Optimal design of pump-and-treat systems under uncertain hydraulic conductivity and plume distribution

In this work, we present a stochastic optimal control framework for assisting the management of the cleanup by pump-and-treat of polluted shallow aquifers. In the problem being investigated, hydraulic conductivity distribution and dissolved contaminant plume location are considered as the uncertain variables. The framework considers the subdivision of the cleanup horizon in a number of stress periods over which the pumping policy implemented until that stage is dynamically adjusted based upon new information that has become available in the previous stages. In particular, by following a geostatistical approach, we study the idea of monitoring the cumulative contaminant mass extracted from the installed recovery wells, and using these measurements to generate conditional realizations of the hydraulic conductivity field. These realizations are thus used to obtain a more accurate evaluation of the initial plume distribution, and modify accordingly the design of the pump-and-treat system for the remainder of the remedial process. The study indicates that measurements of contaminant mass extracted from pumping wells retain valuable information about the plume location and the spatial heterogeneity characterizing the hydraulic conductivity field. However, such an information may prove quite soft, particularly in the instances where recovery wells are installed in regions where contaminant concentration is low or zero. On the other hand, integrated solute mass measurements may effectively allow for reducing parameter uncertainty and identifying the plume distribution if more recovery wells are available, in particular in the early stages of the cleanup process.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

Influence of temporally variable groundwater flow conditions on point measurements and contaminant mass flux estimations

Monitoring of contaminant concentrations, e.g., for the estimation of mass discharge or contaminant degradation rates, often is based on point measurements at observation wells. In addition to the problem, that point measurements may not be spatially representative, a further complication may arise due to the temporal dynamics of groundwater flow, which may cause a concentration measurement to be not temporally representative. This paper presents results from a numerical modeling study focusing on temporal variations of the groundwater flow direction. “Measurements” are obtained from point information representing observation wells installed along control planes using different well frequencies and configurations. Results of the scenario simulations show that temporally variable flow conditions can lead to significant temporal fluctuations of the concentration and thus are a substantial source of uncertainty for point measurements. Temporal variation of point concentration measurements may be as high as the average concentration determined, especially near the plume fringe, even when assuming a homogeneous distribution of the hydraulic conductivity. If a heterogeneous hydraulic conductivity field is present, the concentration variability due to a fluctuating groundwater flow direction varies significantly within the control plane and between the different realizations. Determination of contaminant mass fluxes is also influenced by the temporal variability of the concentration measurement, especially for large spacings of the observation wells. Passive dosimeter sampling is found to be appropriate for evaluating the stationarity of contaminant plumes as well as for estimating average concentrations over time when the plume has fully developed. Representative sampling has to be performed over several periods of groundwater flow fluctuation. For the determination of mass fluxes at heterogeneous sites, however, local fluxes, which may vary considerably along a control plane, have to be accounted for. Here, dosimeter sampling in combination with time integrated local water flux measurements can improve mass flux estimates under dynamic flow conditions.     

Heuristic space–time design of monitoring wells for contaminant plume characterization in stochastic flow fields

An optimization methodology for designing groundwater quality monitoring networks applicable to stochastic flow fields is presented and evaluated. The approach sets itself apart from previous techniques by incorporating the time dimension directly into the objective function. This function is extremized using a directed partial enumeration strategy guided by physical considerations related to transport processes. The result is a set of monitoring well locations and a sampling schedule that minimizes plume characterization error while satisfying constraints on the maximum number of wells and allowable number of active wells. The method is evaluated using hypothetical plumes with varying degrees of heterogeneity. Results indicate that the proposed approach is successful in generating near-optimal sampling networks that satisfy all imposed constraints. Monitoring networks with as little as three active wells and a total of 12 wells are found to provide adequate plume characterization for low toxicity contaminants.     

Stochastic evaluation of mass discharge from pointlike concentration measurements

The contaminant mass discharge crossing a control plane is an important metric in the assessment of natural attenuation at contaminated sites. For risk-assessment purposes, the mass discharge must be estimated together with a level of uncertainty. We present a conditional Monte Carlo approach that allows estimating the statistical distribution of mass discharge. The approach is based on conditioning multiple realizations of the hydraulic conductivity field on all data available. We jointly determine a first-order decay coefficient in each realization, leading to conditional statistical distributions of all estimated parameters and the total mass discharge. The resulting statistical distribution of contaminant mass discharges can be used in the assessment of risks at the contaminated site. The method is applied to data of hypothetical test cases, which gives the opportunity to compare estimation results to the true field. As concentration data, we account for pointlike measurements obtained in multi-level sampling wells. The obtained empirical distribution of mass discharge crossing the multi-level sampling fence could be well fitted by a log-normal distribution.     

