Results of the Brugge Benchmark Study for Flooding Optimization and History Matching
- Lies Peters (TNO) | Rob Arts (TNO) | Geert Brouwer (TNO) | Cees Geel (TNO) | Stan Cullick (Halliburton) | Rolf J. Lorentzen (International Research Institute of Stavanger) | Yan Chen (University of Oklahoma) | Neil Dunlop (Roxar) | Femke C. Vossepoel (Shell International) | Rong Xu (Schlumberger) | Pallav Sarma (Stanford University) | Ahmed H.H. Alhuthali (Texas A&M University) | Albert Reynolds (University of Tulsa)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- June 2010
- Document Type
- Journal Paper
- 391 - 405
- 2010. Society of Petroleum Engineers
- 5.6.9 Production Forecasting, 5.6.1 Open hole/cased hole log analysis, 5.1.2 Faults and Fracture Characterisation, 5.3.1 Flow in Porous Media, 4.3.4 Scale, 3.3 Well & Reservoir Surveillance and Monitoring, 5.1 Reservoir Characterisation, 5.5 Reservoir Simulation, 7.6.2 Data Integration, 5.1.5 Geologic Modeling, 5.5.3 Scaling Methods, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.5.5 Evaluation of uncertainties, 2.4.3 Sand/Solids Control, 7.6.6 Artificial Intelligence, 5.4.1 Waterflooding, 4.1.5 Processing Equipment, 4.2 Pipelines, Flowlines and Risers, 4.1.2 Separation and Treating, 2.2.2 Perforating, 5.1.7 Seismic Processing and Interpretation, 5.1.9 Four-Dimensional and Four-Component Seismic, 5.5.8 History Matching
- Evaluation of Uncertainties
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In preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008, a unique benchmark project was organized to test the combined use of waterflooding-optimization and history-matching methods in a closed-loop workflow. The benchmark was organized in the form of an interactive competition during the months preceding the ATW. The goal set for the exercise was to create a set of history-matched reservoir models and then to find an optimal waterflooding strategy for an oil field containing 20 producers and 10 injectors that can each be controlled by three inflow-control valves (ICVs). A synthetic data set was made available to the participants by TNO, consisting of well-log data, the structure of the reservoir, 10 years of production data, inverted time-lapse seismic data, and other information necessary for the exercise. The parameters to be estimated during the history match were permeability, porosity, and net-to gross- (NTG) thickness ratio. The optimized production strategy was tested on a synthetic truth model developed by TNO, which was also used to generate the production data and inverted time-lapse seismic. Because of time and practical constraints, a full closed-loop exercise was not possible; however, the participants could obtain the response to their production strategy after 10 years, update their models, and resubmit a revised production strategy for the final 10 years of production. In total, nine groups participated in the exercise. The spread of the net present value (NPV) obtained by the different participants is on the order of 10%. The highest result that was obtained is only 3% below the optimized case determined for the known truth field. Although not an objective of this exercise, it was shown that the increase in NPV as a result of having three control intervals per well instead of one was considerable (approximately 20%). The results also showed that the NPV achieved with the flooding strategy that was updated after additional production data became available was consistently higher than before the data became available.
|File Size||1 MB||Number of Pages||15|
Alhuthali, A., Oyerinde, D., and Datta-Gupta, A. 2007. Optimal Waterflood Management UsingRate Control. SPE Res Eval & Eng 10 (5): 539-551.SPE-102478-PA. doi: 10.2118/102478-PA.
Alhuthali, A.H., Datta-Gupta, A., Yuen, B., and Fontanilla, J.P. 2009. Field Applications of WaterfloodOptimization via Optimal Rate Control With Smart Wells. Paper SPE 118948presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA,2-4 February. doi: 10.2118/118948-MS.
Alhuthali, A.H., Datta-Gupta, A., Yuen, B., and Fontanilla, J.P. 2008. Optimal Rate Control Under GeologicUncertainty. Paper SPE 113628 presented the SPE/DOE Symposium on ImprovedOil Recovery, Tulsa, 20-23 April. doi: 10.2118/113628-MS.
