Smart Well Production Optimization Using An Ensemble-Based Method
- Ho-Jeen Su (Saudi Aramco) | Dean S. Oliver (University of Oklahoma)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2010
- Document Type
- Journal Paper
- 884 - 892
- 2010. Society of Petroleum Engineers
- 2.3 Completion Monitoring Systems/Intelligent Wells, 1.6 Drilling Operations, 5.5.8 History Matching
- production optimization, ensemble optimization, smart well covariance
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- 1,367 since 2007
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Ensemble methods have been applied successfully in assisted history matching and in production optimization. In history matching, the ensemble Kalman filter (EnKF) has been used to estimate the values of hundreds of thousands of variables from various types of data. In production optimization, an ensemble-based method has been used to estimate optimal control settings for problems with thousands of control variables. In both cases, relatively small numbers of random realizations are used to compute update directions for improving estimates.
In this paper, we illustrate the application of the ensemble-based optimization on two fairly complex problems that would be difficult to handle by other methods. In the first example, we show its application to optimize inflow-control-valve (ICV) settings on two horizontal wells in a sector model of 200,000 cells. One hundred layers were used in the reservoir model to capture geological heterogeneity. The two wells were drilled parallel to the edgewater boundary. The optimization objective in this example is to minimize cumulative water production over a 10-year production period while maintaining a constant liquid-production rate. Results after only five optimization iterations with improved control-valve settings showed a 50% reduction in cumulative water production. The fully automated optimization process was completed within a few hours under a parallel-computing environment.
The ensemble-based method was also applied successfully to a 3D case consisting of 10 multilateral wells with ICVs installed at each lateral junction. The interaction of various laterals is difficult to visualize, but the optimization algorithm was again successful in reducing water production. In this example, we demonstrate that proper choice of control variables can be important to the success of the optimization.
|File Size||1 MB||Number of Pages||9|
Annan, J.D. and Hargreaves, J.C. 2004. Efficient parameterestimation for a highly chaotic system. Tellus A 56(5): 520-526. doi:10.1111/j.1600-0870.2004.00073.x.
Brouwer, D.R. and Jansen, J.-D. 2004. Dynamic Optmization of Water FloodingWith Smart Wells Using Optimal Control Theory. SPE J. 9(4): 391-402. SPE-78278-PA. doi: 10.2118/78278-PA.
Carrol, J.A. and Horne, R.N. 1992. Multivariate Optimization ofProduction Systems. J Pet Tech 44 (7): 782-789;Trans., AIME, 293. SPE-22847-PA. doi: 10.2118/22847-PA.
Chen, Y., Oliver, D.S., and Zhang, D. 2008. Efficient Ensemble-Based Closed-LoopProduction Optimization. Paper SPE 112873 presented at the SPE/DOESymposium on Improved Oil Recovery, Tulsa, 21-23 April. doi:10.2118/112873-MS.
Chen, Y. and Oliver, D.S. 2009a. Localization of Ensemble-BasedControl Setting Updates for Production Optimization. Paper SPE 125042presented at the SPE Annual Technical Conference, New Orleans, 4-7 October.doi: 10.2118/125042-MS.
Chen, Y. and Oliver, D.S. 2010. Ensemble-Based Closed-LoopProduction Optimization on Brugge Field. SPE Res Eval & Eng 13 (1): 56-71. SPE-118926-PA. doi: 10.2118/118926-PA.
Davidson, J.E. and Beckner, B.L. 2003. Integrated Optimization for RateAllocation in Reservoir Simulation. SPE Res Eval & Eng 6 (6): 426-432. SPE-87309-PA. doi: 10.2118/87309-PA.
Dogru, A.H., Sunaidi, H.A., Fung, L.S., Habiballah, W.A., Al-Zamel, N., andLi, K.G. 2002. A ParallelReservoir Simulator for Large-Scale Reservoir Simulation. SPE Res Eval& Eng 5 (1): 11-23. SPE-75805-PA. doi:10.2118/75805-PA.