Quantification of mass fluxes and natural attenuation rates at an industrial site with a limited monitoring network: a case study

At many "real world" field sites, the number of available monitoring wells is limited due to economic or geological reasons. Under such restricted conditions, it is difficult to perform a reliable field investigation and to quantify primary lines of evidence for natural attenuation (NA), like the documentation of a decrease of contaminant mass flux in flow direction. This study reports the results of a groundwater investigation at a former manufactured gas plant situated in a Quaternary river valley in southwest Germany. The location, infrastructure and aquifer setting are typical of many industrial sites in Germany. Due to difficult drilling conditions (coarse glaciofluvial gravel deposits and an anthropogenic fill above the aquifer), only 12 monitoring wells were available for the investigation and localisation of the contaminant plume. These wells were situated along three control planes (CP) downgradient from the contaminant source, with four wells along each plane. Based on the sparse set of monitoring wells, field scale mass fluxes and first-order natural attenuation rate constants of benzene, toluene, ethylbenzene, and o-xylene and p-xylene (BTEX) and low molecular weight polycyclic aromatic hydrocarbons (PAH) were estimated utilizing different point scale and also a new integral investigation method. The results show that even at a heterogeneous site with a sparse monitoring network point scale investigation methods can provide reliable information on field scale natural attenuation rates, if a dependable flow model or tracer test data is available. If this information is not available, only the new integral investigation method presented can yield adequate results for the quantification of contaminant mass fluxes under sparse monitoring conditions.     

Fingerprinting and Groundwater Model Application to Evaluate Hydraulic Barrier Efficiency (Italy)

Remediation actions at contaminated sites are based on multiple numerical model scenarios considering different parameter distributions, source positions and contaminant transport paths. In some cases the excess of scenarios is due to uncertainties in the conceptual model as a result of the spread of contamination through heterogeneities in the physical system. Reduction of project hypotheses and conceptual model uncertainty is therefore needed. This result can be achieved by coupling hydrogeological investigations with environmental forensic techniques, better localization of the source and understanding of contamination history. In this respect, in the present study, compositional fingerprinting and groundwater flow modeling were applied to a former oil storage facility where, even though a hydraulic barrier had been built to stop the hydrocarbon plume, the presence of some hydrocarbons was still found in downgradient monitoring wells. The final aim was to evaluate the efficacy of the hydraulic barrier and identify of the source of pollution. Fingerprinting results indicated pollution with a gasoline-diesel mixture much altered by water washing and/or biodegradation. Comparison of seven groundwater samples collected in wells and monitoring wells was performed by analyzing the volatile fraction (BTEX) and the total ion chromatogram (TIC), focusing attention on: n-alkanes (m/z 85), alkylcyclohexanes (m/z 83), isoprenoids (m/z 113), C4-alkylbenzenes (m/z 134), C3-C6 alkylbenzenes and polycyclic aromatic hydrocarbons (PAHs). The most probable scenario was then identified by combining the results of fingerprinting with different contaminant paths obtained using the numerical model.     

Flux-based assessment at a manufacturing site contaminated with trichloroethylene

Groundwater and contaminant fluxes were measured, using the passive flux meter (PFM) technique, in wells along a longitudinal transect passing approximately through the centerline of a trichloroethylene (TCE) plume at a former manufacturing plant located in the Midwestern US. Two distinct zones of hydraulic conductivity were identified from the measured groundwater fluxes; a 6-m-thick upper zone ( approximately 7 m to 13 m below the ground surface or bgs) with a geometric mean Darcy flux (q(0)) of 2 cm/day, and a lower zone ( approximately 13 m to 16.5m bgs) with a q(0) approximately 15 cm/day; this important hydrogeologic feature significantly impacts any remediation technology used at the site. The flux-averaged TCE concentrations estimated from the PFM results compared well with existing groundwater monitoring data. It was estimated that at least 800 kg of TCE was present in the source zone. The TCE mass discharge across the source control plane (85 m x 38 m) was used to estimate the "source strength" ( approximately 365 g/day), while mass discharges across multiple down-gradient control planes were used to estimate the plume-averaged, TCE degradation rate constant (0.52 year(-1)). This is close to the rate estimated using the conventional centerline approach (0.78 year(-1)). The mass discharge approach provides a more robust and representative estimate than the centerline approach since the latter uses only data from wells along the plume centerline while the former uses all wells in the plume.     

