# US 11,898,435 B2 · Correcting borehole images using machine-learning models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,898,435 B2 - Document ID: US11898435B2 - Status: Granted - Kind: B2 - Application number: 17/032,015 - Filed: 2020-09-25 - Published: 2022-03-31 - Granted: 2024-02-13 - Earliest claimed: 2020-09-25 - Assignees: Halliburton Energy Services, Inc. - Applicants: Halliburton Energy Services, Inc. - Inventors: Li Pan; Yijing Fan; Hsu-Hsiang Wu; Jin Ma - Web page: https://patentgrep.com/patent/US11898435B2 ## CPC - E21B 47/002: by visual inspection - E21B 47/0025: {generating an image of the borehole wall using down-hole measurements, e.g. acoustic or electric} - G01V 1/40: specially adapted for well-logging - G01V 3/18: specially adapted for well-logging - G01V 3/20: operating with propagation of electric current - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/063: using electronic means - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06V 10/20: Image preprocessing - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - E21B 47/12: Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Borehole images can be corrected using machine-learning models. For example, a system can train a machine-learning model based on a training dataset. The training dataset can include a first set of borehole images correlated to a second set of borehole images, where the second set of borehole images are less precise versions of the first set of borehole images. The system can then execute the trained machine-learning model in relation to an input borehole image to receive a corrected borehole image as output from the trained machine-learning model. The corrected borehole image can be a visually corrected version of the input borehole image. The system may then perform one or more operations based on the corrected borehole image, such as generating a graphical user interface that includes the corrected borehole image for display on a display device. ## Claims ### Claim 1 A system comprising: a processor; and a memory including instructions that are executable by the processor for causing the processor to: generate a training dataset using a three-dimensional model, the three-dimensional model being for simulating a tool in a geological subterranean formation; train a machine-learning model based on the training dataset to generate a trained machine-learning model, the machine-learning model being different than the three-dimensional model, the training dataset including a first set of borehole images correlated to a second set of borehole images, the second set of borehole images being less precise versions of the first set of borehole images; execute the trained machine-learning model in relation to an input borehole image to receive a corrected borehole image as output from the trained machine-learning model, the corrected borehole image being a visually corrected version of the input borehole image, wherein the input borehole image is a two-dimensional digital image that includes colored pixels representing a property of a subterranean formation into which a borehole is drilled for extracting hydrocarbons from the subterranean formation, and wherein the corrected borehole image is also a two-dimensional digital image; and generate a graphical user interface that includes the corrected borehole image for display on a display device. ### Claim 2 (depends on 1) The system of claim 1, wherein the input borehole image is a resistivity image and the property is a resistivity of the subterranean formation. ### Claim 3 (depends on 1) The system of claim 1, further comprising an imaging tool positionable in the borehole, wherein the memory further includes instructions that are executable by the processor for causing the processor to generate the input borehole image based on sensor measurements from the imaging tool. ### Claim 4 (depends on 1) The system of claim 1, wherein the machine-learning model is a convolutional neural network that includes a convolutional layer, a batch normalization layer, a concatenation layer, a summation layer, a max pooling layer, and an up-sampling layer. ### Claim 5 (depends on 1) The system of claim 1, wherein the tool includes a virtual imaging tool, and wherein the memory further includes instructions that are executable by the processor for causing the processor to: receive simulated measurements of the property of the geological subterranean formation; and generate the second set of borehole images based on the simulated measurements. ### Claim 6 (depends on 5) The system of claim 5, wherein the memory further includes instructions that are executable by the processor for causing the processor to generate the second set of borehole images by performing an inversion process on the simulated measurements. ### Claim 7 (depends on 1) The system of claim 1, wherein the corrected borehole image is more accurate than the input borehole image. ### Claim 8 A method comprising: generating, by a processor, a training dataset using a three-dimensional model, the three-dimensional model being for simulating a tool in a geological subterranean formation; training, by the processor, a machine-learning model based on the training dataset to generate a trained machine-learning model, the machine-learning model being different than the three-dimensional model, the training dataset including a first set of borehole images correlated to a second set of borehole images, the second set of borehole images being less precise versions of the first set of borehole images; executing, by the processor, the trained machine-learning model in relation to an input borehole image to receive a corrected borehole image as output from the trained machine-learning model, the corrected borehole image being a visually corrected version of the input borehole image, wherein the input borehole image is a two-dimensional digital image that includes colored pixels representing a property of a subterranean formation into which a borehole is drilled for extracting hydrocarbons from the subterranean formation, and wherein the corrected borehole image is also a two-dimensional digital image; and generating, by the processor, a graphical user interface that includes the corrected borehole image for display on a display device. ### Claim 9 (depends on 8) The method of claim 8, wherein the property includes a resistivity of the subterranean formation. ### Claim 10 (depends on 8) The method of claim 8, further comprising generating the input borehole image based on sensor measurements from an imaging tool in the borehole. ### Claim 11 (depends on 8) The method of claim 8, wherein the tool includes a virtual imaging tool, and further comprising: receiving simulated measurements of the property of the geological subterranean formation; and generating the second set of borehole images based on the simulated measurements. ### Claim 12 (depends on 11) The method of claim 11, further comprising generating the second set of borehole images by performing an inversion process on the simulated measurements. ### Claim 13 (depends on 8) The method of claim 8, wherein the corrected borehole image has a higher resolution than the input borehole image, or the corrected borehole image includes a visual feature that is absent from the input borehole image. ### Claim 14 A non-transitory computer-readable medium comprising program code that is executable by a processor for causing the processor to: generate a training dataset using a three-dimensional model, the three-dimensional model being for simulating a tool in a geological subterranean formation; train a machine-learning model based on the training dataset to generate a trained machine-learning model, the machine-learning model being different than the three-dimensional model, the training dataset including a first set of borehole images correlated to a second set of borehole images, the second set of borehole images being less precise versions of the first set of borehole images; execute the trained machine-learning model to generate a corrected borehole image based on an input borehole image, the corrected borehole image being visually corrected in at least one way relative to the input borehole image, wherein the input borehole image is a two-dimensional digital image that includes colored pixels representing a property of a subterranean formation into which a borehole is drilled for extracting hydrocarbons from the subterranean formation, and wherein the corrected borehole image is also a two-dimensional digital image; and execute one or more operations based on the corrected borehole image. ### Claim 15 (depends on 14) The non-transitory computer-readable medium of claim 14, wherein the one or more operations involves generating a graphical user interface including the corrected borehole image for display on a display device.