Accounting for Aquifer Heterogeneity from Geological Data to Management Tools

Abstract

A nested workflow of multiple-point geostatistics (MPG) and sequential Gaussian simulation (SGS) was tested on a study area of 6 km(2) located about 20 km northwest of Quebec City, Canada. In order to assess its geological and hydrogeological parameter heterogeneity and to provide tools to evaluate uncertainties in aquifer management, direct and indirect field measurements are used as inputs in the geostatistical simulations to reproduce large and small-scale heterogeneities. To do so, the lithological information is first associated to equivalent hydrogeological facies (hydrofacies) according to hydraulic properties measured at several wells. Then, heterogeneous hydrofacies (HF) realizations are generated using a prior geological model as training image (TI) with the MPG algorithm. The hydraulic conductivity (K) heterogeneity modeling within each HF is finally computed using SGS algorithm. Different K models are integrated in a finite-element hydrogeological model to calculate multiple transport simulations. Different scenarios exhibit variations in mass transport path and dispersion associated with the large- and small-scale heterogeneity respectively. Three-dimensional maps showing the probability of overpassing different thresholds are presented as examples of management tools.

Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models

“…AMag data provided a continuous spatial input to the MPS code that does not rely solely on sparse borehole data to constrain the output. Additionally, the existing geological models provided a conceptual understanding, which was found to present an efficient input for MPS algorithms (Blouin et al 2013;Renard 2007) This study is not bound by complex 3D training image problems as detailed in existing literature (Blouin et al 2013;Comunian et al 2011Comunian et al , 2012, as it has been estimated through magnetic modelling at two field sites (Burns et al 2010;Wilson 2011;Comte et al 2012) that the dykes are sub-vertical cuboids, emplaced within the sandstone. Therefore, the spatial distribution in the Z direction was not computed and data were treated as horizontal two-dimensional (2D).…”

Groundwater Simulations and Uncertainty Analysis Using MODFLOW and Geostatistical Approach with Conditioning Multi-Aquifer Spatial Covariance

“…The high degree of spatial variability in hydraulic conductivity and the complexity of causes of this variability are widely recognized as major impediments to making precise predictions [8]. Stochastic methods are widely applied to estimate, simulate, and delineate representative heterogeneous field distributions of hydrogeological properties for groundwater models based on a limited number of groundwater samples [1,[9][10][11]. Stochastic analysis allows a quantitative evaluation of the effects of variability and thereby provides a means of addressing uncertainty in the resultant heads and flows that are caused by uncertainty in the hydraulic conductivity field [10,12].…”

“…Stochastic analysis allows a quantitative evaluation of the effects of variability and thereby provides a means of addressing uncertainty in the resultant heads and flows that are caused by uncertainty in the hydraulic conductivity field [10,12]. Monte Carlo simulation is commonly applied in groundwater modeling, where hundreds-or even thousands-of simulations are performed using generated parameters such as hydraulic conductivity, from which the probability of occurrence of each simulated response can be computed based on statistical data [9,11]. Typically, the parameters are generated by integrating random instances of parameter values from user-defined probability distribution functions or by generating a spatial distribution of parameters using such approaches as geostatistical simulation [13,14].…”

“…Geostatistical simulation, including lower upper (LU) decomposition, turning bands, sequential gaussian (SG) simulation and simulated annealing (SA) simulation, is a well-known technique for modeling the spatial uncertainty of a regionalized variable [1,2,10,11,[16][17][18] such as hydraulic conductivity. The typical geostatistical simulation is performed using a semivariogram model and a simulation algorithm to generate many realizations (conditional or unconditional) of the random function model [18].…”

Combining Hydraulic Head Analysis with Airborne Electromagnetics to Detect and Map Impermeable Aquifer Boundaries

“…The transport paths and dispersion characteristics of contaminant plumes are heavily influenced by such boundaries [1,2]. Drawdown accelerates when a cone of depression intersects an impermeable boundary [3,4].…”