I N NOVATI N G WH I LE DR I LLI N G
Case study: Laser measurements,
machine learning boost motor
performance in the Permian
As the Laser-AI algorithm evolved, it
allowed the stator to be rejected with the
same criteria as a dyno-test, but results
were realized before the motor was built,
rather than after it was assembled and
shipped to a dyno-testing facility.
Results BY LONNIE SMITH, TURNCO
An operator in the Permian Basin was
having inconsistent runs caused by
motors that were either not producing
the horsepower expected or were failing
prematurely for power section chunking.
In both scenarios, the motors exhibited
the same power section fit and were run
in similar drilling circumstances, yet did
not perform with the same consistency.
To reduce nonproductive time (NPT), the
operator sought to accurately measure and
test its motors prior to installation .
The power section of positive displace-
ment motors consists of an internal (rotor)
and external (stator) component . When
paired , they create a “fit” and maintain a
critical seal at the bottom of the well. The
rubber lining within the stator can swell or
shrink when exposed to heat and drilling
fluids . If the stator cannot handle these
conditions, it will disable the power sec-
tion’s ability to uphold the fit required to
maintain a seal. This failure can cause the
rubber lining to chunk or stall, resulting in
motor failure or weakness.
Traditionally, the power section fit of
a motor is determined by a vector gauge.
Because so many manual measurements
are required with this method , miscalcu-
lations can occur frequently. To resolve
performance inconsistencies, the opera-
tor instituted dyno-testing on all motors
before deployment. The additional testing
proved useful, and motor run reliability
improved. However, the additional test-
ing was costly and time intensive. It also
put additional strain on vendors’ supply
chains as each motor had to be sent for
testing prior to installation.
According to results provided by a
leading dyno-testing provider in North
America approximately 6% of approxi-
mately 14,400 motors failed between 2018
and 2021. A large group of manufacturers
of power sections and motors were repre-
sented in this data set.
More cost-effective solution
As an alternative to the dyno-tests, the
operator sought out Turnco’s Laser-AI . In
November 2021, Turnco began taking mea-
surements of stators with an inspection
system laser device. Each measurement
took up to 3,600 laser measurements and
produced a 360° profile of the stator. These
images were then stitched together into
one electronic file . The 360° profile mea-
surement gave the operator a digital log
and comprehensive view of the stator, not
just the ID minor (a limitation of the vec-
tor technology). After being measured, the
stators were assembled onto a motor and
shipped to dyno-testing.
Part of Turnco’s initial efforts included
testing its data set against the dyno-test,
ensuring the accuracy of its algorithm .
Ultimately, Laser-AI combined each
motor’s data set with supervised machine
learning techniques and was able to pre-
dict stator performance on the dyno-test
with greater than 95% accuracy.
The operator was able to modify its
quality program to only dyno-test a sam-
ple of the motors, rather than every single
one, reducing spend on dyno-testing by
80%. Additionally, the operator shortened
its supply chain, enabling motors to go
directly to the rig, saving two days of
mobilization and transportation efforts.
Most importantly, vector measurements
were taken for each stator and compared
with measurements the laser produced. It
was discovered that, on average, the sta-
tor measurements were more accurate by
0.004 in. when utilizing Turnco’s method,
resulting in more consistent motor per-
formance outcomes. In 11% of instances,
stators were more then 0.011 in. off or were
completely out of tolerance.
Turnco’s methodology enabled the oper-
ator’s motor vendor to perform the laser
measurements internally and upload files
into the Laser-AI software .
Ultimately, the technology eliminated
the inconsistencies in stator performance
and improved NPT by rejecting motors
prior to use. DC
A histogram of the absolute difference between laser and vector measurements of
the stator minor. On average, the laser results were more accurate by 0.004 in.
D R I L L I N G C O N T R AC T O R • M A R C H/A P R I L 2023
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