Anatoly I Baumstein

20210041589, Feb 11, 2021

May 6, 2020

15/929499

- Spring TX, US
Valeriy Brytik - Houston TX, US
Anatoly I. Baumstein - Houston TX, US
Yaxun Tang - Spring TX, US
Sirui Tan - Spring TX, US

G01V 1/36
G01V 1/30
G01V 1/28
G06F 30/20

Seismic data processing may include computing the travel time shift between two seismic signals or the depth shift between two seismic images. In Full Waveform Inversion (FWI), the travel time difference between an observed trace and a simulated trace may be computed such that the two traces match after the travel time shift is applied to the observed trace. The travel time shift may be computed based on a constrained optimization that maximizes the windowed cross-correlation between the two seismic traces by constraining the time derivative of the travel time shift to be less than a constant while maximizing the windowed cross-correlation. Further, the travel time shift may be computed during the model line search in FWI by computing a plurality of travel time shifts where a first travel time shift is dependent on the observed trace and a second travel time shift is independent of the observed trace.

20200124752, Apr 23, 2020

Sep 16, 2019

16/571950

Anatoly I. Baumstein - Houston TX, US
Gboyega Ayeni - Humble TX, US
Carey M. Marcinkovich - The Woodlands TX, US
Jaewoo Park - The Woodlands TX, US
Sirui Tan - Spring TX, US
Peter Traynin - Houston TX, US

G01V 1/28
G01V 1/30

Iterative methods for inversion of seismic data to update a physical property model are disclosed. Such methods may comprise iteratively updating the model until a first predetermined resolution is achieved, using full wavefield inversion of the seismic data up to a first frequency threshold and assuming the seismic data is free of attenuation effects; extracting geobodies from the updated model; obtaining a Q model using the geobodies; and updating the physical property model using an inversion process, wherein the Q model is incorporated into the inversion process. These steps may be repeated until a second predetermined resolution of the physical property model is achieved, wherein the first frequency threshold is progressively increased in each repetition. The Q model may be updated with seismic data at all available frequencies to obtain a full-band Q model; and the physical property model may be updated using full-band migration and the full-band Q model.

20180275300, Sep 27, 2018

Feb 12, 2018

15/893898

Volkan Akcelik - Spring TX, US
Anatoly I. Baumstein - Houston TX, US
Valeriy V. Brytik - Houston TX, US
Sunwoong Lee - Houston TX, US
Yaxun Tang - Spring TX, US

G01V 1/28
G01V 1/30
G01V 99/00

A computer-implemented method for updating subsurface models including: using an offset continuation approach to update the model, and at each stage defining a new objective function where a maximum offset for each stage is set, wherein the approach includes, performing a first stage iterative full wavefield inversion with near offset data, as the maximum offset, to obtain velocity and density or impedance models, performing subsequent stages of iterative full wavefield inversion, each generating updated models, relative to a previous stage, wherein the subsequent stages include incrementally expanding the maximum offset until ending at a full offset, wherein a last of the stages yields finally updated models, the subsequent stages use the updated models as starting models, and the full wavefield inversions include constraining scales of the velocity model updates at each stage of inversion as a function of velocity resolution; and using the finally updated models to prospect for hydrocarbons.

20180045839, Feb 15, 2018

Jun 6, 2017

15/614954

Yaxun Tang - Spring TX, US
Sunwoong Lee - Houston TX, US
Anatoly I Baumstein - Houston TX, US
Volkan Akcelik - Spring TX, US

G01V 1/28
G06F 17/10
G01V 1/30
G06F 17/50
G01V 1/36

A computer-implemented method for updating a physical properties model of a subsurface region in an iterative inversion of seismic data using a gradient of a cost function that compares the seismic data to model-simulated data, said method comprising: obtaining a contrast model of a subsurface physical parameter that is sensitive to data dynamics and a kinematic model of a subsurface physical parameter; determining a gradient of a cost function using the contrast model and the kinematic model, wherein the cost function compares seismic data to model-simulated data; updating the kinematic model using a search direction derived from the gradient; adapting the contrast model according to an update to the kinematic model performed in the updating step; iteratively repeating the determining, updating, and adapting steps until a predetermined stopping criteria is reached, and generating a subsurface image from a finally updated kinematic model; and using the subsurface image to prospect for hydrocarbons.

