DEEP LEARNING SEISMIC FACIES CLASSIFICATION

DEEP LEARNING SEISMIC FACIES CLASSIFICATION

Interpreters routinely analyze seismic data to find patterns that represent geologically important seismic facies. Built on deep learning technology, the Seismic Facies Classification tool in the Paradise AI workbench is fit-for-purpose for this important pattern-recognition task. The Seismic Facies Classification application finds patterns that have been selected by the human interpreter, identifying similar features in the seismic throughout the area of interest or in entirely different data sets. Using Seismic Facies Classification, interpreters work faster and with greater precision of results.

Interpreters routinely analyze seismic data to find patterns that represent geologically important seismic facies. Built on deep learning technology, the Seismic Facies Classification tool in the Paradise AI workbench is fit-for-purpose for this important pattern-recognition task. The Seismic Facies Classification application finds patterns that have been selected by the human interpreter, identifying similar features in the seismic throughout the area of interest or in entirely different data sets. Using Seismic Facies Classification, interpreters work faster and with greater precision of results.

Key Features:

CNN_facies

Identify

Identify and calibrate detailed stratigraphy/facies tracts

Classify

Classify geologically important seismic facies

Interpret

Interpret a range of geobodies – across the region or down to the reservoir level

We have maintained for years that more can be gained from seismic data when it is analyzed using machine learning technology at a single sample resolution, and there is now an abundance of evidence to support this observation. We will continue to introduce off-the-shelf, fit-for-purpose applications to Paradise that have a strong return-on-investment for our customers.”

– Tom Smith, President & CEO of Geophysical Insights

Paradise seismic interpretation software monitor

Paradise uses robust, unsupervised learning and supervised deep learning technologies to accelerate interpretation and generate greater insights from seismic and well data. Apply the guided ThoughtFlow® in the Paradise AI workbench to…

  • Identify seismic facies based on distinctive seismic patterns using deep learning
  • Reduce the time required to identify faults and improve fault detection accuracy
  • Extract greater insights from seismic than is possible using traditional tools
  • Identify and estimate potential reservoirs using machine learning
  • Generate a more accurate and complete understanding of the subsurface

KEY TECHNOLOGIES IN PARADISE AI WORKBENCH:

Seismic Facies Classification

Deep learning Seismic Facies Classification enables the identification of structural and stratigraphic facies patterns using Convolutional Neural Network (CNN) as an image recognition process.

Automatic Fault Detection

Equipped with general pre-trained deep learning engines (conservative and aggressive), Fault Detection in Paradise can be applied to a wide range of seismic data without the need of user-provided fault examples for training.

Multi-Attribute Classification

Applies machine learning to reveal thin beds below conventional tuning thickness.

Attribute Generation

The Paradise AI workbench has a world-class library of instantaneous, geometric, and spectral decomposition attributes. Over 100 attribute can be generated.

Geobody Detection

Uses machine learning to identify potential reservoirs and estimate reserves.

Attribute Selection

Principal Component Analysis (PCA), a guided ThoughtFlow® process, is to identify attributes that are contributing the most energy to a region.

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