Research Talk — Deep Learning for 4D Pressure Saturation Inversion [Youtube]

I presented in Amsterdam during the Practical Reservoir Monitoring Workshop in Amsterdam. This is the accompanying video.

Abstract

In this work, we present a deep neural network inversion on map-based 4D seismic data for pressure and saturation. We present a novel neural network architecture that trains on synthetic data and provides insights into observed field seismic. The network explicitly includes AVO gradient calculation within the network as physical knowledge to stabilize pressure and saturation changes separation. We apply the method to Schiehallion field data and go on to compare the results to Bayesian inversion results. Despite not using convolutional neural networks for spatial information, we produce maps with good signal to noise ratio and coherency.

Video

Links

📝 Preprint: https://doi.org/10.31223/osf.io/zytp2
📝 Paper: https://doi.org/10.3997/2214-4609.201900028
📝 Presentation: https://doi.org/10.6084/m9.figshare.7963775

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... is a geophysicist by heart. He works at the intersection of machine learning and geoscience. He is the founder of The Way of the Geophysicist and a deep learning enthusiast. Writing mostly about computational geoscience and interesting bits and pieces relevant to post-grad life.
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