In this work, we develop an innovative stochastic nonlinear inversion method based on the Ensemble Smoother algorithm and data re-parameterization to estimate elastic as well as rock and fluid properties from seismic. The Ensemble Smoother is an iterative stochastic optimization method and in our work, it is applied to simultaneously update an ensemble of prior geostatistical models until a satisfactory match between the predicted seismic response and the measured seismic data is achieved. The model uncertainty is assessed from the multiple equiprobable posterior realizations. The proposed method is appropriate for non-linear inverse problems, and it can be applied to the prestack AVA inversion and petrophysical inversion based on the exact Zoeppritz equations and non-linear rock physics models. Yet, the proposed optimization method typically underestimates the uncertainty of the inverted results. To address this limitation, we propose a re-parameterization of the seismic dataset based on the singular value decomposition (SVD). The proposed methodology is validated on a synthetic seismic dataset and applied to a real seismic dataset in the North Sea.
Presentation Date: Tuesday, September 26, 2017
Start Time: 9:20 AM
Presentation Type: ORAL