How it works
TocSight applies advanced AI and machine learning to integrate seismic and well data for predicting Total Organic Content (TOC) across the subsurface. By combining multiple post-stack seismic volumes with well measurements, TocSight identifies subtle seismic signatures associated with organic-rich source rocks.
The system builds a physics-consistent model linking seismic response to TOC, producing volumetric predictions that reveal the distribution and richness of source rocks beyond well control.
Key benefits
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Source rock insight: Map TOC variations across the subsurface to identify potential hydrocarbon kitchens and understand the quality and distribution of organic-rich intervals.
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Data-driven confidence: Predictions are calibrated to well TOC measurements and constrained by seismic attributes, providing reliable insights for exploration and prospect evaluation.
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Seamless workflow integration: Outputs are delivered in standard seismic formats, ready to load into interpretation platforms for integration with existing geological and geophysical data.
TocSight transforms complex seismic and well data into actionable insight, helping exploration teams make informed decisions about source rock potential and prospectivity.
Data Requirements
Deliverables
TocSight 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 — TocSight 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 TOC.
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At least three wells with TOC measurements at the reservoir level — these wells provide calibration between the seismic response and measured organic content. TOC logs anchor the prediction in real subsurface data and allow the model to learn the relationship between seismic response and reservoir TOC.
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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, TocSight generates a TOC prediction volume calibrated to well control and constrained by the seismic response, extending organic content insight across the full seismic survey area while quantifying uncertainty where data support is limited.
TocSight provides interpreter-ready outputs designed to integrate directly into existing subsurface workflows, extending Total Organic Content (TOC) insight across the full seismic survey area.
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Reservoir TOC prediction cube — A seismic-derived volume predicting TOC within the area of interest across the entire survey. The prediction is calibrated to well data and constrained by the seismic response, allowing interpreters to map organic-rich and poor intervals 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 TOC prediction. These volumes provide transparency into the drivers of the prediction and offer additional tools for analysing organic content distribution and heterogeneity.
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Uncertainty products — Uncertainty estimates indicate where the seismic data strongly supports the TOC prediction and where confidence is lower, helping interpreters assess prediction reliability and reservoir risk.
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Project delivery timeline — Typical TocSight projects are delivered within a few weeks from project kick-off, depending on dataset size and complexity. This allows teams to incorporate TOC predictions into ongoing interpretation and reservoir evaluation workflows without long processing cycles.