Geologic stratigraphic scenario testing via deep learning

Geologic stratigraphic scenario testing via deep learning: towards imaging beyond seismic resolution

Karimzadanzabi, A. (TU Delft Applied Geophysics and Petrophysics)
Cuesta Cano, A. (TU Delft Applied Geology)
Verschuur, D.J. (TU Delft Applied Geophysics and Petrophysics; TU Delft ImPhys/Verschuur group)

2023

In the process of seismic subsurface imaging, there is no acceptable forward model reflecting the AVO response in a laterally inhomogeneous medium for reservoir characterization. This means that even when inversion is performed in full waveform, local heterogeneity is typically not fully incorporated while emplying a local 1.5D assumption. Thus, it is impossible to image and classify the subsurface features with these local heterogeneities. Still, the angle-dependent response encodes heterogeneity information that assists overcoming this issue if used properly. To exploit its capabilities, we present a way for identifying reservoir characteristics in the presence of local heterogeneity by linking encoded angle-dependent responses created using angle-dependent Full Wavefield Migration with their originating source - the relevant geological context. To accomplish this purpose, a pipeline technique that integrates the produced angle-dependent responses with a pattern categorization deep-learning tool is proposed. For a basic test on synthetic data, the method successfully identified the produced different stratigraphic architectures and classified them in the training stage. The method is then validated on angle gathers generated from different models with comparable geological circumstances.

http://resolver.tudelft.nl/uuid:74de3af3-066e-4d9b-84c1-ea363d69c946

https://doi.org/10.3997/2214-4609.2023101151

84th EAGE ANNUAL Conference and Exhibition 2023, 2023-06-05 → 2023-06-08, Vienna, Austria

Institutional Repository

conference paper

© 2023 A. Karimzadanzabi, A. Cuesta Cano, D.J. Verschuur