Course Details
Your Growth, Our Mission
Course Description
The course is ideal for geoscientists, engineers, and data analysts at all experience levels. Concepts are supported with ample illustrations and case studies, complemented by mathematical rigor befitting the subject. Aspects of supervised learning, unsupervised learning, classification, and reclassification are introduced to illustrate how these methods apply to seismic data.
The Training Course Will Highlight ?
Training Objective
In this course, you will learn:
- 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 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?
Target Audience
This training course is suitable for a wide range of professionals but will greatly benefit:
- Petro physicists
- Geophysicists
- Geo-Modelers
- Geologists (Exploration and Development)
- Sedimentologists
- Seismic Interpreters
- Drilling Engineers
- Reservoir Engineers
- Technical Support Personnel
- Team Leaders
- Managers
Training Methods
- Lectures
- Workshop & Work Presentation
- Case Studies and Practical Exercise
- Videos and General Discussions
Daily Agenda
Day 1: Machine Learning
- Introduction to Machine Learning.
- The Rise of Artificial Intelligence.
- Machine Learning.
Day 2: Classification of Machine Learning
- Supervised Machine Learning.
- Unsupervised Machine Learning.
Day 3: Introduction to Seismic Data
- Basic Seismic Technique.
- Petrophysical & Seismic Rock Properties.
- Seismic Wavelet Determination.
- Seismic Data Phase & Polarity.
- Seismic Data Resolution.
Day 4: Machine Learning Techniques for Seismic Data Interpretation
- Principal Component Analysis (PCA).
- Self-Organizing Maps (SOM).
Day 5: Seismic Attributes & DHI Analysis
- Direct Hydrocarbon Indicators.
- DHI Analysis.
- Full Bandwidth Conventional Seismic Attributes.
- Spectral Decomposition Analysis.
- Machine Learning Case Study.
Accreditation
BTS attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.
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