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    Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes

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    Type
    Article
    Authors
    Katterbauer, Klemens cc
    Arango, Santiago
    Sun, Shuyu cc
    Hoteit, Ibrahim cc
    KAUST Department
    Computational Transport Phenomena Lab
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2015-12-24
    Online Publication Date
    2015-12-24
    Print Publication Date
    2016-04
    Permanent link to this record
    http://hdl.handle.net/10754/592603
    
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    Abstract
    Advances in electromagnetic imaging techniques have led to the growing utilization of this technology for reservoir monitoring and exploration. These exploit the strong conductivity contrast between the hydrocarbon and water phases and have been used for mapping water front propagation in hydrocarbon reservoirs and enhancing the characterization of the reservoir formation. The conventional approach for the integration of electromagnetic data is to invert the data for saturation properties and then subsequently use the inverted properties as constraints in the history matching process. The non-uniqueness and measurement errors may however make this electromagnetic inversion problem strongly ill-posed, leading to potentially inaccurate saturation profiles. Another limitation of this approach is the uncertainty of Archie's parameters in relating rock conductivity to water saturation, which may vary in the reservoir and are generally poorly known. We present an Ensemble Kalman Filter framework for efficiently integrating electromagnetic data into the history matching process and for simultaneously estimating the Archie's parameters and the variance of the observation error of the electromagnetic data. We apply the proposed framework to a compositional reservoir model. We aim at assessing the relevance of EM data for estimating the different hydrocarbon components of the reservoir. The experimental results demonstrate that the individual hydrocarbon components are generally well matched, with nitrogen exhibiting the strongest improvement. The estimated observation error standard deviations are also within expected levels (between 5 and 10%), significantly contributing to the robustness of the proposed EM history matching framework. Archie's parameter estimates approximate well the reference profile and assist in the accurate description of the electrical conductivity properties of the reservoir formation, hence leading to estimation accuracy improvements of around 15%.
    Citation
    Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes 2015 Journal of Petroleum Science and Engineering
    Publisher
    Elsevier BV
    Journal
    Journal of Petroleum Science and Engineering
    DOI
    10.1016/j.petrol.2015.12.015
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0920410515302242
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.petrol.2015.12.015
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Computational Transport Phenomena Lab

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