Assimilation of Time-Lapse Temperature Observations and 4D-Seismic Data With the EnKF in SAGD Petroleum Reservoirs

Abstract

This paper presents the applications of the ensemble-Kalmanfilter (EnKF) inverse-modelling technique to petroleum-reservoir characterization of thermally operated oil fields in northern Alberta, Canada. The EnKF is applied to 2D-and 3D-case studies based on the steam-assisted-gravity-drainage heavy-oil-extraction method. The modelling technique integrates effectively both static and dynamic data (petrophysical core data from wellbores, continuous temperature data measured by thermocouples, and 4D-seismic attributes) into a petroleum-reservoir model. Assimilated secondary information provides better insight into geologic properties of a reservoir and improves production forecasting. The method performs well for linear or slightly nonlinear systems that follow a Gaussian distribution, but shows worse performance for nonlinear or non-Gaussian systems. Integration of a large amount of data with a small number of realizations leads to ensemble collapse because of insufficient degrees of freedom. Increasing the ensemble size is a solution, but by increasing forecasting time. To overcome these issues and reduce computational time, matrix localization techniques and a shortcut based on replacement of the model realizations with their mean in forecasting are suggested. Even though the EnKF has been proved to be simple in implementation and effective for modelling of continuous linear systems, special care should be taken for modelling nonlinear systems, including categorical variables (e.g., geologic facies). IntroductionWhile developing a petroleum reservoir, one seeks responsible management and profit maximization. A clear understanding of petroleum-reservoir geology helps to select appropriate technology for oil extraction and place production/injection wells and surface facilities effectively. Modelling of the petroleum system in conjunction with its static and dynamic components in the form of petrophysical properties and time variants of physical conditions of a reservoir leads to efficient field development. This conceptual approach is vital in northern Alberta, where large portions of the heavy-oil and bituminous reservoirs are developed by the steam-assisted-gravity-drainage (SAGD) oil-extraction method.To accurately describe behaviour of the reservoir and predict future performance, various computational tools are available for petroleum-reservoir characterization. Most of them use a concept of data assimilation, in which available data are integrated into the model by minimizing the difference between the simulated data of the current model estimate and the actual data at observation locations. While both static and dynamic data are being assimilated into the model to improve its quality, spatial structure and physical principles embedded in the model should be preserved (Oliver et al. 2008).Reservoir characterization is an inverse problem, where limited data are used to infer the entire spatial configuration of the modelling system (Oliver et al. 2008). A solution to the inverse problem is not unique, which me...