A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework
KAUST DepartmentApplied Mathematics and Computational Science Program
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Online Publication Date2015-11-27
Print Publication Date2015
Permanent link to this recordhttp://hdl.handle.net/10754/622129
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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.
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.
Conference/Event name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014