Constraining a Reservoir Facies Model to Dynamic Data Using a Gradual Deformation Method

In this paper, we propose a new method for gradually deforming Gaussian-related stochastic models while preserving their spatial variability (reproducing their variograms). This method consists in building a continuous stochastic process whose state space is the ensemble of the realizations of a spatial stochastic model. The gradual deformation algorithm is combined with an optimization algorithm to calibrate stochastic models to nonlinear data. This paper focuses particularlyon the gradual deformation and iterative calibration of truncated Gaussian modeIs. The case study on the calibration of a reservoir facies model to weIl-test pressure data illustrates the efficiency of the proposed method. Although the method described in this paper is operational only in the Gaussian framework (e.g. lognormal model, truncated Gaussian model etc.), the idea of gradually deforming realizations through a stochastic process remains general and therefore promising even for calibrating non- Gaussian modeIs.