Cementing lab data validation based on machine learning

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.

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.