Daniel Paradis

Permeability Profiles in Granular Aquifers Using Flowmeters in Direct-Push Wells

Numerical hydrogeological models should ideally be based on the spatial distribution of hydraulic conductivity (K), a property rarely defined on the basis of sufficient data due to the lack of efficient characterization methods. Electromagnetic borehole flowmeter measurements during pumping in uncased wells can effectively provide a continuous vertical distribution of K in consolidated rocks. However, relatively few studies have used the flowmeter in screened wells penetrating unconsolidated aquifers, and tests conducted in gravel-packed wells have shown that flowmeter data may yield misleading results. This paper describes the practical application of flowmeter profiles in direct-push wells to measure K and delineate hydrofacies in heterogeneous unconsolidated aquifers having low-to-moderate K (10(-6) to 10(-4) m/s). The effect of direct-push well installation on K measurements in unconsolidated deposits is first assessed based on the previous work indicating that such installations minimize disturbance to the aquifer fabric. The installation and development of long-screen wells are then used in a case study validating K profiles from flowmeter tests at high-resolution intervals (15 cm) with K profiles derived from multilevel slug tests between packers at identical intervals. For 119 intervals tested in five different wells, the difference in log K values obtained from the two methods is consistently below 10%. Finally, a graphical approach to the interpretation of flowmeter profiles is proposed to delineate intervals corresponding to distinct hydrofacies, thus providing a method whereby both the scale and magnitude of K contrasts in heterogeneous unconsolidated aquifers may be represented.

Resolution analysis of tomographic slug test head data: Two‐dimensional radial case

Hydraulic tomography inverse problems, which are solved to estimate aquifer hydraulic properties between wells, are known to be ill-conditioned and a priori information is often added to regularize numerical inversion of head data. Because both head data and a priori information have effects on the inversed solution, assessing the meaningful information contained in head data alone is required to ensure comprehensive interpretation of inverse solutions, whether they are regularized or not. This study thus aims to assess the amount of information contained in tomographic slug tests head data to resolve heterogeneity in Kh, Kv/Kh, and Ss. Therefore, a resolution analysis based on truncated singular value decomposition of the sensitivity matrix with a noise level representative of field measurements is applied using synthetic data reflecting a known littoral aquifer. As an approximation of the hydraulic behavior of a real aquifer system, synthetic tomographic experiments and associated sensitivity matrices are generated using a radial flow model accounting for wellbore storage to simulate slug tests in a plane encompassing a stressed well and an observation well. Although fine-scale resolution of heterogeneities is limited by the diffusive nature of the groundwater flow equations, inversion of tomographic slug tests head data holds the potential to uniquely resolve coarse-scale heterogeneity in Kh, Kv/Kh, and Ss, as inscribed in the resolution matrix. This implies that tomographic head data can provide key information on aquifer heterogeneity and anisotropy, but that fine-scale information must be supplied by a priori information to obtain finer details.

Field characterization and data integration to define the hydraulic heterogeneity of a shallow granular aquifer at a sub-watershed scale

Providing a sound basis for aquifer management or remediation requires that hydrogeological investigations carried out to understand groundwater flow and contaminant transport be based on representative data that capture the heterogeneous spatial distribution of aquifer hydraulic properties. This paper describes a general workflow allowing the characterization of the heterogeneity of the hydraulic properties of granular aquifers at an intermediate scale of a few km2. The workflow involves characterization and data integration steps that were applied on a 12-km2 study area encompassing a decommissioned landfill emitting a leachate plume and its main surface water receptors. The sediments composing the aquifer were deposited in a littoral–sublittoral environment and show evidence of small-scale transitional heterogeneities. Cone penetrometer tests (CPT) combined with soil moisture and electrical resistivity (SMR) measurements were thus used to identify and characterize spatial heterogeneities in hydraulic properties over the study area. Site-specific statistical relationships were needed to infer hydrofacies units and to estimate hydraulic properties from high-resolution CPT/SMR soundings distributed all over the study area. A learning machine approach was used due to the complex statistical relationships between colocated hydraulic and CPT/SMR data covering the full range of aquifer materials. Application of this workflow allowed the identification of hydrofacies units and the estimation of horizontal hydraulic conductivity, vertical hydraulic conductivity and porosity over the study area. The paper describes and discusses data acquisition and integration methodologies that can be adapted to different field situations, while making the aquifer characterization process more time-efficient and less labor-intensive.

Predicting hydrofacies and hydraulic conductivity from direct-push data using a data-driven relevance vector machine approach: Motivations, algorithms, and application

The spatial heterogeneity of hydraulic conductivity (K) exerts a major control on groundwater flow and solute transport. The heterogeneous spatial distribution of K can be imaged using indirect geophysical data as long as reliable relations exist to link geophysical data to K. This paper presents a nonparametric learning machine approach to predict aquifer K from cone penetrometer tests (CPT) coupled with a soil moisture and resistivity probe (SMR) using relevance vector machines (RVMs). The learning machine approach is demonstrated with an application to a heterogeneous unconsolidated littoral aquifer in a 12 km2 subwatershed, where relations between K and multiparameters CPT/SMR soundings appear complex. Our approach involved fuzzy clustering to define hydrofacies (HF) on the basis of CPT/SMR and K data prior to the training of RVMs for HFs recognition and K prediction on the basis of CPT/SMR data alone. The learning machine was built from a colocated training data set representative of the study area that includes K data from slug tests and CPT/SMR data up-scaled at a common vertical resolution of 15 cm with K data. After training, the predictive capabilities of the learning machine were assessed through cross validation with data withheld from the training data set and with K data from flowmeter tests not used during the training process. Results show that HF and K predictions from the learning machine are consistent with hydraulic tests. The combined use of CPT/SMR data and RVM-based learning machine proved to be powerful and efficient for the characterization of high-resolution K heterogeneity for unconsolidated aquifers.

Comparison of slug and pumping tests for hydraulic tomography experiments: a practical perspective

Hydraulic tomography is the simultaneous analysis of several hydraulic tests performed in multiple isolated intervals in adjacent wells to image heterogeneous hydraulic property fields. In this study, we compare the resolutions associated with hydraulic tomography experiments carried out with slug tests and pumping tests for simple configurations with hydraulic property values representative of an extensively studied littoral aquifer. Associated test designs (e.g., pumping rates, test durations) and the validity of the principle of reciprocity are also assessed. For this purpose, synthetic tomography experiments and their associated sensitivity matrices are generated using a radial flow model accounting for wellbore storage. The resolution analysis is based on a pseudo-inverse analysis of the sensitivity matrix with a noise level representative of field measurements. Synthetic experiments used equivalent perturbations for slug tests and pumping tests. Even though pumping tests induce a drawdown in observation intervals that is three times larger than head changes due to slug tests, resolutions for hydraulic conductivities (horizontal and vertical) are similar for the two tests and slightly lower for specific storage with pumping. However, experiments with pumping require fifty times more water and are seven times longer to perform than experiments with slug tests. Furthermore, reducing pumping rates to limit disposal of water or test durations to decrease field data acquisition time would considerably lower resolutions for either scenario. Analyses are done using all available stressed and observation intervals as required by the non-applicability of the principle of reciprocity for slug tests and pumping tests with important wellbore storage. This study demonstrates concepts that have important implications for the performance and analysis of hydraulic tomography experiments.