Course Catalog

This short course discusses the growing importance of three-component (3C) seismic technology that combines shear waves with compressional waves in the acquisition, processing, and interpretation of surface-seismic and borehole data.

This course consists of 6 modules covering data processing and interpretation techniques used by geoscientists to extract structural, stratigraphic and reservoir properties from seismic data.

The objective of the course is to expose the audience to current trends in visualization and to empower them to adopt these new technologies and to integrate them into their workflows.

This course is a hands-on course that will use open-source Python computational platforms, including numpy, sklearn, pandas, and seaborn. This course will provide working knowledge about data analytics and machine learning tools suitable for petroleum engineers, geophysicists, geologists, and geoscientists. In this course, the participants get access to codes and workflows in Python and they apply these pre-built software tools.

The course is recommended for geophysicists with an undergraduate background who are involved with seismic acquisition, processing or interpretation.

This course will develop participants’ expertise in the writing process and provide insight into the readers’ expectations.

This course is designed to identify a specific operational problem and provide an integrated geophysical tool to help solve that problem.

This field safety program is straightforward, widely applicable, and scalable to a wide range of field activities, from short roadside stops to long, backcountry expeditions.

This course provides participants with knowledge on various aspects of geohazards for shallow water, deepwater environments, as well as an introduction to onshore issues.

An introduction to Python programming with geophysics machine learning applications in mind. This course is 4 hours, and is broken into two, 2-day sessions.

This course focuses on applying Conformal Prediction to probabilistically quantify subsurface uncertainty. It transforms a single prediction from a ML model into a range of possible outcomes, known as a prediction set or interval.

This course is designed to arm the geoscientist with entry to intermediate level oil and gas experience with enhanced geological/geophysical knowledge for lithium exploration and development within a given area.

In this course, the participants get access to codes and workflows in Python and they apply these pre-built software tools on the various types of the datasets. It is a hands-on course that allows participants to learn by assembling various pre-built programming modules to design interesting implementations of machine learning.

This course summarizes the state-of-the-art of seismic reservoir characterization, using anisotropic seismic rock physics.

The course covers the basics and field practices of interferometry, super-virtual interferometry, and parsimonious interferometry for near-surface seismic applications.

The short course will include presentations on passive surface wave theory, data acquisition, data processing, and applications.

In this SEG eight-hour course the basic physics and associated modeled petrophysics relevant to Hydrogen are presented, including the descriptions of the gas, liquid, and super critical phases (scCO₂).

The short course is for physical scientists who have heard about ML and might know some details, but lack enough knowledge to assess ML applications in their specialty.

The course will emphasize on developing and implementing python programming for various types of subsurface data.

The course covers the assessment, quantification, and qualification of the seismic uncertainties in a systematic and technical manner in order to arrive at a risking scorecard.

Earth’s subsurface is heterogeneous, and seismic data have been successfully used in subsurface characterization. We make use of synthetic seismograms in every stages of seismic study: seismic data acquisition, processing and interpretation. In this course, we will focus its uses in seismic interpretation. Synthetic seismograms are computed using a wavelet and a seismic forward modeling method (like, convolution of reflectivity series with a wavelet) to generate seismic response over earth models.

This course is designed for in-person/in-class teaching but can be adjusted for hybrid delivery. There are four thematic modules. Participants are required to follow the course contents, do pre-course reading, and actively participate.

This 16 hour course will include a mix of lectures, short exercises and discussion sessions. The content will be prepared to anticipate the likelihood that participants will have different levels of technical knowledge about petroleum geology.

This course starts with a review of two important new paradigms. The first builds on Wheeler’s work to show why the “Relative Geologic Time” or RGT model takes integrated interpretation to the next level. The second shows how the classic “G&G” approach my many holds us back from true integration, where an “S&S” approach that allows structural geoscientists and stratigraphic scientists to decouple RGT geomodels into structural and stratigraphic workflows in a way that yields superior results.