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    Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter

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    Sana_IGARSS2015.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Sana, Furrukh cc
    Katterbauer, Klemens cc
    Al-Naffouri, Tareq Y. cc
    Hoteit, Ibrahim cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Electrical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2015-11-12
    Online Publication Date
    2015-11-12
    Print Publication Date
    2015-07
    Permanent link to this record
    http://hdl.handle.net/10754/583292
    
    Metadata
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    Abstract
    Recovering information on subsurface geological features, such as flow channels, holds significant importance for optimizing the productivity of oil reservoirs. The flow channels exhibit high permeability in contrast to low permeability rock formations in their surroundings, enabling formulation of a sparse field recovery problem. The Ensemble Kalman filter (EnKF) is a widely used technique for the estimation of subsurface parameters, such as permeability. However, the EnKF often fails to recover and preserve the channel structures during the estimation process. Compressed Sensing (CS) has shown to significantly improve the reconstruction quality when dealing with such problems. We propose a new scheme based on CS principles to enhance the reconstruction of subsurface geological features by transforming the EnKF estimation process to a sparse domain representing diverse geological structures. Numerical experiments suggest that the proposed scheme provides an efficient mechanism to incorporate and preserve structural information in the estimation process and results in significant enhancement in the recovery of flow channel structures.
    Citation
    Sana, F., Katterbauer, K., Al-Naffouri, T., & Hoteit, I. (2015). Enhanced recovery of subsurface geological structures using compressed sensing and the Ensemble Kalman filter. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi:10.1109/igarss.2015.7326474
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
    Conference/Event name
    2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
    DOI
    10.1109/IGARSS.2015.7326474
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7326474
    ae974a485f413a2113503eed53cd6c53
    10.1109/IGARSS.2015.7326474
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Electrical and Computer Engineering Program; Earth Science and Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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