Probabilistic Contaminant Source Assessment-Getting the Most Out of Field Measurements

Abstract

This paper describes a methodology for undertaking probabilistic investigations into the locations at which contaminants have leaked into a groundwater system. The methodology is built with highly parameterized, stochastic history-matching in mind. It is able to reduce uncertainties associated with estimates of subsurface hydraulic properties at the same time as it reduces uncertainties associated with inferences of contaminant sources. Particles are used to simulate contaminant movement. This reduces simulator execution time while increasing simulator stability. Borehole measurements of groundwater chemistry are endowed with a binary classification that indicates the presence, or otherwise, of a contaminant plume. This classification is transferred to passing particles as a detect or nondetect status awarded to their trajectories. Because this status is continuous with respect to model parameters, the latter can be adjusted in order to ensure that the same trajectory cannot possess both a detect status and a nondetect status. Particle trajectory statuses can be assigned to model cells from which they are released. By calculating cell statistics using a large number of history-match-constrained, stochastic parameter fields, probability maps can be drawn. We illustrate two of these. The first maps the probability that a contaminant sourced at a particular location will go undetected by the current observation network. The second maps the probability that a contaminant source cannot exist at a particular location. The method is extended to examine the worth of supplementing an existing observation network with new wells.