Abstract:The assimilation of time-lapse (4D) seismic data is challenging with ensemble-based methods because of the massive number of data points. This situation requires an excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D images and generate a reduced representation of the data. The architecture of the autoencoder is based on the well-known VGG-19 network, from which we take advantage of… Show more
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