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    High-Resolution Regularized Elastic Full Waveform Inversion Assisted by Deep Learning

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    Type
    Conference Paper
    Authors
    Li, Y.
    Alkhalifah, Tariq Ali cc
    Zhang, Z.
    KAUST Department
    King Abdullah University of Science and Technology
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020
    Embargo End Date
    2021-12-30
    Permanent link to this record
    http://hdl.handle.net/10754/668223
    
    Metadata
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    Abstract
    Elastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface. However, in practice, the resolution and illumination of EFWI are limited by the bandwidth and aperture of seismic data. The often-present wells in developed fields as well as some exploratory regions can provide a complementary illumination to the target area. We, thus, introduce a regularization technique, which combines the surface seismic and well log data, to help improve the resolution of EFWI. Using deep fully connected layers, we train our neural network to identify the relation between the means and variances at the well, with the inverted model from an initial EFWI application. The network is used to map the means and variances extracted from the well to the whole model domain. We then perform another EFWI in which we fit the predicted data to the observed one as well as fit the model over a Gaussian window to the predicted means the variances. The tests on the synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and illumination of deep-buried reservoirs, which are less illuminated by seismic data.
    Citation
    Li, Y., Alkhalifah, T., & Zhang, Z. (2020). High-Resolution Regularized Elastic Full Waveform Inversion Assisted by Deep Learning. EAGE 2020 Annual Conference & Exhibition Online. doi:10.3997/2214-4609.202010281
    Sponsors
    We thank Statoil ASA and the Volve license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS, for the release of the Volve data. The Shaheen supercomputing Laboratory in KAUST provides the computational support.
    Publisher
    EAGE Publications
    Conference/Event name
    EAGE2020: Annual Conference Online
    DOI
    10.3997/2214-4609.202010281
    Additional Links
    https://www.earthdoc.org/content/papers/10.3997/2214-4609.202010281
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.202010281
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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