American Oil and Gas Reporter - June 2020 - 52

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SpecialReport: Artificial Lift Technology
The primary daily operational set point
levers to optimize rod lift wells are on/off
time, pump speed (strokes per minute)
and pump fillage. Following the Pareto
principle, these levers represent the lion's
share of the value-added changes that an
engineer or field technician can make to
optimize wells. The third and final step
is to apply a change to the control system-whether a pump-off controller, variable frequency drive, or timer-and observe
the results to judge success.
Ideally, the optimization workflow is
performed for every well on a regular
basis. However, this involves significant
time and resources to accomplish, and
even when done daily, a tiny fraction of
available stroke data receives attention
and contributes to decision making. The
reality is that field personnel have too
many wells, too little time and not enough
of the right analysis or technology to enable a step-change in profitability.
Sisyphean Tasks
The question that operators have to
ask is whether it is beneficial to have
their skilled engineers and production
managers devoting so much time to performing such Sisyphean tasks. With so
many advanced technologies at the ready,
does it make sense for production personnel to carry out repetitive, defined
logic workflows when they could devote
their attention to more complex and nuanced field problems? Would production

operations take that next step by following
the manufacturing model and allowing
machines to automate tasks such as setpoint changes?
The answers to these questions are
obvious considering that doing so would
enable artificial lift experts to do what
they do best: solve problems, design solutions, and think strategically to keep
wells dialed in at all times and add bottom-line value. Physics-based data science
and artificial intelligence, combined with
rod lift domain expertise, deliver operators
a step-change in operating leverage and
optimization capabilities for both horizontal and vertical wells (Figure 1).
Well optimization technology has been
driven by high-rate offshore and horizontal
onshore shale wells, but there are of
course vast numbers of vertical, rodpumped wells that remain unconnected
and uninstrumented. Each of these wells
may only make a couple barrels of oil or
few thousand cubic feet of gas per day,
which tampers the economic case for
retrofitting with conventional automation
technologies beyond a simple timer.
As a result, wells are visited with an
"every well, every day" mentality, where
routine generally substitutes for criteriabased route prioritization. Sometimes,
these fields are closely bunched together,
while other times they span hundreds of
miles. This means if wells go down after
the daily check, they are down until at
least the next scheduled visit.

FIGURE 1A
Artificial Lift Operational Hierarchy of Needs

In reality, operations teams are generally in reactive mode and focused on addressing critical, nonwell-related activities
at the central tank batteries or across
gathering systems. With hundreds of wells
under their management responsibility,
pumpers may not look at wells for long
periods. Moreover, engineers and field
operators charged with managing legacy
fields may have 1,000 or more wells
under their direction, which means they
cannot possibly direct their attention to
more than a small percentage of wells at
any given time.
Adding to the difficulties in managing
these wells is the lack of downhole visibility or remote control capabilities. Resulting optimization efforts occur slowly
and inconsistently, generally following
heuristics to fill the gap. There is no well
data to view, no feedback loop to quantify
the impact, and no confirmation back to
the office that a change was ever made.
This severely restricts any value creation
beyond basic maintenance.
Adaptive Control
When there is more work than manual
processes can handle and vast amounts
of data available to inform decision
making, the stage is set for technology
to drive step-change improvement in
well profitability and even economic
life. However, not any technology will
tackle the challenges of optimizing fields
of wells at scale. AI provides the right

FIGURE 1B
Data Science Hierarchy of Needs

Autonomous Control
AI,
Deep
Learning

Data Science & Predictive
Physics Analysis
Logic Control (Fillage)
Logic Control (Remote On/Off)
Real Time Visibility
High Quality Data
52 THE AMERICAN OIL & GAS REPORTER

LEARN/OPTIMIZE
AGGREGATE/LABEL

A/B Testing,
Expirmentation,
Simple ML Algorithms
Analytics, Metrics,
Segments, Aggregates,
Features, Training Data

EXPLORE/TRANSFORM

Cleaning, Anomaly Detection, Prep

MOVE/STORE

Reliable Data Flow, Infrastructure,
Pipelines, ETL, Structured and
Unstructured Data Storage

COLLECT

Instrumentation, Logging, Sensors,
External Data, User Generated Content



American Oil and Gas Reporter - June 2020

Table of Contents for the Digital Edition of American Oil and Gas Reporter - June 2020

Contents
American Oil and Gas Reporter - June 2020 - Intro
American Oil and Gas Reporter - June 2020 - 1
American Oil and Gas Reporter - June 2020 - 2
American Oil and Gas Reporter - June 2020 - Contents
American Oil and Gas Reporter - June 2020 - 4
American Oil and Gas Reporter - June 2020 - 5
American Oil and Gas Reporter - June 2020 - 6
American Oil and Gas Reporter - June 2020 - 7
American Oil and Gas Reporter - June 2020 - 8
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