Heterogeneity of chlorinated hydrocarbon sorption properties in a sandy aquifer

Hydraulic conductivity and sorption coefficients for chlorinated hydrocarbons (chloroform, carbon tetrachloride and tetrachloroethylene) were evaluated for 216 sediment samples collected across a 15 m transect and a 21 m depth interval in a contaminated aquifer near Schoolcraft, Michigan. Relationships between hydraulic conductivity, linear sorption partition coefficients, grain size classes, and spatial location were investigated using linear regression analysis and geostatistical techniques. Clear evidence of layering was found in sorption properties, hydraulic conductivity and grain sizes. Conductivity correlated well with grain size, as expected, but sorption varied inversely with grain size, contrary to some previous reports. No significant correlation was found between sorption properties and hydraulic conductivity. This is likely due to the unexpected presence of small amounts of highly sorptive coal-like solids, which dominate the sorption behavior but have little effect on conductivity. The results demonstrate that recent findings regarding the high sorption capacity of coal materials found in soils can exert a controlling influence on contaminant transport. Designers of in situ remediation systems should be cautioned that 1) it is not reasonable to assume that sorption capacity and hydraulic conductivity are related, 2) sorption capacity and hydraulic conductivity are critical measurements for contaminant site characterization and subsequent transport modeling, 3) estimating sorption capacity from organic carbon measurement may lead to greater errors than performing sorption isotherms, and 4) it is more important to characterize vertical heterogeneity rather than horizontal heterogeneity because both sorption and hydraulic conductivity are correlated across longer distances in the horizontal plane.     

Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.     

Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions

A new simulation-optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and a global mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OK-based method and results in more potential cost savings. However, the OK-based method leads to more accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model.     

Evaluation of groundwater flow patterns around a dual-screened groundwater circulation well

Dual-screened groundwater circulation wells (GCWs) can be used to remove contaminant mass and to mix reagents in situ. GCWs are so named because they force water in a circular pattern between injection and extraction screens. The radial extent, flux and direction of the effective flow of this circulation cell are difficult to measure or predict. The objective of this study is to develop a robust protocol for assessing GCW performance. To accomplish this, groundwater flow patterns surrounding a GCW are assessed using a suite of tools and data, including: hydraulic head, in situ flow velocity, measured hydraulic conductivity data from core samples, chemical tracer tests, contaminant distribution data, and numerical flow and transport models. The hydraulic head data show patterns that are consistent with pumping on a dual-screened well, however, many of the observed changes are smaller than expected. In situ thermal perturbation flow sensors successfully measured horizontal flow, but vertical flow could not be determined with sufficient accuracy to be useful in mapping flow patterns. Two types of chemical tracer tests were utilized at the site and showed that much of the flow occurs within a few meters of the GCW. Flow patterns were also assessed based on changes in contaminant (trichloroethylene, TCE) concentrations over time. The TCE data clearly showed treated water moving away from the GCW at shallow and intermediate depths, but the circulation of that water back to the well, except very close to the well, was less clear. Detailed vertical and horizontal hydraulic conductivities were measured on 0.3 m-long sections from a continuous core from the GCW installation borehole. The measured vertical and horizontal hydraulic conductivity data were used to construct numerical flow and transport models, the results of which were compared to the head, velocity and concentration data. Taken together, the field data and modeling present a fairly consistent picture of flow and transport around the GCW. However, the time and expense associated with conducting all of those tests would be prohibitive for most sites. As a consequence, a sequential protocol for GCW characterization is presented here in which the number of tools used can be adjusted to meet the needs of individual sites. While not perfect, we believe that this approach represents the most efficient means for evaluating GCW performance.     

