Seismic Attributes – from Interactive Interpretation to Machine Learning

Kurt Marfurt

In this course, we will 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 of 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. Participants are invited to bring case studies from their workplace that demonstrate either the success or failure of seismic attributes to stimulate class discussion.


16 hours

Intended Audience


Prerequisites (Knowledge/Experience/Education required)

Advanced knowledge of seismic theory is not required; this course focuses on understanding and practice.

Who should attend?

  • Seismic interpreters who want to extract more information from their data.
  • Seismic processors and imagers who want to learn how their efforts impact subtle stratigraphic and fracture plays.
  • Sedimentologists, stratigraphers, and structural geologists who use large 3D seismic volumes to interpret their plays within a regional, basin-wide context.
  • Reservoir engineers whose work is based on detailed 3D reservoir models and whose data are used to calibrate indirect measures of reservoir permeability.

Course Outline

  1. Introduction
  2. Spectral decomposition
  3. Geometric attributes
  4. Attribute expression of tectonic deformation
  5. Attribute expression of clastic depositional environments
  6. Attribute expression of carbonate deposition environments
  7. Attribute prediction of fractures and stress
  8. Inversion for acoustic and elastic impedance
  9. Interactive multiattribute analysis
  10. Statistical multiattribute analysis
  11. Unsupervised multiattribute classification
  12. Supervised multiattribute classification

Learner Outcomes

  • Use time slices, phantom horizon slices, and stratal slices through attribute volumes to illuminate stratigraphic features of geologic interest.
  • Apply single and multiattribute color display techniques to effectively communicate attribute images features to others.
  • Identify geological features highlighted by spectral decomposition and wavelet transforms in terms of thin bed tuning.
  • Evaluate the impact of spatial and temporal analysis window size on the resolution of geologic features.
  • Use folds and faults imaged by curvature attributes to predict paleo fractures.
  • Predict which attributes can be used to image the lateral extent of features that fall below vertical seismic resolution.
  • Couple mathematically independent attributes to map different components of the same geologic feature (e.g. bright spots and structural highs, differential compaction seen in curvature, and edges seen in coherence).
  • Recognize acquisition footprint on seismic attribute time and horizon slices.
  • Identify the limits of attribute analysis on data that have been poorly imaged.
  • Differentiate and choose between relative, band-limited, model-driven, and geostatistical inversion algorithms.
  • Choose an appropriate clustering algorithm to combine independent attributes to better delineate geologic features.
  • Use visualization and crossplotting to validate attribute predictions using image logs, microseismic event maps, and well logs.

Instructor Biography

Kurt Marfurt