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    Target-oriented time-lapse waveform inversion using a deep learning-assisted regularization

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    Type
    Conference Paper
    Authors
    Li, Yuanyuan cc
    Alkhalifah, Tariq Ali cc
    Guo, Qiang
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2020-09-30
    Online Publication Date
    2020-09-30
    Print Publication Date
    2020-09-30
    Permanent link to this record
    http://hdl.handle.net/10754/665478
    
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    Abstract
    Detection of the property changes in the subsurface during production is important, yet it is also very challenging considering that these changes are often very settle. The quantitative evaluation of the subsurface property obtained by full waveform inversion (FWI) allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational burden. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We apply a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small target zone. The redatuming technique enables retrieving time-lapse virtual data for the target-oriented inversion. Considering the injection and production wells are often present in the target zone, we can incorporate the well information to the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network (DNN) to learn the mathematical relationship between the inverted model and the facies interpreted from well logs. The trained network is employed to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that the proposed method is capable of inverting for the time-lapse changes effectively in the target zone by incorporating the learned model information from well logs.
    Citation
    Li, Y., Alkhalifah, T., & Guo, Q. (2020). Target-oriented time-lapse waveform inversion using a deep learning-assisted regularization. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425737.1
    Sponsors
    We would like to thank the Shaheen supercomputing Laboratory in KAUST for their computational support. We thank KAUST for its support and SWAG for collaborative environment.
    Publisher
    Society of Exploration Geophysicists
    DOI
    10.1190/segam2020-3425737.1
    Additional Links
    https://library.seg.org/doi/10.1190/segam2020-3425737.1
    ae974a485f413a2113503eed53cd6c53
    10.1190/segam2020-3425737.1
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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