Ninth International Geostatistics Congress, Oslo, Norway
June 11 – 15, 2012

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Abstract No.:



Application of EnKF to Constrain Heavy Oil Reservoir Characterization to Temperature and Time Lapse Seismic Data


Y. Zagayevskiy, University of Alberta (CA)
A.H. Hosseini, Husky Energy (CA)
C.V. Deutsch, University of Alberta (CA)


Steam Assisted Gravity Drainage (SAGD) and other thermal recovery methods are increasingly popular in the oil sands of Northern Alberta. A better understanding of reservoir properties leads to more efficient reservoir management and improved oil recovery. Porosity is a key element in reserve estimation. The vertical permeability determines communication between injection and production wells and oil flow in drainage area above the wells. Thus, estimation of porosity and permeability distributions is a crucial task in reservoir characterization. It is deemed that constraint of these distributions to all available data including core measurements results in better quality reservoir models. 4D seismic data is acquired during production. These data are characterized by large spatial coverage and sufficient resolution to detect subsurface features. Seismic attributes such as P- and S-wave velocities and associated acoustic impedances are highly correlated with porosity and, thereby, used for porosity estimation. The changes in acoustic impedance are used to detect the growth of the steam chamber and predict permeability. Surveillance wells provide a large amount of reservoir temperature data. CMG?s thermal flow simulator STARS is utilized to derive temperature. The distributions of temperature and steam chamber are closely related, therefore, these data can be used to further constraint the permeability distribution. The core derived porosity ? permeability relationship is used to calibrate distributions of these variables. In this paper the Ensemble Kalman Filter (EnKF) is proposed to integrate all available data into a reservoir model to constrain porosity and permeability distributions and history match production data. The EnKF is an ensemble based inverse modeling technique of dynamic systems consisting of nonlinear forecast step and linear analysis step. Spatial linear correlation between different variables should exist in order to update distribution of one variable using data of another. Implementation details are explained and results are shown for a number of heavy oil production scenarios. The EnKF is a simple to use, robust and promising modeling technique.




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