Bailey, W.J., Couët, B., and Wilkinson, D. 2005. Field Optimization Tool forMaximizing Asset Value. SPE Res Eng 8 (1): 7-21.SPE-87026-PA. doi: 10.2118/87026-PA.
Bianco, A., Cominelli, A., Dovera, L., Nævdal, G., and Vallès, B. 2007. History Matching and ProductionForecast Uncertainty by Means of the Ensemble Kalman Filter: A Real FieldApplication. Paper SPE 107161 presented at the EAGE/EUROPEC Conference andExhibition, London, 11-14 June. doi: 10.2118/107161-MS.
Bos, C.F.M. 2000. Production Forecasting with Uncertainty Quantification.PUNQ final report, Fault Analysis Group, Dublin, UK.
Bos, C.F.M. et al. 1999. Production Forecasting with UncertaintyQuantification: PUNQ-2. TNO Report NITG 99-255-A, Fault Analysis Group, Dublin,UK.
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.
Brouwer, D.R., Nævdal, G., Jansen, J.-D., Vefring, E.H., and van Kruijsdijk,C.P.J.W. 2004. Improved ReservoirManagement Through Optimal Control and Continuous Model Updating. Paper SPE90149 presented at the SPE Annual Technical Conference and Exhibition, Houston,26-29 September. doi: 10.2118/90149-MS.
Chen, Y. and Oliver, D.S. 2009. Ensemble-Based Closed-LoopProduction Optimization on Brugge Field. Paper SPE 118962 presented at theSPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 2-4 February.doi: 10.2118/118926-MS.
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.
Christie, M.A. and Blunt, M.J. 2001. Tenth SPE Comparative SolutionProject: A Comparison of Upscaling Techniques. SPE Res Eval &Eng 4 (4): 308-317. SPE-72469-PA. doi: 10.2118/72469-PA.
Cullick, A.S., Johnson, W.D., and Shi, G. 2006. Improved and More Rapid HistoryMatching With a Nonlinear Proxy and Global Optimization. Paper SPE 101933presented at the SPE Annual Technical Conference and Exhibition, San Antonio,Texas, USA, 24-27 September. doi: 10.2118/101933-MS.
Dunlop, K.N.B., Revus, D.E., and Webb, S.J. 2007. Enabling the "BigLoop"--History matching while ensuring consistency with the geological model.Presented at Production Geoscience 2007 "From Geo to Flow--Dealing with theBottlenecks," Norwegian Petroleum Directorate, Stavanger, 5-6 November.
Evensen, G. 2007. Data Assimilation: The Ensemble Kalman Filter.Berlin: Springer Verlag.
Floris, F.J.T., Bush, M.D., Cuypers, M., Roggero, F., and Syversveen, A-R.2001. Methods for quantifying the uncertainty of production forecasts: Acomparative study. Petroleum Geoscience 7 (Supplement, 1May): 87-96.
Gill, P.E, Murray, W., and Saunders, M.A. 2002. User's Guide for SNOPT 6, AFORTRAN Package for Large Scale Nonlinear Programming. Systems OptimizationLaboratory, Stanford University, Stanford, California (December 2002).
Gu, Y. and Oliver, D.S. 2007. An Iterative Ensemble Kalman Filterfor Multiphase Fluid Flow Data Assimilation. SPE J. 12(4): 438-446. SPE-108438-PA. doi: 10.2118/108438-PA.
He, Z. and Datta-Gupta, A. 2002. Streamline-Based Production DataIntegration With Gravity and Changing Field Conditions. SPE J. 7 (4): 423-436. SPE-71333-PA. doi: 10.2118/81208-PA.
Hooke, R. and Jeeves, T.A. 1961. "Direct Search" Solution ofNumerical and Statistical Problems. Journal of the ACM 8 (2): 212-229. doi: 10.1145/321062.321069.
