What is it about?
This work presents a deep-learning method using a Fully Convolutional Transformer (FCT)-based encoder--decoder network for direct estimation of the petrophysical properties from seismic angle stacks and interval velocity.
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Perspectives
The framework is validated at training and blind well locations and across a full 3D seismic cube in the Troll Field, yielding accurate and geologically consistent petrophysical property estimates. The predicted properties are further verified through unseen-well validation and their consistency with seismic signatures, fault patterns acting as seal, and reservoir-scale geological features.
Dr. Nisar Ahmed
Universitetet i Stavanger
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This page is a summary of: Deep learning using FCT-network for petrophysical-property estimation from seismic-angle-stacks: 3D application to the Troll Field, North-Sea, January 2026, EAGE Publications,
DOI: 10.3997/2214-4609.202638039.
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