rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: A
    title: hybrid ensemble Kalman filter for nonlinear dynamics
  name:
    name part: Watanabe, Shingo.
    role:
      role term: creator
  name:
    name part: Datta-Gupta, Akhil
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: In this thesis, we propose two novel approaches for hybrid Ensemble Kalman Filter (EnKF) to overcome limitations of the traditional EnKF. The first approach is to swap the ensemble mean for the ensemble mode estimation to improve the covariance calculation in EnKF. The second approach is a coarse scale permeability constraint while updating in EnKF. Both hybrid EnKF approaches are coupled with the streamline based Generalized Travel Time Inversion (GTTI) algorithm for periodic updating of the mean of the ensemble and to sequentially update the ensemble in a hybrid fashion. Through the development of the hybrid EnKF algorithm, the characteristics of the EnKF are also investigated. We found that the limits of the updated values constrain the assimilation results significantly and it is important to assess the measurement error variance to have a proper balance between preserving the prior information and the observation data misfit. Overshooting problems can be mitigated with the streamline based covariance localizations and normal score transformation of the parameters to support the Gaussian error statistics. The swapping mean and mode estimation approach can give us a better matching of the data as long as the mode solution of the inversion process is satisfactory in terms of matching the observation trajectory. The coarse scale permeability constrained hybrid approach gives us better parameter estimation in terms of capturing the main trend of the permeability field and each ensemble member is driven to the posterior mode solution from the inversion process. However the WWCT responses and pressure responses need to be captured through the inversion process to generate physically plausible coarse scale permeability data to constrain hybrid EnKF updating. Uncertainty quantification methods for EnKF were developed to verify the performance of the proposed hybrid EnKF compared to the traditional EnKF. The results show better assimilation quality through a sequence of …
  note: by Shingo Watanabe.
  note: "Major Subject: Petroleum Engineering"
  note: Title from author supplied metadata (automated record created 2011-02-28 11:02:00).
  note: Electronic resource.
  note: Master of Science Texas A&M University 2009 http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7412
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Petroleum Engineering
  subject:
    topic: Inverse Problem
  subject:
    topic: History Matching
  subject:
    topic: Hybrid Ensemble Kalman Filter
  subject:
    topic: Ensemble Kalman Filter
  subject:
    topic: Reservoir Characterization
  subject:
    topic: Streamline Simulation
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7412
  record info:
    record content source: TXA
    record creation date: 110303
    record change date: 20151202115552.0
    record identifier: in00002669645