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    High-resolution reservoir characterization using deep learning aided elastic full-waveform inversion: The North Sea field data example

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    Type
    Article
    Authors
    Zhang, Zhendong cc
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2020-05-08
    Online Publication Date
    2020-05-08
    Print Publication Date
    2020-07-01
    Submitted Date
    2019-05-27
    Permanent link to this record
    http://hdl.handle.net/10754/661506
    
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    Abstract
    Reservoir characterization is an essential component of oil and gas production, as well as exploration. Classic reservoir characterization algorithms, both deterministic and stochastic, are typically based on stacked images and rely on simplifications and approximations to the subsurface (e.g., assuming linearized reflection coefficients). Elastic full-waveform inversion, which aims to match the waveforms of pre-stack seismic data, potentially provide more accurate high-resolution reservoir characterization from seismic data. However, full-waveform inversion can easily fail to characterize deep-buried reservoirs due to illumination limitations. We present a deep learning aided elastic full-waveform inversion strategy using observed seismic data and available well logs in the target area. Five facies are extracted from the well and then connected to the inverted P- and S-wave velocities using trained neural networks, which correspond to the subsurface facies distribution. Such a distribution is further converted to the desired reservoir-related parameters such as velocities and anisotropy parameters using a weighted summation. Finally, we update these estimated parameters by matching the resulting simulated wavefields to the observed seismic data, which corresponds to another round of elastic full-waveform inversion aided by the a priori knowledge gained from the predictions of machine learning. A North Sea field data example, the Volve Oil Field data set, indicates that the use of facies as prior helps resolve the deep-buried reservoir target better than the use of only seismic data.
    Citation
    Zhang, Z., & Alkhalifah, T. (2020). High-resolution reservoir characterization using deep learning aided elastic full-waveform inversion: The North Sea field data example. GEOPHYSICS, 1–47. doi:10.1190/geo2019-0340.1
    Sponsors
    We thank Jeffrey Shragge, Weichang Li, Wenyi Hu and two anonymous reviewers, for the effort put into the review of the manuscript. We also want to thank Equinor and the former Volve license partners ExxonMobil E&P Norway AS and Bayerngas Norge AS, for the release of the Volve data. The views expressed in this paper are the views of the authors and do not necessarily reflect the views of Equinor and the former Volve field license partners. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.
    Publisher
    Society of Exploration Geophysicists
    Journal
    GEOPHYSICS
    DOI
    10.1190/geo2019-0340.1
    10.1190/segam2019-3198055.1
    Additional Links
    https://library.seg.org/doi/10.1190/geo2019-0340.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/geo2019-0340.1
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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