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1. WO2020185918 - METHOD FOR AUTOMATED STRATIGRAPHY INTERPRETATION FROM BOREHOLE IMAGES

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

WHAT IS CLAIMED IS:

1. A method for automated stratigraphy interpretation from borehole images comprising: constructing, using at least one processor, a training set of images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images;

automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or more machine learning techniques; and automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

2. The method according to claim 1 wherein constructing a training set includes a forward model to generate the synthetic images.

3. The method according to claim 2 wherein constructing a training set further includes an addition of noise to the synthetic images.

4. The method according to claim 1, wherein automatically classifying one or more individual sedimentary geometries includes applying one or more machine learning techniques.

5. The method of claim 1, wherein automatically classifying into priors for depositional environments includes applying one or more machine learning techniques.

6. The method of claim 1, wherein automatically classifying into priors includes building one or more tables of sedimentary geometry successions that represent one or more depositional environments.

7. The method of claim 1, wherein an addition of noise includes at least one of adding one or more masking stripes on the one or more synthetic images, adding one stripe on the one or more synthetic images, adding a one-pixel stripe to the one or more synthetic images, adding white noise to the one or more synthetic images, translating patterns on the one or more synthetic images, truncating the one or more synthetic images, or adding geometric noise.

8. The method of claim 1, further comprising:

utilizing one or more automated individual sedimentary geometry predictions to establish a depositional environment predictor.

9. The method of claim 8, wherein the depositional environment predictor includes a decision tree-based machine-learning, fuzzy-logic based algorithms, or a probabilistic graphical model.

10. The method of claim 1, further comprising:

identifying a longer than standard borehole image: and

applying a sliding window as a spatial sampling technique.

11. A system for automated stratigraphy interpretation from borehole images comprising: a memory configured to store one or more borehole images;

at least one processor configured to construct a training set of images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images, the at least one processor further configured to automatically classify the training set into one or more individual sedimentary geometries using one or more machine learning techniques, the at least one processor further configured to automatically classify the training set into one or more priors for depositional environments.

12. The system of claim 11, wherein constructing a training set includes a forward model to generate the synthetic images.

13. The system of claim 12, wherein constructing a training set further includes an addition of noise to the synthetic images.

14. The system of claim 11, wherein automatically classifying one or more individual sedimentary geometries includes applying one or more machine learning techniques.

15. The system of claim 11, wherein automatically classifying into priors for depositional environments includes applying one or more machine learning techniques.

16. The system of claim 11, wherein automatically classifying into priors includes building one or more tables of sedimentary geometry successions that represent one or more depositional environments.

17. The system of claim 11, wherein an addition of noise includes at least one of adding one or more masking stripes on the one or more synthetic images, adding one stripe on the one or more synthetic images, adding a one-pixel stripe to the one or more synthetic images, adding white noise to the one or more synthetic images, translating patterns on the one or more synthetic images, truncating the one or more synthetic images, or adding geometric noise.

18. The system of claim 11, further comprising:

utilizing one or more automated individual sedimentary geometry predictions to establish a depositional environment predictor.

19. The system of claim 11, wherein the depositional environment predictor includes a decision tree-based machine-learning, fuzzy-logic based algorithms, or a probabilistic graphical model.

20. The system of claim 11, further comprising:

identifying a longer than standard borehole image: and

applying a sliding window as a spatial sampling technique.