3D Seismic Attributes for Reservoir Characterization – SARC
The primary objective of this course is to gain an intuitive understanding of the kinds of seismic features that can be identified by 3D seismic attributes, the sensitivity of seismic attributes to seismic acquisition and processing, and how independent seismic attributes are coupled through geology. We will also discuss alternative workflows using seismic attributes for reservoir characterization as implemented by modern commercial software and practiced by interpretation service companies. Participant discussion centered around case studies, attribute recipes for particular objectives, reservoir workflows and seismic attribute jeopardy exercises will be the main focus of the course.
"The course content was such that it is suitable across experience levels as well as across various geoscience disciplines. Also the Q&A sessions were very useful." - Participant, India
Who should attend?
Seismic interpreters, processors, stratigraphers and structural geologists, reservoir engineers, and students of geophysics.
- Types of attributes
- Impact of seismic data quality on seismic attributes
- Methods for preconditioning of seismic data
- Introduction of various algorithms for attribute computation, their limitations and performance strengths
- Attribute expression of structure and stratigraphy in terms of tectonics and diapirism, clastic and carbonate depositional systems and geologic hazards
- Multi-attribute analysis tools
- Reservoir characterization workflows
- Physical demonstration of attributes on real seismic data
Certification / Credits
You will learn how to:
- Use attributes to enhance subtle faults and folds, as lithologic indicators, and quality control the choice of processing parameters
- Evaluate and exploit attribute expressions for different depositional environments to better characterize reservoirs by adopting appropriate workflows and multi-attribute tools
- Identify geological features highlighted by attributes, limitations to seismic processing through attributes that may result in smeared attribute images from multi-azimuth and multi-offset data, limits of attribute analysis on data that have been poorly imaged and good and bad color display practices
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