20180017690, Jan 18, 2018

Jun 6, 2017

15/614960

Sirui Tan - Spring TX, US
Yaxun Tang - Spring TX, US
Anatoly I. Baumstein - Houston TX, US
Gboyega Ayeni - Humble TX, US
Tetyana Vdovina - Spring TX, US
Thomas A. Dickens - Houston TX, US

G01V 1/28
G01V 1/30
G01V 1/36
G06F 17/11

A method for iteratively inverting seismic data to jointly infer a model for at least P-wave velocity and attenuation parameters of the subsurface, the method including: jointly inverting the P-wave velocity and attenuation parameters with an iterative visco-acoustic full wavefield inversion process, wherein the iterative visco-acoustic full wavefield inversion process includes computing a gradient of an objective function, the objective function measuring a misfit between all or part of the seismic data and corresponding model-simulated seismic data; for each of the P-wave velocity and attenuation parameters, computing a search direction in model space from the gradient; determining line search step sizes α and β for the search directions for the P-wave velocity and attenuation parameters, respectively, wherein a ratio of the step sizes is a function of the P-wave velocity parameter; and using the step sizes α and β and the search directions for each of the P-wave velocity and attenuation parameters, computing a new search direction in model space, then performing a line search along the new search direction to arrive at a new step size, and using the new step size and the new search direction to generate an updated model for a current iteration of the iterative visco-acoustic full wavefield inversion process.

20170307770, Oct 26, 2017

Apr 3, 2017

15/477417

Anatoly I. BAUMSTEIN - Houston TX, US
Di Yang - Spring TX, US
Tetyana Vdovina - Spring TX, US
Yaxun Tang - Spring TX, US

G01V 1/28
G01V 1/30

A method, including performing, with a computer, up/down separation of geophysical data, which produces an approximate up-going wavefield and an approximate down-going wavefield; creating an areal source based at least in part on the down-going wavefield; and performing, with a computer, a full wavefield inversion process with the areal source, and an objective function measuring a misfit between modeled up-going wavefields and recorded up-going wavefields, wherein the full wavefield inversion process generates a final subsurface physical property model.

20170108602, Apr 20, 2017

Aug 30, 2016

15/251298

Di YANG - Spring TX, US
Reeshidev Bansal - Spring TX, US
Spyridon K. Lazaratos - Houston TX, US
Jia Yan - Houston TX, US
Anatoly I. Baumstein - Houston TX, US

G01V 1/28
G01V 1/36
E21B 41/00
G01V 1/32

A method, including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density models; generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections, wherein the transforming includes normalizing the reflectivity sections by their respective bandwidth.

20160238722, Aug 18, 2016

Oct 26, 2015

14/922276

Tetyana VDOVINA - Spring TX, US
Reeshidev Bansal - Spring TX, US
Anatoly Baumstein - Houston TX, US
Yaxun Tang - Spring TX, US
Di Yang - Spring TX, US

G01V 1/28
G01V 1/36

A multi-stage FWI workflow uses multiple-contaminated FWI models to predict surface-related multiples. A method embodying the present technological advancement, can include: using data with free surface multiples as input into FWI; generating a subsurface model by performing FWI with the free-surface boundary condition imposed on top of the subsurface model; using inverted model from FWI to predict multiples; removing predicted multiples from the measured data; using the multiple-free data as input into FWI with absorbing boundary conditions imposed on top of the subsurface model; and preparing a multiple free data set for use in conventional seismic data processing.