Estimation of aquifers hydraulic parameters by three different tecniques: geostatistics, correlation and modeling

• Autores: Marco Barahona Palomo
• Directores de la Tesis: Daniel Fernández García (dir. tes.), Xavier Sánchez Vila (dir. tes.)
• Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2014
• Idioma: inglés
• Tribunal Calificador de la Tesis: Alberto Guadagnini (presid.), Albert Folch Sancho (secret.), Enrique Vázquez Suñé (voc.)
• Materias:
  • Ciencias de la tierra y del espacio
    • Geología
      • Hidrogeología
    • Hidrología
      • Aguas subterráneas
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Characterization of aquifers hydraulic parameters is a difficult task that requires field information. Most of the time the hydrogeologist relies on a group of values coming from different test to interpret the hydrogeological setting and possibly, generate a model. However, getting the best from this information can be challenging. In this thesis, three cases are explored. First, hydraulic conductivities associated with measurement scale of the order of 10?1 m and collected during an extensive field campaign near Tübingen, Germany, are analyzed. Estimates are provided at coinciding locations in the system using: the empirical Kozeny-Carman formulation, providing conductivity values, based on particle size distribution, and borehole impeller-type flowmeter tests, which infer conductivity from measurements of vertical flows within a borehole. Correlation between the two sets of estimates is virtually absent. However, statistics of the natural logarithm of both sets at the site are similar in terms of mean values and differ in terms of variogram ranges and sample variances. This is consistent with the fact that the two types of estimates can be associated with different (albeit comparable) measurement (support) scales. It also matches published results on interpretations of variability of geostatistical descriptors of hydraulic parameters on multiple observation scales. The analysis strengthens the idea that hydraulic conductivity values and associated key geostatistical descriptors inferred from different methodologies and at similar observation scales (of the order of tens of cm) are not readily comparable and should not be embedded blindly into a flow (and eventually transport) prediction model. Second, a data-adapted kernel regression method, originally developed for image processing and reconstruction is modified and used for the delineation of facies. This non-parametric methodology uses both the spatial and the sample value distribution, to produce for each data point a locally adaptive steering kernel function, self-adjusting the kernel to the direction of highest local spatial correlation. The method is shown to outperform the nearest-neighbor classification (NNC) in a number of synthetic aquifers whenever the available number of data is small and randomly distributed. Still, in the limiting case, when the domain is profusely sampled, both the steering kernel method and the NNC method converge to the true solution. Simulations are finally used to explore which parameters of the locally adaptive kernel function yield optimal reconstruction results in typical field settings. It is shown that, in practice, a rule of thumb can be used to get suboptimal results, which are best when key prior information such as facies proportions is used. Third, the effect of water temperature fluctuation on the hydraulic conductivity profile of coarse sediments beneath an artificial recharge facility is model and compared with field data. Due to the high permeability, water travels at a high rate, and therefore also water with different temperature is also present on the sediment under the pond at different moments, this translates into different hydraulic conductivity values within the same layer, even though all the other parameters are the same for this layer. Differences of almost 79% in hydraulic conductivity were observed for the model temperatures (2 °C – 25 °C). This variation of hydraulic conductivity in the sediment below the infiltration pond when water with varying temperature enters the sediment, causes the infiltration velocity to change with time and produces the observed fluctuation on the field measurements.