Sorry, you need to enable JavaScript to visit this website.

HampsonRussell Emerge Workshop (HR-240)

Mar 26 - Mar 26, 2021 10:00 am
Online Houston (CST) , USA

Category: Training

Register Here

Learning Objectives

  • A comprehensive overview of the generation of seismic attributes
  • Understanding how to recognize reliable attributes when estimating reservoir parameters
  • Application of neural network technology in well log prediction, petrophysical volume generation and seismic lithology classification.

Audience

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

Content

This course covers the theory and practical use of Emerge, an interactive program that is fully linked within HampsonRussell software and 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.

Duration: 1-day

Software used: Emerge

Course Format: Workshop

Instructor(s): TBD

Prerequisites: None

Number of Participants:

Price:

US $900.00

SPE Virtual Workshop: Integrated Reservoir Modelling and Simulation Dec 07, 2021 - Dec 09, 2021 MST (UTC+8) Online Tradeshow Add to Calendar
CGG Q4 2021 Results Mar 03, 2022 - Mar 03, 2022 Paris France Investor Add to Calendar
CGG Q1 2022 Results May 04, 2022 - May 04, 2022 Paris France Investor Add to Calendar
CGG Q2 2022 Results Jul 28, 2022 - Jul 28, 2022 Paris France Investor Add to Calendar
CGG Q3 2022 Results Nov 02, 2022 - Nov 02, 2022 Paris France Investor Add to Calendar
Share Link
LinkedIn icon Facebook icon Twitter icon