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Data and Digital
Introduction to Machine Learning/Data Analytics for Subsurface Engineering and Geoscience Applications
Advances in machine learning and computing power now make it possible to extract insight from large, varied and real-time datasets. This course introduces how these methods are applied to subsurface data such as production, drilling, seismic and well data. Participants learn the core concepts, data quality practices and pitfalls of data-driven approaches in the industry.
- Discipline
- Data and Digital
- Level
- Foundation
- Duration
- Flexible length
- Formats
- Classroom, In-house, Blended
Every course can be delivered in-house and tailored to your organisation.
Who should attend
Geoscientists, petrophysicists, engineers and others interested in applying machine learning and analytics to subsurface problems.
What participants will be able to do
- Use the essential terminology of data analytics and machine learning
- Verify and improve data quality
- Perform exploratory data analysis to find relationships and outliers
- Distinguish supervised, unsupervised and reinforcement learning
- Recognise and address pitfalls of data-driven methods
Topics covered
- Machine learning fundamentals
- Industry data types
- Data quality
- Exploratory data analysis
- Learning approaches
- Subsurface use cases