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Data-Driven Proxy Models for Assisted History Matching of SAGD Reservoirs Open Access


Other title
History Matching
SAGD Reservoirs
Karhunen Loeve Expansion
Proxy Models
Artificial Neural Networks
Ensemble Kalman Filter
Polynomial Chaos Expansion
Type of item
Degree grantor
University of Alberta
Author or creator
Jain, Tarang
Supervisor and department
Trivedi, Japan (Civil and Environmental)
Examining committee member and department
Trivedi, Japan (Civil and Environmental)
Li, Huazhou (Civil and Environmental)
Prasad, Vinay (Chemical and Materials)
Department of Civil and Environmental Engineering
Petroleum Engineering
Date accepted
Graduation date
2017-06:Spring 2017
Master of Science
Degree level
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. However, in processing a large set of realizations, this method suffers from high computational time and cost associated with the use of commercial reservoir simulators. Therefore, there is a scope for some improvement in this approach especially in the case of complex thermal recovery process such as steam assisted gravity drainage (SAGD). In this work, the computational cost is reduced significantly by developing proxy models that can substitute the need of reservoir simulator during the assisted history matching process. Different proxy models such as Polynomial Chaos Expansion (PCE) and Artificial Neural Networks (ANN) are tested to represent the outputs of the conventional reservoir simulator. Permeability realizations of the SAGD reservoir are first parameterized using Karhunen- Loeve (KL) series expansion and represented in the form of uncorrelated random variables. The developed proxy models utilize random variables obtained from KL expansion as input parameters. Proxy models are further integrated with EnKF algorithm as a substitute for reservoir simulator. Computational requirement of the proxy model during the development as well as deployment as compared to commercial reservoir simulator is emphasized in this study. The proposed approach is validated using a field-scale SAGD case study of northern Alberta. The observed daily oil rate, cumulative oil production, and cumulative steam to oil ratio are history matched using the proposed method. Results show that as compared to conventional EnKF, the integration of data-driven proxy models can perform assisted history matching in quick, low-cost manner while maintaining the accuracy of results. This work has a potential to cut down the monetary and time constraints during the assisted history matching process.
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