Earth-boring tool rate of penetration and wear prediction system and related methods

1. A method, comprising:

receiving input data;

training a hybrid physics and machine-learning model with the input data by building a coefficient library of drilling parameters of a planned drilling operation, comprising:

determining initial predictions of the drilling parameters of the planned drilling operation based on physics data within the input data; and

determining relative influences and rankings of the drilling parameters of the planned drilling operation based on the physics data; and

providing, via the hybrid physics and machine-learning model, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool during the planned drilling operation.

2. The method of claim 1 , further comprising providing one or more recommendations of the drilling parameters based on the predictive model.

3. The method claim 2 , further comprising drilling a borehole based at least partially on the one or more recommendations of drilling parameters.

4. The method of claim 1 , further comprising:

receiving real-time data from a drilling operation;

retraining the hybrid physics and machine-learning model based on a combination of the input data and the real-time data; and

providing, via the retrained hybrid physics and machine-learning model, an updated predictive model of a rate of penetration of the earth-boring tool and wear of the earth-boring tool during a remainder of the planned drilling operation.

5. The method of claim 4 , further comprising providing one or more updated recommendations of drilling parameters based on the updated predictive model.

6. The method claim 1 , wherein training a hybrid physics and machine-learning model comprises:

identifying drilling parameters having the greatest uncertainties; and

subjecting the drilling parameters to a parameter tuning process.

7. The method of claim 1 , wherein providing a predictive model representing wear of the earth-boring tool comprises utilizing wear state characterization at a cutter level to predict wear of the earth-boring tool.

8. The method of claim 1 , wherein the input data comprises offset well data and the physics data.

9. The method of claim 8 , wherein the offset well data comprises one or more of formation logs, well architecture and design, surface and downhole data, bit and cutter design information, drilling system details, or bit dull information.

10. The method of claim 8 , wherein the physics data comprises one or more of drill bit mechanics simulation models, three-dimensional geometry descriptions of earth-boring tools or formations, rock failure models, cutter-wear progression models, or cutter fracture criteria.

11. The method of claim 1 , further comprising training a plurality of individual modules within the hybrid model.

12. The method of claim 11 , wherein training the plurality of individual modules within the hybrid model comprises training at least a bit mechanics module, a cutter wear module, and a rate-of-penetration limiters module.

13. An earth-boring tool system, comprising:

a drilling assembly for drilling a wellbore; and

a surface control unit operably coupled to the drilling assembly, the surface control unit comprising a prediction system, comprising:

at least one processor; and

at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the prediction system to:

pre-train a plurality of modules individually within a hybrid physics and machine-learning model;

train the plurality of modules together to develop the hybrid physics and machine-learning model based on input data;

provide, via the hybrid physics and machine-learning model, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool during a planned drilling operation;

provide one or more recommendations of drilling parameters based on the predictive model;

utilize the one or more recommendations in a drilling operation;

receive real-time data from the drilling operation;

retrain the hybrid physics and machine-learning model based on a combination of the input data and the real-time data; and

provide, via the retrained hybrid physics and machine-learning model, an updated predictive model of a rate of penetration of the earth-boring tool and wear of the earth-boring tool during a remainder of the planned drilling operation.

14. The earth-boring tool system of claim 13 , further comprising instructions that, when executed by the at least one processor, cause the prediction system to provide one or more updated recommendations of drilling parameters based on the updated predictive model.

15. The earth-boring tool system of claim 13 , wherein providing a predictive model comprises analyzing the input data with one or more of physics models or machine-learning models of the hybrid physics and machine-learning model.

16. The earth-boring tool system of claim 15 , wherein the machine-learning models are selected from a list consisting of a regression analysis, a classification analysis, a neural network, or an ensemble of machine-learning models.

17. A method, comprising:

receiving real-time data from a drilling operation at a trained hybrid physics and machine-learning model;

analyzing the real-time data via the hybrid physics and machine-learning model;

providing, via the hybrid physics and machine-learning model and based at least partially on the analysis, a predictive model representing a rate of penetration of an earth-boring tool and wear of the earth-boring tool throughout at least part of a remainder of the drilling operation;

providing one or more recommendations of drilling parameters based on the predictive model; and

operating at least a portion of the drilling operation using the one or more recommendations of drilling parameters.

18. The method of claim 17 , wherein the drilling operation comprises an operation that involves at least one of a build-up-rate, a turn rate, a lateral ROP, an unconfined compressive strength, a walk rate, a dog leg severity, a WOB, a confined compressive strength, a contact force, a rib force, a bending moment, a pressure, an inclination, an azimuth, a borehole trajectory, a drilling torque, drilling vibrations, or a hole quality.

19. The method of claim 17 , wherein analyzing the real-time data comprises analyzing the real-time data with one or more of physics models or machine-learning models of the hybrid physics and machine-learning model.

20. The method of claim 17 , further comprising continuously retraining the hybrid physics and machine-learning model with real-time data throughout a duration of the drilling operation.