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    Cross-equalization of time-lapse seismic data using recurrent neural networks

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    cross equalization.pdf
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    Accepted manuscript
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    Type
    Conference paper
    Authors
    Alali, Abdullah A. cc
    Kazei, Vladimir cc
    Sun, Bingbing
    Smith, Robert
    Nivlet, Phlippe
    Bakulin, Andrey
    Alkalifah, Tariq
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    King Abdullah University of Science and Technology
    Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/666573
    
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    Abstract
    Time-lapse seismic uses repetitive seismic surveys to monitor the fluid in the subsurface. Ideally, the time-lapse data should be identical except for at the target region (i.e., the reservoir), where the fluid changes occur. Unfortunately, it is almost impossible to have identical data for various reasons, such as the static changes in the near-surface or the varying positioning of sources and receivers between surveys. To increase the accuracy of the 4D signal and reduce the noise, we propose to process the time-lapse data using a machine-learning methodology. Specifically, we train a recurrent neural network (RNN) model to map the data from monitor to baseline. The learned RNN model would reveal 4D overburden changes. Therefore, the difference between the predicted baseline and the actual baseline data stets will represent the target signal. We validate the method on synthetic data and show the improvements of the 4D signal by imaging the reservoir and computing the normalized root mean square.
    Citation
    Alali, A., Kazei, V., Sun, B., Smith, R., Nivlet, P., Bakulin, A., & Alkalifah, T. (2020). Cross-equalization of time-lapse seismic data using recurrent neural networks. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3424773.1
    Publisher
    Society of Exploration Geophysicists
    Conference/Event name
    © 2020 Society of Exploration Geophysicists SEG International Exposition and 90th Annual Meeting
    DOI
    10.1190/segam2020-3424773.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3424773.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3424773.1
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