Dylan R. Harp

Aquifer structure identification using stochastic inversion

This study presents a stochastic inverse method for aquifer structure identification using sparse geophysical and hydraulic response data. The method is based on updating structure parameters from a transition probability model to iteratively modify the aquifer structure and parameter zonation. The method is extended to the adaptive parameterization of facies hydraulic parameters by including these parameters as optimization variables. The stochastic nature of the statistical structure parameters leads to nonconvex objective functions. A multi-method genetically adaptive evolutionary approach (AMALGAM-SO) was selected to perform the inversion given its search capabilities. Results are obtained as a probabilistic assessment of facies distribution based on indicator cokriging simulation of the optimized structural parameters. The method is illustrated by estimating the structure and facies hydraulic parameters of a synthetic example with a transient hydraulic response.

Contaminant remediation decision analysis using information gap theory

Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes our ability to determine a frequency of occurrence of events or conditions that impact the decision; therefore, decision uncertainties due to a severe lack of information cannot be characterized probabilistically. To circumvent this problem, information gap (info-gap) theory has been developed to explicitly recognize and quantify the implications of a severe lack of information in decision making. This paper presents a decision analysis based on info-gap theory developed for a contaminant remediation scenario. The analysis provides decision support in determining the fraction of contaminant mass to remove from the environment. An info-gap uncertainty model is developed to characterize uncertainty due to a lack of information concerning the contaminant flux. The info-gap uncertainty model groups nested, convex sets of functions defining contaminant flux over time based on their level of deviation from a nominal contaminant flux. The nominal contaminant flux defines a best estimate of contaminant flux over time based on existing, though incomplete, information. A robustness function is derived to quantify the maximum level of deviation from nominal that still ensures compliance for alternative decisions. An opportuneness function is derived to characterize the possibility of meeting a desired contaminant concentration level. The decision analysis evaluates how the robustness and opportuneness change as a function of time since remediation and as a function of the fraction of contaminant mass removed.

Adaptive hybrid optimization strategy for calibration and parameter estimation of physical process models

A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physics-based models. Typically, models are calibrated and model parameters are estimated by minimization of the discrepancy between model simulations characterizing the system and existing observations requiring a substantial number of model evaluations. Squads is designed to be computationally efficient and robust in identification of the global optimum (i.e. maximum or minimum value of an objective function). It integrates global and local optimization using Adaptive Particle Swarm Optimization (APSO) and Levenberg-Marquardt (LM) optimization using adaptive rules based on runtime performance. The global strategy (APSO) optimizes the location of a set of solutions (particles) in the parameter space. The local strategy (LM) is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to APSO. Therefore, squads is a global strategy that utilizes a local optimization speedup. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO; i.e. TRIBES), and an existing hybrid optimization strategy (hPSO). All the strategies are tested on 2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a synthetic hydrogeologic application to identify the source of a contaminant plume in an aquifer. Tests are performed using a series of runs with random initial guesses for the estimated parameters. The performance of the strategies are compared based on their robustness, defined as the percentage of runs that identify the global optimum, and their efficiency, quantified by a statistical representation of the number of function evaluations performed prior to identification of the global optimum. Squads is observed to have better performance than the other strategies for the test functions and the hydrogeologic application when both robustness and efficiency are taken into consideration.

Thermal effects of groundwater flow through subarctic fens: A case study based on field observations and numerical modeling

