27_oct_2011 francesco verre - asset management optimization using model based decision support...
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Asset Management Optimization using model based decision support
Speaker: Francesco Verre
SPE Dinner Meeting – 25th October 2011 – London
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Background and Objectives Integration methodologyOptimization methodologyCase studiesConclusions
Presentation outline
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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
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Background and Objectives
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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
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Gathering system
Input Separator pressure Choke opening “FTHP”
Output Well mass flow rate
Integration methodology
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Process model
Integration methodology
Output Gas flowrate Oil flowrate Water flowrate
Input Mass flowrate from each well Process parameters
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8
For each well Oil density Gas gravity GOR
Integration methodology
mass
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Integrated model = two production environments
1. The gathering system (GAP)
2. The process plant (HYSYS)
Optimization particularly challenging: Several variables Several constraints
Optimization methodology
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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
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Steps to build a sound genetic algorithms
1. Define the variables and the constraints of the system2. Define the algorithm parameters3. Define the fitness function4. Generate the initial population5. Find the fitness for each individual6. Convergence check7. Select mates8. Mating9. Mutation10.Go back to step 5
Optimization methodology
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Example: 3 wells and 20 choke openings (5%, 10%.....95%,100%)Definition of the openings with binary representation
20 openings means 5 bits (25 = 32):
0% 000005% 0000110% 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
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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
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Initial Run
Selection
•First best half
•Cost weighting rank
Mating
Crossover
Mutation
Optimization methodology
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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
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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
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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
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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
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+3000 bblsd of condensate recovered through Optimizer application
Month Plant CGRSealine Pressure
BarSales Gas Cri-
condentherm C
Aug-08 36.1 90 23Sep-08 34.8 90 24Oct-08 35.5 96 23Nov-08 35.1 93 19Dec-08 34.3 95 22Jan-09 34.1 95 22Feb-09 34.4 94 19Mar-09 32.1 96 19Apr-09 31.6 95 19May-09 32.4 95 16Jun-09 32.0 94 8Jul-09 31.5 92 8
Aug-09 32.4 96 7
Sep-09 32.3 98 5
Case Study – Condensate recovery
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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 CO2/H2S ratio Wobbe index Oil TVP
Case studies
Oil and associated gas asset
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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
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22
Case studies - Results
pr oduct i on t r ai n opt i mi zat i on
33000
33500
34000
34500
35000
35500
36000
36500
opt. with gas opt. wit gas and FBHP opt. Genetic constrain constrains algorithm
bbl/day
opt i mi zat i onr esul t s
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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
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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
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Thanks
Questions?