Gas Lift Optimization for Long-Term Reservoir Simulations
- Pengju Wang (BP) | Michael L. Litvak (BP America)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2008
- Document Type
- Journal Paper
- 147 - 153
- 2008. Society of Petroleum Engineers
- 5.4.2 Gas Injection Methods, 4.3.4 Scale, 4.2 Pipelines, Flowlines and Risers, 3.1.6 Gas Lift, 4.1.2 Separation and Treating, 5.3.2 Multiphase Flow, 5.6.8 Well Performance Monitoring, Inflow Performance, 5.5 Reservoir Simulation, 5.1.5 Geologic Modeling, 4.1.5 Processing Equipment
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Although various gas lift optimization algorithms have been proposed in literature, few are suitable for long-term reservoir-development studies, which require the gas lift optimizer to be highly efficient, flexible, and powerful enough to handle complicated fluid flows and operational constraints while having minimal impact on simulator convergence rates. This paper investigates methods to address these important issues.
The gas lift optimization problem considered in this paper is to maximize daily hydrocarbon production by selecting optimally the well-production and lift-gas rates subject to pressure and rate constraints in nodes of the surface pipeline network (SPN) and to the amount of lift gas available. The problem is regarded as a well-management problem in a commercial reservoir simulator capable of simulating multiphase compositional fluid flow in reservoirs, well tubing strings, SPN systems, and separation facilities. The problem is solved in selected iterations of a reservoir-simulation timestep.
This paper proposed a method for the described gas lift optimization problem and investigated its performance against multiple existing methods. Case studies showed that the new method is capable of producing high-quality results while requiring less CPU time for optimization and having a smaller impact on reservoir-simulator convergence.
This paper also applied the concept of multiobjective optimization to smooth rate oscillations between adjacent iterations by sacrificing a certain amount of oil production. This method was proved to be successful in tested cases.
In mature-oilfield operations, hydrocarbon production is often assisted by continuous lift-gas injection and constrained by the gas-handling and/or liquid-handling capacities of surface facilities. Optimal allocation of production and lift gas-rates is subject to reservoir deliverability and surface-facility capacities. These can have a big impact on facility design and other capital-investment decisions and should be captured in long-term reservoir studies. Compared to real-time production optimization, the optimal rate allocation in long-term reservoir-simulation studies poses unique problems: the rate-allocation optimizer must be highly efficient, and it must have a minimal impact on simulation convergence while generating quality results.
The stated problem has been addressed in a variety of ways in the petroleum industry. Fang and Lo (1996) proposed a linear programming technique to allocate lift-gas rates and production streams subject to multiple flow-rate constraints. The method was implemented in a reservoir simulator and was demonstrated to be efficient in several field studies. On the basis of Fang and Lo's work, Wang et al. (2002) developed a procedure to optimally allocate the production rate, lift-gas rate, and well connections to surface pipeline systems simultaneously. Their optimization step is invoked at the Newton-iteration level of a commercial reservoir simulator. Hepguler et al. (1997) coupled a separate commercial SPN optimizer with a commercial reservoir simulator through an iterative procedure. The surface-network optimizer uses a sequential quadratic programming (SQP) optimization algorithm and has the ability to perform general operation and design optimizations. Davidson and Beckner (2003) presented an integrated facility and reservoir model in which the rate-allocation problem is solved in the facility model with SQP methods. They also presented a detailed procedure on how to handle unfeasible conditions. Kosmidis et al. (2004) developed a mixed-integer nonlinear optimization formulation to address gas lift optimization problems involving flow interactions through common flowlines. The resulting model is solved by a variation of the sequential linear-programming (SLP) method. Ray and Sarker (2006) applied a multiobjective evolutionary approach to gas lift optimization problems. In their method, the multiobjective formulation eliminates the need to solve gas lift optimization problems on a daily basis while maintaining the quality of solutions.
The Fang and Lo (1996) method is simple and efficient. However, it ignores the pressure interactions among wells through common flowlines and may result in unrealistic results. The methods of Hepguler et al. (1997), Davidson and Beckner (2003), and Kosmidis et al. (2004) relied on powerful nonlinear optimization tools. This paper bridges this gap by presenting a simple, yet robust and efficient, rate-allocation optimization procedure. In addition, this paper presents a methodology that uses the concept of multiobjective optimization (Miettinen 1999) to minimize the impact of lift-gas rate oscillations on simulation convergence. This method was proved to be successful in tested cases.
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Azarm, S. Class Notes for Multiobjective Optimization.http://www.glue.umd.edu/azarm/optimum_notes/multi/multi.html. DownloadedOctober 2002.
Bertsimas, D. and Tsitsiklis, J. 1997. Introduction to LinearOptimization. Belmont, Massachusetts: Athena Scientific.
Davidson, J.E. and Beckner, B.L. 2003. Integrated Optimization for RateAllocation in Reservoir Simulation. SPEREE 6 (6): 426-432.SPE-87309-PA. DOI: 10.2118/87309-PA.
Fang, W.Y. and Lo, K.K. 1996. AGeneralized Well-Management Scheme for Reservoir Simulation. SPERE11 (2): 116-120. SPE-29124-PA. DOI: 10.2118/29124-PA.
Hepguler, G., Barua, S., and Bard, W. 1997. Integration of a Field Surface andProduction Network With a Reservoir Simulator. SPECA 12 (4):88-92. SPE-38937-PA. DOI: 10.2118/38937-PA.
Holland, J.H. 1975. Adaptation in Natural and Artificial Systems. AnnArbor, Michigan: The University of Michigan Press.
Kosmidis, V.D., Perkins, J.D., and Pistikopoulos, E.N. 2004. Optimization of Well Oil RateAllocations in Petroleum Fields. Industrial & Engineering ChemistryResearch 43 (14): 3513-3527. DOI: 10.1021/ie034171z.
Landmark. 2003. VIP-EXECUTIVE Technical Reference. Houston: LandmarkGraphics Corporation.
Litvak, M.L., and Darlow, B.L. 1995. Surface Network and Well TubingheadPressure Constraints in Compositional Simulation. Paper SPE 29125 presentedat the SPE Reservoir Simulation Symposium, San Antonio, Texas, 12-15 February.DOI: 10.2118/29125-MS.
Lo, K.K., Starley, G.P., and Holden, C.W. 1995. Application of Linear Programming toReservoir Development Evaluations. SPERE 10 (1): 52-58.SPE-26637-PA. DOI: 10.2118/26637-PA.
Miettinen, K., 1999. Nonlinear Multiobjective Optimization. Boston,Massachusetts: Kluwer Academic Publishers.
Powell, J.M.D. 1992. A direct search optimization method that models theobjective and constraint functions by linear interpolation. DAMTP/NA5,Cambridge, England.
Ray, T. and Sarker, R. 2006. Multiobjective Evolutionary Approach to theSolution of Gas Lift Optimization Problems. IEEE Congress on EvolutionaryComputation, 16-21 July, 3182-3188.
Wang, P. 2003. Development and Application of Production OptimizationTechniques for Petroleum Fields. PhD dissertation, Stanford, California:Stanford University.
Wang, P., Litvak, M.L., and Aziz, K. 2002. Optimization of Production FromMature Fields. 17th World Petroleum Congress, Rio de Janeiro, 1-5September.