Modeling and observation of ground temperature dynamics are the main tools for understanding current permafrost thermal regimes and projecting future thaw. Until recently, most studies on permafrost have focused on vertical ground heat fluxes. Groundwater can transport heat in both lateral and vertical directions but its influence on ground temperatures at local scales in permafrost environments is not well understood. In this study we combine field observations from a subarctic fen in the sporadic permafrost zone with numerical simulations of coupled water and thermal fluxes. At the Tavvavuoma study site in northern Sweden, ground temperature profiles and groundwater levels were observed in boreholes. These observations were used to set up one- and two-dimensional simulations down to 2 m depth across a gradient of permafrost conditions within and surrounding the fen. Two-dimensional scenarios representing the fen under various hydraulic gradients were developed to quantify the influence of groundwater flow on ground temperature. Our observations suggest that lateral groundwater flow significantly affects ground temperatures. This is corroborated by modeling results that show seasonal ground ice melts 1 month earlier when a lateral groundwater flux is present. Further, although the thermal regime may be dominated by vertically conducted heat fluxes during most of the year, isolated high groundwater flow rate events such as the spring freshet are potentially important for ground temperatures. As sporadic permafrost environments often contain substantial portions of unfrozen ground with active groundwater flow paths, knowledge of this heat transport mechanism is important for understanding permafrost dynamics in these environments.

An agent-based approach to global uncertainty and sensitivity analysis

A novel sampling approach to global uncertainty and sensitivity analyses of modeling results utilizing concepts from agent-based modeling is presented (Agent-Based Analysis of Global Uncertainty and Sensitivity (ABAGUS)). A plausible model parameter space is discretized and sampled by a particle swarm where the particle locations represent unique model parameter sets. Particle locations are optimized based on a model-performance metric using a standard particle swarm optimization (PSO) algorithm. Locations producing a performance metric below a specified threshold are collected. In subsequent visits to the location, a modified value of the performance metric, proportionally increased above the acceptable threshold (i.e., convexities in the response surface become concavities), is provided to the PSO algorithm. As a result, the methodology promotes a global exploration of a plausible parameter space, and discourages, but does not prevent, reinvestigation of previously explored regions. This effectively alters the strategy of the PSO algorithm from optimization to a sampling approach providing global uncertainty and sensitivity analyses. The viability of the approach is demonstrated on 2D Griewank and Rosenbrock functions. This also demonstrates the set-based approach of ABAGUS as opposed to distribution-based approaches. The practical application of the approach is demonstrated on a 3D synthetic contaminant transport case study. The evaluation of global parametric uncertainty using ABAGUS is demonstrated on model parameters defining the source location and transverse/longitudinal dispersivities. The evaluation of predictive uncertainties using ABAGUS is demonstrated for contaminant concentrations at proposed monitoring wells.

Identification of Pumping Influences in Long‐Term Water Level Fluctuations

Identification of the pumping influences at monitoring wells caused by spatially and temporally variable water supply pumping can be a challenging, yet an important hydrogeological task. The information that can be obtained can be critical for conceptualization of the hydrogeological conditions and indications of the zone of influence of the individual pumping wells. However, the pumping influences are often intermittent and small in magnitude with variable production rates from multiple pumping wells. While these difficulties may support an inclination to abandon the existing dataset and conduct a dedicated cross-hole pumping test, that option can be challenging and expensive to coordinate and execute. This paper presents a method that utilizes a simple analytical modeling approach for analysis of a long-term water level record utilizing an inverse modeling approach. The methodology allows the identification of pumping wells influencing the water level fluctuations. Thus, the analysis provides an efficient and cost-effective alternative to designed and coordinated cross-hole pumping tests. We apply this method on a dataset from the Los Alamos National Laboratory site. Our analysis also provides (1) an evaluation of the information content of the transient water level data; (2) indications of potential structures of the aquifer heterogeneity inhibiting or promoting pressure propagation; and (3) guidance for the development of more complicated models requiring detailed specification of the aquifer heterogeneity.

Influences and interactions of inundation, peat, and snow on active layer thickness

The effect of three environmental conditions: 1) thickness of organic soil, 2) snow depth, and 3) soil moisture content or water table height above and below the soil surface, on active layer thickness (ALT) are investigated using an ensemble of 1D thermal hydrology models. Sensitivity analyses of the ensemble exposed the isolated influence of each environmental condition on ALT and their multivariate interactions. The primary and interactive influences are illustrated in the form of color maps of ALT change. Results show that organic layer acts as a strong insulator, and its thickness is the dominant control of ALT, but the strength of the effect of organic layer thickness is dependent on the saturation state. Snow depth, subsurface saturation, and ponded water depth are strongly codependent and positively correlated to ALT.

Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems

The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations are performed based on Latin Hypercube Sampling (LHS) of model inputs drawn from uniform probability distributions. Key sensitive parameters are identified from these simulations, which are fracture zone permeability, well/skin factor, bottom hole pressure, and injection flow rate. The inputs for ROMs are based on these key sensitive parameters. The ROMs are then used to evaluate the influence of subsurface attributes on thermal power production curves. The resulting ROMs are compared with field-data and the detailed physics-based numerical simulations. We propose three different ROMs with different levels of model parsimony, each describing key and essential features of the power production curves. ROM-1 is able to accurately reproduce the power output of numerical simulations for low values of permeabilities and certain features of the field-scale data, and is relatively parsimonious. ROM-2 is a more complex model than ROM-1 but it accurately describes the field-data. At higher permeabilities, ROM-2 reproduces numerical results better than ROM-1, however, there is a considerable deviation at low fracture zone permeabilities. ROM-3 is developed by taking the best aspects of ROM-1 and ROM-2 and provides a middle ground for model parsimony. It is able to describe various features of numerical simulations and field-data. From the proposed workflow, we demonstrate that the proposed simple ROMs are able to capture various complex features of the power production curves of Fenton Hill HDR system. For typical EGS applications, ROM-2 and ROM-3 outperform ROM-1.

Thermal Modeling of High-Level Nuclear Waste Disposal in a Salt Repository

Abstract Salt formations have received recent attention for geologic disposal of heat-generating, high-level nuclear waste (HLW). Existing investigations are summarized and expanded upon using analytical and numerical models to investigate simulated temperatures in the salt after emplacement of HLW. Analytical modeling suggests that temperature variations near canisters will be smooth, indicating that the system can be approximated by a coarsely discretized numerical model. Two multidimensional parameter studies explore canister configuration using characteristics from (a) defense HLW and (b) spent nuclear fuel (SNF) waste. Numerical modeling was conducted for a disposal concept consisting of emplacement of waste canisters on the floor of drifts and covering each with salt backfill. Results indicate that waste forms with U.S. Department of Energy (DOE) waste characteristics can be easily configured to maintain simulated temperatures far below 200·C at spacings as close as 0.3 m (˜1 ft), the minimum feasible spacing that could practically be achieved. For SNF waste packaged into canisters with heat loads of 1500 or 1000 W with canister spacing of 6 m (˜20 ft) and 3 m (˜10 ft), respectively, simulated temperatures can be maintained below 200·C; much higher maximum temperatures would result for designs with higher canister heat loads and smaller spacings. These results indicate that from a thermal loading perspective, in-drift disposal of HLW in salt deposits is feasible for DOE-managed waste as long as the maximum temperature is managed through proper selection of canister heat loads and spacings. The results will aid in the design of potential future field tests to confirm this conclusion.

Near-optimal placement of monitoring wells for the detection of potential contaminant arrival in a regional aquifer at Los Alamos National Laboratory

This research presents a strategy to aid in the development of a decision support toolset in the Advanced Simulation Capability for Environmental Monitoring (ASCEM) modeling platform for determining the near-optimal placement of monitoring wells. There are two scenarios that are studied in determining the near-optimal placement of monitoring wells: (1) placement of an entirely new network and (2) placement of additional monitoring wells within a previously placed network. The key technique utilized in this strategy minimizes the variance of spatial analysis using Geostatistical analysis and optimizes using Monte Carlo analysis. A clustering technique, namely k-means, is used in the second scenario to determine specific locations of importance relative to previously placed monitoring wells. This strategy is applied to chromium contamination at Los Alamos National Laboratory (LANL). The purpose is the determination of monitoring well placement to detect potential contaminant arrival in a regional aquifer located at Sandia and Mortandad Canyons.