Jansen, J.D., Douma, S.D., Brouwer, D.R., Van den Hof, P.M.J., Bosgra, O.H.,and Heemink, A.W. 2009. ClosedLoop Reservoir Management. Paper SPE 119098 presented at the SPE ReservoirSimulation Symposium, The Woodlands, Texas, USA, 2-4 February. doi:10.2118/119098-MS.
Jutila, H.A. and Goodwin, N.H. 2006. Schedule Optimization to ComplementAssisted History Matching and Prediction Under Uncertainty. Paper SPE100253 presented at the SPE Europec/EAGE Annual Conference and Exhibition,Vienna, Austria, 12-15 June. doi: 10.2118/100253-MS.
Killough, J.E. 1995. Ninth SPEComparative Solution Project: A Reexamination of Black-Oil Simulation.Paper SPE 29110 presented at the SPE Reservoir Simulation Symposium, SanAntonio, Texas, USA, 12-15 February. doi: 10.2118/29110-MS.
Kraaijevanger, J.F.B.M., Egberts, P.J.P., Valstar, J.R., and Buurman, H.W.2007. Optimal Waterflood DesignUsing the Adjoint Method. Paper SPE 105764 presented at the SPE ReservoirSimulation Symposium, Houston, 26-28 February. doi: 10.2118/105764-MS.
Lorentzen, R.J., Shafieirad, A., and Nævdal, G. 2009. Closed-Loop Reservoir ManagementUsing the Ensemble Kalman Filter and Sequential Quadratic Programming.Paper SPE 119101 presented at the SPE Reservoir Simulation Symposium, TheWoodlands, Texas, USA, 2-4 February. doi: 10.2118/119101-MS.
Nævdal, G., Johnsen, L.M., Aanonsen, S.I., and Vefring, E.H. 2005. Reservoir Monitoring and ContinuousModel Updating Using Ensemble Kalman Filter. SPE J. 10(1): 66-74. SPE-84372-PA. doi: 10.2118/84372-PA.
Nikolaou, M., Cullick, A.S., and Saputelli, L. 2006. Production Optimization: AMoving-Horizon Approach. Paper SPE 99358 presented at the IntelligentEnergy Conference and Exhibition, Amsterdam, 11-13 April. doi:10.2118/99358-MS.
Nocedal, J. and Wright, S.J. 1999. Numerical Optimization. New York:Springer.
Sarma, P., Chen, W.H., Durlofsky, L.J., and Aziz, K. 2008. Production Optimization With AdjointModels Under Nonlinear Control-State Path Inequality Constraints. SPERes Eval & Eng 11 (2): 326-339. SPE-99959-PA. doi:10.2118/99959-PA.
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.
Schittkowski, K. 1985. NLQPL: A FORTRAN-Subroutine Solving ConstrainedNonlinear Programming Problems. Annals of Operations Research 5 (1-4): 485-500.
Skjervheim, J.-A., Evensen, G., Aanonsen, S.I., Ruud, B.O., and Johansen,T.A. 2007. Incorporating 4DSeismic Data in Reservoir Simulation Model Using Ensemble Kalman Filter.SPE J. 12 (3): 282-292. SPE-95789-PA. doi:10.2118/95789-PA.
Vasco, D.W. and Karasaki, K. 2006. Interpretation and inversion oflow-frequency head observations. Water Resources Research 42: W05408. doi:10.1029/2005WR004445.
Wang, C., Li, G., and Reynolds, A.C. 2007. Production Optimization inClosed-Loop Reservoir Management. Paper SPE 109805 presented at the SPEAnnual Technical Conference and Exhibition, Anaheim, California, USA, 11-14November. doi: 10.2118/109805-MS.
Webb, S.J., Bayless, J.S., and Dunlop, K.N.B. 2007. Enabling the "BigLoop"--Ensuring Consistency of Geological and Reservoir Simulation Models.Presented at the AAPG Annual Convention and Exhibition, Long Beach, California,USA, 1-4 April.