I N NOVATI N G WH I LE DR I LLI N G
A point cloud map of a scanned bit can help companies
decide if they want to change drilling parameters to avoid
the same damage seen in the visualization.
sic digitization system can be used immediately after a run . The
goal is to eliminate the inherent inconsistency and unreliability
in human interpretation, while also freeing up engineers to focus
on other critical tasks.
Before such technology was available, laboratory staff would
manually sift through and analyze post-run data. This was
always a laborious task, made more daunting by variation in the
quality of data from the field. Due to the discrepancy in the type
and quality of data coming in, aggregation of the information
into an analytical software package for diagnosis was difficult.
Furthermore, attempting to overlay this data with relevant drill-
ing parameters to truly assess bit performance was extremely
challenging, if not impossible.
“We wanted to build a machine that could be easily operated by
someone with minimal training in a shop, at a lab, at a rig site, or
The bit scanner from Trax Electronics is a roughly 4-ft cube that
can be placed on a rig, in a shop or in a lab. It automatically
takes pictures of the bit to build a 3D visualization.
18 potentially anywhere, and reliably build a 3D visualization,” said
Ron Schmitz, Executive Advisor at Trax. “We use photogramme-
try, which is basically taking photographs from various perspec-
tives around the bit, and build a visualization with that.”
Robotic-controlled, AI-enhanced photogrammetry, or the sci-
ence of making measurements from photographs, is at the core of
the system. The user places the bit within the scanner, a roughly
4-ft cube, and lets the system get to work, with a camera taking
pictures automatically at all relevant angles.
Once those images are input, the output is typically a point
cloud map, which is a drawing, measurement or 3D visualization
of some real-world object or scene. The scan takes approximately
15 to 20 minutes depending on the size of the bit, while it takes
approximately 90 minutes for the AI to compute the dull bit
forensic characteristics. By leveraging and applying this technol-
ogy to dull bit grading, the system produces highly accurate and
repeatable measurements of individual cutter wear in PDC bits, as
well as machine-generated base parameters for the IADC dull bit
grading protocol.
Trax says it sees quantifiable value in having its system on a
rig, in a shop or in a lab. “We feel that there’s a big advantage in
being able to obtain an independent analysis from a third party,”
Mr Schmitz said. “There are also time and cost factors; since we
can provide photographs on site, at a lab or in a shop, you can
view the visualization in a few hours. It may not help you decide
which bit to run, but it could help you decide that you want to
vary drilling parameters to avoid some of the damage you saw
in the scan.”
The point of collecting this data is not simply to understand
what happened to the bit, of course, but also to use the data to opti-
mize drilling. By obtaining reliable, independent cutter-by-cutter
forensics, Trax sees five key areas of bit forensics that will enable
better drilling performance.
First, companies can improve bit design and quality control,
enabling better operator/vendor collaboration and potentially
enhancing drilling and directional performance, particularly in
unconventional wells that are longer and more complex. Then,
drilling dysfunctions can be identified to eliminate cutter dam-
age, ultimately prolonging bit and BHA life and reducing the time
needed for tripping .
Next, bit wear can be managed, which Mr Schmitz said is
critical. “There are a lot of issues around bit wear instead of bit
damage,” he explained. “For example, looking at where the cutter
is located on the bit and if it’s spalled, chipped or broken, people
can get a good idea of what was going on downhole to cause that
damage.” He also noted that often, pulling a bit with no or only
“smooth” cutter wear may not be optimum, as this could indicate
that performance wasn’t maximized. Being able to assess smooth
wear down to “sub-0” levels could be very important in some
applications, as that could mean higher ROP could be achieved
without causing damage. Less wear does not necessarily mean
an optimum run.
The fourth area is increasing data granularity to make big data
analytics possible. “A typical dull code involves an overall average
for the bit as a whole,” Mr Schmitz said. “The new protocol being
considered by the IADC has information across all the cutters on
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I N NOVATI N G WH I LE DR I LLI N G
UT Austin’s automated forensics process
relies on software to accurately evaluate
bit damage and failure so that changes
can be made to the bit and/or BHA before
the next run.
every blade, distinguishing if the damage is in the cone, nose or
shoulder, how much is the loss area, and what kind of damage
occurred.” The next step, which could be enabled via Trax’s bit
forensics, is an even more detailed analysis on a per-cutter basis,
down to the wear or damage seen at a micro level.
The final objective is to improve overall quality assurance,
which is critical in iterative bit design improvements. “When we
analyzed one bit with a depth-of-cut (DOC) limiter, for example,
we noted a difference in the visualization between what it was
supposed to be and what it actually was,” Mr Schmitz noted.
“When we’re talking about the very precise designs that they’re
trying to develop now, in terms of limiting the DOC to exactly
what they want in order to avoid damage while maximizing DOC
to drill as fast as possible, differences of that magnitude start to
become important.”
Drill bit failure forensics using field
photography Drill bit forensics is also a topic of study in the world of aca-
demia. An ongoing research initiative at the University of Texas
(UT) at Austin involves the development of a software algorithm
that can automatically analyze photos taken of the bit at the rig
site and identify, from those photos, the root cause of bit damage
and failure. The goal of the project is for the software to be able to
accurately evaluate a used bit so that changes can potentially be
made to the BHA and/or bit before the next run .
The methodology for this automated forensics process involves
four steps. First, the algorithm is given a set of drill bit photo-
graphs that clearly show individual blades, allowing the software
to identify all the cutters on that bit. The software then quanti-
fies the damage to each cutter, drawing on a database of surface
sensor and downhole vibration data, as well as offset well rock
strength information, to characterize drilling dysfunction relative
to the damage seen on the bit. After calculating cutter location, the
software then uses a classifier to determine the average damage
in various parts of the blades, thereby enabling it to infer the root
cause of damage.
Jian Chu, a PhD student working on bit forensics research,
explained that the initiative continues to advance. Previous
versions of the software were not always able to detect all the
cutters based on the photographs available and lacked precision.
Additionally, not all damage could be calculated, and when it was,
it was only quantified as a whole number. Further, the algorithm
was affected by lighting, and the forensics process did not include
EDR data. However, the team is now focusing on identifying the
damage type (such as worn, chipped) and improving the precision
of damage grading via semantic segmentation.
Mr Chu said it was critical that noise from the drilling environ-
ment is removed in order to develop something truly useful. “One
big issue we had is that we didn’t have a lot of data,” he explained.
“For traditional machine learning algorithms, they would need
millions of data points to train the neural network. We didn’t have
that much data, so we had to remove all the noise and really focus
on what we had.”
Through precise area isolation, damage categories identifica-
tion and integration of expert systems, the process will eventually
be entirely automated, with the algorithm becoming more and
more accurate. Since the photography used to drive the algorithm
is still prone to human error, Mr Chu said that maybe one day,
computer vision on the rig floor could take photographs of the bit
as the BHA is pulled, capturing all angles and feeding that data to
the algorithm.
There is additional potential should a company choose to
develop an application for the algorithm that could be deployed
on a smartphone or tablet, which would make the process even
easier and reduce the concerns associated with limited connec-
tivity on remote sites. For now, Mr Chu said he isn’t worried about
the commercial potential of the platform. He is more interested in
advancing the field of drill bit forensics: “I want to make some-
thing useful for the industry .” DC
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