- Boolean operators
- This OR that
This AND that
This NOT that
- Must include "This" and "That"
- This That
- Must not include "That"
- This -That
- "This" is optional
- This +That
- Exact phrase "This That"
- "This That"
- (this AND that) OR (that AND other)
- Specifying fields
- publisher:"Publisher Name"
author:(Smith OR Jones)
Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter
- Mario Trani (Delft University of Technology) | Rob Arts (TNO) | Olwijn Leeuwenburgh (TNO)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2012
- Document Type
- Journal Paper
- 159 - 171
- 2012. Society of Petroleum Engineers
- 5.1.9 Four-Dimensional and Four-Component Seismic, 5.1.8 Seismic Modelling, 4.1.5 Processing Equipment, 4.1.2 Separation and Treating, 5.5.8 History Matching, 5.2 Reservoir Fluid Dynamics, 5.4.1 Waterflooding
- 2 in the last 30 days
- 412 since 2007
- Show more detail
Time-lapse seismic data provide information on the dynamics of multiphase reservoir fluid flow in places where no production data from wells are available. This information, in principle, could be used to estimate unknown reservoir properties. However, the amount, resolution, and character of the data have long posed significant challenges for quantitative use in assisted-history matching workflows. Previous studies, therefore, have generally investigated methods for updating single models with reduced parameter-uncertainty space. Recent developments in ensemble-based history-matching methods have shown the feasibility of multimodel history and matching of production data while maintaining a full uncertainty description. Here, we introduce a robust and flexible reparameterization for interpreted fluid fronts or seismic attribute isolines that extends these developments to seismic history matching. The seismic data set is reparameterized, in terms of arrival times, at observed front positions, thereby significantly reducing the number of data while retaining essential information. A simple 1D example is used to introduce the concepts of the approach. A synthetic 3D example, with spatial complexity that is typical for many waterfloods, is examined in detail. History-matching cases based on both separate and combined use of production and seismic data are examined. It is shown that consistent multimodel history matches can be obtained without the need for reduction of the parameter space or for localization of the impact of observations. The quality of forecasts based on the history-matched models is evaluated by simulating both expected production and saturation changes throughout the field for a fixed operating strategy. It is shown that bias and uncertainty in the forecasts of production both at existing wells and in the flooded area are reduced considerably when both production and seismic data are incorporated. The proposed workflow, therefore, enables better decisions on field developments that require optimal placement of infill wells.
Aanonsen, S.I., Nævdal, G., Oliver, D.S. et al. 2009. The Ensemble KalmanFilter in Reservoir Engineering--A Review. SPE J. 14 (3):393-412. http://dx.doi.org/10.2118/117274-PA.
Arenas, E., van Kruijsdijk, C., and Oldenziel, T. 2001. Semi-AutomaticHistory Matching Using the Pilot Point Method Including Time-Lapse SeismicData. Paper SPE-71634 presented at the 2001 SPE Annual Technical Conference andExhibition, New Orleans, Louisiana, 30 September-3 October. http://dx.doi.org/10.2118/71634-MS.
Arroyo, E., Devegowda, D., Datta-Gupta, A. et al. 2008. Streamline-AssistedEnsemble Kalman Filter for Rapid Continuous Reservoir Model Updating. SPERes Eval & Eng 11 (6): 1046-1060. http://dx.doi.org/10.2118/104255-PA.
Arts, R.J., Trani, M., Chadwick, R.A. et al. 2009. Acoustic and ElasticModeling of Seismic Time-Lapse Data from the Sleipner CO2 StorageOperation. In Carbon Dioxide Sequestration in Geological Media—State of theScience, AAPG Studies in Geology, eds. M. Grobe, J.C. Pashin, andR.L. Dodge, Vol. 59, 391-403. Tulsa, Oklahoma: AAPG. http://dx.doi.org/10.1306/13171251St593387.
