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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
- 7 in the last 30 days
- 273 since 2007
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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.
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