10.6084/m9.figshare.7422629.v1
Jesper Soeren Dramsch
Jesper Soeren
Dramsch
Mikael Lüthje
Mikael
Lüthje
Deep Learning: From Cats to 4D Seismic - Reducing cycle time and model training cost in asset management
figshare
2018
geophysics
4D
seismic
time-lapse seismic
deep learning
transfer learning
Seismology and Seismic Exploration
Geophysics
2018-12-05 06:01:15
Poster
https://figshare.com/articles/poster/Deep_Learning_From_Cats_to_4D_Seismic_-_Reducing_cycle_time_and_model_training_cost_in_asset_management/7422629
4D Seismic data has proven invaluable in O&G asset management, however, it’s engineering challenges are still plentiful. These challenges include non-repeatable noise, tie-in and match with production curves, as well as, separation of imaging, pressure and saturation effects. Deep learning has proven robust at separating effects [1] with a strong data-dependent prior and has been shown effective in modelling physics-based systems [2]. We present work that reduces training times and thus reduces cost of implementation and enables rapid prototyping of<br>experiments. This can be used in seismic modelling, physical effect separation, time series alignment and automatic seismic interpretation.
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