Hydrocarbon Processing - April 2021 - GP-37

DIGITALIZATION
and compressors. DCP Midstream's engineers have configured more than 400 nocode digital twin templates in an evolutionary, agile way to form an operational
digital twin of their entire enterprise, with
more than 11,000 digital twin instances.
" Layers of analytics " strategy for gas
plants. Terms such as advanced analyt-

ics, machine learning, big data and artificial intelligence (AI) appear pervasively
in marketing literature today, but they
can lead to confusion, failed projects and
significant lost-opportunity costs.
The most successful gas plant operators achieve value from analytics by first
defining an analytics framework and the
types of analytics required, and then
selecting fit-for-purpose technologies.
They use a layers of analytics strategy,
which considers incremental cost vs. incremental value as they move to more
complex analytical methods. The costs
include not only the technology, but also
the costs associated with lost time to
value, scalability, configuration, sustainment and risk of attainment.
The foundation of this layers of analytics approach (FIG. 8) relies on the use
of an operational data infrastructure to
enable SMEs-not IT-to configure
real-time descriptive, diagnostic and
simple predictive analytics using formulas, empirical correlations and rule-based
expressions. These lower-level analytics
form the foundation for more advanced
predictive, prescriptive and adaptive
analytics that use machine learning and
other methods and require collaborative
support from data science teams.
These foundational analytical layers
generally provide more than 80% of the
value for about 20% of the cost vs. more
advanced analytical layers that use only
technologies such as machine learning.
Once higher layers of analytics are utilized,
it is imperative to feed back the results of
these advanced layers to the lower-level
layers as forecasts or targets to operationalize the advanced analytical output. This
is key to the development of the smart gas
plant, as results from the integration with
process simulation optimization models
and financial data for real-time gas plant
financial optimization are fed back to the
operational data infrastructure.
Takeaway. Natural gas gathering and
processing operators face numerous op-

FIG. 8. A " layers of analytics " strategy-disciplined evolution of analytical layers to deliver value.

erational and market challenges that inhibit their ability to achieve optimal operating and financial performance. Leading
gathering and processing companies are
responding to these challenges by aggressively adopting digital technologies
that enable agility, flexibility, operational
excellence and proactive decision support to optimize performance through
the development of smart gas plants and
gathering and processing super systems.
The overall benefits of this optimized
natural gas gathering and processing super system can be significant in terms of
lower operating and maintenance expenses, increased reliability and asset utilization, higher-value product recovery rates
resulting in $1 MM/yr per gas plant, and
increases in effective gathering and processing system capacity by as much as 3%.
Adopting an operational data infrastructure with no-code operational
digital twins based on a layer of analytics
strategy, as well as building smart dashboards, are the keys to a successful digital transformation. The enablement of
SMEs to develop, configure and evolve
these no-code digital twins with minimal
IT support is the secret to the smart gas
plant of the 21st century. GP
NOTES
The PI System functions as a real-time operational
data infrastructure for critical operations and enables
SMEs to configure no-code digital twins to create
self-serve access to contextualized operational intelligence and support a layer of analytics strategy.
b
No-code digital twins refer to the PI System's Asset
Framework (AF) that includes an integration,
abstraction and contextualization layer via data references to other data sources; a portfolio of 110
analytical functions optimized for time-series data
that leverage a wizard capability for ease of use; event
a

framing that enables configurable event start and
end times with event analytics; and a configurable
notification engine to trigger alerts via SMS, email
or work order in a maintenance management system.
LITERATURE CITED
1

Hill, J., " DCP update on the use of the PI System, "
PI World Conference, March 2019, San Francisco,
California.
2
Johnson, W., " DCP Midstream-Enabling business
transformation with the PI System: The DCP 2.0
journey, " PI World Conference, April 2019, San
Francisco, California.
ANDREW NATHAN is a Senior
Systems Engineer and the
Midstream Oil and Gas Industry
Champion at OSIsoft. He has
12 yr of experience in the oil
and gas industry, specifically data
infrastructure with the PI System,
analytics, visualization, process engineering, plant and
pipeline modeling, operator training, 3D visualization
and virtual environments. He is passionate about
operational performance optimization for the process
industries through utilization of data infrastructure
and simulation software tools. He holds a PhD in
polymer science from the University of Akron in
Ohio and a BS degree in chemical engineering
from Michigan State University in East Lansing.
CRAIG HARCLERODE is a Global Oil
and Gas and HPI Industry Principal
at OSIsoft. He focuses on digitalenabled business transformation
addressing the dimensions of
people/culture and process
reengineering leveraging the
PI System in the areas of IIOT/edge computing,
PI AF digital twins, layers of analytics, and big data to
deliver transformative business value. Mr. Harclerode's
40-yr career has included engineering, operations
and automation in supervisory, executive management
and consulting roles at Amoco Oil, Honeywell IAC
and Aspen Tech. He holds a BS degree in chemical
engineering from Texas A&M University in College
Station and an MBA degree from Rice University in
Houston, Texas, and he is a former Project Management
Professional. He has numerous publications to his
credit and is a regular thought leader presenter at
conferences and events globally on " going digital. "
Gas Processing & LNG | MARCH/APRIL 2021

Hydrocarbon Processing - April 2021

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