Optimization using model

libyantawdryAI and Robotics

Oct 23, 2013 (3 years and 10 months ago)

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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?