Seismic Attributes and Their Applications in Seismic Interpretation



  Dr Behzad Alaei (Earth Science Analytics, Bergen, Norway)


  1 day


  Geophysics –Integrated Geophysics




  English, Norwegian, Persian


  5 CPD points




Course description

Seismic attributes have been increasingly used in both exploration and reservoir characterization and has been integrated in the seismic interpretation process. Seismic attribute analysis can extract information from seismic data that is otherwise hidden and have been used to identify prospects, ascertain depositional environments (e.g. fluvial or deep water channels, carbonate buildups), detect and enhance faults and fracture sets to unravel structural history, and even provide direct hydrocarbon indicators. They have proven to be useful in different geological settings such as clastic, carbonate, and salt related basins as well as different tectonic regimes including extensional, strike-slip, and compressional. Developments in digital recording and modern visualization techniques had great impact on the growth of seismic attributes in the past decades. The purpose of this course is to introduce seismic attributes with their applications in seismic interpretation using examples from different sedimentary basins and also through certain attribute workflows. It is aimed to provide geoscientists with the minimum required theory of how each attribute is generated, with a greater emphasis on the application in the exploration and reservoir characterization.

The course is divided into two parts: attributes review/applications and workflows. The first part starts with a review of seismic attributes and discusses the noise (random and coherent) reduction as one essential step of all attribute studies. The number of seismic attributes has recently increased dramatically causing confusion for geoscientists to select appropriate ones. In this course, trace-based attributes, volumetric dip and azimuth, fault detection and enhancement attributes, volumetric curvature, and frequency decomposition are presented using examples from different geological settings. Frequency decomposition is briefly presented with different decomposition methods such as wavelet transform, Fourier transform and matching pursuit analysis. Examples illustrate the interpretation challenges associated with frequency decomposition data interpretation. The concept of multi-attributes and geobody extraction is introduced at the end of the first part of the course with examples on combinations of amplitude, phase, discontinuity and frequency attributes to visualize different geological objects.

In the second part of the course stratigraphic and structural workflows are presented. The workflows (and the elements for their planning) aim to show the integration of several attributes for specific interpretation purposes, with examples of stratigraphic (fluvial/shallow marine clastic systems, attribute expressions of deep water turbidites and carbonate settings) and structural imaging workflows. Lastly, the course analyses the importance of the integration of seismic attribute analysis processes with the other seismic interpretation (qualitative or quantitative) workflows.


Course objectives

Upon completion, participants will be familiar with a range of relevant attributes used in seismic exploration and reservoir characterization. They will know the basics of how those attributes were calculated and will gain understanding of their applications in seismic interpretation. They will be able to plan some attribute workflows and they will know how to integrate attribute analysis with other disciplines of qualitative/quantitative seismic interpretation.


Course outline

Part I: Seismic Attributes

  1. Introduction:
    • Definition and historical review
    • Structure of the short course
  2. Input data cleaning:
    • Noise reduction applications with examples
    • Workflow oriented noise removal process
    • Focus on structurally oriented edge preserving methods to remove noise
    • Mean and median filters for noise removal
  3. Trace-based attributes:
    • Complex trace analysis and the elementary attributes of envelope (reflection strength), instantaneous phase, instantaneous frequency, and cosine of phase attributes
    • Simple examples with interpretation applications
  4. Dip and Azimuth volumes:
  • Quantitative estimate of dip and azimuth through seismic volumes to map morphology of seismic texture
  • Introduction and theory
  • Dip and Azimuth calculation methods including:
    1. Calculating temporal and spatial derivatives of the phase estimated using complex trace analysis
    2. Explicit dip scan to find the most coherent reflector
    3. Gradient structure tensor
  • Examples with applications for both structural and stratigraphic interpretation aspects.

5. Coherence (Measurements of the similarity of seismic waveform)

  • Introduction
  • Different approaches including:
    1. Cross correlation
    2. Semblance
    3. Variance
    4. Eigenstructure
    5. Gradient structure Tensor-based coherence
  • Role of dip and azimuth steering volumes on coherence calculation
  • Several examples and interpretation criteria

6. Fault attributes, attribute enhancement approaches:

  • Identify objects representing faults from background noise.
  • Apply filters to enhance already detected faults from background noise.
  • Plan different filter sizes to enhance faults with different scales (regional to small scale).

7. Curvature attribute:

  • Definition and background theory
  • Surface and volume curvature measurements
  • Interpretation applications using some examples

8. Frequency decomposition:

  • Introduction and mathematics of spectral decomposition using graphic illustrations
  • Review of decomposition methods:
    • DFT (discrete Fourier transform)
    • CWT (continuous wavelet transform)
    • MPD (matching pursuit decomposition)
  • Examples and applications in layer thickness estimation, stratigraphic variations (seismic facies) and Direct Hydrocarbon detection
  • Non-uniqueness will be addressed together with resultant challenges in interpretation of frequency decomposed data

9. Multi attributes, geobody extraction, and iso proportional slicing:

  • Some attribute blending methods such as RGB blending, and opacity blending
  • Geological object identification
  • Machine learning examples of multi attributes
  • Selection of appropriate attributes
  • Quantitative extraction of certain attribute volumes
  • Iso proportional slicing as an important interpretation tool

Part II: Workflows

  1. Seismic attribute analysis workflow planning:
    • Stratigraphic, structural, reservoir characterization
    • Factors controlling the seismic attribute workflow planning
  2. Workflow examples: fault imaging, carbonate imaging
  3. Integration of attribute analysis with other disciplines of seismic interpretation


Participants' profile

The course addresses geoscientists involved in exploration and production projects where seismic studies play a role and who wish to learn the basic theory of the main seismic attributes used in exploration and production, as well as their applications and how to integrate them in exploration and reservoir characterization studies.



Participants should have knowledge of seismic interpretation. Mathematical concepts of attributes are presented with minimum required equations and graphic illustrations. Some basic knowledge of seismic exploration may help.


About the instructor

Behzad Alaei

Dr. Behzad Alaei is geophysicist and co-founder of Earth Science Analytics AS. He has PhD in exploration seismology from University of Bergen, Norway. He has 25 years of industry and research experience focused on seismic exploration, forward modelling of complex structures, seismic imaging, seismic attributes, and machine learning applications in geoscience. He carried out several seismic attribute studies over different sedimentary basins from Asia to Norwegian continental shelf and Gulf of Mexico. In the recent years, he has been involved in the integration of seismic fault attributes with structural geological investigations of faults as well as development of machine learning techniques in geoscience.
He is a member of EAGE, SEG, and CSEG.




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