This training course is available upon request. When requesting a course, please know there is a minimum number of participants needed for the course to be held. Request more information in the form to notify our team of your interest.
Learning Objectives
This course covers the theory and practical use of Emerge, is an interactive program that that is fully linked within HampsonRussell Software and performs multi-attribute seismic analysis for seismic reservoir characterization using multivariate statistics and neural networks.
Audience
Geophysicists, geologists, engineers and technical staff who want to understand the theory and learn how to apply these increasingly critical techniques. Experience with HampsonRussell Software is NOT a prerequisite for this workshop, although students will become familiar with the functionality through attendance
Content
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 include:
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
Duration: 1 day Software used: HampsonRussell Emerge Course Format: Instructor-led, workflow-based, in-person classroom or virtual Prerequisites: Experience with HampsonRussell software is NOT a prerequisite for this training course, although students will become familiar with the functionality through attendance of this class.