An Efficient Seismic AVA Inversion Method with Uncertainty Quantification Using Ensemble Kalman Filter Scheme

Abstract

The Ensemble Kalman Filter (EnKF) has attracted large amount of attention and it has become a relatively competitive method. The rationale underpinning the Ensemble Kalman-based methods is that it uses the Kalman formula to generate an ensemble of inverse estimates of the geophysical and geologic properties. Each ensemble member may be updated via an iterative or non-iterative way and in sequential (filtering) or all-at-once (smoothing) basis. We try to perform AVA inversion via EnKF to extract the subsurface elastic parameters including P-wave velocity, S-wave velocity and density, which is of significance for further delineation in oil reservoir. We apply this method on a synthetic AVA dataset of 40 degrees using a well data. Numerical tests with the synthetic data have revealed the success of the proposed methods.