In a reservoir characterization perspective it is important to introduce a consistent parameterization to make an improvement of the reservoir model and provide more reliable predictions of the future production. In this paper we present a methodology for history matching and uncertainty quantification of reservoir simulation models using the Ensemble Kalman filter (EnKF). In the updating sequence, we propose a method for estimating coarse-scale relative permeability curves, based on a Corey function representation. In addition traditionally static (permeability, porosity) and dynamic (pressure, saturation) variables are adjusted. During the assimilation we used the oil production rate, gas-oil ratio and water-cut as history data. The EnKF is applied on a StatoilHydro operated reservoir field in the North Sea, where the relative permeability is identified to be highly uncertain. In this work a Corey function parameterization is used to estimate the relative permeability, and in the history matching process the Corey exponent, the end point saturation and the end point of the relative permeability curve are treated as poorly known parameters. The influence of the relative permeability has been investigated on real field application, and the results of the field study show that a significant improvement in the history match can be achieved by additionally updating the coarse-scale relative permeability properties. The final estimated ensemble shows a reduction in uncertainty for the relative permeability curves, which demonstrate the capability of the EnKF to quantify the uncertainty in the reservoir model. Furthermore, the final estimated ensemble is used to predict the future production performance of the reservoir. Technical contributions: The paper shows that the EnKF is capable to adjust relative permeability curves, based on the information contained in the assimilated production measurements, and it is shown that estimating coarse-scale relative permeability may be crucial to obtain satisfactory history matching results in real field applications.


Article metrics loading...

Loading full text...

Full text loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error