SYSTEM AND METHOD FOR QUANTITATIVE ANALYSIS OF BOREHOLE IMAGES

• US 20200134803A1
• Filed: 10/15/2019
• Published: 04/30/2020
• Est. Priority Date: 10/25/2018
• Status: Active Grant

• Current Assignee: Chevron USA Incorporated (Chevron Corp.)
• Original Assignee: Chevron USA Incorporated (Chevron Corp.)
• Inventors: Gong, Bo, Keele, Dustin J., Toumelin, Emmanuel, Clinch, Simon
• Granted Patent: US 10,997,705 B2
• Time in Patent Office: Days
• CPC Class Codes: G01V 11/00 Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
• CPC Class Codes: G01V 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 7/0002 Inspection of images, e.g. flaw detection

Abstract

A method is described for ways to generate a Fraction of Sand (F_sand) estimate and net-to-gross (NTG) estimate of sand in a formation using a machine-learning algorithm such as a neural network based on borehole image logs. The method may use the F_sand and other information to estimate hydrocarbons in place in a subsurface formation. The method may be executed by a computer system.

Claims

1. A computer-implemented method comprising:

a. receiving, at one or more computer processors, at least one borehole image wherein the at least one borehole image traverses a subsurface formation including sand and non-sand layers;

b. preparing, via the one or more computer processors, the at least one borehole image to generate prepared borehole images;

c. selecting, via the one or more computer processors, a subset of the prepared borehole images to generate training data;

d. labeling, via the one or more computer processors, the training data with sand fraction values based on core analysis;

e. training, via the one or more computer processors, a neural network using the training data to generate a trained neural network; and

f. predicting, via the one or more computer processors, a fraction of sand (F_sand) for the prepared borehole images using the trained neural network.

2. The method of claim 1 further comprising augmenting the training data prior to the labeling.

3. The method of claim 2 wherein the augmenting the training data includes azimuthal shifts.

4. The method of claim 2 wherein the augmenting the training data includes depth shifts.

5. The method of claim 1 further comprising using the F_sand to estimate at least one of cumulative sand and hydrocarbon in place.

6. A computer system comprising:

one or more processors;

memory; and

one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the computer system to;

a. receive, at the one or more processors, at least one borehole image wherein the at least one borehole image traverses a subsurface formation including sand and non-sand layers;

b. prepare, via the one or more processors, the at least one borehole image to generate prepared borehole images;

c. select, via the one or more processors, a subset of the prepared borehole images to generate training data;

d. label, via the one or more processors, the training data with sand fraction values based on core analysis;

e. train, via the one or more processors, a neural network using the training data to generate a trained neural network; and

f. predict, via the one or more computer processors, a fraction of sand (F_sand) for the prepared borehole images using the trained neural network.

7. The computer system of claim 6 further including instructions that when executed by the one or more processors cause the computer system to augment the training data prior to the labeling.

8. The computer system of claim 6 further including instructions that when executed by the one or more processors cause the computer system to use the F_sand to estimate at least one of cumulative sand and hydrocarbon in place.

9. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to execute:

a. receive, at one or more processors, at least one borehole image wherein the at least one borehole image traverses a subsurface formation including sand and non-sand layers;

b. prepare, via the one or more processors, the at least one borehole image to generate prepared borehole images;

c. select, via the one or more processors, a subset of the prepared borehole images to generate training data;

d. label, via the one or more processors, the training data with sand fraction values based on core analysis;

e. train, via the one or more processors, a neural network using the training data to generate a trained neural network; and

f. predict, via the one or more computer processors, a fraction of sand (F_sand) for the prepared borehole images using the trained neural network.

10. The non-transitory computer readable storage medium of claim 9 further comprising instructions that when executed by the one or more processors cause the device to augment the training data prior to beginning the labeling.

11. The computer system of claim 9 further comprising instructions that when executed by the one or more processors cause the computer system to use the F_sand to estimate at least one of cumulative sand and hydrocarbon in place.