Deep Learning: From Cats to 4D Seismic - Reducing cycle time and model training cost in asset management

2018-12-05T06:01:15Z (GMT) by Jesper Soeren Dramsch Mikael Lüthje
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
experiments. This can be used in seismic modelling, physical effect separation, time series alignment and automatic seismic interpretation.