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Augmenting reservoir characterization with machine learning

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Paris, France
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Empowering geoscientists with the latest machine learning toolkit and innovative software on the Azure Cloud

Machine learning continues to make the headlines at industry events this year with dedicated workshops and technical sessions.

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Porosity predictions from HampsonRussell Emerge comparing a traditional multi-linear regression (top) to the prediction from a deep feed-forward neural network (DFNN) (bottom). The DFNN result shows a more detailed prediction of the reservoir with improved lateral continuity and a better match at the wells.
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CGG GeoSoftware Adds Machine Learning Applications for Reservoir Characterization Using Python Ecosystem Technology

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Paris, France
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CGG GeoSoftware, part of CGG’s Geoscience division, has announced that machine learning technology in Python ecosystems will be available in upcoming releases of its flagship HampsonRussell and Jason reservoir characterization solutions.

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Machine learning application predicts Density using a deep neural network (left-side section and horizon) that shows more detail and better lateral continuity compared to multi-linear regression (back section) (image courtesy of CGG GeoSoftware).
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Delaware Basin: lifting the Cenozoic veil

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Paris, France
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The latest seismic technology and multi-client products provide a new perspective on Northwest Texas plays

Booming hydrocarbon production in the Permian basin over the last few years allowed the United States to become, once again, one of the world’s top exporters.

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Time-lag full waveform inversion (TLFWI) results from CGG’s Gini 3D survey in the Delaware Basin. Top panel: 22Hz TLFWI output velocity model. Bottom: Velocity perturbation showing how TLFWI has updated the initial smooth input model. Final migrated seismic is overlain on both sections for reference (image courtesy of CGG Multi-Client & New Ventures).
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