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Efficient Ensemble-Based Closed-Loop Production Optimization
- Yan Chen (Chevron ETC) | Dean S. Oliver (University of Oklahoma) | Dongxiao Zhang (University of Southern California)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2009
- Document Type
- Journal Paper
- 634 - 645
- 2009. Society of Petroleum Engineers
- 5.4.1 Waterflooding, 4.3.4 Scale, 5.5.8 History Matching, 4.1.2 Separation and Treating, 1.7.5 Well Control, 6.5.2 Water use, produced water discharge and disposal, 4.1.5 Processing Equipment, 2.3 Completion Monitoring Systems/Intelligent Wells, 5.1 Reservoir Characterisation, 5.6.11 Reservoir monitoring with permanent sensors, 5.5 Reservoir Simulation, 5.2 Reservoir Fluid Dynamics, 2.4.3 Sand/Solids Control, 5.1.5 Geologic Modeling, 3.3 Well & Reservoir Surveillance and Monitoring
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With the advances in smart well technology, substantially higher oil recovery can be achieved by intelligently managing the operations in a closed-loop optimization framework. The closed-loop optimization consists of two parts: geological model updating and production optimization. Both of these parts require gradient information to minimize or maximize an objective function: squared data mismatch or the net present value (or other quantities depending on financial goals), respectively. Alternatively, an ensemble-based method can acquire the gradient information through the correlations provided by the ensemble. Computation of the optimal controls in this way is nearly independent of the number of control variables, reservoir simulator and simulation solver. In this paper, we propose an ensemble-based closed-loop optimization method that combines a novel ensemble-based optimization scheme (EnOpt) with the ensemble Kalman filter (EnKF). EnKF has recently been found suitable for sequential data assimilation in large-scale nonlinear dynamics. It adjusts reservoir model variables to honor observations and propagates uncertainty in time. EnOpt optimizes the expectation of the net present value based on the updated reservoir models. The proposed method is fairly robust, completely adjoint-free and can be readily used with any reservoir simulator. The ensemble-based closed-loop optimization method is illustrated with a waterflood example subject to uncertain reservoir description. Results are compared with other possible reservoir operation scenarios, such as wells with no controls, reactive control, and optimization with known geology. The comparison shows that the ensemble-based closed-loop optimization is able to history match the main geological features and increase the net present value to a level comparable with the hypothetical case of optimizing based on known geology.
Agbalaka, C. and Oliver, D.S. 2008. Application of the EnKF and localizationto automatic history matching of facies distribution and production data.Mathematical Geosciences 40 (4): 353-374.
Arroyo-Negrete, E., Devegowda, D., Datta-Gupta, A., and Choe, J. 2008. Streamline-Assisted Ensemble KalmanFilter for Rapid and Continuous Reservoir Model Updating. SPE Res Eval& Eng 11 (6): 1046-1060. SPE-104255-PA. doi:10.2118/104255-PA.
Bertino, L., Evensen, G., and Wackernagel, H. 2003. Sequential DataAssimilation Techniques in Oceanography. International StatisticalReview 71 (2): 223-241.
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.
Burgers, G., van Leeuwen, P.J., and Evensen, G. 1998. AnalysisScheme in the Ensemble Kalman Filter. Monthly Weather Review 126 (6): 1719-1724.doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
Chen, Y. and Zhang, D. 2006. Data assimilation fortransient flow in geologic formations via ensemble Kalman filter.Advances in Water Resources 29 (8): 1107-1122.doi:10.1016/j.advwatres.2005.09.007.
Chen, Y., Oliver, D.S., and Zhang, D. 2009. Data assimilation for nonlinearproblems by ensemble Kalman filter with reparameterization. J. Pet. Sci.Eng. 66 (1-2), 1-14.
Evensen, G. 1994. Sequential data assimilation with a nonlinearquasi-geostrophic model using Monte Carlo methods to forecast error statistics.J. of Geophysical Research 99 (C5): 10143-10162.
Evensen, G. 2003. TheEnsemble Kalman Filter: Theoretical formulation and practicalimplementation. Ocean Dynamics 53 (4): 343-367. doi:10.1007/s10236-003-0036-9.
Foss, B.A. 1990. Optimal inputdesign--a method for enhancing data quality. SPE Form Eval 5 (3): 303-309. SPE-13402-PA. doi: 10.2118/18402-PA.
Gu, Y. 2006. History Matching Production Data Using the Ensemble KalmanFilter. PhD dissertation, University of Oklahoma, Norman, Oklahoma (October2006).
