HGL’s PlumeSeekerTM technology optimizes monitoring well networks to efficiently and accurately delineate contaminant plumes in groundwater. PlumeSeekerTM combines geostatistics and 3-D groundwater flow and contaminant transport modeling to predict the spatial distribution of groundwater plumes that exceed a specified screening or remedial concentration.

The technology is particularly effective for defining large, diffuse plumes or contamination in complex hydrogeologic settings. PlumeSeekerTM identifies the minimum number and optimal locations of monitoring wells needed to delineate the boundary of a plume and provides a quantitative measure of confidence in how well that plume is defined. Kalman filtering is used to tune temporal results to observed water levels and concentrations, and optimal new well locations are determined based on statistical measures, as well as on logistical considerations such as access limitations. Cost savings realized with optimally chosen monitoring networks can include reduced well installation costs, especially in deep or complex subsurface environments, and reduced monitoring and O&M costs.

PlumeSeekerTM conducts stochastic simulations of groundwater flow and contaminant transport using HGL’s MODFLOW-SURFACTTM code and a conditional Monte Carlo procedure that honors available data and the correlation structure of site parameters such as hydraulic conductivity. PlumeSeekerTM builds an uncertainty model of the plume in the form of a covariance matrix. PlumeSeekerTM simulations can be run in serial mode on a standard PC or in parallel mode in the Cloud environment. HGL is currently applying the PlumeSeekerTM technology at an oil refinery and at PFAS sites. The figure below summarizes the Quantify, Identify, Save methodology employed by PlumeSeekerTM.

PlumeSeekerTM Methodology

See animation below describing HGL’s PlumeSeekerTM technology:

PlumeSeekerTM Technology (Runtime 8:32)

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