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مشخصه سازی بهنگام مخازن نفتی با استفاده از همجوشی داده های تولید
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مشخصه سازی بهنگام مخازن نفتی با استفاده از همجوشی داده های تولید

بی نیاز دلیجانی، ابراهیم Biniaz Delijani, Ebrahim

Real-time oil Reservoir Characterization by Assimilation of Production Data

Biniaz Delijani, Ebrahim | 2014

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 45781 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Pishvaie, Mahmoud Reza; Bozorgmehry Boozarjomehry, Ramin
  7. Abstract:
  8. Hydrocarbon reservoirs development and management is based on their dynamic models. To encounter various types of error during model building, model parameters are adjusted to produce reservoir historical data by assimilation (history matching) of reservoir production or 4D seismic data. Among the existing sequential methods for automatic history matching, ensemble Kalman filter and its variants have displayed promising results. The innovations of this thesis for ensemble Kalman filter (EnKF) are presented into three major orients; these includes adaptive localization/regularization, characterization of original PUNQ test model and characterization of channelized reservoir.
    To mitigate the under-sampling issues in EnKF, several covariance localization and Kalman gain regularization methods are demonstrated on some different nonlinear and heterogeneous examples. Finally in this orient, a new adaptive thresholding localization /regularization with some particular features is presented.
    In the second orient, six different variants of EnKF are compared on characterization of a 3D- 3phase test case known as PUNQ reservoir model. These methods include distance-based localization, bootstrap Kalman gain regularization, universal/adaptive thresholding of covariance and finally adaptive thresholding of Kalman gain.
    In the third orient, characterization of channelized reservoir is investigated. Existing of multi-modal properties in this reservoir violate the Gaussian assumption within EnKF which results in incorrect updating during assimilation cycles. In this section, applicability of several localization/regularization methods for estimation of uncertain and non- Gaussian parameters of such reservoirs is examined.
  9. Keywords:
  10. Ensemble Kalman Filter ; Data Assimilation ; Automatic History Matching ; Covariance Localization ; Kalman Gain Regularization ; PUNQ Reservoir Model ; Channelized Reservoirs

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