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[1] Subsurface aquifer characterization often involves high parameter dimensionality and requires tremendous computational resources if employing a full Bayesian approach. Ensemble-based data assimilation techniques, including filtering and smoothing, are computationally efficient alternatives. Despite the increasing use of ensemble-based methods in assimilating flow and transport related data for subsurface aquifer characterization, most applications have been limited to synthetic studies or twodimensional problems. In this study, we applied ensemble-based techniques adapted for parameter estimation, including the p-space ensemble Kalman filter and ensemble smoother, for assimilating field tracer experimental data obtained from the Integrated Field Research Challenge (IFRC) site at the Hanford 300 Area. The forward problem was simulated using the massively parallel three-dimensional flow and transport code PFLOTRAN to effectively deal with the highly transient flow boundary conditions at the site and to meet the computational demands of ensemble-based methods. This study demonstrates the effectiveness of ensemble-based methods for characterizing a heterogeneous aquifer by assimilating experimental tracer data, with refined prior information obtained from assimilating other types of data available at the site. It is demonstrated that high-performance computing enables the use of increasingly mechanistic nonlinear forward simulations for a complex system within the data assimilation framework with reasonable turnaround time.Citation: Chen, X., G. E. Hammond, C. J. Murray, M. L. Rockhold, V. R. Vermeul, and J. M. Zachara (2013), Application of ensemble-based data assimilation techniques for aquifer characterization using tracer data at Hanford 300 area, Water Resour. Res., 49,[7064][7065][7066][7067][7068][7069][7070][7071][7072][7073][7074][7075][7076]

Table 3.1. Drilling, sampling, geophysical logging, and well construction information for IFRC wells Well Name Well ID Function Samples Drilling Order Start Drilling End Drilling Geophysical Logging Total Depth (ft) Length Screen (ft) Top Screen Depth (ft) Bottom Screen Depth (ft) Bottom End Cap (ft) Instrument Depth (ft) % Recovery Hanford/Ringold Contact Depth (ft)

SummaryThe objective of this project is to develop a method to emplace apatite precipitate in the 100-N Area vadose zone, resulting in sorption and ultimately incorporation of Sr-90 into the apatite structure. The Ca-citrate-phosphate (Ca-citrate-PO 4 ) solution can be infiltrated into unsaturated sediments to result in apatite precipitate to provide effective treatment of Sr-90 contamination. Microbial redistribution during solution infiltration and a high rate of citrate biodegradation for river water microbes (water used for solution infiltration) produces a relatively even spatial distribution of the citrate biodegradation rate and ultimately, apatite precipitate in the sediment. Manipulation of the Ca-citrate-PO 4 solution infiltration strategy can be used to induce apatite precipitation in the lower half of the vadose zone (where most of the Sr-90 is located) and within low-K layers (which may have higher Sr-90 concentrations):• infiltration rate: more rapid Ca-citrate-PO 4 solution infiltration resulted in greater depth of apatite precipitate, but less lateral spreading• decrease in infiltration rate: rapid then slow solution infiltration resulted in greater lateral spreading of the apatite precipitate at depth• sequential solution then water infiltration: water infiltration after solution infiltration effectively moved the apatite mass to depth in homogeneous sediment systems• solution concentration: infiltration of higher Ca-citrate-PO 4 solution concentrations resulted in a greater depth of apatite precipitate• infiltration cycles: repeated infiltration of the Ca-citrate-PO 4 solution with time between cycles to allow for water drainage increased depth and greater lateral spread of apatite• low-K zones had a higher apatite precipitate due to higher residual water content• solution then water infiltration into a complex heterogeneous system with low-K and high-K discontinuous zones (8 ft high) showed high apatite precipitate in low-K and medium-grained sediment, but low treatment in high-K zones due to low water contentThe most effective infiltration strategy to precipitate apatite at depth (and with sufficient lateral spread) was to infiltrate a high concentration solution (6 mM Ca, 15 mM citrate, 60 mM PO 4 ) at a rapid rate (near ponded conditions), followed by rapid, then slow water infiltration. Repeated infiltration events, with sufficient time between events to allow water drainage in the sediment profile can be used to build up the mass of apatite precipitate at greater depth. Low-K heterogeneities were effectively treated, as the higher residual water content maintained in these zones resulted in higher apatite precipitate concentration. High-K zones did not receive sufficient treatment by infiltration, although an alternative strategy of air/surfactant (foam) was demonstrated effective for targeting high-K zones. The flow rate manipulation used in this study to treat specific depths and heterogeneities is not as easy to implement at field scale due to the lack of characterization of heterogene...

This study presents a stochastic, three-dimensional characterization of a heterogeneous hydraulic conductivity field within DOE's Hanford 300 Area site, Washington, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity field, using zeroth-order temporal moments of pressure buildup curves. We applied a newly developed Bayesian geostatistical inversion framework, the method of anchored distributions (MAD), to obtain a joint posterior distribution of geostatistical parameters and local log-transmissivities at multiple locations. The unique aspects of MAD that make it suitable for this purpose are its ability to integrate multi-scale, multi-type data within a Bayesian framework and to compute a nonparametric posterior distribution. After we combined the distribution of transmissivities with depth-discrete relative-conductivity profile from the EBF data, we inferred the three-dimensional geostatistical parameters of the log-conductivity field, using the Bayesian model-based geostatistics. Such consistent use of the Bayesian approach throughout the procedure enabled us to systematically incorporate data uncertainty into the final posterior distribution. The method was tested in a synthetic study and validated using the actual data that was not part of the estimation. Results showed broader and skewed posterior distributions of geostatistical parameters except for the mean, which suggests the importance of inferring the entire distribution to quantify the parameter uncertainty