Asset Management
Optimization using model
based decision support
Speaker: Francesco
Verre
SPE Dinner Meeting
–
25
th
October 2011
–
London
Background and Objectives
Integration methodology
Optimization methodology
Case studies
Conclusions
Presentation outline
Background
Integrated Asset Modeling established methodology for
asset performance
Need to exploit further the integration philosophy through
optimization
Objectives
Development of an optimization and integration tool to
support daily operations
Choke valve settings, well routing
Separator pressure,
reboiler
temperature etc.
Maximize asset performance objectives taking into
account possible constraints
Reservoir limits (minimum FBHP)
Erosion velocity
Process constraints
Background and Objectives
Background and Objectives
Integration methodology
Hypotheses
Constant fluid composition for each well (independent
from FTHP)
Steady state conditions
The tool is not able to reproduce time dependence
effect like slugs, shut down or ramp up conditions
Well performances such as Production Index PI,
reservoir pressure are considered not time dependent
The tool is not designed to have forecasts
Boundaries of the system. The tool is designed to
simulate asset performance from sand face to delivery
point
Gathering system
Input
Separator pressure
Choke opening “FTHP”
Output
Well mass flow rate
Integration methodology
Process model
Integration methodology
Output
Gas flowrate
Oil flowrate
Water flowrate
Input
Mass flowrate from each
well
Process parameters
8
For each well
Oil density
Gas gravity
GOR
Integration methodology
mass
Integrated model = two
production environments
1. The gathering system
(GAP)
2. The process plant
(HYSYS
)
Optimization particularly challenging:
Several variables
Several constraints
Optimization methodology
Interaction between the different production
environments and search of the optimum through
genetic algorithms
3 basics requirements:
Find the true global optimum
Fast convergence
Limited number of control parameters
Optimization methodology
Steps
to
build
a
sound
genetic
algorithms
1.
Define
the
variables
and
the
constraints
of
the
system
2.
Define
the
algorithm
parameters
3.
Define
the
fitness
function
4.
Generate
the
initial
population
5.
Find
the
fitness
for
each
individual
6.
Convergence
check
7.
Select
mates
8.
Mating
9.
Mutation
10.
Go
back
to
step
5
Optimization methodology
Example: 3 wells and 20 choke openings (5%, 10%.....95%,100%)
Definition of the openings with binary representation
20 openings means 5 bits (2
5
= 32):
0%
00000
5%
00001
10%
00010
…
…
100%
10100
Building
randomly the
population of
rabbits
Rabbit 1 =
00001
10100
01110
Well1
Choke
5%
Well2
Choke
100%
Well3
Choke
70%
Rabbit n =
00100
00010
10100
Well1
Choke
20%
Well2
Choke
10%
Well3
Choke
100%
.
.
.
.
.
. . .
. . .
. . .
Optimization methodology
Rabbit 1 =
00001
10100
01110
Well1
Choke
5%
Well2
Choke
100%
Well3
Choke
70%
Rabbit n =
00100
00010
10100
Well1
Choke
20%
Well2
Choke
10%
Well3
Choke
100%
.
.
.
.
.
. . .
. . .
. . .
OLGA
Prosper
HYSYS
Q 1
Q n
.
.
.
.
.
flowrates
Optimization methodology
Initial Run
Selection
•
First best half
•
Cost weighting
rank
Mating
Crossover
Mutation
Optimization methodology
Rabbit 1 =
00010
10100
01110
Well1
Choke
10%
Well2
Choke
100%
Well3
Choke
70%
Rabbit n =
00100
00010
10100
Well1
Choke
20%
Well2
Choke
10%
Well3
Choke
100%
.
.
.
.
.
. . .
. . .
. . .
OLGA
Prosper
HYSYS
After x iterations we obtain the last generation
MAX Q!!!
Optimization methodology
Case Study
–
Network
Find the maximum
flowrate
for a network of water wells
The objective is to change the WHP for the 3 wells in order to obtain
the maximum water
flowrate
as output
Case Study
–
Gas Lift Optimization
Find the maximum liquid
flowrate
for gas lift network avoiding excessive
fuel gas consumption for the gas lift compression
The objective is to vary the gas lift
flowrate
and the percentage for
each well in order to obtain the maximum oil
flowrate
and minimum
fuel gas consumption
10% oil
recovery
increase
Case Study
–
Condensate recovery
Find the best combination of operating parameters to increase
condensate recovery from Abu Fares field.
The objective is to vary the
sealine
pressure, the separation
pressures and the
stabilisation
process in order to obtain the
maximum condensate recovery
+3000
bblsd
of condensate recovered through Optimizer application
Month
Plant CGR
Sealine
Pressure
Bar
Sales Gas Cri

condentherm C
Aug

08
36.1
90
23
Sep

08
34.8
90
24
Oct

08
35.5
96
23
Nov

08
35.1
93
19
Dec

08
34.3
95
22
Jan

09
34.1
95
22
Feb

09
34.4
94
19
Mar

09
32.1
96
19
Apr

09
31.6
95
19
May

09
32.4
95
16
Jun

09
32.0
94
8
Jul

09
31.5
92
8
Aug

09
32.4
96
7
Sep

09
32.3
98
5
Case Study
–
Condensate recovery
14 Variables:
8 inlet choke ΔP
2 separators’ P
ΔP slug

catcher
Stabilizer head P
Stabilizer T reboiler
Stabilizer middle T
15 Constraints:
8 FBHP
Oil, Gas and Water
entering the plant
Volume flow to the
treating section
CO
2
/H
2
S ratio
Wobbe index
Oil TVP
Case studies
Oil and associated gas asset
Tested
3
different
optimization
methodologies
Combination of separated optimization:
Gathering optimization with max gas flow rate
Process optimization
Combination of separated optimization:
Gathering optimization with max gas flow rate and
minimum FBHP
Process optimization
Genetic algorithm optimization of integrated
system with process and well constraints
Case studies
22
Case studies

Results
Case Study
–
NGL optimization
Find the best combination of rich gas wells to increase NGL recovery
The objective is to segregate and find the best wells combination
and process parameters in order to obtain the
maximum NGL
recovery
From 19000 boepd to 23000 boepd
The integrated model allows the evaluation of
potential production with constraints
The optimization of the integrated asset is a key
live activity to obtain the optimum solution for all
the configuration changes
The integration and optimization unleash
unforeseen potentials
Conclusions
Thanks
Questions?
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