System for generating bit wear model predictions and related methods
An earth-boring tool system may include a drill string and at least one or more sensors. The earth-boring tool system may receive field drilling data, logging while drilling (LWD) data, and data indicative of wear of at least part of a drilling tool, train a predictive bit wear model based on the received data, fit a wear trajectory regression curve to a latent space representation of the received data, and generate one or more predicted dull states of the at least one drilling tool based, at least in part, on the predictive bit wear model and the wear trajectory regression curve. The earth-boring tool system may also generate one or more similarity scores responsive to comparing the first drilling data to target data and filter the first drilling data or a latent space representation of the first drilling data responsive to the one or more similarity scores.
1 . An earth-boring tool system comprising:
a drill string comprising at least one drilling tool;
one or more sensors configured to sense wear of the at least one drilling tool;
at least one processor;
at least one non-transitory computer readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the earth-boring tool system to:
receive first drilling data including field drilling data, logging while drilling (LWD) data and data indicative of wear of at least part of the at least one drilling tool;
train at least one predictive bit wear model for predicting one or more dull states of the at least one drilling tool based, at least in part, on the first drilling data, the predictive bit wear model configured to encode the first drilling data into a latent space representation of the first drilling data;
fit a wear trajectory regression curve to the latent space representation of the first drilling data;
generate one or more predicted dull states of the at least one drilling tool based, at least in part, on the predictive bit wear model and the wear trajectory regression curve;
receive second drilling data including field drilling data, logging while drilling (LWD) data and data indicative of wear of at least part of the at least one drilling tool via the one or more sensors;
utilize the at least one predictive bit wear model to encode the second drilling data into a latent space representation of the second drilling data; and
project the latent space representation of the second drilling data onto the wear trajectory regression curve.
2 . The earth-boring tool system of claim 1 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
generate one or more predicted dull states of the at least one drilling tool responsive to the projected latent space representation of the second drilling data via the predictive bit wear model.
3 . The earth-boring tool system of claim 1 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
project the latent space representation of the second drilling data onto the wear trajectory regression curve responsive to the latent space representation of the second drilling data exceeding a predetermined threshold.
4 . The earth-boring tool system of claim 3 , wherein the predetermined threshold is based, at least in part, on a distance, in latent space, between the latent space representation of the second drilling data and a closest point on the wear trajectory regression curve.
5 . The earth-boring tool system of claim 1 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
decode the projected latent space representation of the second drilling data to generate at least one predicted dull state of the at least one drilling tool.
6 . The earth-boring tool system of claim 5 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
modify one or more drilling parameters of the at least one drilling tool responsive to the at least one predicted dull state.
7 . The earth-boring tool system of claim 1 , wherein the first drilling data includes one or more of rock strength, rate of penetration (ROP), weight on bit (WOB), rotations per minute (RPM), torque on bit, formation logging data, well geometry, formation geometry, formation density, tool geometry, formation composition, or tool rotation.
8 . The earth-boring tool system of claim 1 , wherein the first drilling data includes labeled data from one or more historical drilling operations.
9 . The earth-boring tool system of claim 1 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
generate one or more similarity scores responsive to comparing the latent space representation of the first drilling data to target drilling data; and
filter the first drilling data or the latent space representation of the first drilling data responsive to the one or more similarity scores.
10 . The earth-boring tool system of claim 1 , wherein the instructions stored on the at least one computer readable storage medium, when executed by the at least one processor, cause the earth-boring tool system to:
generate a warning indicative of a need to replace at least part of the at least one drilling tool; and
provide to an operator via a display of the earth-boring tool system, the generated warning.
11 . A method for obtaining a bit wear model, the method comprising:
receiving first drilling data from an earth-boring tool system, the first drilling data including field drilling data, logging while drilling (LWD) data and data indicative of wear of at least part of the at least one drilling tool;
training at least one predictive bit wear model for predicting one or more dull states of the at least one drilling tool based, at least in part, on the first drilling data, the predictive bit wear model configured to encode the first drilling data into a latent space representation of the first drilling data;
generating one or more similarity scores responsive to comparing the latent space representation of the first drilling data to target drilling data; and
filtering the first drilling data or the latent space representation of the first drilling data responsive to the one or more similarity scores.
12 . The method of claim 11 , further comprising:
receiving second drilling data including field drilling data, logging while drilling (LWD) data and data indicative of wear of at least part of the at least one drilling tool via one or more sensors;
fitting a wear trajectory regression curve to the filtered latent space representation of the first drilling data; and
utilizing the at least one predictive bit wear model to encode the second drilling data into a latent space representation of the second drilling data.
13 . The method of claim 12 , further comprising:
projecting the latent space representation of the second drilling data onto the wear trajectory regression curve; and
generating one or more predicted dull states of the at least one drilling tool based, at least in part, on the predictive bit wear model and the wear trajectory regression curve.
14 . The method of claim 13 , wherein generating one or more predicted dull states of the at least one drilling tool based, at least in part, on the predictive bit wear model and the wear trajectory regression curve comprises generating one or more predicted dull states of the at least one drilling tool responsive to decoding the projected latent space representation of the second drilling data via the at least one predictive bit wear model.
15 . The method of claim 13 , wherein projecting the latent space representation of the second drilling data onto the wear trajectory regression curve comprises projecting the latent space representation of the second drilling data onto the wear trajectory curve responsive to the latent space representation of the second drilling data exceeding a predetermined threshold.
16 . The method of claim 15 , wherein the predetermined threshold is based, at least in part, on a distance, in latent space, between the latent space representation of the second drilling data and a closest point on the wear trajectory regression curve.
17 . The method of claim 11 , further comprising:
receiving third drilling data from an earth-boring tool system;
receiving labeled drilling data correlating to the third drilling data; and
validating the at least one predictive bit wear model responsive to the third drilling data and the labeled drilling data.
18 . The method of claim 11 , wherein the first drilling data includes labeled data from one or more historical drilling operations.
19 . A non-transitory computer-readable medium storing instructions thereon that, when executed by the at least one processor, cause the at least one processor to perform the steps comprising:
receiving first drilling data including field drilling data, logging while drilling (LWD) data and data indicative of wear of at least part of the at least one drilling tool;
training at least one predictive bit wear model for predicting one or more dull states of the at least one drilling tool based, at least in part, on the first drilling data, the predictive bit wear model configured to encode the first drilling data into a latent space representation of the first drilling data;
generating one or more similarity scores responsive to comparing the latent space representation of the first drilling data to target drilling data;
filtering the first drilling data or the latent space representation of the first drilling data responsive to the one or more similarity scores,
fitting a wear trajectory regression curve to the filtered latent space representation of the first drilling data; and
generating one or more predicted dull states of the at least one drilling tool based, at least in part, on the predictive bit wear model and the wear trajectory regression curve.