Conditioning Multilayered Geologic Models to Well-Test and Production-Logging Data Using the Ensemble Kalman Filter

Abstract

Abstract Reservoir models utilize highly heterogenous permeability and porosity fields embedded in a structural model derived from geophysical and geological data. It is not uncommon for permeabilities in the initial model derived from static data to differ significantly from values obtained from the interpretation of well-test data. This paper presents the application of the ensemble Kalman Filter (EnKF) to assimilate pressure transient and production-logging data to update permeabilities, estimate layer skin factors and "effective" well skin factor in multilayered reservoir models. The methodology is applied to two synthetic cases and a field case. For the field case application, we consider two approaches. In the first approach we simply use multipliers to adjust the heterogeneous permeability field in each layer. In the second approach, we adjust the prior mean of each layer log-permeability field together with individual gridblock log-permeabilities.