IMPROVING FRACKING POWER & EFFICIENCY
was no human intervention to accept the
model’s design suggestions.
“What we did get to was that we were
able to stream the model outputs to the
user interface of the intelligent fracturing
system so it was just a quick acceptance
and a copy-paste to the blender outputs,”
Ms Butler said. Future iterations of the
technology will remove that human step
to enable seamless, closed-loop communi-
cation between the operator and service-
provider systems.
To ensure that the optimizer model was
functioning correctly, the pilot required
human oversight of the design change
protocols recommended by the model. This
oversight involved a sign-off from the field
supervisor and the engineer, who con-
ducted a “final check that we were adher-
ing to our boundary conditions and that
there was no outlandish suggestion by the
model,” Ms Butler said. “Then we gave the
nod to the operator to adopt these model
changes.” Such manual requirements
would be phased out in future iterations
following further vetting of the data archi-
tecture and boundary condition adherence.
Because the project used a joint service
company-operator data stream, an impor-
tant feature of the data architecture was
the ability for both parties to maintain the
security of their respective networks.
“Integration of the operator’s model
with the service company’s automation-
enabling equipment was developed with-
out risk to either party in terms of security
or the loss of intellectual property, provid-
ing mutual benefit,” Ms Butler said.
Proving the technology
The 2021 pilot took place on a four-well
pad operated by Hess in the Bakken. For
the first two wells, the team tested only the
functionality of the Halliburton automated
fracking system, without activating the
machine-learning model. The automat-
ed system successfully and consistently
placed the Hess fracs.
For the second two wells, tested in July
2021, the team brought the integrated
machine-learning model into play and
met the project’s goals of ensuring that
the intelligent fracturing system received
the model’s outputs at the designated fre-
quency and under established boundary
conditions. They also met another key
A pilot project conducted last year in the Bakken used the operator’s machine-
learning model to provide tailored frac design guidance to the service provider’s
automated fracking system. Erin Butler, Senior Specialist Engineer for Hess,
discussed the project in a presentation at the SPE Hydraulic Fracturing Technology
Conference in The Woodlands, Texas, on 2 February.
goal: to avoid any downtime associated
with the modeling process and acceptance
of design changes.
A secondary goal of the pilot project was
to better understand the machine-learning
model’s performance and how it achieved
the optimization targets. That analysis is
now under way, Ms Butler said.
“Right now, we are investigating a few
hypotheses that we have for why the
model is behaving in the ways it did, and
then we would like to take steps to fine-
tune this optimization tool – the run times,
the frequencies, the methodology – to best
achieve our operational efficiency targets.”
Change, she added, is the only constant
when it comes to developing automation.
“Things that work well are going to stick,
and things that don’t work well we need
to learn from quickly and move on to our
next task.”
She noted that there is no set meth-
odology for hydraulic fracturing automa-
tion, and different operators pursuing the
vision of a push-button frac might order
their processes or data flows differently
depending on their value drivers. However,
she advised that necessary capabilities
should include digital workflows, real-
time data aggregation, edge devices and
cloud computing, machine learning and
artificial intelligence tool development,
AI-enabled equipment controls and an
integrated operations center.
Efficiencies like those emerging from
this pilot align with Hess’ strategy for gen-
erating cash flow from its Bakken assets,
which remain a critical component of its
portfolio. Hess produced approximately
155 MBOED from its Bakken assets in 2021,
and its 2022 production budget includes
$790 million to fund a three-rig program
in the Bakken. With those funds, the com-
pany expects to drill approximately 85
gross-operated wells and bring online
approximately 85 wells this year. DC
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