- 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)
Hybrid Parameterization for Robust History Matching
- Mohammadreza M. Khaninezhad | Behnam Jafarpour
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2014
- Document Type
- Journal Paper
- 487 - 499
- 2013. Society of Petroleum Engineers
- 5 in the last 30 days
- 257 since 2007
- Show more detail
Identification of reservoir connectivity is critical for reliable productionpredictions and field-development planning. Field-scale connectivity isparticularly important at early stages when costly development decisions aremade. However, in developing fields, knowledge about reservoir flow-propertydistribution is subject to significant uncertainty. In addition, initialmeasurements of the dynamic response of the reservoir are too limited toresolve reservoir properties at high-enough resolution. Therefore, reservoiridentification problems must account for the limited data resolution andsignificant geologic uncertainty, and emphasize the importance of field-scalereservoir connectivity estimation. Under such conditions, parameterization ofreservoir properties should primarily describe the large-scale flowconnectivity. Parameterization techniques that are derived from priorinformation, such as the principle component analysis (PCA) or Karhunen-Loevetransform (KLT), can be biased by errors in the prior knowledge, whereasprior-independent methods such as the Wavelet or Fourier-basedimage-compression techniques are robust but do not take advantage of priorknowledge. We propose an effective approach for describing reservoir continuityby combining prior-dependent and prior-independent parameterizations to form ahybrid technique that possesses the advantages of both methods. We introduce arobust hybrid parameterization approach that is less sensitive to possibleerrors in the prior model and yet quite effective in reproducing geologicfeatures if the prior knowledge is reliable. We apply the new method withconventional parameter reduction and sparse history-matching methods and showthat the proposed method can identify reservoir continuity from availabledynamic data under both correct and incorrect prior knowledge. Inidentification of reservoir continuity from limited (low-resolution) availabledata (particularly at early stages of development), accounting for geologicuncertainty becomes imperative. Hybrid parameterization offers a robustparameterization option that incorporates the prior knowledge about reservoirconnectivity when it is reliable and reduces the degrading effect of priorinformation when the prior model is incorrect.
Aziz, K. and Settari, A. 1979. Petroleum Reservoir Simulation. NewYork: Springer.
Bhark, E., Jafarpour, B. and Datta-Gupta, A. 2011a. An Adaptively ScaledFrequency-Domain Parameterization for History Matching. J. Pet. Sci.Eng. 75 (3-4): 289-303. http://dx.doi.org/10.1016/j.petrol.2010.11.026.
Bhark, E., Jafarpour, B. and Datta-Gupta, A. 2011b. A GeneralizedGrid-Connectivity-Based Parameterization for Subsurface Flow Data Assimilation.Water Resour. Res. 47 (6). http://dx.doi.org/10.1029/2010WR009982.
Britanak, V., Yip, P.C. and Rao, K.R. 2007. Discrete Cosine and SineTransforms: General Properties, Fast Algorithms and Integer Approximations.Burlington, Massachusetts: Academic Press.
Carrera, J., Alcolea, A., Medina, A., et al. 2005. Inverse Problem inHydrogeology. Hydrogeol. J. 13 (1): 206-222. http://dx.doi.org/10.1007/s10040-004-0404-7.
Chavent, G. and Bissell, R. 1998. Indicators for the Refinement ofParametrization. In Inverse Problems in Engineering Mechanics, eds. M.Tanaka and G.S. Dulikravich, 309-314. Oxford, UK: Elsevier Science Ltd.
Chen, S.S., Donoho, D.L. and Saunders, M.A. 1998. Atomic Decomposition byBasis Pursuit. SIAM J. Sci. Comput. 20 (1): 33-61. http://dx.doi.org/10.1137/S1064827596304010.
Donoho, D.L. 2006. Compressed Sensing. IEEE Trans. Inform. Theory. 52 (4): 1289-1306. http://dx.doi.org/10.1109/TIT.2006.871582.
Elad, M. 2010. Sparse and Redundant Representations: From Theory toApplication in Signal and Image Processing. New York City, New York:Springer.
Feng, T. and Mannseth, T. 2009. Improvements on a Predictor-CorrectorStrategy for Parameter Estimation with Several Data Types. InverseProbl. 25 (10): 1-21. http://dx.doi.org/10.1088/0266-5611/25/10/105012.
Gavalas, G.R., Shah, P.C. and Seinfeld, J.H. 1976. Reservoir HistoryMatching by Bayesian Estimation. SPE J. 16 (6): 337-350. http://dx.doi.org/10.2118/5740-PA.
Grimstad, A.-A., Mannseth, T., Aanonsen, S. I., et al. 2004. Identificationof Unknown Permeability Trends From History Matching of Production Data. SPEJ. 9 (4): 419-428. http://dx.doi.org/10.2118/77485-PA.
Hill, M.C. and Tiedeman, C.R. 2007. Effective Groundwater ModelCalibration: With Analysis of Data, Sensitivities, Predictions, andUncertainty. Hoboken, New Jersey: John Wiley & Sons, Inc.
Jafarpour, B. 2010. Wavelet Reconstruction of Geologic Facies from NonlinearDynamic Flow Measurements. IEEE T. Geosci. Remote 49 (5):1520-1535. http://dx.doi.org/10.1109/TGRS.2010.2089464.
