公开(公告)号：US20190293813A1

公开(公告)日：2019-09-26

申请号：US16196809

申请日：2018-11-20

Applicant: ConocoPhillips Company

Inventor： Zhengxue LI ,  Yunqing SHEN ,  Jianxing HU ,  Yong MA ,  Feng CHEN ,  Yu ZHANG ,  Chengbo LI ,  Charles C. MOSHER ,  Frank D. JANISZEWSKI ,  Laurence S. WILLIAMS ,  Jeffrey MALLOY ,  Bradley BANKHEAD ,  Jon ANDERSON

IPC: G01V1/28 ,  G01V1/38

Abstract: Method for acquiring seismic data is described. The method includes obtaining undersampled seismic data acquired from a non-uniform sampling grid. Attenuating multiples from the undersampled seismic data.

公开(公告)号：US20170168181A1

公开(公告)日：2017-06-15

申请号：US15370412

申请日：2016-12-06

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Yu ZHANG ,  Haiyan ZHANG

IPC: G01V1/36 ,  G01V1/30

CPC classification number: G01V1/36 ,  G01V1/303 ,  G01V2210/56

Abstract: Methods for processing seismic data are described. The method includes: obtaining seismic data; solving a series of partial differential wave equations, wherein a first partial differential wave equation describes propagation of a seismic wave going from a first reflector to a second reflector, wherein a second partial differential wave equation describes propagation of a seismic wave going from a second reflector to a third reflector, and wherein a third partial differential wave equation describes propagation of a seismic wave going from a third reflector to a seismic receiver, wherein outputting predicted internal multiples for further imaging or attenuation.

公开(公告)号：US20220236436A1

公开(公告)日：2022-07-28

申请号：US17587765

申请日：2022-01-28

Applicant: CONOCOPHILLIPS COMPANY

Inventor： Chengbo LI ,  Yu ZHANG

IPC: G01V1/36

Abstract: Leveraging migration and demigration, here we propose a learning-based approach for fast denoising with applications to fast-track processing. The method is designed to directly work on raw data without separating each noise type and character. The automatic attenuation of noise is attained by performing migration/demigration guided sparse inversion. By discussing examples from a Permian Basin dataset with very challenging noise issues, we attest the feasibility of this learning-based approach as a fast turnaround alternative to conventional denoising methodology.