IMPROVING FRACKING POWER & EFFICIENCY
Integrated platform aims
to improve real-time decision
making in frac operations
Technology gives engineers actionable insights
for optimization while aiming for balance
between frac efficiency and effectiveness
BY STEPHEN WHITFIELD, ASSOCIATE EDITOR
Recent breakthroughs in connectivity and
digital technologies are enabling the mon-
itoring and analyses of hydraulic frac-
turing operations in real time through
data streaming and analytics. While most
third-party frac monitoring solutions offer
things like real-time frac treatment data
charts, post-stage frac analytics and key
operational efficiency metrics , there was
still a need for software that could identify
opportunities for frac optimization in real
time , augmenting and improving real-time
decision making at the frac site.

At the 2022 SPE Hydraulic Fracturing
Technology Conference in The Woodlands,
Texas, on 2 February, Shell presented a
software platform it developed to host
and execute an ensemble of third-party
frac models and visualizations. It com-
municates actionable insights within
minutes of identifying a potential event
during a frac stage. The platform is a plug-
Somnath Mondal, Research Production Technologist at Shell, discussed a software
platform for frac optimization at the 2022 SPE Hydraulic Fracturing Technology
Conference in The Woodlands, Texas, on 2 February. The platform makes use of
third-party analytics to interpret data about ongoing frac operations and provide
actionable insights for on-site engineers.

38 and-play system, where users can insert
and remove third-party models measur-
ing different variables at a frac site, said
Somnath Mondal, Research Production
Technologist at Shell.

“Everyone wants to pump the best frac
that we can at every stage, but a good frac
is not just about pumping away a given
volume of water or sand at the lowest
cost, but also to get effective stimulation
distribution. We’re trying to get a balance
of efficiency and effectiveness at every
stage,” Dr Mondal said. “We’ve made a
tremendous amount of progress in getting
real-time data acquisition and running
analytics on it, but most market solutions
are still not there.”
The platform is based on a fairly sim-
ple framework: Real-time sensor data is
fed into software for processing and then
transferred into third-party models for
visualization. To limit the number of vari-
ables during lab testing and initial field
testing, only models for measuring injec-
tion rate and sand concentration were
used. However, Dr Mondal said multiple
models can be executed simultaneously
within the platform. Users can even estab-
lish a hierarchical optimization for the
whole system based on priority.

The platform identifies potential oper-
ating states for optimization by taking
the output from the third-party models
and running it through a separate model
designed to predict a given action’s impact
on pump pressure. This model looks for
points where sufficient pressure head-
room is available and the pressure trend is
favorable, to ensure that any action taken
would not increase the pressure outside
an acceptable limit for the well. It then
identifies the valid operating states that
can result from a given change to different
variables, and assigns a score.

Another model utilizes historical data to
estimate an action’s impact on the comple-
tion time and simulates a stage under
the assumption of pre-determined control
variables. From that simulation, the plat-
form can estimate the cost for each pos-
sible operating state and recommend an
operating state that is most optimal.

The platform’s recommendations are
stored in a data lake, and notifications are
sent to engineers about potential events.

The recommended actions are presented
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
Top: This fl owchart illustrates the steps
for optimizing a frac operation using
Shell’s integrated platform .

Bottom: A recommendation “card”
displays the suggested change along
with contextual information to assist
engineers in real-time decision making .

Additional details about the system
are available in SPE 209127, “Effi ciency
and Effectiveness – A Fine Balance: An
Integrated System to Improve Deci-
sions in Real-Time Hydraulic Fracturing
Operations.” in the form of a “card” that can be dis-
played on the user interface. This card
includes the suggested change, as well as
contextual information to assist engineers
in decision making, such as average pres-
sure, predicted pressure and estimated
savings. While he did not provide exact
figures, Dr Mondal said the estimated sav-
ings is typically in the thousands of dollars
per recommendation.

The platform restricts the frequency of
recommendations given within a particu-
lar time frame, both to ensure engineers
have time to implement the recommenda-
tions already given and to see the response
to any optimization action taken before a
new one is generated.

Dr Mondal stressed that the model is
purely a recommendation tool. Engineers
at the frac site must decide whether to
implement the optimization action. “The
idea here is not to replace the human. It’s
to provide the human making the decision
with the tools they need and the data they
need to make the best decision without
having to rely solely on experience or
recall at a given moment,” he said.

System limitations
While specific results from initial field
testing were not discussed, Dr Mondal said
the software has consistently identified
optimization opportunities and augment-
ed decision making that led to cost sav-
ings while preserving frac effectiveness.

However, there are limitations. Frequently,
the team saw scenarios during testing
where the pressure became negatively
correlated to the proppant or showed non-
linear dynamics due to wellbore complexi-
S tep 1
Identify potential
operating states
S tep 5
S tep 2
Generate Estimate
optimization pressure response
S tep 4
S tep 3
Calculate Select
cost/time impact
valid states
ty. Since the pressure response model used
in testing was a linear model, the system’s
ability to predict effectively became lim-
ited under those scenarios. When the team
increased the prediction time frame from
1 minute to 3 minutes during these sce-
narios, the model became insufficient at
modeling the complexities of the system.

To help remedy this issue, the team
explored the possibility of incorporating a
gradient boosting, or “learning tree,” model
into the pressure response. In a gradi-
ent boosting model, a base model trains
additional models sequentially to try to
reduce error. This model leverages histori-
cal data and contextual data, such as stage
depth. Shell structured the model to input
all variables available at a given time and
contextual information about the well and
stage, such as the depth or formation type.

This model showed impressive predic-
tive power, Dr Mondal said, as it was able
to predict the pressure response up to 3
minutes ahead of an event with reasonable
accuracy. However, like with the linear
model, this model struggled to accurately
predict an event in the early ramp phase of
the frac stage because there was too much
variability. Shell is currently investigating how to
integrate a gradient boosting model into
the software platform, which is a challeng-
ing task because a large gradient boosting
model requires hosting a sizeable number
of parameters associated with it. A host
would also need to be developed that can
cover all possible well types, which is an
exhaustive process.

“This model is computationally very
heavy,” Dr Mondal said. “You have to keep
an evergreen training set, which is very
time consuming, and this set needs to con-
stantly update the model. We are trying
to keep a balance between the practical
implementation challenges and having
optimal model accuracy.” DC
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 202 2
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