Jafarpour, B. and McLaughlin, D. 2009. Reservoir Characterization With theDiscrete Cosine Transform. SPE J. 14 (1): 181-202. http://dx.doi.org/10.2118/106453-PA.
Jafarpour, B., Goyal, V.K., McLaughlin, D.B., et al. 2010. CompressedHistory Matching: Exploiting Transform-Domain Sparsity for Regularization ofNonlinear Dynamic Data Integration Problems. Math. Geosci. 42(1): 1-27. http://dx.doi.org/10.1007/s11004-009-9247-z.
Jahns, O. J. 1966. A Rapid Method for Obtaining a Two-Dimensional ReservoirDescription From Well Pressure Response Data. SPE J. 6 (4):315-327. http://dx.doi.org/10.2118/1473-PA.
Jain, A.K. 1989. Fundamentals of Digital Image Processing. EnglewoodCliffs, New Jersey: Prentice Hall.
Khaninezhad, M. R. M., Jafarpour, B. and Li, L. 2012a. Sparse GeologicalDictionaries for Subsurface Flow Model Calibration: Part I. InversionFormulation. Adv. Water Resour. 39 (April): 106-121. http://dx.doi.org/10.1016/j.advwatres.2011.09.002.
Khaninezhad, M. R. M., Jafarpour, B. and Li, L. 2012b. Sparse GeologicalDictionaries for Subsurface Flow Model Calibration: Part II. Robustness toUncertainty. Adv. Water Resour. 39 (April): 122-136. http://dx.doi.org/10.1016/j.advwatres.2011.10.005.
Last, B.J. and Kubik, K. 1983. Compact Gravity Inversion. Geophysics48 (6): 713-721. http://dx.doi.org/10.1190/1.1441501.
Li, L., and Jafarpour, B. 2010. Effective Solution of Nonlinear SubsurfaceFlow Inverse Problems in Sparse Bases. Inverse Probl. 26(10). http://dx.doi.org/10.1088/0266-5611/26/10/105016.
Lu, P.B. and Horne, R.N. 2000. A Multiresolution Approach to ReservoirParameter Estimation Using Wavelet Analysis. Paper SPE 62985 presented at theSPE Annual Technical Conference and Exhibition, Dallas, 1-4 October. http://dx.doi.org/10.2118/62985-MS.
Moore, C. and Doherty, J. 2005. Role of the Calibration Process in ReducingModel Prediction Error. Water Resour. Res. 41 (5). http://dx.doi.org/10.129/2004WR003501.
Natarajan, B.K. 1995. Sparse Approximate Solutions to Linear Systems.SIAM J. Comput. 24 (2): 227-234. http://dx.doi.org/10.1137/S0097539792240406.
Oliver, D.S. and Chen, Y. 2011. Recent Progress on Reservoir HistoryMatching: A Review. Computat. Geosci. 15 (1): 185-221. http://dx.doi.org/10.1007/s10596-010-9194-2.
Oliver, D.S., Reynolds, A. and Liu, N. 2008. Inverse Theory for PetroleumReservoir Characterization and History Matching. Cambridge, UK: CambridgeUniversity Press.
Peaceman, D.W. and Rachford, H.H. 1955. The Numerical Solution of Parabolicand Elliptic Differential Equations. J. Soc. Ind. Appl. Math. 3 (1): 28-41. http://www.jstor.org.proxyau.wrlc.org/stable/2098834.
Portniaguine, O. and Zhdanov, M. 1999. Focusing Geophysical InversionImages. Geophysics 64 (3): 874-887. http://dx.doi.org/10.1190/1.1444596.
Reynolds, A.C., He, N., Chu, L., et al. 1996. Reparameterization Techniquesfor Generating Reservoir Descriptions Conditioned to Variograms and Well-TestPressure Data. SPE J. 1 (4): 413-426. http://dx.doi.org/10.2118/30588-PA.
Sahni, I. and Horne R. 2005. Multiresolution Wavelet Analysis for ImprovedReservoir Description. SPE Res Eval & Eng 8 (1): 53-69. http://dx.doi.org/10.2118/87820-PA.
Sarma, P., Durlofsky, L.J. and Aziz, K. 2008. Kernel Principal ComponentAnalysis for Efficient, Differentiable Parameterization of MultipointGeostatistics. Math. Geosci. 40 (1): 3-32. http://dx.doi.org/10.1007/s11004-007-9131-7.
Tonkin, M. and Doherty, J. 2009. Calibration-Constrained Monte CarloAnalysis of Highly Parameterized Models Using Subspace Techniques. WaterResour. Res. 45 (12). http://dx.doi.org/10.1029/2007WR006678.
Yeh, T., Lee, C., Hsu, K., et al. 2007. Fusion of Active and PassiveHydrologic and Geophysical Tomographic Surveys: The Future of SubsurfaceCharacterization. In Subsurface Hydrology: Data Integration for Propertiesand Processes, eds. D. W. Hyndman, F. D. Day-Lewis, and K.Singha, Vol. 170, 109-120. Washington, D.C.: Geophysical Monograph Series,American Geophysical Union.
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.