Instructor: Tom Smith
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 course is presented in English only. The video recording was originally produced as part of a GSH webinar.
The 24 high-quality videos present over 11 hours of material on the application of machine learning to seismic Interpretation. Your purchase of this course provides access for 180 days. Return to view the videos as often as you like. Please let us know if you find a graphic or two that you would like and we’ll be glad to share it. The course is offered with and without certification, as described below.
If you would like to be considered for certification with distinction, submit answers to the four optional written questions corresponding to each of the four units of the course. Your answers will be evaluated by a geoscientist in consideration for the certificate with distinction.
Machine learning is foundational to the digital transformation of the oil & gas industry and will have a dramatic impact on the exploration and production of hydrocarbons. Dr. Tom Smith, the founder and CEO of Geophysical Insights, conducts a comprehensive survey of machine learning technology and its applications to seismic interpretation in this 24-part series. Machine learning is presented in a clear, cogent way that identifies a whole new set of tools that will transform interpretation workflows. Using machine learning technology, geoscientists and engineers will operate on data at a level of insight that promises to reduce risks and identify features that might otherwise be missed.
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 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?
Machine Learning Essentials can be taken either with or without certification. The certification requires correct answers to 80% or greater of the questions at the end of each of the four major sections; however, you may take the quizzes as often as you like. The course contains a total of 88 questions divided up in four sections, one for each unit. The certificate of completion will be presented in printable form upon successful completion of the course. Please see the non-certification course below if you prefer to take the course without the certification.