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Re-Sampling the Ensemble Kalman Filter for Improved History Matching and Characterizations of Non-Gaussian and Non-Linear Reservoir Models Open Access


Other title
History Matching
Ensemble Kalman Filter
discrete fracture network
Type of item
Degree grantor
University of Alberta
Author or creator
nejadi, siavash
Supervisor and department
Trivedi, Japan (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
Leung, Juliana (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
Examining committee member and department
Gates, Ian Donald (University of Calgary, Department of Chemical and Petroleum Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Okuno, Ryosuke (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
Deutsch, Clayton (Civil and Environmental Engineering, School of Mining and Petroleum Engineering)
Department of Civil and Environmental Engineering
Petroleum Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
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 relationship between production performance and model parameters is vastly non-linear, rendering history matching process a challenging task. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo based technique for assisted history matching and real-time updating of reservoir models. EnKF works efficiently with Gaussian variables, but it often fails to honor the reference probability distribution of the model parameters where the distribution of model parameters are non-Gaussian and the system dynamics are strongly nonlinear. In this thesis, novel sampling procedures are proposed to honor geologic information in reservoirs with non-Gaussian model parameters. The methodologies include generating multiple geological models and updating the uncertain parameters using dynamic flow responses using iterative EnKF technique. Two new re-sampling steps are presented for characterization of multiple facies reservoirs. After certain number of assimilation steps, the updated ensemble is used to generate a new ensemble that is conditional to both the geological information and the early production data. Probability field simulation and a novel probability weighted re-sampling scheme are introduce to re-sample a new ensemble. After the re-sampling step, iterative EnKF is again applied on the ensemble members to assimilate the remaining production history. A new automated dynamic data integration workflow is implemented for characterization and uncertainty assessment of fracture reservoir models. This new methodology includes generating multiple discrete fracture network (DFN) models, upscaling the models for flow simulation, and updating the DFN model parameters using dynamic flow responses. The assisted history matching algorithm entails combining a probability weighted sampling with iterative EnKF. The performances of the introduced methodologies are evaluated by performing various simulation studies for different synthetic and field case studies. The qualities of the final matching results are assessed by examining the geological realism of the updated ensemble using the reference probability distribution of the model parameters and computing the predicted dynamic data mismatch.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Nejadi, S., Leung, J., Trivedi, J., 2014. Characterization of Non-Gaussian Geologic Facies Distribution Using Ensemble Kalman Filter with Probability Weighted Re-Sampling. Mathematical Geosciences. DOI:10.1007/s11004-014-9548-8Nejadi, S., Trivedi, J., Leung, J., 2014. Estimation of Facies Boundaries Using Categorical Indicators with P-Field Simulation and Ensemble Kalman Filter (EnKF). Natural Resources Research. DOI:10.1007/s11053-014-9233-0Nejadi, S., Trivedi, J., Leung, J., 2014. History Matching and Uncertainty Quantification of Discrete Fracture Network Models in Fractured Reservoirs. DFNE. Vancouver, British Columbia, Canada, October 19 - 22.Nejadi, S., Leung, J., Trivedi, J., Virues, C.J.J., 2014. Integrated Characterization of Hydraulically Fractured Shale Gas Reservoirs Production History Matching. Paper SPE 171664, Canadian Unconventional Resources Conference. Calgary, Alberta, Canada. 30 September–2 October.

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