Burgers, G., van Leeuwen, P.J., and Evensen, G. 1998. Analysis Scheme in theEnsemble Kalman Filter. Mon. Weather Rev. 126: 1719-1724.http://dx.doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
Calvert, R. 2005. Insight and Methods for 4D Reservoir Monitoring andCharacterization. SEG Distinguished Instructor Series 8: 51-83. http://dx.doi.org/10.1190/1.9781560801696.
Chen, Y., Oliver, D.S., and Zhang, D. 2009. Data Assimilation for NonlinearProblems by Ensemble Kalman Filter with Reparameterization. J. Pet. Geosci.Eng. 66 (1-2): 1-14. http://dx.doi.org/10.1190/1.9781560801696.
Chen, Y. and Oliver, D.S. 2010. Cross-Covariances and Localization for EnKFin Multiphase Flow Data Assimilation. Comput. Geosci. 14(4): 579-601. http://dx.doi.org/10.1007/s10596-009-9174-6.
Dadashpour, M., Kleppe, J., and Landrø, M. 2007. Porosity and PermeabilityEstimation by Gradient-Based History Matching Using Time-Lapse Seismic Data.Paper SPE 104519 presented at the SPE Middle East Oil and Gas Show andConference, Kingdom of Bahrain, 11-14 March. http://dx.doi.org/10.2118/104519-MS.
Datta-Gupta, A. and King, M. 2007. Streamline Simulation, Theory andPractice, Richardson, Texas: Society of Petroleum Engineers, TextbookSeries, Vol. 11.
De Waal, H., and Calvert, R. 2003. Overview of Global 4D SeismicImplementation Strategy. Pet. Geosci. 9 (1): 1-6.
Dong, Y., and Oliver, D.S. 2005. Quantitative Use of 4D Seismic Data forReservoir Description. SPE J. 10 (1): 91-99. http://dx.doi.org/10.2118/84571-PA.
Dovera, L. and Della Rossa, E. 2010. Multimodal Ensemble Kalman FilteringUsing Gaussian Mixture Models. Comput. Geosci. 15 (2):307-323. http://dx.doi.org/10.1007/s10596-010-9205-3.
Fahimuddin, A., Aanonsen, S.I., and Skjervheim, J.A. 2010. Ensemble Based 4DSeismic History Matching: Integration of Different Levels and Types of SeismicData. Paper SPE-131453 presented at SPE EUROPEC/EAGE Annual Conference andExhibition, Barcelona, Spain, 14-17 June. http://dx.doi.org/10.2118/131453-MS.
Emerick, A.A., and Reynolds, A.C. 2011. History Matching a Field Case Usingthe Ensemble Kalman Filter with Covariance Localization. SPE Res Eval &Eng 14 (4):423-432. http://dx.doi.org/10.2118/141216-PA.
Fagervik, K., Lygren, M., Valen, T.S. et al. 2001. A Method for PerformingHistory Matching of Reservoir Flow Models Using 4D Seismic. SEG Exp.Abstr. 20: 1636. http://dx.doi.org/10.1190/1.1816429.
Foster, D.G. 2007. The BP 4-D story: Experience Over The Last 10 Years andCurrent Trends. Paper 11757 presented at the International Petroleum TechnologyConference, Dubai, U.A.E., 4-6 December. http://dx.doi.org/10.2523/11757-MS.
Gabriels, P., Horvei, A., Koster, J.K. et al. 1999. Time Lapse SeismicMonitoring of the Draugen Field. SEG Exp. Abstr. 18:2035-2037. http://dx.doi.org/10.1190/1.1820970.
Gosselin, O., van den Berg, S., and Cominelli, A. 2001. IntegratedHistory-Matching of Production and 4D Seismic Data. Paper SPE 71599 presentedat the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana,30 September-3 October. http://dx.doi.org/10.2523/71599-MS.
Gu, Y., and Oliver, D.S. 2006. The Ensemble Kalman Filter for ContinuousUpdating of Reservoir Simulation Models. J. Energy Resour. Technol. 128 (1): 79-87. http://dx.doi.org/10.1115/1.2134735.
