Geophysics for Data Scientists (GEO 132)
COURSE SCHEDULE
| Code | Date | Location | price (€)* |
|---|---|---|---|
| GEO 132 | 3 - 7 Aug 2026 | Online | 3300 |
| GEO 132 | 2 - 6 Nov 2026 | Online | 3300 |
| GEO 132 | 7 - 11 Sep 2026 | Dubai | 4440 |
| GEO 132 | 5 - 9 Oct 2026 | Stavanger | 4440 |
COURSE OVERVIEW
In discussions at the EAGE, it was emphasized that not only the Subject Matter Experts (SME’s) had to become familiar with the terminology and methods used by the Data Scientists, but also the Data Scientists must understand what geology and geophysics is about. That doesn’t mean they need to know the ins-and-outs of these subjects but at least know the terminology and the overall context for which they need to provide the Machine / Deep learning tools. Therefore, this course will be a first step in providing the necessary geophysical background. Assumed that the Data Scientists are familiar with mathematics and statistics, the course will include advanced geophysical subjects. A general overview of seismic and non-seismic acquisition, processing and interpretation will be followed by various uses of Machine / Deep learning for Geophysical Applications. Use will be made of a whole range of open-source Deep Learning algorithms for geophysical applications.
COURSE OUTLINE
5 days
Day 1:
o Geophysical Methods
o Seismic Acquisition
o Sampling & Aliasing
o Field Record
o Seismic Processing
Day 2:
o Wave propagation
o Reflection & Transmission
o Fourier Transform
o Correlation, Convolution
o Deconvolution
Day 3:
o Diffraction curves
o Migration using diffraction curves
o Migration using wavefronts
o Depth Migration
Dayb 4:
o Stretch TD Conversion
o Ray tracing TD Conversion
o ML: Lithology Classification
o ML Facies Clustering
Day 5:
o ML Oil saturation Regression
o ML Salt Segmentation
o AI Inversions
INSTRUCTOR
Instructor Profile
Instructor has a PhD from Utrecht University on “Full wave theory and the structure of the lower mantle” and joined Shell Research to develop methods to predict lithology and pore-fluid based on seismic, petrophysical and geological data. Subsequently worked for Shell in London to interpret seismic data from the Central North Sea Graben.
As part of a Quantitative Interpretation assignment, he was actively involved in managing, processing and interpreting Well Seismic Profiling data, while heading a team for the development of 3D interpretation methods using multi-attribute statistical and pattern recognition analysis. Subsequently he was responsible for Geophysics in the Shell Learning Centre and at the same time part-time professor in Applied Geophysics at the University of Utrecht. From 2001 till 2005 he worked on the development of Potential Field Methods (Gravity, Magnetics) for detecting oil and gas. From 2008 til 2013 he was visiting professor at the German Technical University in Muscat. Finally, he became a champion on the use of EM methods and involved in designing acquisition, processing and interpretation for Marine Controlled Source EM (CSEM) methods.
FAQ
DESIGNED FOR
Data Scientists who will be cooperating with geoscientists to develop AI methods for exploration and development of hydrocarbons or mineral resources. Applications for geothermal and CO2 storage are discussed.
LEARNING OBJECTIVES
The course's primary learning objectives are:
o Which geophysical methods are available
o How can seismic be acquired and processed to obtain subsurface models
o Methods based on potential field data: gravity, magnetic
o Applications of electrical and electromagnetic surveys.
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Ph.D. students, group and early bird registrants are eligible to DISCOUNT!
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