Non-uniform optimal survey design principles

    公开(公告)号:US10809402B2

    公开(公告)日:2020-10-20

    申请号:US15641916

    申请日:2017-07-05

    Abstract: Method for acquiring seismic data is described. The method includes determining a non-uniform optimal sampling design that includes a compressive sensing sampling grid. Placing a plurality of source lines or receiver lines at a non-uniform optimal line interval. Placing a plurality of receivers or nodes at a non-uniform optimal receiver interval. Towing a plurality of streamers attached to a vessel, wherein the plurality of streamers is spaced apart at non-uniform optimal intervals based on the compressive sensing sampling grid. Firing a plurality of shots from one or more seismic sources at non-uniform optimal shot intervals. Acquiring seismic data via the plurality of receivers or nodes.

    Methods for simultaneous source separation

    公开(公告)号:US11294088B2

    公开(公告)日:2022-04-05

    申请号:US16833975

    申请日:2020-03-30

    Abstract: A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.

    Methods for simultaneous source separation

    公开(公告)号:US10605941B2

    公开(公告)日:2020-03-31

    申请号:US14974060

    申请日:2015-12-18

    Abstract: A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.

    SYSTEMS AND METHODS OF GENERATING HIGH RESOLUTION SEISMIC USING SUPER RESOLUTION INVERSION

    公开(公告)号:US20240045091A1

    公开(公告)日:2024-02-08

    申请号:US18230815

    申请日:2023-08-07

    CPC classification number: G01V1/302 G01V2210/632 G01V2210/665

    Abstract: Systems and methods for reservoir modeling include a super resolution seismic data conversion platform for converting input seismic data into high resolution output seismic data. The super resolution seismic data conversion platform can perform a super resolution inversion on the input seismic data by imposing sparsity and/or coherency assumptions on geophysical parameters represented by wavelet information of the input seismic data. For instance, a seismic trace interval can be determined, and both a reflection coefficient and an acoustic impedance of the seismic trace interval can be constrained. An optimization problem, using the constrained reflection coefficient and the constrained acoustic impedance, can be generated and/or solved by a sparse inversion. As such, a vertical resolution, as well as a seismic bandwidth, of super resolution output seismic data can be increased, improving subterranean feature (e.g., sand and/or shale characteristics) interpretation and well planning and construction.

    Compressive sensing
    6.
    发明授权

    公开(公告)号:US09632193B2

    公开(公告)日:2017-04-25

    申请号:US14529690

    申请日:2014-10-31

    Abstract: Computer-implemented method for determining optimal sampling grid during seismic data reconstruction includes: a) constructing an optimization model, via a computing processor, given by minu∥Su∥1s.t. ∥Ru−b∥2≦σ wherein S is a discrete transform matrix, b is seismic data on an observed grid, u is seismic data on a reconstruction grid, and matrix R is a sampling operator; b) defining mutual coherence as μ ≤ C S ⁢ m ( log ⁢ ⁢ n ) 6 , wherein C is a constant, S is a cardinality of Su, m is proportional to number of seismic traces on the observed grid, and n is proportional to number of seismic traces on the reconstruction grid; c) deriving a mutual coherence proxy, wherein the mutual coherence proxy is a proxy for mutual coherence when S is over-complete and wherein the mutual coherence proxy is exactly the mutual coherence when S is a Fourier transform; and d) determining a sample grid r*=arg minr μ(r).

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