A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework

Mohamad El Gharamti, YOUSSEF M. MARZOUK, Xun Huan, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Optimizing wells placement may help in better understanding subsurface solute transport and detecting contaminant plumes. In this work, we use the ensemble Kalman filter (EnKF) as a data assimilation tool and propose a greedy observational design algorithm to optimally select aquifer wells locations for updating the prior contaminant ensemble. The algorithm is greedy in the sense that it operates sequentially, without taking into account expected future gains. The selection criteria is based on maximizing the information gain that the EnKF carries during the update of the prior uncertainties. We test the efficiency of this algorithm in a synthetic aquifer system where a contaminant plume is set to migrate over a 30 years period across a heterogenous domain.
Original languageEnglish (US)
Title of host publicationDynamic Data-Driven Environmental Systems Science
PublisherSpringer Nature
Pages301-309
Number of pages9
ISBN (Print)9783319251370
DOIs
StatePublished - Nov 27 2015

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