This presentation contains an overview of tracking plant performance, with its application in two case studies, including gas compression train monitoring (Aspen) and production facility surveillance system (HYSYS).

1. /Process-­‐Ecology @processecology
PLANT
PERFORMANCE
MONITORING
SEPTEMBER
14,
2017
27/05/18
2017©COPYRIGHT
PROCESS
ECOLOGY
INC. 1

2. WHO
WE
ARE
2017©COPYRIGHT
PROCESS
ECOLOGY
INC.
2
Founded 2003, Calgary, AB
Engineering consulting, process simulation & optimization,
software development, air emissions estimation and
management
Track record of advanced modelling, simulation and
process design, combining and extending simulators and
rigorous engineering calculations to handle complex
scenarios
CORE COMPETENCIES

3. OUTLINE
27/05/182017©COPYRIGHT
PROCESS
ECOLOGY
INC.
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• Tracking
Plant
Performance
• Case
Study
#1
– Gas
Compression
Train
Monitoring
(Aspen+)
• Case
Study
#2
– Production
Facility
Surveillance
System
(HYSYS)

4. PERFORMANCE
MONITORING
-­‐ OVERVIEW
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ECOLOGY
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• Performance
Monitoring
Options
1. Process
Simulation
– Tuned
to
represent
reality;
requires
engineer
to
interpret
results
2. Simulation
/
Offline
Spreadsheet
– Ability
to
look
at
case
studies;
Simple
to
develop,
but
requires
manual
update,
difficult
to
maintain
3. Simulation
/
Linked
spreadsheet
– More
work
to
set
up;
automatic
update,
difficult
to
maintain
4. Simulation
/
Data
connection
/
User
Interface
– more
effort
to
set
up;
automated

5. APPLICATION #1:
GAS
COMPRESSION
TRAIN
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• Operating
Overview
• 3
separate
crude
processing
facilities
• Each
facility
has
produced
gas
compression
train
• Produced
gas
used
for
lift
gas
injection,
EOR
gas
injection,
fuel
supply
• Operating
Challenges
• Compression
trains
were
designed
for
lower
ambient
operating
conditions
• Efficiency
of
compressors
reduced,
less
hp
– need
to
optimize
performance

6. APPLICATION #1:
GAS
COMPRESSION
TRAIN
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• Application
Goals
• Incremental
gas
production
• Operation
Improvement;
Ability
to
respond
to
compressor
“degradation”
•Determine
optimal
times
to
correct
fouling
of
compressor
fins
and
air
cooler
fans

7. PERFORMANCE
MONITORING
SOLUTION
Customer Business Objective:
Asset returns tie directly to field production.
Increased compressor throughput leads to
increased lift and injection gas rates, translating
to incremental crude productivity.
Field Operational Challenges:
Performance challenges from fluctuations in ambient,
reservoir operation, and operating conditions. Limited
ability to monitoring equipment performance. Production is
not maximized
Solution:
Oil field real time and historian data
Constantly tuned online
model of compressors
& heat exchangers
Look-­back
5 years for
trends
Run every 5
minutes
Engineer interprets,
considers choices, takes
action
KPI for equipment
performance, and trends for
operator
Current and Expected Results:
Improved gas compressor train
operations (goal 1-­5%):
-­ Operating strategies
-­ Better exchanger and
-­ Compressor maintenance
Incremental lift gas and EOR gas
Incremental crude production

8. COMPRESSOR
TRAIN
OVERVIEW
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• Model
incorporates
the
low
pressure
and
high
pressure
compressor
stages,
encompassing
four
separate
compressors,
and
four
air-­‐cooled
exchangers
(or
“fin-­‐fans”),
and
the
gas
turbine
subsystem.

9. SIMULATION
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• Model
developed
in
the
equation-­‐oriented
mode
(E/O)
of
Aspen
Plus
(steady-­‐state
mode)
• Model
set
up
to
run
every
five
minutes
• Incorporates
detailed
exchanger
models
-­‐ Aspen
Air
Cooled
EDR
– allows
specification
of
geometry
of
heat
exchangers

10. OPERATING
MODES
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• Performance
Analytics
Mode:
Provide
compressor
performance
KPIs
to
engineers,
operators,
and
management.
Two
types
of
displays:
1. Datasheets
-­‐ report
on
compressor
and
heat
exchanger
performance.
Key
parameters
include
temperature,
pressure
and
horsepower.

11. OPERATING
MODES
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2. Visual
data
displays
-­‐provided
through
the
aspenONE Process
Explorer,
visualization
“front
end”.
Review
data
trends
and
patterns.

12. OPERATING
MODES
27/05/182017©COPYRIGHT
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ECOLOGY
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2. Visual
data
displays
-­‐provided
through
the
aspenONE Process
Explorer,
visualization
“front
end”.
Review
data
trends
and
patterns.

13. OPERATING
MODES
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• Historical
Trends
Mode:
five
years
of
historical
operating
data
extracted
from
the
IP.21
archive
• historical
time
series
run
through
the
model.
• Used
to
validate
model
and
develop
confidence
in
predictions
through
analysis
of
historical
trends
• This
provides
the
ability
to
look
at
historical
performance
of
the
system,
and
to
evaluate
current
trends,
perturbations
and
upsets
in
the
context
of
historical
information.

