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Version WellGen -Standard Supervised Machine Learning
In standard supervised machine learning approaches, the seismic-to-rock property relationship is learned using available data. These methods, particularly deep learning, depend on having enough labeled data to adequately train the neural network. WellGen overcomes this challenge by generating synthetic data, simulating many pseudo-wells based on existing well statistics and rock physics modeling.
- Scarcity of wells within the study area
- The difficulty of tying well data with seismic
- High variability in the well curves not depicting geological variations
- Inability to link geological and geophysical observations
- Reservoir complexity that cannot be resolved by inversion alone
Achieve your exploration and development objectives at every stage of your reservoir characterization and interpretation workflows.
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.