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

Abstract

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.