EMERGE: Machine Learning Multi-Attribute Prediction for Reservoir Properties Workshop

Course overview

This course covers the theory and practical use of Emerge, an interactive program fully integrated within HampsonRussell Software that performs multi-attribute seismic analysis for seismic reservoir characterization using multivariate statistics and neural networks.

Topics covered include:

• Theory of seismic attributes, linear, non-linear, and neural network methodologies for attribute selection, cross-validation, and attribute ranking
• Application of attributes to convert seismic data volumes into geological or petrophysical volumes.
• Application of attributes to predict missing log data
• Attributes exercises using seismic data and well logs

 Course benefits

• A comprehensive overview of the generation of seismic attributes
• Provides a mechanism for the user to derive complex relationships between seismic attributes and petrophysical parameters
• Understanding of how to recognize reliable attributes when estimating reservoir parameters.
• Basic theory of neural network technologies
• Application of neural network technology in well log prediction, petrophysical volume generation, and seismic lithology classification
• Structured to teach theory alongside practical exercises, equipping the user in the operation of the Emerge software

Software covered:

HampsonRussell Emerge

 Who should attend?

Geophysicists, geologists, engineers, and technical staff who want to understand the theory and learn how to apply these increasingly critical techniques

Pre-requisite

Experience with HampsonRussell Software is not a prerequisite for this workshop; however, students will become familiar with its functionality through participation.

Duration

2 days (4 hours per day)

Format

Instructor-led, workflow-based, virtual training

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