Method to adapt a template dataset to a target dataset by using curvelet representations

• US 8,280,695 B2
• Filed: 08/28/2008
• Issued: 10/02/2012
• Est. Priority Date: 10/17/2007
• Status: Active Grant

• Current Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Original Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Inventors: Neelamani, Ramesh, Baumstein, Anatoly, Ross, Warren S.
• Primary Examiner(s): Le, Toan M
• Application Number:
  US12/675,083
• Publication Number:
  US 20110147004A1
• Time in Patent Office: 1,496 Days
• Field of Search: 702 1- 2, 702 11- 14, 702 16- 17, 702/81, 702/84, 702182-183, 702189-191, 702194-197, 166/369, 367/14, 367/21, 367 37- 38, 367 43- 48, 367/64, 367/73, 382/109, 700 28- 32, 703/2, 703/5, 703/10
• US Class Current: 702/191
• CPC Class Codes: G01V 1/36 Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
• CPC Class Codes: G01V 2210/32 Noise reduction
• CPC Class Codes: G01V 2210/56 De-ghosting; Reverberation compensation

Abstract

Method for adapting a template to a target data set. The template may be used to remove noise from, or interpret noise in, the target data set. The target data set is transformed (550) using a selected complex-valued, directional, multi-resolution transform (‘CDMT’) satisfying the Hubert transform property at least approximately. An initial template is selected, and it is transformed (551) using the same CDMT. Then the transformed template is adapted (560) to the transformed target data by adjusting the template'"'"'s expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set. Multiple templates may be simultaneously adapted to better fit the noise or other component of the data that it may be desired to represent by template.

Claims

1. A method for developing at least one template to use to remove noise from or interpret noise in a target data set of geophysical data, wherein the template is adapted to the target dataset, said method comprising:

(a) transforming the target data using a selected complex-valued, directional, multi-resolution transform (“

CDMT”

) satisfying the Hilbert transform property at least approximately to obtain a set of expansion coefficients that are complex numbers having a magnitude and phase;

(b) selecting an initial template representing noise in the target data set and transforming the initial template using said selected CDMT;

(c) adapting the transformed template to the transformed target data by adjusting the template'"'"'s expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set, thereby creating an adapted template; and

(d) using the adapted template to suppress noise, further process, or interpret the target data set or other geophysical data in order to prospect for or produce hydrocarbons;

wherein at least one of (a)-(c) are performed using a computer.

2. The method of claim 1, further comprising subtracting the adapted template from the target data in the transform domain and inverse transforming back to the data domain, or inverse transforming the adapted template to the data domain and then subtracting it from the target data, thereby creating a noise-suppressed target dataset.

3. The method of claim 2, further comprising using the noise-suppressed target dataset to identify potential commercial hydrocarbon reservoirs.

4. The method of claim 1, wherein the CDMT is a complex wavelet transform, complex contourlet transform, or a complex curvelet transform.

5. The method of claim 1, wherein the complex expansion coefficients for template are provided a range of adjustment for the magnitude of each complex coefficient and a range of adjustment for the phase of each complex coefficient, thereby constraining the adapting of the template, said ranges of adjustment being selected to balance tradeoff between noise subtraction and signal preservation when the adapted template is subtracted from the target dataset.

6. The method of claim 1, wherein each CDMT expansion coefficient of the template is multiplied by λ

exp(jε

), where j=√

{square root over ((−

1))} and λ and

ε

are factors that are solved for by minimizing the magnitude of the difference between corresponding coefficients in the CDMT expansions of the target data and the template.

7. The method of claim 1, further comprising iterating the adapting step, after resetting the adjustment ranges, until convergence within a pre-selected tolerance or other stopping condition is reached.

8. The method of claim 1, further comprising selecting at least one additional initial template and transforming it using said selected CDMT, and simultaneously adapting all templates to the transformed target.

9. The method of claim 8, wherein the target data are seismic data and the noise includes multiple reflection data, and each template is selected to represent, in the CDMT transform domain, a reflector identified as contributing to multiple reflection noise.

