Geoplat AI/Features

Seismic Data Enhancement

Get More From Your Seismic Data With AI

Pre-trained seismic interpretation models run locally, integrate into existing workflows, and can be adapted to your geological context — keeping your data within your infrastructure.

Fault Prediction

Paleochannel Delineation

Seismic Facies Classification

Geobody Detection

Seismic Interpretation is Still a Bottleneck

The cost of a missed structure or incorrect interpretation

Can be far greater than the cost of the interpretation itself

2000+ PB

$5–100M per well

from onshore prospects to complex offshore drilling

for a full seismic interpretation study

of seismic data in global E&P archives

AI makes it possible to screen more seismic, test more geological hypotheses, and focus expert attention where it matters most

AI makes it possible
to screen more seismic, test more geological hypotheses, and focus expert attention where it matters most

Complex geology, variable data quality, and limited interpretation time

Make it difficult to evaluate every relevant scenario before a decision has to be made

Up to 6 months

Automation Without Replacing Expert Interpretation

AI+Expert

Less Manual Routine Work in Selected Workflows

Up to 80%

Faster Key Interpretation Stages

Integrated Asset Knowledge with Less Subjectivity

3–7x

Workflow builder — combine, repeat, and refine AI steps for each geological task

Expert QC with optional transfer learning for project-specific adaptation

Outputs ready for interpretation, structural modeling, and reservoir characterization

Families of models for seismic conditioning, fault detection, geobody detection, facies classification, and horizon interpretation

The Geoplat AI Platform

Geoplat AI applies pre-trained models consistently across seismic volumes, reducing dependence on individual picking style and inherited local interpretation bias.

Build flexible workflows with AI models, run computations on seismic volumes, and export interpretation-ready results directly into existing G&G workflows.

Full AI Tool Library.
Your Workflow, Your Rules

Build workflows for specific geological tasks by choosing and combining AI tools in any order — from random noise suppression to fault surface export and geobody extraction.

Not a Black Box: Synthetic Ground Truth, Real-Seismic Validation

Pre-trained networks are no longer rare. The real difference is what they are trained on. Models trained only on interpreted field data can inherit regional bias, inconsistent labels, and the limitations of existing interpretations.
Geoplat AI takes a different approach: our models are pre-trained on synthetic geological data with known ground truth and validated on real seismic surveys.

Learn more about Technology

Pre-trained universal models for first-pass prediction plus transfer learning for local adaptation

Physics-guided generation of structural frameworks, rock properties, wavelets, noise and acquisition effects

Reserves growth in carbonate reservoirs

Geologically realistic synthetic seismic with known faults, horizons, facies and geobody labels

Structural risks to caprock seal integrity

2

1

Structural model update for reserves re-estimation

Pinch-out and stratigraphic trap detection

2

1

Amplitude-preserved data conditioning for seismic inversion

6

5

4

3

Amplitude anomaly screening ahead of well placement

Reserves growth in carbonate reservoirs

Structural risks to caprock seal integrity

Structural model update for reserves re-estimation

Pinch-out and stratigraphic trap detection

Amplitude-preserved data conditioning for seismic inversion

Amplitude anomaly screening ahead of well placement

4

3

6

5

Validated on Real Basins

Salt bodies in the North Sea

Detailed salt delineation with substantially less manual tracking

Paleo Channels Discovery

Full-volume paleochannel mapping on a standard workstation

Resolving Sub-Salt Strata

Improved quality and resolution below evaporites in the Southern Permian Basin

Improving
Faults Imaging

Structural model where conventional attribute workflows struggle

Automatic Horizon Interpretation

All stratigraphic horizons extracted in hours, fault zones preserved

Seismic Data Quality

Structural model where conventional attribute workflows struggle

Salt Delineation

386 km²

Mean Conditioning

Faults

Geobody Detection

680 km²

LGT Volume

Horizons

Structure-Oriented Conditioning

Zero setup

2,671 km²

Hi-Res Conditioning

Geoplat Features

Fault Prediction

Paleo-Channels Detection

Seismic Facies Identification

Geobody Detection

Seismic Data Enhancement

Bring us real field data — sub-salt, noisy vintage seismic, or complex tectonic settings. We run a live technical demo and compare Geoplat AI outputs with your current interpretation workflow

Time to Level Up

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