Publications

This work develops a new approach for generating stochastic permeability fields from complex three-dimensional fracture networks to support physical and economic performance analyses of enhanced geothermal systems (EGS). The approach represents multiple fracture sets with different dips, orientations, apertures, spacing, and lengths by homogenizing discrete fracture permeabilities onto a regular grid using continuum methods. A previously developed algorithm is used for combining multiple fracture sets at arbitrary orientations into a full anisotropic permeability tensor for every grid block. Fracture properties for each grid cell can either be independently specified or spatially correlated using a variety of probability distributions. The generated stochastic permeability fields are used in mass and heat transport models to represent a variety of complex fracture networks to provide realistic simulations of long-term thermal performance.

Assessing the impact of parameter estimation accuracy in models of heterogeneous, three-dimensional (3-D) groundwater systems is critical for predictions of solute transport. A unique experimental data set provides concentration breakthrough curves (BTCs) measured at a 0.253 cm 3 scale over the 13 × 8 × 8 cm3 domain (∼53,000 measurement locations). Advective transport is used to match the first temporal moments of BTCs (or mean arrival times, m1) averaged at 0.253 and 1.0 cm3 scales through simultaneous inversion of highly parameterized heterogeneous hydraulic conductivity (K) and porosity (φ) fields. Pilot points parameterize the fields within eight layers of the 3-D medium, and estimations are completed with six different models of the K-φ relationship. Parameter estimation through advective transport shows accurate estimation of the observed m1 values. Results across the six different K-φ relationships have statistically similar fits to the observed m1 values and similar spatial estimates of m1 along the main flow direction. The resulting fields provide the basis for forward transport modeling of the advection-dispersion equation (ADE). Using the estimated K and φ fields demonstrates that advective transport coupled with inversion using dense spatial field parameterization provides an efficient surrogate for the ADE. These results indicate that there is not a single set of model parameters, or a single K-φ relationship, that leads to a best representation of the actual experimental sand packing pattern (i.e., nonuniqueness). Additionally, knowledge of the individual sand K and values along with their arrangement in the 3-D experiment does not reproduce the observed transport results at small scales. Small-scale variation in the packing and mixing of the sands causes large deviations from the expected transport results as highlighted in forward ADE simulations. Highly parameterized inverse estimation is able to identify those regions where variations in mixing and packing alter the expected property values and significantly improve results relative to the nave application of the experimentally derived property values. Impacts of the observation scale, the scale over which results are averaged and the number of observations and parameters on the final estimations are also examined. Results indicate existence of a representative element volume (REV) at 0.25 cm3, the existence of subgrid scale heterogeneity that impacts transport and the accuracy of highly parameterized models with even relatively small amounts of observations. Finally, this work suggests that local-heterogeneity features below the REV scale are difficult to incorporate into parameterized models, highlighting the importance of addressing prediction uncertainty for small-scale variability (i.e., uncaptured variability) in modeling practice. © 2012. American Geophysical Union. All Rights Reserved.

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Water quality signals from sensors provide a snapshot of the water quality at the monitoring station at discrete sample times. These data are typically processed by event detection systems to determine the probability of a water quality event occurring at each sample time. Inherent noise in sensor data and rapid changes in water quality due to operational actions can cause false alarms in event detection systems. While the event determination can be made solely on the data from each signal at the current time step, combining data across signals and backwards in time can provide a richer set of data for event detection. Here we examine the ability of algebraic combinations and other transformations of the raw signals to further decrease false alarms. As an example, using operational events such as one or more pumps turning on or off to define a period of decreased detection sensitivity is one approach to limiting false alarms. This method is effective when lag times are known or when the sensors are co-located with the equipment causing the change. The CANARY software was used to test and demonstrate these combinatorial techniques for improving sensitivity and decreasing false alarms on both background data and data with simulated events. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. © 2012 ASCE.

We present a mixed-integer linear programming formulation to determine optimal locations for manual grab sampling after the detection of contaminants in a water distribution system. The formulation selects optimal manual grab sample locations that maximize the total pair-wise distinguishability of candidate contamination events. Given an initial contaminant detection location, a source inversion is performed that will eliminate unlikely events resulting in a much smaller set of candidate contamination events. We then propose a cyclical process where optimal grab samples locations are determined and manual grab samples taken. Relying only on YES/NO indicators of the presence of contaminant, source inversion is performed to reduce the set of candidate contamination events. The process is repeated until the number of candidate events is sufficiently small. Case studies testing this process are presented using water network models ranging from 4 to approximately 13000 nodes. The results demonstrate that the contamination event can be identified within a remarkably small number of sampling cycles using very few sampling teams. Furthermore, solution times were reasonable making this formulation suitable for real-time settings. © 2012 ASCE.

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