System and method for optimizing seismic data analysis

• US 10,114,135 B2
• Filed: 12/31/2015
• Issued: 10/30/2018
• Est. Priority Date: 12/31/2015
• Status: Expired due to Fees

• Current Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation), General Electric Company
• Original Assignee: General Electric Company
• Inventors: Huang, Xiaojie, Can, Ali, Bas, Erhan, Ge, Weina, Liu, Shubao, Dobin, Mark W., Johns, Mary
• Primary Examiner(s): Oh, Jaehwan
• Application Number:
  US14/985,860
• Publication Number:
  US 20170192114A1
• Time in Patent Office: 1,034 Days
• CPC Class Codes: G01V 1/30 Analysis G01V1/50 takes precedence
• CPC Class Codes: G01V 1/34 Displaying seismic recordings or visualisation of seismic data or attributes

Abstract

A method includes accessing a seismic image comprising a plurality of features of interest. The method also includes defining a plurality of configuration files for a plurality of graphical models. The method further includes applying the plurality of graphical models to the seismic image. The method also includes generating a plurality of scores for each feature of interest, wherein each graphical model generates a score for each feature of interest. The method further includes combining the plurality of scores for each feature of interest into a plurality of combined scores, wherein each feature of interest has a combined score.

Claims

1. A method, comprising:

accessing a seismic image comprising a plurality of features of interest;

defining a plurality of configuration files for a plurality of graphical models;

applying the plurality of graphical models to the seismic image;

generating a plurality of scores for each feature of interest, wherein each graphical model generates a score for each feature of interest, and wherein each score is representative of each respective feature'"'"'s degree of agreement with one of the plurality of graphical models;

combining the plurality of scores for each feature of interest into a plurality of combined scores, wherein each feature of interest has a combined score; and

ranking the plurality of features of interest based on the plurality of combined scores.

2. The method of claim 1, comprising:

generating a plurality of user ratings based on a user rating the plurality of combined scores.

3. The method of claim 1, comprising:

generating a plurality of user ratings based on a user rating the ranked plurality of features of interest.

4. The method of claim 1, comprising:

generating an adjusted plurality of scores for the plurality of features of interest, wherein the plurality of scores for each feature of interest is adjusted based at least on the plurality of user ratings.

5. The method of claim 3, comprising:

generating an adjusted plurality of scores for the plurality of features of interest, wherein the plurality of scores for each feature of interest are adjusted based on the plurality of user ratings.

6. The method of claim 4, comprising:

generating an adjusted ranked plurality of features of interest based on the adjusted plurality of scores.

7. The method of claim 5, comprising:

generating an adjusted ranked plurality of features of interest based on the adjusted plurality of scores.

8. A system, comprising:

a processor, configured to;

access a seismic image comprising a plurality of features of interest;

define a plurality of configuration files for a plurality of graphical models;

apply the plurality of graphical models to the seismic image;

generate a plurality of scores for each of the plurality of features of interest, wherein each graphical model generates a score for each feature of interest, and wherein each score is representative of each respective feature'"'"'s degree of agreement with one of the plurality of graphical models;

combine the plurality of scores into a plurality of combined scores for the plurality of features of interest; and

rank the plurality of features of interest based on the plurality of combined scores.

9. The system of claim 8, wherein the at least one feature of interest is a carbonate mound.

10. The method of claim 9, wherein the at least one feature of interest is a direct hydrocarbon indicator.

11. The system of claim 8, wherein the processor is configured to combine the plurality of scores into a plurality of combined scores for the plurality of features of interest by:

normalizing the plurality of scores to generate a plurality of normalized scores; and

combine the plurality of normalized scores.

12. The system of claim 8, wherein the processor is configured to combine the plurality of scores into a plurality of combined scores for the plurality of features of interest by using correlation-based combining.

13. A method comprising:

accessing a seismic image comprising a plurality of features of interest;

defining a plurality of configuration files for a plurality of graphical models;

applying the plurality of graphical models to the seismic image, comprising acts of;

generating a plurality of scores for each feature of interest, wherein each graphical model generates a score for each feature of interest, and wherein each score is representative of each respective feature'"'"'s degree of agreement with one of the plurality of graphical models; and

generating a plurality of rankings of the plurality of features of interest for each graphical model based on the plurality of scores; and

combining the plurality of rankings into a respective combined ranking for each feature of interest.

14. The method of claim 13, wherein defining a plurality of configuration files comprises:

defining geophysical attributes; and

defining relationships between the geophysical attributes.

15. The method of claim 13, wherein combining the plurality of rankings comprises using average combining.

16. The method of claim 13, wherein combining the plurality of rankings comprises using maximum combining.

17. The method of claim 13, wherein combining the plurality of rankings comprises using correlation-based combining.

18. The method of claim 17, wherein combining the plurality of rankings using correlation-based combining comprises removing at least some correlations between the graphical models in the plurality of graphical models.