By the end of this training course, participants should be able to:

• What is machine learning and how does it apply to seismic exploration and unconventional resource development?
• What is the difference between supervised and unsupervised machine learning?
• When is an analysis statistical and when is it machine learning?
• What is attribute space and what is the mathematical foundation of this technology?
• How do you know if the results are any good?
• What are some case histories that illustrate machine learning principles?
• What are some practical tips?

This training course is suitable to a wide range of professionals but will greatly benefit:

• Petrophysicists
• Geophysicists
• Geo-Modelers
• Geologists (Exploration and Development)
• Sedimentologists
• Seismic Interpreters
• Drilling Engineers
• Reservoir Engineers
• Technical Support Personnel
• Team Leaders
• Managers

This  Training will be highly interactive with opportunities to advance your opinions and ideas and will include:

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Module-1: Machine Learning

• Introduction to Machine Learning.
• The Rise of Artificial Intelligence.
• Machine Learning.

Module-2: Classification of Machine Learning

• Supervised Machine Learning.
• Unsupervised Machine Learning.

Module-3: Introduction to Seismic Data

• Basic Seismic Technique.
• Petrophysical & Seismic Rock Properties.
• Seismic Wavelet Determination.
• Seismic Data Phase & Polarity.
• Seismic Data Resolution.

Module-4: Machine Learning Techniques for Seismic Data Interpretation

• Principal Component Analysis (PCA).
• Self-Organizing Maps (SOM).

Module-5: Seismic Attributes & DHI Analysis

• Direct Hydrocarbon Indicators.
• DHI Analysis.
• Full Bandwidth Conventional Seismic Attributes.
• Spectral Decomposition Analysis.
• Machine Learning Case Study.

CDGA attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.