Face-to- Face Course

June 23rd to 25th, 2025

Duration : 3 days

WHO SHOULD ATTEND: Seismic interpreters incorporating depth imaging into their evaluations, and depth-processing imagers looking to better interact with interpreters. Training is conducted with a combination of lectures, demonstrations, and illustrative datasets. Participants should be familiar with the seismic method and have several years of interpretation or processing experience.

OBJECTIVES:

An interpreter-oriented approach to the theory, practical application, and interpretive aspects of depth imaging. The course covers the nature of velocities and time-to-depth conversion. Next, is an intuitive overview of migration theory, Kirchhoff (ray) versus RTM (wave) algorithms, tomographic velocity updates, and advances in full-waveform inversion. This course introduces intuitive quality controls and quantitative spreadsheet analysis to plan and ensure stable depth solutions during the iterative depth-imaging process. Advanced database-validation methods are used to identify and remove inconsistencies before deriving anisotropic parameters. The course continues with a robust approach to well-top calibration of the final depth deliverables. Additionally, freeware is provided to provide a statistical method for estimating depth uncertainty. The course reviews advanced attributes derived from depth imaging, including practical aspects of implementing machine learning for classification and estimation

BENEFITS:

You will learn concepts and limitation time and depth imaging.

You will be able to quality control a depth-imaging project.

You will be able calibrate depth-migration deliverables with well tops

You will be able to assess uncertainty in depth calibrations

Course content includes:

• Reviewing time-to-depth conversion methodologies
• Differentiating between time and depth migration imaging capabilities
• Defining target velocity resolution for tomography and related imaging grids
• Establishing database consistency between well tops and interpreted surfaces
• Assessing methods for defining anisotropic imaging parameters
• Performing well-top calibration of depth-imaged volumes
• Defining depth-conversion uncertainty using stochastic analysis software (provided)
• Reviewing practical aspects of machine-learning classification and estimation