Structural Uncertainty Modelling and Updating Using the Ensemble Kalman Filter – Parameters and State Consistency

In previous work, the authors presented an elastic grid approach to handle horizon and fault geometric uncertainties in the reservoir model and established an assisted history-matching workflow for updating the structural model using the EnKF. In this paper we consider a gas-flooding experiment in which the flow path is controlled by the reservoir top structure. Uncertainties in the top horizon are accounted for and updated by sequential assimilation of production data using the Ensemble Kalman Filter (EnKF). The result is an ensemble of history-matched models, with reduced and quantified uncertainty in the top horizon. The updated estimate of the top horizon captures the main features of the reference structure. We focus on the consistency between the reservoir top horizon and the gas saturation, as it has been shown that when the assumption of Gaussian priors in the EnKF is violated, the EnKF update scheme may lead to inconsistencies between the state and model parameters. We study in detail the sequential updating of the gas saturation and investigate if the updated state is a better description of the reservoir condition given the uncertainties in the reservoir description and modelling errors, or if a reinitialization with the updated parameters is needed to solve the inconsistency issues before predictions.