科研之友
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摘要
Identification of reservoir connectivity is critical for reliable production predictions and field-development planning. Field-scale connectivity is particularly important at early stages when costly development decisions are made. However, in developing fields, knowledge about reservoir flow-property distribution is subject to significant uncertainty. In addition, initial measurements of the dynamic response of the reservoir are too limited to resolve reservoir properties at a high-enough resolution. Therefore, reservoir identification problems must account for the limited data resolution and significant geologic uncertainty, and emphasize the importance of field-scale reservoir connectivity estimation. Under such conditions, parameterization of reservoir properties should primarily describe the large-scale flow connectivity. Parameterization techniques that are derived from prior information, such as the principal component analysis (PCA) or Karhunen-Loeve transform (KLT), can be biased by errors in the prior knowledge, whereas prior-independent methods such as the Wavelet or Fourier-based image-compression techniques are robust but do not take advantage of prior knowledge. We propose an effective approach for describing reservoir continuity by combining prior-dependent and prior-independent parameterizations to form a hybrid technique that possesses the advantages of both methods. We introduce a robust hybrid parameterization approach that is less sensitive to possible errors in the prior model and yet quite effective in reproducing geologic features if the prior knowledge is reliable. We apply the new method with conventional parameter reduction and sparse history-matching methods and show that the proposed method can identify reservoir continuity from available dynamic data under both correct and incorrect prior knowledge. In identification of reservoir continuity from limited (low-resolution) available data (particularly at early stages of development), accounting for geologic uncertainty becomes imperative. Hybrid parameterization offers a robust parameterization option that incorporates the prior knowledge about reservoir connectivity when it is reliable and reduces the degrading effect of prior information when the prior model is incorrect.
- 出版日期2014-6