An analytical solution for two-dimensional contaminant transport during groundwater extraction

We obtain an analytical solution for two-dimensional steady-state transport of conservative contaminant between injecting and pumping wells. Flow and transport are considered in the vertical cross-section. The Dupuit approximation and conformal mapping onto the complex potential domain are employed to determine the velocity and concentration distributions, respectively. We use this solution to derive a priori conditions under which widely used 1-D analytical solutions with constant velocity and dispersion coefficients provide accurate approximations. These conditions are formulated in terms of aquifer parameters, such as hydraulic conductivity, porosity and dispersivities, and remediation strategy, e.g., well spacing and pumping regimes.     

Bayesian inference for source determination with applications to a complex urban environment

The problem of determining the source of an emission from the limited information provided by a finite and noisy set of concentration measurements obtained from real-time sensors is an ill-posed inverse problem. In general, this problem cannot be solved uniquely without additional information. A Bayesian probabilistic inferential framework, which provides a natural means for incorporating both errors (model and observational) and prior (additional) information about the source, is presented. Here, Bayesian inference is applied to find the posterior probability density function of the source parameters (location and strength) given a set of concentration measurements. It is shown how the source–receptor relationship required in the determination of the likelihood function can be efficiently calculated using the adjoint of the transport equation for the scalar concentration. The posterior distribution of the source parameters is sampled using a Markov chain Monte Carlo method. The inverse source determination method is validated against real data sets acquired in a highly disturbed flow field in an urban environment. The data sets used to validate the proposed methodology include a water-channel simulation of the near-field dispersion of contaminant plumes in a large array of building-like obstacles (Mock Urban Setting Trial) and a full-scale field experiment (Joint Urban 2003) in Oklahoma City. These two examples demonstrate the utility of the proposed approach for inverse source determination.     

Pulsed pumping process optimization using a potential flow model

A computational model is applied to the optimization of pulsed pumping systems for efficient in situ remediation of groundwater contaminants. In the pulsed pumping mode of operation, periodic rather than continuous pumping is used. During the pump-off or trapping phase, natural gradient flow transports contaminated groundwater into a treatment zone surrounding a line of injection and extraction wells that transect the contaminant plume. Prior to breakthrough of the contaminated water from the treatment zone, the wells are activated and the pump-on or treatment phase ensues, wherein extracted water is augmented to stimulate pollutant degradation and recirculated for a sufficient period of time to achieve mandated levels of contaminant removal. An important design consideration in pulsed pumping groundwater remediation systems is the pumping schedule adopted to best minimize operational costs for the well grid while still satisfying treatment requirements. Using an analytic two-dimensional potential flow model, optimal pumping frequencies and pumping event durations have been investigated for a set of model aquifer-well systems with different well spacings and well-line lengths, and varying aquifer physical properties. The results for homogeneous systems with greater than five wells and moderate to high pumping rates are reduced to a single, dimensionless correlation. Results for heterogeneous systems are presented graphically in terms of dimensionless parameters to serve as an efficient tool for initial design and selection of the pumping regimen best suited for pulsed pumping operation for a particular well configuration and extraction rate. In the absence of significant retardation or degradation during the pump-off phase, average pumping rates for pulsed operation were found to be greater than the continuous pumping rate required to prevent contaminant breakthrough.     

Use of tandem circulation wells to measure hydraulic conductivity without groundwater extraction

Conventional methods to measure the hydraulic conductivity of an aquifer on a relatively large scale (10-100 m) require extraction of significant quantities of groundwater. This can be expensive, and otherwise problematic, when investigating a contaminated aquifer. In this study, innovative approaches that make use of tandem circulation wells to measure hydraulic conductivity are proposed. These approaches measure conductivity on a relatively large scale, but do not require extraction of groundwater. Two basic approaches for using circulation wells to measure hydraulic conductivity are presented; one approach is based upon the dipole-flow test method, while the other approach relies on a tracer test to measure the flow of water between two recirculating wells. The approaches are tested in a relatively homogeneous and isotropic artificial aquifer, where the conductivities measured by both approaches are compared to each other and to the previously measured hydraulic conductivity of the aquifer. It was shown that both approaches have the potential to accurately measure horizontal and vertical hydraulic conductivity for a relatively large subsurface volume without the need to pump groundwater to the surface. Future work is recommended to evaluate the ability of these tandem circulation wells to accurately measure hydraulic conductivity when anisotropy and heterogeneity are greater than in the artificial aquifer used for these studies.     

