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مشخصه سازی و تخمین پارامترهای مخازن نفتی با استفاده ار الگوریتم فیلتر کالمن تجمیعی
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مشخصه سازی و تخمین پارامترهای مخازن نفتی با استفاده ار الگوریتم فیلتر کالمن تجمیعی

ابراهیم خانی، محمد جواد Ebrahimkhani, Mohammad Javad

Reservoir Characterization and Parameter Estimation Using Ensemble Kalman Filter

Ebrahimkhani, Mohammad Javad | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41624 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Pishvaei, Mahmood Reza; Bozorgmehri Boozarjmehri, Ramin
  7. Abstract:
  8. Management decisions, enhanced oil recovery, and reservoir development plans in petroleum industries are based on predictions by reservoir simulation. Due to uncertainties in model parameters or engineering assumptions, the simulation results are not accurate, while they are correct. For more accurate estimation of unknown production quantities, it is required to characterize the unknown parameters and its uncertainty. By using static data alone the result of characterization is unreliable and unsure, therefore dynamic data use practically. In reservoir engineering literature, this is called “History Matching”.The ensemble Kalman filter is an optimal recursive data processing algorithm based on Monte Carlo integration approach that assimilates parameters with measured data. This filter is as an optimal estimator of states or parameters which can overcome uncertainties of measured data, this property of Kalman filter is particularly effective in estimation of states or parameters in reservoir characterization. The filter procedure for integration consists of two steps: a forecast step and an update step. The two-step procedure is repeated at each measurement time till the last measurements are assimilated into model.In this study EnKF have been implemented for characterizing a 2-dimensional 2-phase flow with multiple injection-production wells in a synthetic reservoir. Results shown that this method can estimate permeability distribution and reduce its uncertainties. Also sensitivity analysis of some parameters of Kalman filter and derivative usage effects of measurement data has been done. One of the other applications of ensemble kalman filter is implementation of it in interpretation of well-test data. Results from this study reveal strong ability of this filter to extract information from noisy data for estimation of permeability and skin factor.
  9. Keywords:
  10. Water Flooding ; Reservoir Rock Characterization ; Ensemble Kalman Filter ; History Matching ; Data Assimilation ; Well Test Data Interpretation

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