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Detailed stratigraphic and structural geomodelling within high-resolution, 3D seismic data is a tedious and time-consuming process. This is especially the case within the geologically complex Santos Basin, offshore Brazil.

In recent years, there have been several deep learning approaches with sophisticated neural network architectures that show a lot of potential when applied to tedious seismic imaging and interpretation tasks. These deep learning workflows accelerate such interpretations, shifting the focus of petroleum geoscientists from exhaustively digitizing features on a workstation, to critical evaluation and risk/resource analysis of the play.

Bluware’s InteractivAI Deep Learning methodology utilizing neural networks are revolutionizing traditional seismic interpretation tasks by accelerating the speed at which geologic features can be mapped. Using data from the structurally complex Santos Basin, offshore Brazil, we introduce this interactive deep learning methodology which enables the interpreter to exert more control over network predictions in real-time.


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