IMPROVING FRACKING POWER & EFFICIENCY
Automated predictive frac
control system taps operator’s
machine-learning model
to improve completion execution
Hess, Halliburton partner on Bakken pilot
pairing automation with a predictive model
to optimize for new surface efficiencies
BY JESSICA WHITESIDE, CONTRIBUTOR
The technology behind what Hess called a
“push-button frac” pilot is complex – com-
bining automated hydraulic fracturing
with predictive machine learning – but
the potential payoff is as simple as it gets:
a faster frac with better economics.
“What we are using this specific
model for is surface efficiency optimiza-
tion. Overall, we want to reduce time and
material costs,” said Erin Butler, Senior
Specialist Engineer for Hess, which con-
ducted a pilot in the Bakken in partnership
with Halliburton in 2021.
The project demonstrated the ability
to upgrade completion performance with
minimal human intervention by using
the operator’s machine-learning model to
accept data from, and provide tailored frac
design guidance to, the service provider’s
automated fracking system.
In a presentation at the SPE Hydraulic
Fracturing Technology Conference in The
Woodlands, Texas, on 2 February, the proj-
ect team described the venture as “the first
time a hydraulic fracture was conducted
via automation with algorithmic integrat-
ed design improvement.”
According to Ms Butler, the successful
pilot provides “the groundwork for opera-
tors and service companies to progress
toward automation of hydraulic fracturing
operations” and make further step-change
improvements in frac design and execu-
tion. “The overall vision of automation in
hydraulic fracturing isn’t going to be tack-
led in one fell swoop,” she said. “It’s going
36 to take small incremental steps to get there
and really capture value from automation.”
Removing human bias
Traditionally, decisions to modify frac
design during a job are made by engi-
neers or supervisors on location. However,
this can lead to inconsistent execution
because, in addition to subsurface hetero-
geneity, these decisions are influenced by
variability in the experiences and capabili-
ties of the individuals and crews involved.
It can also be challenging for these profes-
sionals to gain approval for modifications
in a timely manner, leading to missed
optimization opportunities.
Pairing automation with machine-learn-
ing models that can run 24/7 and process
more data more quickly than possible for a
human could avoid some of these pitfalls.
“Automating hydraulic fracturing pro-
cesses can add value by driving consis-
tency, removing human bias, reducing
our EHS risk, eliminating waste and even
enhancing our well performance,” Ms
Butler said.
Automation and digital workflows are
still relatively new to hydraulic fractur-
ing, with testing of automated equip-
ment beginning in the initial breakdown
stage of completions in 2016. By 2020,
Halliburton had introduced an automated
fracturing system that the company says
is capable of running completions from
beginning to end. The system is described
as a “co-pilot” that uses subsurface sen-
sors, 3D visualization and other features
to provide operators with greater control
over fracture placement through “intel-
ligent automation.” The system provides
real-time diagnostic insights into factors
such as pressure, rate and proppant con-
centration and enables automatic changes
to fracking equipment, such as the blender
and hydraulic pumps.
What Hess wanted to determine through
the 2021 pilot, however, was whether the
Halliburton system could be configured to
accept inputs and direction remotely from
an external source – a machine-learning
model developed by Hess that would ana-
lyze both historical and real-time data
from the job. The model is designed to
assess pump curve characteristics and
make on-the-fly recommendations for
design changes to improve the frac based
on specific optimization targets (e.g.,
reduce time or execution cost, maximize
rate, etc). The model would then need
to communicate these changes directly
to the intelligent automation system for
execution. Hess trained its optimizer model on a
data set of more than 150 Bakken wells.
The model retrains itself with real-time
data it receives during the completions
process, improving its performance and
prediction accuracy as the frac progresses.
With this model, Ms Butler said, you can
better capture why and when a decision
was made and use that information in
additional analytics.
Developing data workflows
The companies worked together to
develop a data architecture that facili-
tated the secure delivery of data between
the operator’s model in the office and
the service provider’s system at the frac
site. The resulting automated process
feeds the frac equipment with the updated
design change recommendations from the
machine-learning model at an agreed-
upon frequency.
“What we had to develop was a transla-
tion on how to take this prediction table
and put it into a format that was digestible
by the intelligent fracturing service,” Ms
Butler said.
While the project team had intended for
this process to happen autonomously, time
constraints during the trial meant they
weren’t able to get to the point where there
M A R C H/A P R I L 202 2 • D R I L L I N G C O N T R AC T O R
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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