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    Multi Data Reservoir History Matching using the Ensemble Kalman Filter

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    Name:
    Katterbauer, Klemens Final Dissertation.pdf
    Size:
    12.84Mb
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    PDF
    Description:
    Katterbauer, Klemens Final Dissertation
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    Type
    Dissertation
    Authors
    Katterbauer, Klemens cc
    Advisors
    Hoteit, Ibrahim cc
    Sun, Shuyu cc
    Committee members
    Schuster, Gerard T. cc
    Jonsson, Sigurjon cc
    Marsala, Alberto
    Program
    Earth Science and Engineering
    KAUST Department
    Physical Science and Engineering (PSE) Division
    Date
    2015-05
    Embargo End Date
    2016-05-20
    Permanent link to this record
    http://hdl.handle.net/10754/555580
    
    Metadata
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    Access Restrictions
    At the time of archiving, the student author of this dissertation opted to temporarily restrict access to it. The full text of this dissertation became available to the public after the expiration of the embargo on 2016-05-20.
    Abstract
    Reservoir history matching is becoming increasingly important with the growing demand for higher quality formation characterization and forecasting and the increased complexity and expenses for modern hydrocarbon exploration projects. History matching has long been dominated by adjusting reservoir parameters based solely on well data whose spatial sparse sampling has been a challenge for characterizing the flow properties in areas away from the wells. Geophysical data are widely collected nowadays for reservoir monitoring purposes, but has not yet been fully integrated into history matching and forecasting fluid flow. In this thesis, I present a pioneering approach towards incorporating different time-lapse geophysical data together for enhancing reservoir history matching and uncertainty quantification. The thesis provides several approaches to efficiently integrate multiple geophysical data, analyze the sensitivity of the history matches to observation noise, and examine the framework’s performance in several settings, such as the Norne field in Norway. The results demonstrate the significant improvements in reservoir forecasting and characterization and the synergy effects encountered between the different geophysical data. In particular, the joint use of electromagnetic and seismic data improves the accuracy of forecasting fluid properties, and the usage of electromagnetic data has led to considerably better estimates of hydrocarbon fluid components. For volatile oil and gas reservoirs the joint integration of gravimetric and InSAR data has shown to be beneficial in detecting the influx of water and thereby improving the recovery rate. Summarizing, this thesis makes an important contribution towards integrated reservoir management and multiphysics integration for reservoir history matching.
    Citation
    Katterbauer, K. (2015). Multi Data Reservoir History Matching using the Ensemble Kalman Filter. KAUST Research Repository. https://doi.org/10.25781/KAUST-3D3I9
    DOI
    10.25781/KAUST-3D3I9
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-3D3I9
    Scopus Count
    Collections
    PhD Dissertations; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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