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Skip to Search Results- 5Ensemble Kalman Filter
- 2EnKF
- 2History Matching
- 1Artificial Neural Networks
- 1Assisted History Matching
- 1Characterisation
- 1Deutsch, Clayton (Civil and Environmental Engineering)
- 1Leung, Juliana (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
- 1Trivedi, Japan (Civil and Environmental Engineering)
- 1Trivedi, Japan (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
- 1Trivedi, Japan (Civil and Environmental)
- 1Trivedi, Japan (Department of Civil and Environmental Engineering)
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Advanced Closed-Loop Reservoir Management for Computationally Efficient Data Assimilation and Real-Time Production Optimization of SAGD Reservoirs
DownloadSpring 2018
Steam-assisted gravity drainage (SAGD), an in-situ thermal oil recovery method is successfully utilized to extract bitumen from the Canadian oil sands. To improve the reservoir performance, an idea of closed-loop reservoir management (CLRM) was proposed that comprises near-continuous data...
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Applications of Ensemble Kalman Filter for characterization and history matching of SAGD reservoirs
DownloadFall 2011
Steam-assisted gravity drainage (SAGD) is the most robust thermal recovery process that has unlocked western Canadian heavy oil and bitumen reserves into economical recovery. The prime challenges in SAGD heavy oil developments and well planning in the Northern Alberta formations are:...
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Constraining 3D Petroleum Reservoir Models to Petrophysical Data, Local Temperature Observations, and Gridded Seismic Attributes with the Ensemble Kalman Filter (EnKF)
DownloadSpring 2012
A methodology based on the Ensemble Kalman Filter (EnKF) is proposed for petroleum reservoir characterization, using continuous integration of petrophysical core data, reservoir temperature observations, and gridded time-lapse acoustic impedances. The localization of updating and covariance...
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Spring 2017
In reservoir simulation studies, history matching is extensively used for uncertainty reduction and reservoir management. History matching using Ensemble Kalman Filter (EnKF) is a promising approach due to its non-iterative nature and ability to assimilate a large number of model parameters....
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Re-Sampling the Ensemble Kalman Filter for Improved History Matching and Characterizations of Non-Gaussian and Non-Linear Reservoir Models
DownloadSpring 2015
Reservoir simulation models play an important role in the production forecasting and field development planning. To enhance their predictive capabilities and capture the uncertainties in model parameters, stochastic reservoir models should be calibrated to both geologic and flow observations. The...