14. OPERATING
MODES
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• What-­‐if
Mode:
• The
model
has
the
flexibility
to
be
run
in
analytical
mode
• This
can
be
used
to
look
at
the
expected
result
of
different
operating
strategies
and
responses
to
compressor
performance
modes.
• Can
be
used
to
look
at
the
economics
and
performance
impact
of
alternative
maintenance
strategies
for
air
cooler
and
compressor
fouling.

15. KEY
BENEFITS
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1. Improved
ability
to
measure
compressor
system
performance.
2. Better
ability
to
react
to
compressor
train
performance
degradation.
3. Closer
to
optimal
compressor
train
efficiency.
4. Various
operating
modes
(
“what
if”,
historical,
performance
analysis)
5. Ability
to
conduct
tradeoffanalysis
of
the
potential
benefits
of
equipment
replacement
or
other
CAPEX
spending
in
relationship
to
impact
on
maintenance
time,
production
uptime,
and
gas
production.
6. Achieve
increased
lift
and
injection
gas
availability,
with
incremental
crude
production
(target
1-­‐5%
improvement).

16. APPLICATION
#2:
PRODUCTION
FACILITIES
SURVEILLANCE
SYSTEM
• Objectives:
• Monitor
performance
of
equipment
to
minimize
the
downtime.
• Make
decision
to
switch
to
various
production
modes
of
operation
such
as
artificial
lift.
• Calculate
instant
well
production
to
detect
problems
such
as
improper
valve
alignment.

17. PURPOSE
OF
MODEL
• Fill
in
measurement
gaps
and
compare
model
results
with
measured
values
in
real
time.
• Estimate
production
based
on
system
conditions
for
Production
Allocation.
• A
tool
for
assisting
operators
in
understanding
the
asset
behaviour
and
in
making
better
operational
decisions

18. MODEL
DEVELOPMENT
• Pioneer
Resources
-­‐ Oooguruk production
facilities
were
modeled
in
dynamic
mode
• Model
consists
of
15
wells
operating
in
free
flowing,
ESP
(electrical
submersible
pump)
or
shut-­‐in
modes
• Sub-­‐sea
transportation
pipelines
• Three
phase
flow
(oil,
water,
gas)

19. KEY
PROCESS
ELEMENTS
• Wells:
• Three
modes
of
operation;
Free
flowing,
ESP,
Gas
Lift
controlled
by
chokes
• Down
hole
gas-­‐oil
separation
for
ESP
operation
• Thermal
gradient

20. KEY
PROCESS
ELEMENTS
• Man
made
gravel
island
where
the
production
of
all
the
wells
were
collected
• Well
heads
• Chokes
• Three
phase
test
meter
for
single
well
• Sub-­‐Sea
and
surface
pipeline
• On
shore
separation
and
metering
of
composite
stream

21. ASPEN
HYSYS
DYNAMIC
MODEL
Well tubingWell casingBottomhole
ESP Separator
Gas control choke Liquid control choke
Sub-­sea pipe
Gathering island
surface pipe

22. MODEL
TUNING
• Model
was
calibrated
to
historical
data
• Oil
Production
change
due
to
step-­‐change
in
bottomhole
pressure
2500
3000
3500
4000
4500
5000
12:00
AM 4:48
AM 9:36
AM 2:24
PM 7:12
PM 12:00
AM 4:48
AM
OilProduction
Date/Time
Grey: Data
Red: HYSYS Model

23. ONLINE
DATABASE
CONNECTIVITY
• A
multi-­‐threaded
application
was
developed
to
provide
“on-­‐
line”
and
“real
time”
connectivity
between
the
Aspen
HYSYS
Dynamics
model
of
the
production
system
and
historian
database
• The
application
imports
the
recorded
well
and
production
facilities
data
from
the
database
at
predefined
time
intervals
and
transfers
them
to
the
HYSYS
dynamic
model
• The
dynamic
response
of
the
model
is
then
transferred
to
the
corresponding
database.
• Developed
in
C#
using
.NET
platform.

24. ONLINE
DATABASE
CONNECTIVITY
Process
Historian:
Production
Data
Connector
Application
Aspen HYSYS Dynamic Model
Real-­Time
On-­line
Data Read
Process
Historian:
Simulation
Data
Real-­Time
On-­line
Data Write
Wells and
Production Facilities

25. BENEFITS
• Calculation
of
real
time
well
flow
rates
when
not
physically
metered
• Model
helps
on-­‐shore
operators
anticipate
changes
from
island
events
• Diagnostics
for
wells
that
are
not
behaving
according
to
model
(GOR
or
GLR
different
than
well
test)
• What-­‐if
scenarios
(pipeline
flow
regime,
proration/shut-­‐ins)

26. SUMMARY
27/05/182017©COPYRIGHT
PROCESS
ECOLOGY
INC.
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• Performance
Monitoring
Applications
built
on
simulation
models
can
yield
significant
process
insight
• This
can
result
in
process
improvements
(e.g.,
1-­‐
5%
incremental
production
in
Case
Study
#1)
• Revenue
Benefit
can
be
significant
and
in
many
cases
worth
the
effort

27. THANK
YOU!
james@processecology.com
+1
(403)
313
8931