Cutter/rock interaction modeling
A computer-implemented method may include receiving test data representing a cutter/rock interaction for a cutter/rock pair; calibrating an analytical model to represent the cutter/rock interaction mechanism for a cutter/rock pair; applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets; generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interaction between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.
1. A computer-implemented method comprising:
receiving test data representing a cutter/rock interaction for a cutter/rock pair;
calibrating an analytical model to represent the cutter/rock interaction for the cutter/rock pair;
applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets;
generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interactions between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; and
generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.
2. The method of claim 1 , further comprising:
determining cutter force estimates using the second neural network model, wherein the cutter force estimates are determined for the non-tested cutter/rock interactions; and
executing a computer-based instruction based on the second neural network model or the cutter force estimates, wherein the computer-based instruction includes at least one selected from the group consisting of:
executing a computer-based simulation based on the cutter force estimates;
adjusting a drilling plan based on results of the computer-based simulation;
adjusting a maintenance plan based the results of the computer-based simulation;
adjusting operations of a cutter based on the results of the computer-based simulation;
modifying a workflow based on the results of the computer-based simulation;
providing the calibrated analytical model or data derived from the calibrated analytical model to a simulation system;
providing the first neural network model or data derived from the first neural network model to a simulation system;
providing the second neural network model or data derived from the second neural network model to a simulation system;
setting up automatic synthetic rock file generation workflow; and
visually presenting the second neural network model or visually present cutter/rock interaction information.
3. The method of claim 1 , further comprising refining the first neural network model or the second neural network model based on experimental data.
4. The method of claim 1 , wherein the calibrated analytical model is calibrated using model-based inversion.
5. The method of claim 1 , wherein the calibrated analytical model is a 3D model.
6. The method of claim 1 , wherein the training the first neural network and the training the second neural network are based on machine learning techniques.
7. The method of claim 1 , wherein:
the first neural network is used to obtain cutter rock forces based on:
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth; and
the second neural network model is used to obtain cutter rock forces based on:
rock type,
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth.
8. A computing system, comprising:
one or more processors; and
a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
receiving test data representing a cutter/rock interaction for a cutter/rock pair;
calibrating an analytical model to represent the cutter/rock interaction for the cutter/rock pair;
applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets;
generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interactions between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; and
generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.
9. The computing system of claim 8 , wherein the operations further comprise:
determining cutter force estimates using the second neural network model, wherein the cutter force estimates are determined for the non-tested cutter/rock interactions; and
executing a computer-based instruction based on the second neural network model or the cutter force estimates, wherein the computer-based instruction includes at least one selected from the group consisting of:
executing a computer-based simulation based on the cutter force estimates;
adjusting a drilling plan based on results of the computer-based simulation;
adjusting a maintenance plan based the results of the computer-based simulation;
adjusting operations of a cutter based on the results of the computer-based simulation;
modifying a workflow based on the results of the computer-based simulation;
providing the calibrated analytical model or data derived from the calibrated analytical model to a simulation system;
providing the first neural network model or data derived from the first neural network model to a simulation system;
providing the second neural network model or data derived from the second neural network model to a simulation system;
setting up automatic synthetic rock file generation workflow; and
visually presenting the second neural network model or visually present cutter/rock interaction information.
10. The computing system of claim 8 , wherein the operations further comprise refining the first neural network model or the second neural network model based on experimental data.
11. The computing system of claim 8 , wherein the calibrated analytical model is calibrated using model-based inversion.
12. The computing system of claim 8 , wherein the calibrated analytical model is a 3D model.
13. The computing system of claim 8 , wherein the training the first neural network and the training the second neural network are based on machine learning techniques.
14. The computing system of claim 8 , wherein:
the first neural network is used to obtain cutter rock forces based on:
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth; and
the second neural network model is used to obtain cutter rock forces based on:
rock type,
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth.
15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:
receiving test data representing a cutter/rock interaction for a cutter/rock pair;
calibrating an analytical model to represent the cutter/rock interaction for the cutter/rock pair;
applying the calibrated analytical model to expand the test data to form one of a plurality of expanded test datasets;
generating a first neural network model, of a plurality of first neural network models, representing cutter/rock interactions between a plurality of cutters of different cutter sizes and a particular rock type, wherein the first neural network is generated using the plurality of expanded test datasets as training input; and
generating a second neural network model using the plurality of first neural network models as training input, wherein the second neural network model represents non-tested cutter/rock interactions between a plurality of cutters of different cutter sizes and a plurality of rock types.
16. The computer-readable medium of claim 15 , wherein the operations further comprise:
determining cutter force estimates using the second neural network model, wherein the cutter force estimates are determined for the non-tested cutter/rock interactions; and
executing a computer-based instruction based on the second neural network model or the cutter force estimates, wherein the computer-based instruction includes at least one selected from the group consisting of:
executing a computer-based simulation based on the cutter force estimates;
adjusting a drilling plan based on results of the computer-based simulation;
adjusting a maintenance plan based the results of the computer-based simulation;
adjusting operations of a cutter based on the results of the computer-based simulation;
modifying a workflow based on the results of the computer-based simulation;
providing the calibrated analytical model or data derived from the calibrated analytical model to a simulation system;
providing the first neural network model or data derived from the first neural network model to a simulation system;
providing the second neural network model or data derived from the second neural network model to a simulation system;
setting up automatic synthetic rock file generation workflow; and
visually presenting the second neural network model or visually present cutter/rock interaction information.
17. The computer-readable medium of claim 15 , wherein the operations further comprise refining the first neural network model or the second neural network model based on experimental data.
18. The computer-readable medium of claim 15 , wherein the calibrated analytical model is calibrated using model-based inversion.
19. The computer-readable medium of claim 15 , wherein the training the first neural network and the training the second neural network are based on machine learning techniques.
20. The computer-readable medium of claim 15 , wherein:
the first neural network is used to obtain cutter rock forces based on:
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth; and
the second neural network model is used to obtain cutter rock forces based on:
rock type,
cutter size,
confinement pressure,
back rake angle,
side rake angle, and
depth.