Method of, and apparatus for, full waveform inversion

Patent metadata

Field	Value
Publication number	US-2016238729-A1
Application number	US-201415033045-A
Country	US
Kind code	A1
Filing date	Jan 14, 2014
Priority date	Oct 29, 2013
Publication date	Aug 18, 2016
Grant date	—

Abstract

A method of subsurface exploration includes generating a representation of a portional volume of the Earth from a seismic measurement of a physical parameter. The method includes providing observed seismic dataset having three distinct nonzero data values derived from three distinct nonzero seismic measured values of said portional volume of the Earth, generating a predicted seismic dataset having three distinct nonzero data values, generating a nontrivial convolutional filter including three nonzero filter coefficients, generating a convolved observed dataset by convolving the convolutional filter with said observed seismic dataset, generating primary objective functions to measure the similarity between said convolved observed dataset and said predicted dataset, maximizing and/or minimizing said primary objective functions by modifying at least one filter coefficient of the convolutional filter, generating predetermined reference filters having at least three reference coefficients generating secondary objective functions to measure the similarity between filter coefficients for the nontrivial filter and reference coefficients for the predetermined reference filters, and minimizing and/or maximizing said secondary objective functions by modifying a model coefficient of a subsurface model of a portion of the Earth to produce an updated subsurface model of a portion of the Earth.

First claim

1 . A method of subsurface exploration, the method comprising generating a geophysical representation of a portion of the volume of the Earth from a seismic measurement of at least one physical parameter, and comprising the steps of:
a) providing an observed seismic data set comprising at least three distinct non-zero data values derived from at least three distinct non-zero seismic measured values of said portion of the volume of the Earth; b) generating, using a subsurface model of a portion of the Earth comprising a plurality of model coefficients, a predicted seismic data set comprising at least three distinct non-zero data values; c) generating at least one non-trivial convolutional filter, the or each filter comprising three or more non-zero filter coefficients; d) generating a convolved observed data set by convolving the or each convolutional filter with said observed seismic data set; e) generating one or more primary objective functions operable to measure the similarity and/or mismatch between said convolved observed dataset and said predicted dataset; f) maximising and/or minimising at least one of said primary objective functions by modifying at least one filter coefficient of the or each convolutional filter; g) generating one or more pre-determined reference filters comprising at least three reference coefficients; h) generating one or more secondary objective functions operable to measure the similarity and/or mismatch between the filter coefficients for the or each non-trivial filter and the reference coefficients for the or each pre-determined reference filters; i) minimising and/or maximising at least one of said secondary objective functions by modifying at least one model coefficient of said subsurface model of a portion of the Earth to produce an updated subsurface model of a portion of the Earth; and j) providing an updated subsurface model of a portion of the Earth for subsurface exploration.

2 . A method according to claim 1 , wherein said at least one convolutional filter is operable to transform at least a portion of said observed seismic data set to render the observed seismic data set and predicted data set approximations of one another.

3 . A method according to claim 1 , wherein, subsequent to step c), the method further comprises:
k) generating at least one further non-trivial convolutional filter, the or each further filter comprising three or more non-zero filter coefficients; and l) generating a convolved predicted data set by convolving the or each further convolutional filter with said predicted seismic data set; and
wherein step e) comprises generating one or more primary objective functions operable to measure the similarity and/or mismatch between said convolved observed dataset and said convolved predicted dataset.

4 . A method according to claim 3 , wherein the or each convolutional filter is operable to transform at least a portion of said observed seismic data set and the or each further convolutional filter is operable to transform said predicted data set to render the observed seismic data set and predicted data set approximations of one another.

5 . A method of subsurface exploration, the method comprising generating a geophysical representation of a portion of the volume of the Earth from a seismic measurement of at least one physical parameter, and comprising the steps of:
a) providing an observed seismic data set comprising at least three distinct non-zero data values derived from at least three distinct non-zero seismic measured values of said portion of the volume of the Earth; b) generating, using a subsurface model of a portion of the Earth comprising a plurality of model coefficients, a predicted seismic data set comprising at least three distinct non-zero data values; c) generating at least one non-trivial convolutional filter, the or each filter comprising three or more non-zero filter coefficients; d) generating a convolved predicted data set by convolving the or each convolutional filter with said predicted seismic data set; e) generating one or more primary objective functions operable to measure the similarity and/or mismatch between said observed seismic dataset and said convolved predicted dataset; f) maximising and/or minimising at least one of said primary objective functions by modifying at least one filter coefficient of the or each convolutional filter; g) generating one or more pre-determined reference filters comprising at least three reference coefficients; h) generating one or more secondary objective functions operable to measure the similarity and/or mismatch between the filter coefficients for the or each non-trivial filter and the reference coefficients for the or each pre-determined reference filters; and i) minimising and/or maximising at least one of said secondary objective functions by modifying at least one model coefficient of said subsurface model of a portion of the Earth to produce an updated subsurface model of a portion of the Earth; and j) providing an updated subsurface model of a portion of the Earth for subsurface exploration.

6 . A method according to claim 5 , wherein said at least one convolutional filter is operable to transform at least a portion of said predicted seismic data set to render the observed seismic data set and predicted data set approximations of one another.

7 . A method according to claim 5 , wherein, subsequent to step c), the method further comprises:
k) generating at least one further non-trivial convolutional filter, the or each further filter comprising three or more non-zero filter coefficients; and l) generating a convolved observed data set by convolving the or each further convolutional filter with said observed seismic data set; and
wherein step e) comprises generating one or more primary objective functions operable to measure the similarity and/or mismatch between said convolved observed dataset and said convolved predicted dataset.

8 . A method according to claim 7 , wherein the or each convolutional filter is operable to transform at least a portion of said predicted seismic data set and the or each further convolutional filter is operable to transform said observed data set to render the observed seismic data set and predicted data set approximations of one another.

9 . A method according to any one of the preceding claims, wherein, subsequent to step h), the method further comprises:
m) normalising the or each secondary objective function to form one or more normalised secondary objective functions operable to measure the similarity and/or mismatch between the filter coefficients for the or each non-trivial filter and the reference coefficients for the or each pre-determined reference filters, wherein the or each normalised secondary objective function is insensitive or has reduced sensitivity to the relative scaling of the filter coefficients for the or each non-trivial filter with respect to the reference coefficients for the or each pre-determined reference filter.

10 . A method according to any one of claims 1 to 8 , wherein step h) comprises generating one or more normalised secondary objective functions operable to measure the similarity and/or mismatch between the filter coefficients for the or each non-trivial filter and the reference coefficients for the or each pre-determined reference filters, wherein the or each normalised secondary objective function is insensitive or has reduced sensitivity to the relative scaling of the filter coefficients for the or each non-trivial filter with respect to the reference coefficients for the or each pre-determined reference filter.

11 . A method according to claim 9 or 10 , whe

Assignees

• Imp Innovations Ltd

Inventors

• Warner Mike
• Guasch Batalla Lluis

Classifications

• G01V1/364 Primary

  Seismic filtering (G01V1/37 takes precedence) · CPC title

• G01V2210/27

  Other pre-filtering · CPC title

• G01V1/306

  for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles · CPC title

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V2210/614

  Synthetically generated data · CPC title