Asset Management Optimization using model based decision support

Speaker: Francesco Verre

SPE Dinner Meeting – 25th October 2011 – London

Background and Objectives Integration methodologyOptimization methodologyCase studiesConclusions

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 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

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

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 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

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

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

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

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?