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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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Background and Objectives Integration methodology Optimization methodology Case studies Conclusions 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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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 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

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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 (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

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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 MonthPlant CGR Sealine Pressure Bar Sales Gas Cri- condentherm C Aug-0836.1 90 23 Sep-0834.8 90 24 Oct-0835.5 96 23 Nov-0835.1 93 19 Dec-0834.3 95 22 Jan-0934.1 95 22 Feb-0934.4 94 19 Mar-0932.1 96 19 Apr-0931.6 95 19 May-0932.4 95 16 Jun-0932.0 94 8 Jul-0931.5 92 8 Aug-0932.4 96 7 Sep-0932.3 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 CO 2 /H 2 S 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

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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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