# US 12,595,715 B2 · Cementing lab data validation based on machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,595,715 B2 - Document ID: US12595715B2 - Status: Granted - Kind: B2 - Application number: 17/558,810 - Filed: 2021-12-22 - Published: 2023-06-22 - Granted: 2026-04-07 - Earliest claimed: 2021-12-22 - Assignees: Halliburton Energy Services, Inc. - Applicants: Halliburton Energy Services, Inc. - Inventors: Shohreh Amini; John Paul Bir Singh; Siva Rama Krishna Jandhyala - Web page: https://patentgrep.com/patent/US12595715B2 ## CPC - E21B 33/13: Methods or devices for cementing, for plugging holes, crevices or the like - C04B 40/0032: {Controlling the process of mixing, e.g. adding ingredients in a quantity depending on a measured or desired value (B28C7/00 takes precedence)} - C09K 8/46: containing inorganic binders, e.g. Portland cement - E21B 47/005: Monitoring or checking of cementation quality or level - G06N 20/00: Machine learning - G16C 20/30: Prediction of properties of chemical compounds, compositions or mixtures - E21B 2200/20: Computer models or simulations, e.g. for reservoirs under production, drill bits - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - C04B: LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE {(roofing granules E04D7/005)}; CERAMICS (devitrified glass-ceramics C03C10/00); REFRACTORIES; TREATMENT OF NATURAL STONE - C09K: MATERIALS FOR MISCELLANEOUS APPLICATIONS, NOT PROVIDED FOR ELSEWHERE - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE ## Abstract Techniques of the present disclosure relate to validating data for a composition design. A method comprises applying a machine learning model to at least two inputs comprising parameters of a cement composition and experimental conditions such that the machine learning model outputs at least one predicted property of the cement composition; performing a laboratory experiment to determine at least one experimental property of the cement composition; calculating an error between the at least one predicted property and the at least one experimental property; and recording the experimental data in a cement property database if the error is within an error range or repeating the performing the laboratory experiment if the error is outside the error range. ## Claims ### Claim 1 A method comprising: applying a machine learning model trained on previously recorded experimental data comprising parameters of a plurality of historical cement composition and historical experimental condition, to at least two inputs comprising parameters of a cement composition comprising a cement and water and experimental conditions such that the machine learning model outputs at least one predicted property of the cement composition, wherein properties of a cement composition comprise at least one of compressive strength, thickening time, or gel strength and wherein experimental conditions comprise at least of pressure, temperature, or time; performing a laboratory experiment to determine at least one experimental property of the cement composition; calculating an error between the at least one predicted property and the at least one experimental property; recording the experimental data in a cement property database if the error is within an error range or repeating the performing the laboratory experiment if the error is outside the error range; and preparing an additional cement composition based on the cement composition, such that at least one of the parameters of the additional cement composition has a second error within the error range. ### Claim 2 (depends on 1) The method of claim 1, further comprising training the machine learning model to predict properties of cement compositions. ### Claim 3 (depends on 1) The method of claim 1, further comprising sending a notification to a user about deviation in material performance if the error is outside the error range. ### Claim 4 (depends on 1) The method of claim 1, further comprising determining a range of values for the two or more inputs to screen the inputs prior to an application of the machine learning model to the inputs. ### Claim 5 (depends on 1) The method of claim 1, wherein the parameters of the cement composition each individually comprise at least one parameter selected from the group consisting of an experimental condition, an amount of a component of the cement composition, an identify of a component of the cement composition, and combinations thereof. ### Claim 6 (depends on 1) The method of claim 1, wherein the at least one predicted property comprises at least one cement property selected from the group consisting of compressive strength, thickening time, fluid loss, and combinations thereof. ### Claim 7 (depends on 1) The method of claim 1, further comprising training the machine learning model with previous recorded cement data. ### Claim 8 (depends on 1) The method of claim 1, further comprising screening the at least two inputs with a predetermined range. ### Claim 9 (depends on 1) The method of claim 1, further comprising pumping the additional cement composition in a wellbore. ### Claim 10 (depends on 1) The method of claim 1 wherein the laboratory experiment comprises a QA/QC test, wherein the QA/QC test determines the performance of cement composition materials and/or wherein the laboratory experiment comprises a formulation test, wherein the formulation test determines cement composition properties. ### Claim 11 (depends on 1) The method of claim 1 further comprising allowing the additional cement composition to set to form a set composition. ### Claim 12 A method comprising: calculating an acceptable range of values for properties of cement compositions based on previously recorded experimental data; receiving new experimental data for a cement composition, wherein the experimental data comprises at least one experimental property of the cement composition comparing the at least one experimental property to determine if the experimental data is within the acceptable range of values; applying a machine learning model to at least two inputs comprising parameters of a cement composition comprising a cement and water and experimental conditions such that the machine learning model outputs at least one predicted property of the cement composition, wherein properties of a cement composition comprise at least one of compressive strength, thickening time, or gel strength and wherein experimental conditions comprise at least of pressure, temperature, or time; calculating an error between at least one predicted property and the at least one experimental property; recording the experimental data in a cement property database, wherein-if the error is within an error range or repeating receiving new experimental data from a repeated laboratory experiment if the error is outside the error range; and producing an additional cement composition based on the cement composition, such that at least one of the parameters of the additional cement composition has a second error within the error range. ### Claim 13 (depends on 12) The method of claim 12, further comprising training the machine learning model with previously recorded cement data, prior to application of the machine learning model to the inputs. ### Claim 14 (depends on 12) The method of claim 12, further comprising training the machine learning model with previously recorded data. ### Claim 15 (depends on 12) The method of claim 12, further comprising sending a notification to a user about deviation in material performance if the error is outside the error range. ### Claim 16 (depends on 12) The method of claim 12, further comprising disposing the additional cement composition into a wellbore. ### Claim 17 A method comprising: training a machine learning model to predict properties of cement compositions based on previously recorded experimental data; calculating an acceptable range of values for one or more properties in the experimental data; performing a laboratory experiment to determine at least one experimental property of a cement composition; applying the machine learning model to at least two inputs comprising parameters of a cement composition comprising a cement and water and experimental conditions such that the machine learning model outputs at least one predicted property of the cement composition, wherein properties of a cement composition comprise at least one of compressive strength, thickening time, or gel strength and wherein experimental conditions comprise at least of pressure, temperature, or time; calculating an error between the at least one predicted property and the at least one experimental property, wherein the at least one experimental property is within the acceptable range of values; recording the experimental data in a cement property database if the error is within a range or repeating the performing the laboratory experiment if the error is outside the error range; and preparing an additional cement composition based on the cement composition, such that at least one of the parameters of the additional cement composition has a second error within the error range. ### Claim 18 (depends on 17) The method of claim 17, wherein the parameters of the cement composition each individually comprise at least one parameter selected from the group consisting of an experimental condition, an amount of a component of the cement composition, an identify of a component of the cement composition, and combinations thereof. ### Claim 19 (depends on 17) The method of claim 17, wherein the at least one predicted property comprises at least one cement property selected from the group consisting of compressive strength, thickening time, fluid loss, and combinations thereof. ### Claim 20 (depends on 17) The method of claim 17, further comprising sending a notification to a user about deviation in material performance if the error is outside the error range.