Haverl, M.C., Aga, M., and Reiso, E. 2005. Integrated Workflow forQuantitative use of Time-Lapse Seismic Data in History Matching: A North SeaField Case. Paper SPE 94453 presented at SPE Europec/EAGE Annual Conference,Madrid, Spain, 13-16 June. http://dx.doi.org/10.2523/94453-MS.
He, Z., Datta-Gupta, A., and Yoon, S.S. 2001. Streamline-Based ProductionData Integration Under Changing Field Conditions. Paper SPE 71333 presented atthe SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 30September-3 October 2001. http://dx.doi.org/10.2523/71333-MS.
Jin, L., Castineira, D., Fu, S. et al. 2011. 4D Seismic History MatchingUsing Flood Front Information. Paper J040 presented at the EAGE Conference& Exhibition incorporating SPE EUROPEC, Vienna, Austria, 23-26 May2011.
Kretz, V., Valles, B., and Sonneland, L. 2004. Fluid Front History MatchingUsing 4D Seismic and Streamline Simulation. Paper SPE 90136 presented at theSPE Annual Technical Conference and Exhibition, Houston, Texas, 26-29September. http://dx.doi.org/10.2523/90136-MS.
Landrø, M., Veire, H.H., Duffaut, K. et al. 2001. Discrimination BetweenPressure and Fluid Saturation Changes From Time-Lapse Seismic Data.Geophysics 66: 836-844. http://dx.doi.org/10.1190/1.1444973.
Leeuwenburgh, O., Brouwer, J.H., and Trani, M. 2011. Ensemble-BasedConditioning of Reservoir Models to Seismic Data. Comput. Geosci. 15 (2): 359-378. http://dx.doi.org/10.1007/s10596-010-9209-z.
Leguijt, J. 2001. A Promising Approach to Subsurface InformationIntegration. Paper L-35 presented at 63rd Conference and Technical Exhibition,Amsterdam, The Netherlands, 11-15 June 2001.
Leguijt, J. 2009. Seismically Constrained Probabilistic Reservoir Modeling.The Leading Edge 28: 1478-1484. http://dx.doi.org/10.1190/1.3272703.
Lie, K.-A., Krogstad, S., Ligaarden, I.S. et al. 2012. Open Source MATLABImplementation of Consistent Discretisations on Complex Grids. Comput.Geosci. 16 (2): 297-322. http://dx.doi.org/10.1007/s10596-011-9244-4.
Lorentzen, R.J., Flornes, K.M., and Nævdal, G. 2011. History MatchingChannelized Reservoirs Using the Ensemble Kalman Filter. SPE J. 17 (1): 137-151. http://dx.doi.org/10.2118/143188-PA.
Lorentzen, R. J., Nævdal, G., Vallés, B. et al. 2005. Analysis of theEnsemble Kalman Filter for the Estimation of Permeability and Porosity inReservoir Models. Paper SPE 96375 presented at SPE Annual Technical Conference& Exhibition, Dallas, Texas, 9-12 October. http://dx.doi.org/10.2523/96375-MS.
Nævdal, G., Mannseth, T., and Vefring, E. 2002. Instrumented Wells andNear-Well Reservoir Monitoring Through Ensemble Kalman Filter. Paper presentedat the 8th European Conference of the Mathematics of Oil Recovery, Freiberg,Germany, 3-6 September.
Peters, L., R. J. Arts, G. K. Brouwer et al. 2010. Results of the BruggeBenchmark Study For Flooding Optimization And History Matching. SPE Res Eval& Eng. 13: 391-405. http://dx.doi.org/10.2118/119094-PA.
Peters, E. 2011. Performance of the Ensemble Kalman Filter Outside ofExisting Wells for a Channelized Reservoir. Comput. Geosci. 15(2): 345-358. http://dx.doi.org/10.1007/s10596-010-9215-1.
