A Greedy Approach for Placement of Subsurface Aquifer Wells in an Ensemble Filtering Framework
KAUST DepartmentEarth Science and Engineering Program
Applied Mathematics and Computational Science Program
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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.
PublisherSpringer Science + Business Media
Conference/Event name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014