Hydrocarbon Processing - April 2021 - GP-35

DIGITALIZATION
created by defining a rule-based start and
end event analytics. Another powerful
capability is " backcasting " these event
analytics (or any expression) on highfidelity historical data to enable insight
into when the defined event occurred in
the past. These events can automatically
trigger notifications to the operators,
SMEs and other relevant parties or workflows to external systems, such as work
order creation.
For maintenance personnel, equipment-centric views into all heat exchangers, air coolers, compressors and pumps in
a gas plant can be developed. When an asset class, such as a heat exchanger, is selected from the list in FIG. 2, a display listing all
relevant operating KPIs for each exchanger can be configured with exception-based
reporting rules to visually indicate which
exchangers need attention (FIG. 4).
The gas plant digital twin becomes increasingly " smart " when integrated with
a robust plant process simulation tool
that has validated models of all necessary
plant units. Model intelligence is further
enhanced by bi-directionally integrating
real-time plant DCS/sensor data from
a historian as inputs to the simulation
model and historizing the simulation
results to perform real-time comparisons between simulation and operating
parameters or validating the model. The
display (FIG. 5) illustrates ethane and propane recovery comparisons from both real-time and simulated data. The live link
between the operating and engineering
models, coupled with financial data (e.g.,
ethane and propane spot pricing), enables real-time, recovery-based financial
performance optimization to be implemented in the smart gas plant.

portfolio performance and take appropriate actions.
Creation of a gathering and processing super system. Once the portfolio

of gas plants has near-real-time optimization, the same strategy and methodology
can be extended to pipeline gathering assets, including associated systems, such
as compressor stations and meters, to
build a smart pipeline system.
The smart digital replica of the gas
plants and the digital replicas of the associated gathering systems can be combined to form an integrated, intelligent
gas gathering and processing super system (FIG. 6). By leveraging the super system optionality, such as gating a gathering system to the most optimal gas
plant, the gathering and processing operator can gain as much as 1.5 times of
financial performance across the super

system vs. optimizing a single gas plant.
Several gathering and processing operators have reported an increase in super
system effective capacity by more than
3%, enabling the idling of low-efficiency
gas plants or the ability to secure additional gathering and processing volumes.
This integrated super system can now
be utilized to holistically monitor and
optimize the entirety of a midstream operator's asset portfolio. The super system
helps maximize production and financial
returns, minimize costs and ensure safe
and reliable operations, leading to optimum gas volumes and routing from the
gathering network to high-efficiency gas
plants based on spot prices. In addition,
based on this super system, management
decisions can be made to either sell or
idle under-performing or low-efficiency
plants that are not needed based on gas
demand and present contract pricing.2

FIG. 4. Exception-based equipment reporting display for all heat exchangers in a gas plant.

Portfolio and fleet-wide gas plant
business intelligence. Once this re-

covery-based financial performance optimization methodology is developed
for a single gas plant, it can then be applied throughout the plant portfolio in
a midstream company. Fleet-wide business intelligence (BI) dashboards can be
developed by streaming this data in real
time to tools such as Microsoft Power BI,
Tableau or Spotfire.
Users can quickly view these recovery
values vs. model-validated targets for all
plants in a central display, taking advantages of features such as slicing and dicing in these BI platforms to understand

FIG. 5. Comparison of C2 and C3 recoveries from operating and simulated data, with recoverybased financial performance optimization.
Gas Processing & LNG | MARCH/APRIL 2021

Hydrocarbon Processing - April 2021

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