公开(公告)号：US20190302293A1

公开(公告)日：2019-10-03

申请号：US16049941

申请日：2018-07-31

Applicant: CGG SERVICES SAS

Inventor： Zhigang ZHANG ,  Ping WANG ,  Adriano GOMES ,  Jiawei MEI ,  Feng LIN ,  Rongxin HUANG

IPC: G01V1/30 ,  G01V1/28

Abstract: Methods and devices use improved FWI techniques for seismic exploration of subsurface formations including salt bodies using a travel-time cost function. In calculating the travel-time cost function, time-shifts may be weighted using cross-correlation coefficients of respective time-shifted recorded data and synthetic data generated based on current velocity model. The improved methods enhance the resulting image while avoiding cycle-skipping and issues related to amplitude difference between synthetic and recorded data.

公开(公告)号：US20210003728A1

公开(公告)日：2021-01-07

申请号：US16870274

申请日：2020-05-08

Applicant: CGG SERVICES SAS

Inventor： Min WANG ,  Yi XIE ,  Tengfei WANG ,  Adriano GOMES

IPC: G01V1/28 ,  G01V1/30 ,  G01V1/00 ,  G01V1/36

Abstract: Methods and apparatuses for processing seismic data acquired with multicomponent sensors build an accurate S-wave velocity model of a surveyed underground formation using a full waveform inversion (FWI) approach. PS synthetic data is generated using approximative acoustic equations in anisotropic media with a P-wave model, a current S-wave velocity model and a reflectivity model as inputs. The current S-wave velocity model is updated using FWI to minimize an amplitude-discrepancy-mitigating cost function that alleviates the amplitude mismatch between the PS observed data and the PS synthetic data due to the use of the approximative acoustic equations.

公开(公告)号：US20180196154A1

公开(公告)日：2018-07-12

申请号：US15865905

申请日：2018-01-09

Applicant: CGG SERVICES SAS

Inventor： Adriano GOMES ,  Nicolas CHAZALNOEL

IPC: G01V1/36 ,  G01V1/28

CPC classification number: G01V1/362 ,  G01V1/282 ,  G01V1/303 ,  G01V1/3808 ,  G01V2210/44 ,  G01V2210/6161 ,  G01V2210/6222 ,  G01V2210/6224

Abstract: A reflection full waveform inversion method updates separately a density model and a velocity model of a surveyed subsurface formation. The method includes generating a model-based dataset corresponding to the seismic dataset using a velocity model and a density model to calculate an objective function measuring the difference between the seismic dataset and the model-based dataset. A high-wavenumber component of the objective function's gradient is used to update the density model of the surveyed subsurface formation. The model-based dataset is then regenerated using the velocity model and the updated density model, to calculate an updated objective function. The velocity model of the surveyed subsurface formation is then updated using a low-wavenumber component of the updated objective function's gradient. A structural image of the subsurface formation is generated using the updated velocity model.