科研之友
微信
新浪微博
Facebook
分享链接
摘要
This paper presents the applications of the ensemble-Kalman-filter (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).
- 出版日期2015-5
