Seismic data processing

Patent metadata

Field	Value
Publication number	US-9625593-B2
Application number	US-201213614932-A
Country	US
Kind code	B2
Filing date	Sep 13, 2012
Priority date	Apr 26, 2011
Publication date	Apr 18, 2017
Grant date	Apr 18, 2017

Abstract

The invention includes a method for reducing noise in migration of seismic data, particularly advantageous for imaging by simultaneous encoded source reverse-time migration (SS-RTM). One example embodiment includes the steps of obtaining a plurality of initial subsurface images; decomposing each of the initial subsurface images into components; identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and generating an enhanced subsurface image using the identified set of components. For SS-RTM, each of the initial subsurface images is generated by migrating several sources simultaneously using a unique random set of encoding functions. Another embodiment of the invention uses SS-RTM for velocity model building.

First claim

What is claimed is:

1. A method for processing seismic data, the method comprising:
a. obtaining a plurality of initial subsurface images, wherein each of the initial subsurface images is generated using a unique random set of encoding functions, and the initial subsurface images are obtained by simultaneous-source reverse-time migration;
b. decomposing each of the initial subsurface images into components, wherein the decomposing is performed by a transform generating a set of transform coefficients for each initial subsurface image;
c. identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images, wherein step c includes averaging the transform coefficients to generate a preliminary signal transform coefficient estimate and computing a variance of at least a subset of the transform coefficients to determine a noise level in the preliminary signal transform coefficient estimate; and
d. generating an enhanced simultaneous-source reverse-time migration subsurface image using the set of components identified in step c, wherein step d includes attenuating noise in the transform coefficients using the determined noise level and the preliminary signal transform coefficient estimate to generate attenuated transform coefficients, and performing an inverse transform on the attenuated transform coefficients to generate the enhanced simultaneous-source reverse-time migration subsurface image;

wherein steps a-d are performed using a computer.

2. The method of claim 1 further comprising the step of attenuating components not identified as having at least one substantially similar characteristic across the plurality of initial subsurface images.

3. The method of claim 1 wherein the transform is a curvelet transform.

4. The method of claim 1 wherein the transform is a Fourier transform, wavelet transform, F-K transform, or radon transform.

5. The method of claim 1 wherein the initial subsurface images are generated by:
a. obtaining a set of shot gathers comprising forward and backward wave component data;
b. selecting first and second random encoding functions;
c. encoding the forward wave component data for each source in the set of shot gathers using the first random encoding function to form an Encoded Source Super-Shot Wave Component;
d. encoding the backward wave component data for each receiver in the set of shot gathers using the second random encoding function to form an Encoded Receiver Super-Shot Wave Component;
e. forward propagating the Encoded Source Super-Shot Wave Component to generate a Forward Propagated Wave Component;
f. back propagating the Encoded Receiver Super-Shot Wave Component to generate a Back Propagated Wave Component;
g. applying an imaging condition to the Forward and Back Propagated Wave Components to generate a subsurface image; and
h. iteratively repeating steps b-g until a predetermined condition is satisfied, wherein the first and second random encoding functions are selected so that the functions are unique for each iteration.

6. The method of claim 5 wherein the encoding the forward and backward wave components is performed using scalars in the time domain.

7. The method of claim 5 wherein the encoding the forward and backward wave components is performed using scalars in the frequency domain.

8. The method of claim 5 wherein the first and second random encoding functions are reciprocal.

9. The method of claim 8 wherein the first and second reciprocal random encoding functions are unit-magnitude encoding functions.

10. The method of claim 9 wherein the first or second reciprocal random encoding function includes a unit-magnitude complex number encoding function.

11. The method of claim 5 wherein the first and second random encoding functions include reciprocal random encoding functions on plane waves with different angles of incidence.

12. The method of claim 5 wherein the first random encoding function is equivalent to the second random encoding function.

13. The method of claim 1 , further comprising first iteratively inverting the seismic data, said inversion involving computing gradients of objective functions associated with the seismic data, and then performing the method with the gradients being regarded as the initial subsurface images.

14. The method of claim 1 , wherein the encoding functions have unit magnitude.

15. A method for processing seismic data, the method comprising:
a. forming shot gathers from the seismic data and binning the shot gathers into at least two bins, each offset bin having shot gathers with a specified range of offsets;
b. obtaining a plurality of initial subsurface images, wherein each of the initial subsurface images is generated using a unique random set of encoding functions, wherein step b includes encoding the gathers in each offset bin using a unique random set of encoding functions to form composite gathers of simultaneous source data, then repeating at least once using a different random set of encoding functions, thereby forming at least two realizations of each offset-bin composite gather, said at least two realizations becoming, after migration using an assumed velocity model, the plurality of initial subsurface images for step c;
c. decomposing each of the initial subsurface images into components,
d. identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and
e. generating an enhanced subsurface image using the set of components identified in step c;

wherein steps a-e are performed using a computer.

16. The method of claim 15 , wherein the migration is SS-RTM, and further comprising examining coherency or consistency of the enhanced subsurface image for different offset bins to assess accuracy of the assumed velocity model.

17. The method of claim 16 , further comprising using mis-positioning of one or more reflection events between different offset bins to estimate a corresponding update to the assumed velocity model.

Assignees

• Neelamani Ramesh
• Routh Partha S
• Krebs Jerome R
• Baumstein Anatoly
• Dickens Thomas A
• Ross Warren S
• Palacharla Gopalkrishna
• Exxonmobil Upstream Res Co

Inventors

• Neelamani Ramesh
• Routh Partha S
• Krebs Jerome R
• Baumstein Anatoly
• Dickens Thomas A
• Ross Warren S
• Palacharla Gopalkrishna

Classifications

• G01V1/28 Primary

  Processing seismic data, e.g. for interpretation or for event detection (G01V1/48 takes precedence) · CPC title

• G01V2210/51

  Migration · CPC title

• G01V2210/40

  Transforming data representation · CPC title

• G01V2210/32

  Noise reduction · CPC title