Seismic data enhancement

Speed up seismic interpretation

Revive seismic archive data

Skip reprocessing,
save $

Seismic data enhancement

Speed up seismic interpretation

Revive seismic archive data

Skip reprocessing,
Save $

Problematics

Unlocking the full potential of oil and gas fields relies on the precision of seismic interpretation. Yet, legacy data quality limitations and subsurface complexity often obscure critical insights. This can lead to the following problems in oil and gas industry:

Leading to well loss, which саn cost uр to $3 mln per well

As high as 50%, especially in case of legacy data

Leading to reputation risks and investment decrease

Problematics

Unlocking the full potential of oil and gas fields relies on the precision of seismic interpretation. Yet, legacy data quality limitations and subsurface complexity often obscure critical insights. This can lead to the following problems in oil and gas industry:

Leading to well loss, which саn cost uр to $3 mln per well

As high as 50%, especially in case of legacy data

Leading to reputation risks and investment decrease

The answer lies in neural networks that have been pre-trained on a big amount of synthetic data. As a result the neural network learned how to separate noise from signal in various geological conditions. This data have been stored and available as a ready-to-use solution in Geoplat AI.

How to solve these problems?

Results

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2D/3D seismic data with suppressed noise

Highlighted horizons and fault zones

Requirements

From several minutes to 2-3 days

1 or 2 parameters only per procedure

Stacked seismic data of аnу quality

TrueAmp

Increasing reliability of the dynamic interpretation

Preserving true amplitudes

Random noise filtering

Unlocking the full potential of oil and gas fields relies on the precision of seismic interpretation. Yet, legacy data quality limitations and subsurface complexity often obscure critical insights. This can lead to the following problems in oil and gas industry:

True Amplitude Noise Suppression

Increasing reliability of the dynamic interpretation

Preserving true amplitudes

Random noise filtering

Structural conditioning

Increasing reliability of the dynamic interpretation

Preserving true amplitudes

Random noise filtering

Footprints removal

Separate and combined processing of inline and crossline directions

Clear image of faults and horizons without loss of amplitude information

Automatic recognition and elimination of footprints and other coherent noise