# US 2017/0286802 A1 · Automated core description > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0286802 A1 - Document ID: US20170286802A1 - Status: Published application - Kind: A1 - Application number: 15/471,500 - Filed: 2017-03-28 - Published: 2017-10-05 - Earliest claimed: 2016-04-01 - Assignees: Saudi Arabian Oil Company - Applicants: Saudi Arabian Oil Company - Inventors: Mokhles Mustapha Mezghani; Salem Hamoud Shammari; Fatai A. Anifowose - Web page: https://patentgrep.com/patent/US20170286802A1 ## CPC - G06T 7/0002: {Inspection of images, e.g. flaw detection} - E21B 49/02: by mechanically taking samples of the soil - G01N 15/08: Investigating permeability, pore-volume, or surface area of porous materials - G01N 33/24: Earth materials (G01N33/42 takes precedence) - G06F 18/2413: based on distances to training or reference patterns - E21B 47/0025: {generating an image of the borehole wall using down-hole measurements, e.g. acoustic or electric} - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06V 2201/06: Recognition of objects for industrial automation - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An image associated with a core sample is received. The image represents a property of the core sample. A plurality of values of at least one image attribute are determined from the received image. A core description of the core sample is determined from a set of core descriptions. The core description describes the property of the core sample. The set of core descriptions are associated with a set of training core samples. Each training core sample has a corresponding core description and is associated with a set of plurality of values. Determining the core description of the core sample is based on a comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples. The core description of the core sample is provided to an output device. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving an image associated with a core sample, the image representing a property of the core sample; determining a plurality of values of at least one image attribute from the received image; determining a core description of the core sample from a set of core descriptions, wherein the core description describes the property of the core sample, the set of core descriptions are associated with a set of training core samples, each training core sample has a corresponding core description and is associated with a set of plurality of values, and determining the core description of the core sample is based on a comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples; and providing the core description of the core sample to an output device. ### Claim 2 (depends on 1) The method of claim 1, further comprising selecting the at least one image attribute based on correlations between the at least one image attribute and the property of the core sample. ### Claim 3 (depends on 1) The method of claim 1, further comprising: detecting, by a computer, a plurality of areas in the image, the plurality of areas corresponding to areas at a plurality of depths; determining the plurality of values of the at least one image attribute for the plurality of areas; and recording the determined plurality of values in a pseudo log. ### Claim 4 (depends on 3) The method of claim 3, wherein each of the plurality of areas represents an area in which the property of the core sample is substantially homogeneous, and the plurality of areas can be overlapping. ### Claim 5 (depends on 1) The method of claim 1, wherein each set of plurality of values includes values of the at least one image attribute associated with the corresponding training core sample. ### Claim 6 (depends on 1) The method of claim 1, wherein the comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples includes determining a difference between the plurality of values and at least one set of plurality of values associated with at least one training core sample. ### Claim 7 (depends on 1) The method of claim 1, wherein determining the core description of the core sample includes providing a core description of a training core sample. ### Claim 8 A non-transitory, computer-readable medium storing one or more instructions executable by a computer-implemented system to perform operations comprising: receiving an image associated with a core sample, the image representing a property of the core sample; determining a plurality of values of at least one image attribute from the received image; determining a core description of the core sample from a set of core descriptions, wherein the core description describes the property of the core sample, the set of core descriptions are associated with a set of training core samples, each training core sample has a corresponding core description and is associated with a set of plurality of values, and determining the core description of the core sample is based on a comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples; and providing the core description of the core sample to an output device. ### Claim 9 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein the operations further comprise selecting the at least one image attribute based on correlations between the at least one image attribute and the property of the core sample. ### Claim 10 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein the operations further comprise: detecting, by a computer, a plurality of areas in the image, the plurality of areas corresponding to areas at a plurality of depths; determining the plurality of values of the at least one image attribute for the plurality of areas; and recording the determined plurality of values in a pseudo log. ### Claim 11 (depends on 10) The non-transitory, computer-readable medium of claim 10, wherein each of the plurality of areas represents an area in which the property of the core sample is substantially homogeneous, and the plurality of areas can be overlapping. ### Claim 12 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein each set of plurality of values includes values of the at least one image attribute associated with the corresponding training core sample. ### Claim 13 (depends on 8) The non-transitory, computer-readable medium of claim 8, wherein the comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples includes determining a difference between the plurality of values and at least one set of plurality of values associated with at least one training core sample. ### Claim 14 (depends on 8) The non-transitory, computer-readable medium of claim of claim 8, wherein determining the core description of the core sample includes providing a core description of a training core sample. ### Claim 15 A computer-implemented system, comprising: a computer memory; and a hardware processor interoperably coupled with the computer memory and configured to perform operations comprising: receiving an image associated with a core sample, the image representing a property of the core sample; determining a plurality of values of at least one image attribute from the received image; determining a core description of the core sample from a set of core descriptions, wherein the core description describes the property of the core sample, the set of core descriptions are associated with a set of training core samples, each training core sample has a corresponding core description and is associated with a set of plurality of values, and determining the core description of the core sample is based on a comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples; and providing the core description of the core sample to an output device. ### Claim 16 (depends on 15) The computer-implemented system of claim 15, wherein the operations further comprise: detecting, by a computer, a plurality of areas in the image, the plurality of areas corresponding to areas at a plurality of depths; determining the plurality of values of the at least one image attribute for the plurality of areas; and recording the determined plurality of values in a pseudo log. ### Claim 17 (depends on 16) The computer-implemented system of claim 16, wherein each of the plurality of areas represents an area in which the property of the core sample is substantially homogeneous, and the plurality of areas can be overlapping. ### Claim 18 (depends on 15) The computer-implemented system of claim 15, wherein each set of plurality of values includes values of the at least one image attribute associated with the corresponding training core sample. ### Claim 19 (depends on 15) The computer-implemented system of claim 15, wherein the comparison between the plurality of values associated with the core sample and sets of plurality of values associated with the set of training core samples includes determining a difference between the plurality of values and at least one set of plurality of values associated with at least one training core sample. ### Claim 20 (depends on 15) The computer-implemented system of claim 15, wherein determining the core description of the core sample includes providing a core description of a training core sample.