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Version GeoAI -Encompasses A Novel Methodology
GeoAI encompasses a novel methodology for seismic reservoir characterization with limited well control, speeding up reservoir property predictions with a rock physics driven machine learning technique. Rock Physics theory and statistical simulations generate synthetic data for various geological scenarios. A simplified machine learning approach employs Convolutional Neural Networks (CNN) estimating multiple rock property volumes in a greatly simplified workflow.
- Improves Reservoir Characterization for low well-control areas
- Allows direct prediction of facies and reservoir properties
- Utilizes rock physics guided machine learning to optimize extraction of information and value addition from all available data
Versatile Workflows
Built-in, fully customizable workflows simplify projects by guiding you through the required steps while linking parameters from one step to the next.
Broad Capabilities
Whether your goals are prospect ranking, field development or maximizing recovery from mature or unconventional reservoirs, HampsonRussell software offers a unique combination of technology and expertise.
Intuitive and Interactive
Visualize, interpret and manage seismic reservoir characterization projects easily and efficiently, so you can better understand reservoir complexity.
Training and Support
GeoSoftware provides HampsonRussell support and training through a global network of offices to help you get the most from your geophysical data. We offer both public workshops and custom in-house training based on your ongoing projects.