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High Resolution Seismic Conditioning
Mean Seismic Conditioning
Fault Interpretation
Horizon Interpretation
Structure-Oriented Seismic Conditioning
Paleo-Channels Detection
Salt Bodies Delineation
Resolving Sub-Salt Strata on Groningen Field
Improving Faults Imaging on Opunake 3D Data
Improving Seismic Data Quality on Waka Field
Delineation of Salt Bodies
Automatic Horizon Interpretation
Automatic Fault Tracking
Paleo Channels Discovery
Geoplat
Artificial Intelligence
AI based integrated geoscience solutions
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Reduced operational time
Intuitive ML workflow building that significantly decreases processing time and delivers fast and accurate prediction results
Applying your knowledge
Intelligent ML workflow Customisation aimed at retaining your competences and experience
Advanced hybrid neural network
Pretrained Models on more than 1M data samples including multiple seismic attributes
Data model at a new level
Robust prediction of the most complex geological features based on varying seismic quality and complexity of geological strata
8
Years of ML research and development

15

Leading Academic Institutions participating in the R&D program
80
Successful consultancy projects
сompleted by the Geoplat team
Up to 70 %
Productivity improvement

500

Geoscientists have already
applied Geoplat AI on their data
1M
Data samples are included in the AI models
Synthetic seismic
data sets
The training accumulates and analyzes information from thousands of synthetic datasets. This way the algorithm is fully prepared to look for faults, noise data, specific geobodies, etc.
It is used to correct Machine Learning results in the areas where the user can specify complex faults, geobodies, seismic facies. This way the user gets to combine learning on synthetic models and real data.
Together these three anchors make Geoplat AI a unique product for geoscience.
Machine leaning is a well-known approach that Geoplat has managed to adapt to the needs of seismic survey. Our method is based on three anchors:
With different conditions: including noise, different kind of faults, horizontal and dipping reflections, geobodies, etc.
Over the years over 1 mln datasets have been generated.
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Transfer learning
Machine Learning approach in Geoplat AI
Pre-trained neural networks

Our Solutions

Initial Data
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Significant increase of Signal-to-noise ratio for further detailed interpretations
Ability to detect thin layers in case of low data resolution
Precise determination of fault zones and real amplitude shifts up to several meters
AI
Key Features
Seismic Data Enhancement
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Significant increase of Signal-to-noise ratio for further detailed interpretations
Ability to detect thin layers in case of low data resolution
Precise determination of fault zones and real amplitude shifts up to several meters
Key Features
Fault Determination
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Geobodies Detection
Significant increase of Signal-to-noise ratio for further detailed interpretations
Ability to detect thin layers in case of low data resolution
Precise determination of fault zones and real amplitude shifts up to several meters
Key Features
Learn more
Significant increase of Signal-to-noise ratio for further detailed interpretations
Ability to detect thin layers in case of low data resolution
Precise determination of fault zones and real amplitude shifts up to several meters
Key Features
Seismic Facies Identification
AI Webinar Series
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    • Case Studies
    Check out an effective application of the Geoplat technologies on real seismic data
    Improving Seismic Data Quality on Waka field
    Seismic data quality increasing with a help of seismic data AI-based cleaning functions
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    Вelineation of Salt Bodies on F3 Seismic Survey
    Salt geobody delineation outcome based on the AI capabilities
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    Automatic Horizon Interpretation at Opunake Area
    Results of automatic horizon interpretation, propagated over the entire survey area with a help of AI-driven tool
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    Paleo Channels Discovery on Tui Area
    Detecting paleo channels on the entire survey area enabled by the AI geobody solution
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