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Hampson Russell 2024 (One PC)

HampsonRussell software products have been providing innovative geophysical solutions since 1987, in comprehensive and easy-to-use packages.

Why Choose HampsonRussell

Reduce the risks and costs associated with exploration and production with world-class advanced geophysical interpretation and analysis from our HampsonRussell software.

This comprehensive suite of reservoir characterization tools integrates well logs, seismic data and geophysical processes into an easily navigated, intuitive package for fast results. Known for its ease of use, HampsonRussell makes sophisticated geophysical techniques accessible.

  • circleExperience powerful speed improvements for your processing projects with multi-node processing
  • circleGet the flexibility to design and code any process with the Python ecosystem while taking advantage of the HampsonRussell project structure and data access
  • circleQuickly import and use shapefiles
  • circleWork with your AWS and Microsoft Azure cloud data

HampsonRussell offers a comprehensive suite of reservoir characterization software tools:

GeoAI encompasses a novel methodology for seismic reservoir characterization with limited well control, speeding up reservoir property predictions with a rock physics driven machine learning technique. Rock Physics theory and statistical simulations generate synthetic data for various geological scenarios. A simplified machine learning approach employs Convolutional Neural Networks (CNN) estimating multiple rock property volumes in a greatly simplified workflow.

Benefits of GeoAI:

  • Improves Reservoir Characterization for low well-control areas
  • Allows direct prediction of facies and  reservoir properties
  • Utilizes rock physics guided machine learning to optimize extraction of information and value addition from all available data

In standard supervised machine learning approaches, the seismic-to-rock property relationship is learned using available data. These methods, particularly deep learning, depend on having enough labeled data to adequately train the neural network.

WellGen overcomes this challenge by generating synthetic data, simulating many pseudo-wells based on existing well statistics and rock physics modeling.WellGen addresses common machine learning challenges, including:

Scarcity of wells within the study area The difficulty of tying well data with seismic

High variability in the well curves not depicting geological variations

Inability to link geological and geophysical observations

Reservoir complexity that cannot be resolved by inversion alone

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