ESTIMATE RESERVES EARLY IN THE PROJECT LIFE-CYCLE

ESTIMATE RESERVES EARLY IN THE PROJECT LIFE-CYCLE

Machine Learning Geobody generation and editing are unique to the Paradise AI workbench. Geobodies are used to generate a three-dimensional visualization of geological features and interpreted or drilled reservoirs. Once generated, industry-standard volumetric calculations are available in the tool to estimate the amount of in-place and/or recoverable oil or gas where reservoir data is available.

Machine Learning Geobody generation and editing are unique to the Paradise AI workbench. Geobodies are used to generate a three-dimensional visualization of geological features and interpreted or drilled reservoirs. Once generated, industry-standard volumetric calculations are available in the tool to estimate the amount of in-place and/or recoverable oil or gas where reservoir data is available.

Key Features:

thumbnail for video gi3xier1mn 3

Identify geobodies through machine learning classification

Calculate volumetrics, including hydrocarbon pore volume down to the sample interval level

Edit/clean-up selected geobodies by filling in areas or pruning extraneous samples

Export geobodies to an interpretation system for further analysis

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 machine learning and supervised deep learning technologies to accelerate interpretation and generate greater insights from seismic and well data. Apply the guided ThoughtFlows® in the Paradise AI workbench to:

  • Estimate volume of reserves/resources
  • Interpret amplitude anomalies potentially related to Direct Hydrocarbon Indicators (DHIs)
  • Analyze reservoir properties related to seismic facies contrasts
  • Distinguish thin beds below conventional seismic tuning
  • Identify and calibrate detailed stratigraphy/facies tracts
  • Detect faults and fracture trends

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.

  • Registration confirmation will be emailed to you.

  • We're committed to your privacy. Geophysical Insights uses the information you provide to us to contact you about our relevant content, events, and products. You may unsubscribe from these communications at any time. For more information, check out our Privacy Policy

    Scroll to Top