Phale, H.A., and Oliver, D.S. 2011. Data Assimilation Using the ConstrainedEnsemble Kalman Filter. SPE J. 16 (2): 331-342. http://dx.doi.org/10.2118/125101-PA.
Portella, R.C.M., and Emerick, A.A. 2005. Use of Quantitative 4D-SeismicData in Automatic History Match. Paper SPE 94650 presented at the SPE LatinAmerican and Caribbean Petroleum Engineering Conference, Rio de Janeiro,Brazil, 20-23 June. http://dx.doi.org/10.2523/94650-MS.
Roggero, F., Ding, D.Y., Berthet, P. et al. 2007. Matching of ProductionHistory and 4D Seismic Data—Application to the Girassol Field. Paper SPE 109929presented at SPE Annual Technical Conference and Exhibition, Anaheim,California, 11-14 November. http://dx.doi.org/10.2523/109929-MS.
Skjervheim, J.A., Evensen, G., Aanonsen, S.I. et al. 2007. Incorporating 4DSeismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter.SPE J. 12 (3): 282-292. http://dx.doi.org/10.2118/95789-PA.
Stenerud, V.R. 2007. Multiscale-Streamline Inversion for High-ResolutionReservoir Models, PhD dissertation, Norwegian University of Science andTechnology, Trondheim, Norway.
Stephen, K.D., Soldo, J., MacBeth, C. et al. 2006. Multiple-Model Seismicand Production History Matching: A Case Study. SPE J. 11(4): 418-430. http://dx.doi.org/10.2118/94173-PA.
Thiele, M.R., Batycky, R.P., and Fenwick, D.H. 2010. Streamline Simulationfor Modern Reservoir-Engineering Workflows. J Pet Technol. 62(1): 64-70. http://dx.doi.org/10.2118/118608-MS.
Thiele, M.R., Fenwick, D.H., and Batycky, R.P., 2007. Streamline-AssistedHistory Matching. Paper presented at the 9th International Forum on ReservoirSimulation, Abu Dhabi, United Arab Emirates, 9-13 December.
Thulin, K., Nævdal, G., Skaug, H.J. et al. 2011. Quantifying Monte CarloUncertainty in the Ensemble Kalman Filter. SPE J. 16 (1):172-182. http://dx.doi.org/10.2118/123611-PA.
Trani, M., Arts, R., Leeuwenburgh, O. et al. 2011. Estimation of Changes inSaturation and Pressure From 4D Seismic AVO and Time-Shift Analysis.Geophysics 76: 1-17. http://dx.doi.org/10.1190/1.3549756.
van Ditzhuijzen, R, Oldenziel, T., and van Kruijsdijk, C.P.J.W. 2001.Geological Parameterization of a Reservoir Model for History MatchingIncorporating Time-Lapse Seismic Based on a Case Study of the Stafjord Field.Paper SPE 71318 presented at the SPE Annual Technical Conference andExhibition, New Orleans, Louisiana, 30 September-3 October. http://dx.doi.org/10.2118/71318-MS.
Wang, Y., Li, G., and Reynolds, A.C. 2010. Estimation of Depths of FluidContacts by History Matching Using Iterative Ensemble-Kalman Smoothers. SPEJ. 15 (2): 509-525. http://dx.doi.org/10.2118/119056-PA.
Wu, Z., and Datta-Gupta, A. 2002. Rapid History Matching Using a GeneralizedTravel-Time Inversion Method. SPE J. 7 (2): 113-222. http://dx.doi.org/10.2118/78359-PA.
Zhao, Y., Reynolds, A.C., and Li, G. 2008. Generating Facies Maps byAssimilating Production Data and Seismic Data with the Ensemble Kalman Filter,SPE Improved Oil Recovery Symposium. http://dx.doi.org/10.2118/113990-MS.
Not finding what you're looking for? Some of the OnePetro partner societies have developed subject- specific wikis that may help.
The SEG Wiki
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.