10. The method of claim 8, further comprising subtracting the adapted templates from the target data in the transform domain and inverse transforming back to the data domain, or inverse transforming the adapted templates to the data domain and then subtracting them from the target data.

11. The method of claim 1, wherein the selected template is a first template and further comprising:

(d) subtracting the adjusted coefficients of the first template from the target dataset'"'"'s expansion coefficients;

(e) selecting an initial second template and transforming it using said selected CDMT; and

(f) adapting the transformed second template to the transformed target resulting from step (d) by adjusting the expansion coefficients for the template within allowed ranges of adjustment so as to better match the expansion coefficients of the target dataset after the subtraction of step (d), thereby creating an adapted second template.

12. The method of claim 11, further comprising:

(g) subtracting the adjusted coefficients of the second template from the target dataset'"'"'s expansion coefficients as they result from step (d); and

(h) taking the target dataset expansion resulting from step (g) and the two template expansions resulting from steps (c) and (f) as starting quantities for an iterative cycle, and repeating steps (c)-(d) and (f)-(g).

13. The method of claim 12, further comprising repeating step (g) until a predetermined convergence criterion or other stopping point is reached.

14. The method of claim 13, further comprising subtracting both adapted templates resulting after convergence or stopping point from the target data from step (a) in the transform domain and inverse transforming back to the data domain, or inverse transforming the adapted templates to the data domain and then subtracting them from the target data, thereby creating a noise-suppressed target dataset.

15. The method of claim 13, wherein each template adapting is performed within pre-selected adjustment ranges, and the ranges are updated after each iterative cycle.

16. The method of claim 1, wherein adjusting a template expansion coefficient consists of adjusting both the magnitude and the phase of the expansion coefficient.

17. A method for producing hydrocarbons from a subsurface region, comprising:

(a) conducting a seismic survey of the subsurface region;

(b) obtaining survey data with noise removed by steps comprising;

transforming the survey data using a selected complex-valued, directional, multi-resolution transform (“

CDMT”

) satisfying the Hilbert transform property at least approximately to obtain a set of expansion coefficients that are complex numbers having a magnitude and phase;

(ii) selecting an initial template representing noise in the survey data and transforming the initial template using said selected CDMT;

(iii) adapting the transformed template to the transformed survey data by adjusting the template'"'"'s expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the survey data, thereby creating an adapted template; and

(iv) subtracting the adapted template from the survey data in the transform domain and inverse transforming back to the data domain, or inverse transforming the adapted template to the data domain and then subtracting it from the survey data, thereby creating a noise-suppressed survey dataset;

(c) drilling a well into the subsurface region based at least in part on interpretation of the noise-suppressed survey dataset; and

(d) producing hydrocarbons from the well.

18. A computer-implemented method for adapting a template to a target data set of geophysical data so that the template better represents a selected component of the target data set, said method comprising:

(a) transforming the target data using a selected complex-valued, directional, multi-resolution transform (“

CDMT”

) satisfying the Hilbert transform property at least approximately to obtain a set of expansion coefficients that are complex numbers having a magnitude and phase;

(b) selecting an initial template representing a selected component of the target data set and transforming the initial template using said selected CDMT;

(c) adapting the transformed template to the transformed target data by adjusting the template'"'"'s expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set, thereby creating an adapted template; and

(d) using the adapted template to suppress noise, further process, or interpret the target data set or other geophysical data in order to prospect for or produce hydrocarbons;

wherein at least one of (a)-(c) are performed using a computer.

19. The method of claim 18, wherein the selected component of the target data set is noise in the data set.

20. The method of claim 18, wherein the allowed ranges of adjustment are determined based on considerations of signal preservation and estimated error in construction of the initial template.

21. The method of claim 20, wherein error in construction of the initial template includes at least a time shift, wherein the range of possible time shift values is ascertainable.

22. The method of claim 18, wherein the target data set is acquired from a monitor survey of a subsurface region, and wherein the selected initial template is, or is determined from, an earlier base survey of the subsurface region, and the adapted template is subtracted from the target data set to highlight differences between the base survey and the monitor survey.