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

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.

Publication
Dynamic Data-Driven Environmental Systems Science, First International Conference, DyDESS 2014
Date
Links
Citation
M. E. Gharamti, Y. M. Marzouk, X. Huan, and I. Hoteit. A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework. In Dynamic Data-Driven Environmental Systems Science, First International Conference, DyDESS 2014, pp. 301–309, Cambridge, MA, 2015. https://dx.doi.org/10.1007/978-3-319-25138-7_27

BibTeX

@inproceedings{Gharamti2015,
address = {Cambridge, MA},
author = {Gharamti, Mohamad E. and Marzouk, Youssef M. and Huan, Xun and Hoteit, Ibrahim},
booktitle = {Dynamic Data-Driven Environmental Systems Science (First International Conference, DyDESS)},
doi = {10.1007/978-3-319-25138-7_27},
pages = {301--309},
title = {{A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework}},
year = {2015}
}