Pattern recognition template construction applied to oil exploration and production
View Patent ↗An apparatus, system, and set of methods are provided for building templates that can be used in the exploration and production oil and gas deposits. A personal computer is used to perform calculations on known data to generate hyperdimensional templates that are stored within a pattern database. The hyperdimensional templates are generated in a layered series of cutting, attributing, and statisizing phases using data known to represent existing oil and gas reservoirs. The hyperdimensional templates are stored in a pattern database. Methods are provided for identifying misclassifications and otherwise determine the quality of the templates in the pattern database.
1. A method for generating templates in a computer system comprising:
generating a pattern pyramid having:
a data level;
a feature level;
a pattern level; and
a texture level;
classifying one or more attributes based on the data level and at least one level selected from the group of levels consisting of the feature level, the pattern level, and the texture level; and
outputting one or more attribute classification results to an interpreter.
2. The method of claim 1 , wherein said step of generating includes receiving raw data for said data level.
3. The method of claim 1 , wherein said pattern pyramid has an inline fragment direction.
4. The method of claim 1 , wherein said pattern pyramid has a time fragment direction.
5. The method of claim 1 , wherein said pattern pyramid as an xline fragment direction.
6. The method of claim 1 , wherein said pattern pyramid as an inline fragment direction, a time fragment direction, and an xline fragment direction.
7. The method of claim 1 , wherein said method of generating has a pre-stack phase for said data level.
8. The method of claim 7 , wherein said method of generating has a post stack phase for said data level.
9. The method of claim 1 , wherein said method of generating has a cut phase for said feature level.
10. The method of claim 9 , wherein said method of generating has an attribute phase for said feature level.
11. The method of claim 9 , wherein said method of generating has a statistic phase for said feature level.
12. The method of claim 1 , wherein said method of generating has a cut phase for said pattern level.
13. The method of claim 12 , wherein said method of generating has all attribute phase for said pattern level.
14. The method of claim 12 , wherein said method of generating has a statistic phase for said pattern level.
15. The method of claim 1 , wherein said method of generating has a cut phase for said texture level.
16. The method of claim 15 , wherein said method of generating has an attribute phase for said texture level.
17. The method of claim 15 , wherein said method of generating has a statistic phase for said texture level.
18. The method of claim 1 , wherein said step of generating produces a hyperdimensional template having at least one point in each of said levels.
19. The method of claim 18 , wherein one or more of said hyperdimensional templates is stored on a storage media.
20. The method of claim 1 , wherein said step of generating includes a step of identifying target hyperdimensional fragments.
21. The method of claim 1 , wherein said step of generating includes a step of identifying non-target hyperdimensional fragments.
22. The method of claim 1 , wherein said step of generating includes a step of identifying target hyperdimensional fragments in the presence of well data.
23. The method of claim 1 , wherein said step of generating includes a step of identifying target hyperdimensional fragments in the absence of well data.
24. The method of claim 1 , wherein said step of generating includes a step of identifying non-target hyperdimensional fragments in the presence of well data.
25. The method of claim 1 , wherein said step of generating includes a step of identifying non-target hyperdimensional fragments in the absence of well data.
26. The method of claim 1 , wherein said step of generating includes a step of modifying a template to reduce misclassification.
27. The method of claim 1 , wherein said step of generating includes a step of modifying a template to reduce the potential for a false positive.
28. The method of claim 1 , wherein said step of generating includes a step of modifying a feature attribute selection.
29. The method of claim 1 , wherein said step of generating includes a step of modifying a feature statistic selection.
30. The method of claim 1 , wherein said step of generating includes a step of modifying a feature decision surface selection.
31. The method of claim 1 , wherein said step of generating includes a step of modifying a pattern attribute selection.
32. The method of claim 1 , wherein said step of generating includes a step of modifying a pattern statistic selection.
33. The method of claim 1 , wherein said step of generating includes a step of modifying a pattern decision surface selection.
34. The method of claim 1 , wherein said step of generating includes a step of modifying a texture attribute selection.
35. The method of claim 1 , wherein said step of generating includes a step of modifying a texture statistic selection.
36. The method of claim 1 , wherein said step of generating includes a step of modifying a texture decision surface selection.
37. An apparatus for generating templates in a computer system having a central processing unit, system memory operatively connected to said central processing unit, and a storage unit operative with said central processing unit and said system memory, said apparatus further comprising:
a pattern database on said storage unit for storing one or more of said templates; and
said central processing unit constructed and arranged to generate a pattern pyramid having:
a data level;
a feature level;
a pattern level; and
a texture level;
where the data level and at least one level selected from the group of levels consisting of the feature level, the pattern level, and the texture level are used to classify one or more attributes; and
where said central processing unit is further constructed and arranged to output one or more attribute classification results to an interpreter.
38. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to receive raw data for said data level.
39. The apparatus of claim 37 , wherein said pattern pyramid has an inline fragment direction.
40. The apparatus of claim 37 , wherein said pattern pyramid has a time fragment direction.
41. The apparatus of claim 37 , wherein said pattern pyramid as an inline fragment direction.
42. The apparatus of claim 37 , wherein said pattern pyramid as an inline fragment direction, a time fragment direction, and an xline fragment direction.
43. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to compute a pre-stack phase for said data level.
44. The apparatus of claim 43 , wherein said central processing unit constructed and arranged to compute a post stack phase for said data level.
45. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to compute a cut phase for said feature level.
46. The apparatus of claim 45 , wherein said central processing unit constructed and arranged to compute an attribute phase for said feature level.
47. The apparatus of claim 45 , wherein said central processing unit constructed and arranged to compute a statistic phase for said feature level.
48. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to compute a cut phase for said pattern level.
49. The apparatus of claim 48 , wherein said central processing unit constructed and arranged to compute an attribute phase for said pattern level.
50. The apparatus of claim 48 , wherein said central processing unit constructed and arranged to compute a statistic phase for said pattern level.
51. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to compute a cut phase for said texture level.
52. The apparatus of claim 51 , wherein said central processing unit constructed and arranged to compute an attribute phase for said texture level.
53. The apparatus of claim 51 , wherein said central processing unit constructed and arranged to compute a statistic phase for said texture level.
54. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to produce a hyperdimensional template having at least one point in each of said layers.
55. The apparatus of claim 54 , wherein one or more of said hyperdimensional templates is stored on a storage media.
56. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to identify target hyperdimensional fragments.
57. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to identify non-target hyperdimensional fragments.
58. The apparatus of claim 37 , wherein said step central processing unit constructed and arranged to identity target hyperdimensional fragments in the presence of well data.
59. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to identify target hyperdimensional fragments in the absence of well data.
60. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to identify, non-target hyperdimensional fragments in the presence of well data.
61. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to identify non-target hyperdimensional fragments in the absence of well data.
62. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a template to reduce misclassification.
63. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a template to reduce the potential for a false positive.
64. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a feature attribute selection.
65. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a feature statistic selection.
66. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a feature decision surface selection.
67. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a pattern attribute selection.
68. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a pattern statistic selection.
69. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a pattern decision surface selection.
70. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a texture attribute selection.
71. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a texture statistic selection.
72. The apparatus of claim 37 , wherein said central processing unit constructed and arranged to modify a texture decision surface selection.