Coarse-gradient Langevin algorithms for dynamic data integration and uncertainty quantification
Journal of Computational Physics - Special issue: Uncertainty quantification in simulation science
Data Assimilation: The Ensemble Kalman Filter
Data Assimilation: The Ensemble Kalman Filter
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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 out. In particular, the method does not prevent petrophysical realizations from departing from prior information. In addition, petrophysical properties can reach extreme (non-physical) values. In this work, we propose to combine the EnKF with two parameterization methods designed to preserve second-order statistical properties: pilot points and gradual deformation. The aim is to prevent the departure of the constrained petrophysical property distributions from prior information. Over/under estimations should also be avoided. The two algorithms are applied to a synthetic case. Several parameter configurations are investigated in order to identify solutions improving the performance of the method.