A hybrid method for inverse characterization of subsurface contaminant flux

The methods presented in this work provide a potential tool for characterizing contaminant source zones in terms of mass flux. The problem was conceptualized by considering contaminant transport through a vertical “flux plane” located between a source zone and a downgradient region where contaminant concentrations were measured. The goal was to develop a robust method capable of providing a statement of the magnitude and uncertainty associated with estimated contaminant mass flux values.In order to estimate the magnitude and transverse spatial distribution of mass flux through a plane, the problem was considered in an optimization framework. Two numerical optimization techniques were applied, simulated annealing (SA) and minimum relative entropy (MRE). The capabilities of the flux plane model and the numerical solution techniques were evaluated using data from a numerically generated test problem and a nonreactive tracer experiment performed in a three-dimensional aquifer model. Results demonstrate that SA is more robust and converges more quickly than MRE. However, SA is not capable of providing an estimate of the uncertainty associated with the simulated flux values. In contrast, MRE is not as robust as SA, but once in the neighborhood of the optimal solution, it is quite effective as a tool for inferring mass flux probability density functions, expected flux values, and confidence limits.A hybrid (SA-MRE) solution technique was developed in order to take advantage of the robust solution capabilities of SA and the uncertainty estimation capabilities of MRE. The coupled technique provided probability density functions and confidence intervals that would not have been available from an independent SA algorithm and they were obtained more efficiently than if provided by an independent MRE algorithm.     

A hybrid method for inverse characterization of subsurface contaminant flux

The methods presented in this work provide a potential tool for characterizing contaminant source zones in terms of mass flux. The problem was conceptualized by considering contaminant transport through a vertical "flux plane" located between a source zone and a downgradient region where contaminant concentrations were measured. The goal was to develop a robust method capable of providing a statement of the magnitude and uncertainty associated with estimated contaminant mass flux values. In order to estimate the magnitude and transverse spatial distribution of mass flux through a plane, the problem was considered in an optimization framework. Two numerical optimization techniques were applied, simulated annealing (SA) and minimum relative entropy (MRE). The capabilities of the flux plane model and the numerical solution techniques were evaluated using data from a numerically generated test problem and a nonreactive tracer experiment performed in a three-dimensional aquifer model. Results demonstrate that SA is more robust and converges more quickly than MRE. However, SA is not capable of providing an estimate of the uncertainty associated with the simulated flux values. In contrast, MRE is not as robust as SA, but once in the neighborhood of the optimal solution, it is quite effective as a tool for inferring mass flux probability density functions, expected flux values, and confidence limits. A hybrid (SA-MRE) solution technique was developed in order to take advantage of the robust solution capabilities of SA and the uncertainty estimation capabilities of MRE. The coupled technique provided probability density functions and confidence intervals that would not have been available from an independent SA algorithm and they were obtained more efficiently than if provided by an independent MRE algorithm.     

Probabilistic study of well capture zones distribution at the Lauswiesen field site

The delineation of well capture zones is of utmost environmental and engineering relevance as pumping wells are commonly used both for drinking water supply needs, where protection zones have to be defined, and for investigation and remediation of contaminated aquifers. We analyze the probabilistic nature of well capture zones within the well field located at the "Lauswiesen" experimental site. The test site is part of an alluvial heterogeneous aquifer located in the Neckar river valley, close to the city of Tübingen in South-West Germany. We explore the effect of different conceptual models of the structure of aquifer heterogeneities on the delineation of three-dimensional probabilistic well catchment and time-related capture zones, in the presence of migration of conservative solutes. The aquifer is modeled as a three-dimensional, doubly stochastic composite medium, where distributions of geo-materials and hydraulic properties are uncertain. We study the relative importance of uncertain facies geometry and uncertain hydraulic conductivity and porosity on predictions of catchment and solute time of travel to the pumping well by focusing on cases in which (1) the facies distribution is random, but the hydraulic properties of each material are fixed, and (2) both facies geometry and material properties vary stochastically. The problem is tackled within a conditional numerical Monte Carlo framework. Results are provided in terms of probabilistic demarcations of the three-dimensional well catchment and time-related capture zones. Our findings suggest that the uncertainty associated with the prediction of the location of the outer boundary of well catchment at the "Lauswiesen" site is significantly affected by the conceptual model adopted to incorporate the heterogeneous nature of the aquifer domain in a predictive framework. Taking into account randomness of both lithofacies distribution and materials hydraulic conductivity allows recognizing the existence of preferential flow paths that influence the extent of the well catchment and the solute travel time distribution at the site.