SYSTEM AND METHOD FOR AUTOMATED AND ACCURATE CORE PHOTOS LABELING IN MACHINE LEARNING BASED CORE PROPERTIES PREDICTION

1 . A method for analyzing rock cores of a subterranean formation, the method comprising:

capturing a first plurality of core images of the rock cores that are collected from a plurality of geographical locations in the subterranean formation;

generating, by a computer processor and from the first plurality of core images, a first plurality of sub-images by sub-dividing each of the first plurality of core images;

classifying, using a secondary machine learning model that automatically identifies artifacts induced from preparation of the rock cores, the first plurality of sub-images into a plurality of artifact-free sub-images and a plurality of artifact-containing sub-images; and

analyzing, using a primary machine learning model, the plurality of artifact-free sub-images to generate a core analysis result.

2 . The method according to claim 1 , further comprising:

capturing a second plurality of core images of the rock cores;

generating, from the second plurality of core images, a second plurality of sub-images by sub-dividing each of the second plurality of core images;

forming, based on user assigned labels to designate each of the second plurality of sub-images as artifact-free or artifact-containing, a secondary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the secondary machine learning dataset; and

training, based on the secondary machine learning dataset during a secondary training phase prior to classifying the first plurality of sub-images, the secondary machine learning model.

3 . The method according to claim 1 ,

wherein the artifacts comprise one or more of a hand written text, a core plug location, a core breakage, and a missing portion of the rock cores.

4 . The method according to claim 2 , further comprising:

forming, based on user assigned geological characteristic values to designate each artifact-free sub-image of the second plurality of sub-images, a primary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the primary machine learning dataset; and

training, based on the primary machine learning dataset during a primary training phase prior to analyzing the plurality of artifact-free sub-images to generate the core analysis result, the primary machine learning model.

5 . The method according to claim 1 , further comprising:

performing, based on the core analysis result, a field operation of the subterranean formation.

6 . The method according to claim 5 , further comprising:

selecting, from the plurality of geographical locations and based on the core analysis result, a target location,

wherein the core analysis result comprises geological characteristics of the plurality of geographical locations, and

wherein the field operation is performed at the target location.

7 . The method according to claim 6 ,

wherein the geological characteristics comprise one or more of porosity, permeability, fluid saturation, and grain density of the rock cores.

8 . A core image analyzer for analyzing rock cores of a subterranean formation, comprising:

a processor; and

a memory coupled to the processor and storing instruction, the instructions, when executed by the processor, comprising functionality for:

capturing a first plurality of core images of the rock cores that are collected from a plurality of geographical locations in the subterranean formation;

generating, by a computer processor and from the first plurality of core images, a first plurality of sub-images by sub-dividing each of the first plurality of core images;

classifying, using a secondary machine learning model that automatically identifies artifacts induced from preparation of the rock cores, the first plurality of sub-images into a plurality of artifact-free sub-images and a plurality of artifact-containing sub-images; and

analyzing, using a primary machine learning model, the plurality of artifact-free sub-images to generate a core analysis result.

9 . The core image analyzer according to claim 8 , the instructions, when executed by the processor, further comprising functionality for:

capturing a second plurality of core images of the rock cores;

generating, from the second plurality of core images, a second plurality of sub-images by sub-dividing each of the second plurality of core images;

forming, based on user assigned labels to designate each of the second plurality of sub-images as artifact-free or artifact-containing, a secondary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the secondary machine learning dataset; and

training, based on the secondary machine learning dataset during a secondary training phase prior to classifying the first plurality of sub-images, the secondary machine learning model.

10 . The core image analyzer according to claim 8 ,

wherein the artifacts comprise one or more of a hand written text, a core plug location, a core breakage, and a missing portion of the rock cores.

11 . The core image analyzer according to claim 9 , the instructions, when executed by the processor, further comprising functionality for:

forming, based on user assigned geological characteristic values to designate each artifact-free sub-image of the second plurality of sub-images, a primary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the primary machine learning dataset; and

training, based on the primary machine learning dataset during a primary training phase prior to analyzing the plurality of artifact-free sub-images to generate the core analysis result, the primary machine learning model.

12 . The core image analyzer according to claim 8 , the instructions, when executed by the processor, further comprising functionality for:

performing, based on the core analysis result, a field operation of the subterranean formation.

13 . The core image analyzer according to claim 12 , the instructions, when executed by the processor, further comprising functionality for:

selecting, from the plurality of geographical locations and based on the core analysis result, a target location,

wherein the core analysis result comprises geological characteristics of the plurality of geographical locations, and

wherein the field operation is performed at the target location.

14 . The core image analyzer according to claim 13 ,

wherein the geological characteristics comprise one or more of porosity, permeability, fluid saturation, and grain density of the rock cores.

15 . A system, comprising:

a wellbore penetrating a subterranean formation;

a well control system of the wellbore; and

a core image analyzer comprising functionality for:

capturing a first plurality of core images of rock cores that are collected from a plurality of geographical locations in the subterranean formation;

generating, by a computer processor and from the first plurality of core images, a first plurality of sub-images by sub-dividing each of the first plurality of core images;

classifying, using a secondary machine learning model that automatically identifies artifacts induced from preparation of the rock cores, the first plurality of sub-images into a plurality of artifact-free sub-images and a plurality of artifact-containing sub-images; and

analyzing, using a primary machine learning model, the plurality of artifact-free sub-images to generate a core analysis result.

16 . The system according to claim 15 , the core image analyzer further comprising functionality for:

capturing a second plurality of core images of the rock cores;

generating, from the second plurality of core images, a second plurality of sub-images by sub-dividing each of the second plurality of core images;

forming, based on user assigned labels to designate each of the second plurality of sub-images as artifact-free or artifact-containing, a secondary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the secondary machine learning dataset; and

training, based on the secondary machine learning dataset during a secondary training phase prior to classifying the first plurality of sub-images, the secondary machine learning model.

17 . The system according to claim 15 ,

wherein the artifacts comprise one or more of a hand written text, a core plug location, a core breakage, and a missing portion of the rock cores.

18 . The system according to claim 16 , the core image analyzer further comprising functionality for:

forming, based on user assigned geological characteristic values to designate each artifact-free sub-image of the second plurality of sub-images, a primary machine learning dataset, wherein any sub-image designated as artifact-containing is excluded from the primary machine learning dataset; and

training, based on the primary machine learning dataset during a primary training phase prior to analyzing the plurality of artifact-free sub-images to generate the core analysis result, the primary machine learning model.

19 . The system according to claim 15 , the core image analyzer further comprising functionality for:

performing, based on the core analysis result, a field operation of the subterranean formation.

20 . The system according to claim 19 , the core image analyzer further comprising functionality for:

selecting, from the plurality of geographical locations and based on the core analysis result, a target location,

wherein the core analysis result comprises geological characteristics of the plurality of geographical locations, and

wherein the field operation is performed at the target location.