Gu, Y. and Oliver, D.S. 2005. History Matching of the PUNQ-S3Reservoir Model Using the Ensemble Kalman Filter. SPE J. 10 (2): 217-224. SPE-89942-PA. doi: 10.2118/89942-PA.
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.
Hamill, T.M., Whitaker, J.S., and Snyder, C. 2001. Distance-dependentfiltering of background error covariance estimate in an ensemble Kalmanfilter. Monthly Weather Review 129 (11): 2776-2790.doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.
Haugen, V., Nævdal, G., Natvik, L.-J., Evensen, G., Berg, A.M., and Flornes,K.M. 2008. History Matching Usingthe Ensemble Kalman Filter on a North Sea Field Case. SPE J. 13 (4): 382-391. SPE-102430-PA. doi: 10.2118/102430-PA.
Houtekamer, P.L. and Mitchell, H.L. 2001. Asequential ensemble Kalman filter for atmospheric data assimilation.Monthly Weather Review 129 (1): 123-137. doi:10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2.
Li, G. and Reynolds, A.C. 2007. An Iterative Ensemble Kalman Filterfor Data Assimilation. Paper SPE 109808 presented at the SPE AnnualTechnical Conference and Exhibition, Anaheim, California, USA, 11-14 November.doi: 10.2118/109808-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.
Liu, G., Chen, Y., and Zhang, D. 2008. Investigation of flow and transportprocesses at the MADE site using ensemble Kalman filter. Advances in WaterResources 31 (7): 975-986.
Liu, N. and Oliver, D.S. 2006. Ensemble Kalman filterfor automatic history matching of geologic facies. J. Pet. Sci. Eng. 47 (3-4): 147-161. doi: 10.1016/j.petrol.2005.03.006.
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.
Mitchell, H.L., Houtekamer, P.L., and Pellerin, G. 2002. EnsembleSize, Balance, and Model-Error Representation in an Ensemble Kalman Filter.Monthly Weather Review 130 (11): 2791-2808-433, 2002.doi:10.1175/1520-0493(2002)130<2791:ESBAME>2.0.CO;2.
Nævdal, G., Brower, D.R., and Jansen, J.-D. 2006. Waterflooding usingclosed-loop control. Computational Geosciences 10 (1):37-60. doi:10.1007/s10596-005-9010-6.
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.
Nwaozo, J. 2006. Dynamic optimization of a water flood reservoir. MS thesis,University of Oklahoma, Norman, Oklahoma.
Oliver, D.S. 1995. Movingaverages for Gaussian simulation in two and three dimensions.Mathematical Geology 27 (8): 939-960.doi:10.1007/BF02091660.
Oliver, D.S., Reynolds, A.C., and Liu, N. 2008. Inverse Theory forPetroleum Reservoir Characterization and History Matching, first edition.Cambridge: Cambridge University Press.
Sarma, P., Aziz, K., and Durlofsky, L.J. 2005a. 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., and Aziz, K. 2005b. Efficient Closed-Loop ProductionOptimization Under Uncertainty. Paper SPE 94241 presented at the SPEEuropec/EAGE Annual Conference, Madrid, Spain, 13-16 June. doi:10.2118/94241-MS.
Tarantola, A. 2005. Inverse Problem Theory and Methods for ModelParameter Estimation. Philadelphia, Pennsylvania: SIAM.
van Doren, J.F.M., van den Hof, P.M.J., Jansen, J.D., and Bosgra, O.H. 2008.Determining Identifiable Parameterizations for Large-scale Physical Models inReservoir Engineering. Proc., 17th Int. Fed. Autom. Control (IFAC) WorldCongress, Seoul, Korea, 6-11 July.
van Essen, G.M., Zandvliet, M.J., van den Hof, P.M.J., Bosgra, O.H., andJansen, J.D. 2009. RobustWaterflooding Optimization of Multiple Geological Scenario. SPE J. 14 (1): 202-210. SPE-102913-PA. doi: 10.2118/102913-PA.
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.
Wittenmark, B. 1995. Adaptive Dual Control Methods: An Overview.Proc., 5th IFAC Symposium on Adaptive Systems in Control and SignalProcessing, Budapest, Hungary, 14-16 June, 67-73.
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The SEG Wiki is a useful collection of information for working geophysicists, educators, and students in the field of geophysics. The initial content has been derived from : Robert E. Sheriff's Encyclopedic Dictionary of Applied Geophysics, fourth edition.