sets and lithological information into large-scale hydrological models. Clayfraction maps are produced by translating geophysical resistivity into clay-fraction values using lithological borehole information. Voxel models of electrical resistivity and clay fraction are classified into hydrostratigraphic zones using kmeans clustering. Hydraulic conductivity values of the zones are estimated by hydrological calibration using hydraulic head and stream discharge observations. The method is applied to a Danish case study. Benchmarking hydrological performance by comparison of performance statistics from comparable hydrological models, the cluster model performed competitively. Calibrations of 11 hydrostratigraphic cluster models with 1-11 hydraulic conductivity zones showed improved hydrological performance with an increasing number of clusters. Beyond the 5-cluster model hydrological performance did not improve. Due to reproducibility and possibility of method standardization and automation, we believe that hydrostratigraphic model generation with the proposed method has important prospects for groundwater models used in water resources management.

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