American Oil and Gas Reporter - June 2020 - 54

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SpecialReport: Artificial Lift Technology
FIGURE 2
Machine Algorithm Analyses of Human Movement (Left) and Well Optimization (Right)
Underpumping
BEFORE

AFTER

Overpumping
BEFORE

AFTER

mix of complex problem solving and
adaptive control to deliver both pump
by exception and optimization at scale.
AI connotes many things to many people,
but it is technology that is widely deployed in different industries and is
well suited for the repetitive, high-value
optimization tasks that overwhelm today's operational teams.
The foundation of any AI application
is the acquisition of sufficiently complete
and detailed data. When connected to a
supervisory control and data acquisition
network, AI ingests that data easily. When
wells lack communications, employing
an edge device with embedded communications such as satellite, LTE or WiFi
enables sufficiently high-resolution data
that can feed an AI engine for consistent
analysis and closed-loop control.
Machine learning algorithms analyze
an unlimited number of strokes and
identify patterns in well behavior. Much
like humans, however, machines also
require training by domain experts to
become truly effective at their jobs.
Studies across multiple industries show
that even the most accurate machine
learning systems involve "humans in
the loop," which can account for up to
20% of the solution. This primarily is
54 THE AMERICAN OIL & GAS REPORTER

accomplished either through helping label training datasets or correcting inaccurate predictions to refine the algorithm.
In the case of artificial lift optimization,
this methodology requires having production engineers collaborate with developers and data scientists alike.
The left-hand image in Figure 2 demonstrates how a machine algorithm effectively
detects whether a human is walking or
running. Through the use of trained data
and neural networks, AI can recognize
nuanced differences to classify motion
accurately. The human brain, of course,
understands the difference immediately.
Machines have the same ability, but they
rely on different mechanisms to do so.
For an electric motor operating on a
marginal well, a similar algorithm can
determine on/off cycle well optimization,
in which a computer identifies underpumping (walking) or overpumping (running) conditions by interpreting current
and torque trends, and turning them
into an inferred downhole fillage. This
enables insights into both surface and
downhole conditions. Optimization occurs without the need for expensive additional sensors. Going a step further,
those inputs help classify the well into
overpumping, dialed in or underpumping

(at right in Figure 2). From there, closedloop control optimizes the on/off time
of the well automatically.
AI reduces wasteful, damaging strokes
on overpumping wells, saving costs on
electricity and failure remediation. Many
of these wells become unprofitable with
another failure, and yet their production
helps retain leases for future horizontal
drilling. Consequently, it is critical to extend the well's run life in the most costeffective way, and know for certain it
produces in paying quantities to avoid
losing leases. On the few underpumping
wells that do exist on the stripper well
side, AI finds the extra barrels and frees
critical cash flow.
Similar to a timer, edge-based controller
devices tie into the cross/soft starter panel
of the electric motor. Above and beyond
a timer, the latest generation devices also
are connected via embedded communications to provide real-time status and
remote set point change capabilities.
What's more, these devices can automatically classify wells into one of three
categories: overpumping, underpumping
or dialed-in.
This is similar to the methodology
used for wells with pump-off controllers
and variable frequency drives. In lieu of



American Oil and Gas Reporter - June 2020

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Contents
American Oil and Gas Reporter - June 2020 - Intro
American Oil and Gas Reporter - June 2020 - 1
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American Oil and Gas Reporter - June 2020 - Contents
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