Method and apparatus for deblending seismic data using a non-blended dataset

Patent metadata

Field	Value
Publication number	US-11112518-B2
Application number	US-201514967679-A
Country	US
Kind code	B2
Filing date	Dec 14, 2015
Priority date	Feb 24, 2015
Publication date	Sep 7, 2021
Grant date	Sep 7, 2021

Abstract

A non-blended dataset related to a same surveyed area as a blended dataset is used to deblend the blended dataset. The non-blended dataset may be used to calculate a model dataset emulating the blended dataset, or may be transformed in a model domain and used to derive sparseness weights, model domain masking, scaling or shaping functions used to deblend the blended dataset.

First claim

What is claimed is:

1. A seismic exploration method for imaging gas and oil reservoirs in an underground formation, the method comprising:
obtaining a first non-blended dataset acquired over the underground formation;
acquiring a second blended dataset over the underground formation using plural sources whose listening times overlap;
calculating a model dataset emulating the second blended dataset by:
dividing the first dataset into spatial blocks, data in each spatial block having a common acquisition characteristic,
interpolating the data in each spatial block of the first non-blended dataset to match source and receiver positions during the acquiring of the second blended dataset, and
merging the interpolated data blocks to obtain the model dataset;

deblending the second blended dataset using the model dataset to obtain at least one deblended second dataset corresponding to one of plural seismic sources; and
generating an image of the underground formation based on the at least one deblended second datasets,
wherein the common acquisition characteristic is one of mid-point, offset and receiver position.

2. The method of claim 1 , wherein the model dataset is used to mitigate cross-talk noise in the second blended dataset by:
blending the model dataset to form a continuous recording trace;
pseudo-blending the continuous recording trace;
calculating a cross-talk estimate based on the pseudo-blended continuous recording trace; and
subtracting the cross-talk noise estimate from the second blended dataset.

3. The method of claim 2 , further comprising:
generating a changemap including anticipated signal to blend noise ratios evaluated based on the model dataset.

4. The method of claim 3 , wherein the changemap is used to derive space-time sparseness weights used to deblend the second blended dataset.

5. The method of claim 3 , wherein the changemap is used to derive filters to be applied to the second blended dataset.

6. The method of claim 1 , wherein
the model dataset is calculated by numerically blending the first non-blended dataset based on locations and times extracted from the second blended dataset, and
the deblending includes comparing blend noise of the blended first non-blended dataset and the second blended dataset.

7. The method of claim 1 , wherein the first non-blended dataset was acquired earlier than the second blended dataset, and evolution of the underground formation is observed by comparing the image of the underground formation generated using the at least one deblended second dataset with an image of the underground formation generated using the first non-blended dataset.

8. The method of claim 1 , wherein the first non-blended dataset and the second blended dataset are acquired intertwined during a same survey.

9. A seismic data processing apparatus for imaging gas and oil reservoirs in an underground formation, the apparatus comprising:
an interface configured to obtain a first non-blended dataset acquired over the underground formation and a second blended dataset acquired over the underground formation using plural sources whose listening times overlap; and
a data processing unit including one or more processors and configured
to calculate a model dataset emulating the second blended dataset by:
dividing the first dataset into spatial blocks, data in each spatial block having a common acquisition characteristic,
interpolating the data in each spatial block of the first non-blended dataset to match source and receiver positions with the second blended dataset, and
merging the interpolated data blocks to obtain the model dataset;

to deblend the second blended dataset using the model dataset to obtain at least one deblended second dataset corresponding to one of plural seismic sources; and
to generate an image of underground formation based on the at least one deblended second dataset,

wherein the common acquisition characteristic is one of mid-point, offset and receiver position.

10. The apparatus of claim 9 , wherein the first non-blended dataset was acquired earlier than the second blended dataset, or
the first non-blended dataset and the second blended dataset are acquired intertwined during a same survey.

11. The apparatus of claim 9 , wherein the data processing unit is configured to mitigate cross-talk noise in the second blended dataset using the model dataset by:
blending the model dataset to form a continuous recording trace;
pseudo-blending the continuous recording trace;
calculating a cross-talk estimate based on the pseudo-blended continuous recording trace; and
subtracting the cross-talk noise estimate from the second blended dataset.

12. The apparatus of claim 9 , wherein the data processing unit is configured:
to generate a changemap including anticipated signal to blend noise ratios evaluated based on the model dataset, and
to derive space-time sparseness weights used to deblend the second blended dataset or filters to be applied to the second blended dataset, based on the changemap.

13. The apparatus of claim 9 , wherein the data processing unit is configured
to calculate the model dataset by numerically blending the first non-blended dataset based on locations and times extracted from the second blended dataset, and
to compare blend noise of the blended first dataset and the second blended dataset while deblending the second blended dataset.

14. The apparatus of claim 9 , wherein the data processing unit is further configured:
to transform the first non-blended dataset in a model domain; and
to deblend the second blended dataset using the transformed first dataset.

15. The apparatus of claim 14 , wherein the data processing unit is configured
to deblend the second dataset by an inversion method using sparseness weights derived from the transformed first dataset,
to deblend the second blended dataset using an anti-leakage/matching method, and/or
to deblend the second blended dataset using a model domain masking, scaling or shaping function derived using the transformed first dataset.

16. The apparatus of claim 9 , further comprising a non-transitory computer readable recording medium storing executable codes, which when executed by the data processing unit make the data processing unit to calculate the model dataset emulating the second blended dataset based on the first dataset, and to deblend the second blended dataset using the model dataset.

Assignees

• Cgg Services Sa
• Cgg Services Sas

Inventors

• Poole Gordon
• Hoeber Henning
• Khalil Adel

Classifications

• G01V2210/324

  Filtering · CPC title

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V1/364 Primary

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

• G01V1/368

  Inverse filtering · CPC title

• G01V1/32

  Transforming one recording into another {or one representation into another} · CPC title