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

Handle URI:
http://hdl.handle.net/10754/622129
Title:
A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework
Authors:
El Gharamti, Mohamad ( 0000-0002-7229-8366 ) ; Marzouk, Youssef M.; Huan, Xun; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
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.
KAUST Department:
Earth Science and Engineering Program; Applied Mathematics and Computational Science Program
Citation:
Gharamti ME, Marzouk YM, Huan X, Hoteit I (2015) A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework. Lecture Notes in Computer Science: 301–309. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_27.
Publisher:
Springer Science + Business Media
Journal:
Dynamic Data-Driven Environmental Systems Science
Conference/Event name:
1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014
Issue Date:
26-Nov-2015
DOI:
10.1007/978-3-319-25138-7_27
Type:
Conference Paper
ISSN:
0302-9743; 1611-3349
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorEl Gharamti, Mohamaden
dc.contributor.authorMarzouk, Youssef M.en
dc.contributor.authorHuan, Xunen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2017-01-02T08:10:20Z-
dc.date.available2017-01-02T08:10:20Z-
dc.date.issued2015-11-26en
dc.identifier.citationGharamti ME, Marzouk YM, Huan X, Hoteit I (2015) A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework. Lecture Notes in Computer Science: 301–309. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_27.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-319-25138-7_27en
dc.identifier.urihttp://hdl.handle.net/10754/622129-
dc.description.abstractOptimizing 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.en
dc.publisherSpringer Science + Business Mediaen
dc.titleA Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Frameworken
dc.typeConference Paperen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalDynamic Data-Driven Environmental Systems Scienceen
dc.conference.date2014-11-05 to 2014-11-07en
dc.conference.name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014en
dc.conference.locationCambridge, MA, USAen
dc.contributor.institutionMohn-Sverdrup Center, Nansen Environmental and Remote Sensing Center, Bergen, Norwayen
dc.contributor.institutionDepartment of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, United Statesen
kaust.authorEl Gharamti, Mohamaden
kaust.authorHoteit, Ibrahimen
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