Application of the ensemble Kalman filter for assisted layered history matching
نویسندگان
چکیده
منابع مشابه
History matching of petroleum reservoir models by the Ensemble Kalman Filter and parameterization methods
The Ensemble Kalman Filter (EnKF) has been successfully applied in petroleum engineering during the past few years to constrain reservoir models to production or seismic data. This sequential assimilation method provides a set of updated static variables (porosity, permeability) and dynamic variables (pressure, saturation) at each assimilation time. However, several limitations can be pointed o...
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During history match reservoir models are calibrated against production data to improve forecasts reliability. Often, the calibration ends up with a handful of matched models, sometime achieved without preserving the prior geological interpretation. This makes the outcome of many history matching projects unsuitable for a probabilistic approach to production forecast, then motivating the quest ...
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Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is shown that this collapse is caused by positive coupling of the ensemble members due to use of one common estimate of the Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution i...
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In ensemble Kalman filter methods, localization is applied for both avoiding the spurious correlations of distant observations and increasing the effective size of the ensemble space. The procedure is essential in order to provide quality assimilation in large systems; however a severe localization can cause imbalances that impact negatively on the accuracy of the analysis. We want to understan...
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ژورنال
عنوان ژورنال: Advances in Geo-Energy Research
سال: 2018
ISSN: 2207-9963,2208-598X
DOI: 10.26804/ager.2018.04.09