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Asset Management Optimization using model based decision support Speaker: Francesco Verre SPE Dinner Meeting – 25 th October 2011 – London.

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Presentation on theme: "Asset Management Optimization using model based decision support Speaker: Francesco Verre SPE Dinner Meeting – 25 th October 2011 – London."— Presentation transcript:

1 Asset Management Optimization using model based decision support Speaker: Francesco Verre SPE Dinner Meeting – 25 th October 2011 – London

2  Background and Objectives  Integration methodology  Optimization methodology  Case studies  Conclusions Presentation outline

3 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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5 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

6 Gathering system Input  Separator pressure  Choke opening “FTHP” Output  Well mass flow rate Integration methodology

7 Process model Integration methodology Output  Gas flowrate  Oil flowrate  Water flowrate Input  Mass flowrate from each well  Process parameters

8 8 For each well  Oil density  Gas gravity  GOR Integration methodology mass

9 Integrated model = two production environments 1. The gathering system (GAP) 2. The process plant (HYSYS) Optimization particularly challenging:  Several variables  Several constraints Optimization methodology

10 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

11 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

12 Example: 3 wells and 20 choke openings (5%, 10% %,100%) Definition of the openings with binary representation 20 openings means 5 bits (2 5 = 32): 0% % %00010 … 100%10100 Building randomly the population of rabbits Rabbit 1 = Well1 Choke 5% Well2 Choke 100% Well3 Choke 70% Rabbit n = Well1 Choke 20% Well2 Choke 10% Well3 Choke 100% Optimization methodology

13 Rabbit 1 = Well1 Choke 5% Well2 Choke 100% Well3 Choke 70% Rabbit n = Well1 Choke 20% Well2 Choke 10% Well3 Choke 100% OLGA Prosper HYSYS Q 1 Q n flowrates Optimization methodology

14 Initial Run Selection First best half Cost weighting rank Mating Crossover Mutation Optimization methodology

15 Rabbit 1 = Well1 Choke 10% Well2 Choke 100% Well3 Choke 70% Rabbit n = Well1 Choke 20% Well2 Choke 10% Well3 Choke 100% OLGA Prosper HYSYS After x iterations we obtain the last generation MAX Q!!! Optimization methodology

16 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

17 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

18 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

19  bblsd of condensate recovered through Optimizer application MonthPlant CGR Sealine Pressure Bar Sales Gas Cri- condentherm C Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Case Study – Condensate recovery

20 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

21 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 22 Case studies - Results

23 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 boepd to boepd

24  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

25 Thanks Questions?


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