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Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data

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  • Dramsch, Jesper Sören
  • Corte, Gustavo
  • Amini, Hamed
  • Lüthje, Mikael
  • MacBeth, Colin

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.

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  • Dramsch, Jesper Sören & Corte, Gustavo & Amini, Hamed & Lüthje, Mikael & MacBeth, Colin, 2019. "Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data," Earth Arxiv zytp2, Center for Open Science.
    • Handle: RePEc:osf:eartha:zytp2
    DOI: 10.31219/osf.io/zytp2
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    Cited by:

    1. Cheng Cao & Hejuan Liu & Zhengmeng Hou & Faisal Mehmood & Jianxing Liao & Wentao Feng, 2020. "A Review of CO 2 Storage in View of Safety and Cost-Effectiveness," Energies, MDPI, vol. 13(3), pages 1-45, January.

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