Geoplat AI/Features

Seismic Data Enhancement

See What Your Seismic Data Has Been Hiding

Geoplat AI turns weeks of seismic interpretation into hours. Pre-trained neural network models work out of the box on data from any basin, helping geos detect geobodies and build a more objective geological understanding of the asset

Fault Prediction

Paleo-Channels Detection

Seismic Facies Identification

Geobody Detection

Why Seismic Interpretation Still Slows Projects Down?

Picking, correlation and contouring consume expert time under tight CAPEX discipline

Most companies already own enough seismic data to answer more questions. The problem is that poor data quality, complex geology and limited interpretation time keep much of that information locked inside the cube.

A single unsuccessful deepwater well can cost tens to hundreds of millions of dollars

Petabytes of archive seismic remain underused because reprocessing is expensive and slow

Automation without replacing interpretation authority

AI+Expert

Lower additional processing / routine costs in selected workflows

Up to 80%

Faster key interpretation stages

Integrated Asset Knowledge with Less Subjectivity

3–7x

Deliverables ready for interpretation, structural modeling and reservoir characterization

Workflow constructor: combine, repeat and refine AI steps for each geological task

Human-in-the-loop quality control and optional transfer learning

Model library for conditioning, faults, geobodies and facies

The Geoplat AI Platform

Geoplat AI helps convert underused seismic data into consistent, interpretable evidence for exploration and development decisions. Routine enhancement, correlation and object outlining are accelerated, while experts remain focused on geological reasoning and final decisions.

A complete AI-assisted environment for seismic data enhancement, structural interpretation and geological object detection. Users combine models into adaptive workflows, run calculations on seismic volumes and export interpretation-ready results to existing G&G workflows.

Full AI Tool Library.
Your Workflow, Your Rules

The order of AI tools is defined by the user: combine adaptive workflows for specific geological objectives, from noise attenuation to fault surface export and geobody extraction.

Not a Black Box.
AI Born from Geology

Pre-trained networks are no longer rare. The difference is what they are trained on. Models trained only on interpreted field data inherit regional bias, inconsistent labels and the limits of someone else’s interpretation. Geoplat AI starts from synthetic geological data with known ground truth and validates the models on real 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 model with major time savings compared with auto-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

We run live technical demos on real field data: sub-salt, noisy vintage seismic and complex tectonics are welcome. Compare Geoplat AI outputs with your current interpretation workflow.

Time to Level Up

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