SYSTEM AND METHOD FOR QUANTITATIVE ANALYSIS OF BOREHOLE IMAGES
First Claim
Patent Images
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 (Fsand) for the prepared borehole images using the trained neural network.
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Abstract
A method is described for ways to generate a Fraction of Sand (Fsand) 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 Fsand and other information to estimate hydrocarbons in place in a subsurface formation. The method may be executed by a computer system.
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Citations
11 Claims
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1. A computer-implemented method comprising:
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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 (Fsand) for the prepared borehole images using the trained neural network. - View Dependent Claims (2, 3, 4, 5)
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6. A computer system comprising:
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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 (Fsand) for the prepared borehole images using the trained neural network. - View Dependent Claims (7, 8)
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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:
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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 (Fsand) for the prepared borehole images using the trained neural network. - View Dependent Claims (10, 11)
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Specification