Hydrocarbon Processing - April 2021 - GP-33

DIGITALIZATION

The smart gas plant: An integrated, intelligent
gathering and processing super system
A. NATHAN and C. HARCLERODE, OSIsoft, Houston, Texas

Midstream operators typically own
both the gathering networks that transport natural gas and the downstream treating and processing facilities that convert
this raw feed into marketable products,
such as residue gas and Y-grade NGL.
The optimization of both the gathering system and processing facilities-with
their multitude of associated meters, commercial contracts and variable commodity
pricing-in an integrated fashion is challenging to carry out in real time. System
size, complexity and data requirements
can complicate the real-time optimization
of these facilities.
However, 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,
and increases in effective system capacity.
The heart of this integrated super system is the smart gas plant, a fully realized,
operational digital twin of the plant. When
combined with other operational digital
twins for the compressors, meters and
pipelines, an integrated, intelligent super
system is created.
This article describes the use of an operational data infrastructurea in configuring a smart gas plant for real-time monitoring and optimization of plant operations,
as a first step in creating an integrated super system. The super system enables the
optimization of the complete gathering
and processing system, comprising many
gathering networks and an associated fleet
of processing plants, with optionality to
gate natural gas to several gas plants based
on optimal guidance.
Gas plant operational challenges.
The gas plant is the heart of a gathering- and processing-focused midstream

operator's asset portfolio because of its
role as the treating and processing hub,
which receives raw gas from the gathering
network, removes contaminants such as
H2S, CO2 and water in amine and glycol
units, and separates NGL and sales gas in
a cryogenic unit.
The challenge for gas plant operators
is to optimize plant uptime and equipment maintenance to ensure consistent,
optimum and high-quality sales gas and
NGL production rates, while balancing
numerous issues, including:
* Feed gas rate composition/
quality variability
* H2S and CO2 concentrations
exceeding environmental limits
* Running the cryogenic unit in
ethane recovery or rejection mode
* Controlling residue gas
heating values
* NGL composition ratios,
such as C1/C2 and CO2/C2,
as well as fluctuating methane,
ethane and propane prices.
The capability to carry out this analysis for gas plants and act in near-real time,
vs. an offline weekly or monthly reactive approach, is essential for responding
rapidly to operational and price/market
fluctuations, as well as optimizing production. Near-real time, financial-based
optimization of a single gas plant train
can yield $2,500/d-$3,000/d (approximately $1 MM/yr), and up to 1.5 times
this amount per gas plant in a gathering
and processing super system.
Extracting actionable insights and
optimizing gas plant performance is
challenging due to the large and diverse
amount of operational, meta and financial data from disparate sources: distributed control systems (DCSs), programmable logic controllers (PLCs) and other
sensors; financial/pricing databases;

commodity pricing and enterprise resource planning (ERP) platforms; computerized maintenance management systems (CMMS); plant process simulation
tools; and many others. Examples of such
data types include:
* Plant process sensor data,
such as temperature, flow,
pressure, concentrations, etc.
from the DCS
* Raw gas quantity, quality and
gathering system information
* Manufacturer equipment
performance curves and
related engineering data
* Plant asset maintenance records
* Plant process simulation models
that are validated and provide
optimal operating targets
* Financial information
associated with methane,
ethane and propane pricing
from multiple sources, including
a multitude of contracts and
commodity pricing.
An operational data infrastructurea is
a key foundational tool to enable the creation of a smart gas plant. An operationally intelligent operational digital twin of
the physical gas plant can turn these disparate data sources into actionable operational intelligence and decision support.
This digital infrastructure empowers engineers, operators, maintenance personnel and subject matter experts (SMEs)
to configure, evolve and manage their
gas plant decision support application directly, using the data infrastructurea with
" no-code " digital twins.b This is in contrast to the more traditional approach of
developing or purchasing a customized
digital solution for gas plant operations
with significant IT involvement, associated customization and challenging sustainment effort.
Gas Processing & LNG | MARCH/APRIL 2021

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Hydrocarbon Processing - April 2021

Table of Contents for the Digital Edition of Hydrocarbon Processing - April 2021
