Ensemble-based seismic history matching with data reparameterization using convolutional autoencoder

Liu, Mingliang (University of Wyoming) | Grana, Dario (University of Wyoming)


Summary In this work, we propose an ensemble-based seismic history matching approach to predict reservoir properties, i.e. porosity and permeability, with uncertainty quantification, using both production and time lapse seismic data. To avoid the common underestimation of uncertainty in ensemblebased optimization approaches, and to make the computation feasible, we introduce the convolutional autoencoder to reparameterize seismic data into a lower dimensional space. We then apply the Ensemble Smoother with Multiple Data Assimilation to optimize an ensemble of reservoir models using the production and re-parameterized seismic data. The proposed methodology is tested on a 2D synthetic case. The inversion results indicate that the method can largely improve the characterization of reservoir models compared to the history-matching scenario with production data only.