Rapid advances in AI and machine learning are transforming seismic interpretation and reservoir characterization workflows. Using commercially available AI/ML technologies, geoscientists can now predict lithofacies, detect thin beds, and generate fault volumes faster and more economically than traditional interpretation methods.
This webinar will showcase practical applications within the Paradise® AI workbench, including machine learning lithofacies prediction and deep learning automatic fault detection.
Presenters will demonstrate how unsupervised ML techniques such as Self-Organized Maps (SOM) can integrate seismic and petrophysical data to identify natural lithologic patterns without the need for traditional inversion workflows.
The session will also explore AI-driven fault detection powered by 3D CNN technology, capable of rapidly generating fault geobodies and probability volumes across complex geologic settings. Case studies from the Western Desert of Egypt and Deepwater Offshore India will highlight how these workflows help reduce interpretation time, minimize bias, and improve subsurface understanding in both structural and stratigraphic environments.
Can’t attend the live webinar? All registrants will be emailed a copy of the on-demand recording, regardless of attendance.
Speakers

Fabian Rada

Hal Green

Alvaro Chaveste
Event Contacts

Director of Innovation, Emerging Science and Technology
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