How it works
PoroSight leverages advanced AI and machine learning to translate seismic and well data into high-resolution predictions of reservoir porosity. By integrating all available post-stack seismic volumes with calibration from well logs, PoroSight captures subtle variations in the subsurface that are often invisible to conventional interpretation techniques.
The system builds a physics-consistent model that relates seismic response to porosity, producing a volumetric porosity prediction that is grounded in real data. Interpreters can explore the full survey area with confidence, identifying high-porosity zones, mapping reservoir quality, and reducing uncertainty in reservoir evaluation.
Key benefits
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Enhanced reservoir understanding: See variations in porosity beyond well control, supporting better volumetrics, planning, and development decisions.
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Data-driven confidence: Predictions are anchored to well measurements and constrained by seismic attributes, providing defensible insights for exploration and production teams.
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Integration with existing workflows: Outputs are delivered in standard seismic formats, ready to be loaded into interpretation platforms for seamless integration with existing datasets and models.
PoroSight enables teams to turn complex seismic and well data into actionable insight, improving decision-making and optimizing reservoir development strategies.
Data Requirements
Deliverables
PoroSight is designed to work with the datasets geophysicists already have. To get started, we require:
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All available post-stack seismic data — PoroSight can operate with a single post-stack volume, but benefits from the inclusion of additional stacks such as near, mid, far, angle, or offset stacks where available. The system integrates all provided seismic data within the recorded bandwidth, preserving amplitude, phase, and wavelet behaviour while reducing uncertainty in the predicted rock properties.
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At least three wells with porosity logs at the reservoir level — these wells provide calibration between the seismic response and measured reservoir properties. Porosity logs anchor the prediction in real subsurface data and allow the model to learn the relationship between seismic response and reservoir quality.
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Geological context for the area — such as known stratigraphy, structural framework, or interpreted horizons. These inputs help guide the prediction and ensure results remain interpretable and consistent with the geological setting.
With this dataset, PoroSight generates a porosity prediction volume calibrated to well control and constrained by the seismic response, extending porosity insight across the full seismic survey area while quantifying uncertainty where data support is limited.
PoroSight provides interpreter-ready outputs designed to integrate directly into existing subsurface workflows, extending porosity insight across the full seismic survey area.
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Reservoir porosity prediction cube — A seismic-derived volume predicting porosity at the reservoir level across the entire survey. The prediction is calibrated to well data and constrained by the seismic response, allowing interpreters to map reservoir quality away from well control. Delivered in standard SEG-Y format, ready for loading into your interpretation platform.
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Key attribute volumes — A set of the most influential seismic attribute cubes used by the model to generate the porosity prediction. These volumes provide transparency into the drivers of the prediction and offer additional tools for analysing reservoir heterogeneity.
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Uncertainty products — Uncertainty estimates indicate where the seismic data strongly supports the porosity prediction and where confidence is lower, helping interpreters assess prediction reliability and reservoir risk.
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Project delivery timeline — Typical PoroSight projects are delivered within a few weeks from project kick-off, depending on dataset size and complexity. This allows teams to incorporate porosity predictions into ongoing interpretation and reservoir evaluation workflows without long processing cycles.