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

  • Mohamad E. Gharamti
  • Youssef M. Marzouk
  • Xun Huan
  • Ibrahim Hoteit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8964)

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.

Keywords

Greedy Algorithm Information Gain Observation Well Forecast Ensemble Greedy Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohamad E. Gharamti
    • 1
    • 4
  • Youssef M. Marzouk
    • 2
  • Xun Huan
    • 2
  • Ibrahim Hoteit
    • 1
    • 3
  1. 1.Earth Sciences and EngineeringKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Department of Aeronautics and AstronauticsMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Applied Mathematics and Computational SciencesKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  4. 4.Mohn-Sverdrup Center, Nansen Environmental and Remote Sensing CenterBergenNorway

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