Ebadi, F., Davies, D.R., Gardiner, A.R., and Corbett, P.W.M. 2008.Evaluation of added value in reservoir management by application of flowcontrol with intelligent wells. Petroleum Geoscience 14(2): 183-196.
Gunzburger, M.D. 2003. Perspectives in Flow Control and Optimization.Philadelphia, Pennsylvania, USA: Advances in Design and Control, SIAM.
Handels, M., Zandvliet, M.J., Brouwer, D.R., and Jansen, J.D. 2007. Adjoint-Based Well-PlacementOptimization Under Production Constraints. Paper SPE 105797 presented atthe SPE Reservoir Simulation Symposium, Houston, 26-28 February. doi:10.2118/105797-MS.
Li, R., Reynolds, A.C., and Oliver, D.S. 2003. History Matching of Three-Phase FlowProduction Data. SPE J. 8 (4): 328-340. SPE-87336-PA.doi: 10.2118/87336-PA.
Lien, M., Brouwer, D.R., Mannseth, T., and Jansen, J.D. 2008. Multiscale Regularization of FloodingOptimization for Smart Field Management. SPE J. 13 (2):195-204. SPE-99728-PA. doi: 10.2118/99728-PA.
Lorentz, M., Ratterman, G., and Augustine, J. 2006. Uniform Inflow Completion SystemExtends Economic Field Life: A Field Case Study and Technology Overview.Paper SPE 101895 presented at the SPE Annual Technical Conference andExhibition, San Antonio, Texas, USA, 24-27 September. doi:10.2118/101895-MS.
Lorentzen, R.J., Berg, A.M., Nævdal, G., and Vefring, E.H. 2006. A New Approach for DynamicOptimization of Waterflooding Problems. Paper SPE 99690 presented at theIntelligent Energy Conference and Exhibition, Amsterdam, 11-13 April. doi:10.2118/99690-MS.
Meum, P., Tøndel, P., Godhavn, J.-M., and Aamo, O.M. 2008. Optimization of Smart WellProduction Through Nonlinear Model Predictive Control. Paper SPE 112100presented at the Intelligent Energy Conference and Exhibition, Amsterdam, 25-27February. doi: 10.2118/112100-MS.
Naus, M.M.J.J., Dolle, N., and Jansen, J.-D. 2006. Optimization of Commingled ProductionUsing Infinitely Variable Inflow Control Valves. SPE Prod & Oper 21 (2): 293-301. SPE-90959-PA. doi: 10.2118/90959-PA.
Sarma, P., Aziz, K., and Durlofsky, L.J. 2005. Implementation of Adjoint Solutionfor Optimal Control of Smart Wells. Paper SPE 92864 presented at the SPEReservoir Simulation Symposium, The Woodlands, Texas, USA, 31 January-2February. doi: 10.2118/92864-MS.
Sarma, P., Durlofsky, L.J., Aziz, K., and Chen, W.H. 2006. Efficient real-timereservoir management using adjoint-based optimal control and modelupdating. Computational Geosciences 10 (1): 3-36.doi:10.1007/s10596-005-9009-z.
Spall, J.C. 1992. Multivariatestochastic approximation using a simultaneous perturbation gradientapproximation. IEEE Transactions Automatic Control 37(3): 332-341. doi: 10.1109/9.119632.
Su, H.-J. and Dogru, A.H. 2009. Modeling of Equalizer ProductionSystem and Smart-Well Applications in Full-Field Studies. SPE Res Eval& Eng 12 (2): 318-328. SPE-111288-PA. doi:10.2118/111288-PA.
Tarantola., A. 1987. Inverse Problem Theory: Methods for Data Fitting andModel Parameter Estimation. Amsterdam: Elsevier.
Yeten, B., Durlofsky, L.J., and Aziz, K. 2003. Optimization of Nonconventional WellType, Location, and Trajectory. SPE J. 8 (3): 200-210.SPE-86880-PA. doi: 10.2118/86880-PA.