3D Seismic Attributes for Prospect Identification and Reservoir Characterization (2-days)
Kurt J. Marfurt
Professor of Geophysics
The University of Oklahoma
Abstract
A seismic attribute is any measure of seismic data that helps us better visualize or
quantify features of interpretation interest. Seismic attributes fall into two broad categories
– those that help us quantify the morphological component of seismic data and those that
help us quantify the reflectivity component of seismic data. The morphological attributes
help us extract information on reflector dip, azimuth, and terminations, which can in turn be
related to faults, channels, fractures, diapirs, and carbonate buildups. The reflectivity
attributes help us extract information on reflector amplitude, waveform, and variation with
illumination angle, which can in turn be related to lithology, reservoir thickness, and the
presence of hydrocarbons.
In the reconnaissance mode, 3D seismic attributes help us to rapidly identify
structural features and depositional environments. In the reservoir characterization mode,
3D seismic attributes are calibrated against real and simulated well data to identify
hydrocarbon accumulations and reservoir compartmentalization.
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 can 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.
Course Outline
Module name
Topics addressed
Introduction
An overview of the use of seismic attributes. A quick review of complex
trace, horizon, and formation attributes. A short review of best ways to
display single attributes and co-render multiple attributes.
Spectral decomposition
Theory, workflows, and advantages of the three most commonly used
spectral decomposition algorithms (DFT, CWT, and matching pursuit).
Their use not only in mapping "tuned" lithologies but also as input to
bandwidth extension, Q estimation, and phase discontinuity mapping of
unconformities.
Geometric attributes
A summary of volumetric dip/azimuth, coherence, Sobel filter, amplitud
gradients, amplitude and structural curvature, reflector shapes,
reflector rotation, and reflector convergence.
Attribute expression of
tectonic deformation
Attribute expression of faulting and folding as seen on post stack
volumes by coherence, curvature, and reflector rotation.
Attribute expression of
clastic depositional
environments
Attribute expression of fluvial/deltaic and deepwater systems as seen
on post stack volumes by spectral decomposition, coherence, curvature,
and reflector convergence attributes. Attribute expression of differential
compaction.
Attribute expression of
carbonate deposition
environments
Attribute expression of carbonate buildups and diagenesis as seen on
post stack volumes by coherence, curvature, and texture attributes.
Attribute expression of karst terranes.
Attribute prediction of
fractures and stress
Use of curvature, impedance, and seismic anisotropy to map the
orientation and intensity of natural fractures and/or horizontal stress.
Calibration with lidar data and image logs.
Inversion for acoustic and
elastic Impedance
A hierarchal overview of inversion - emphasizing the assumptions and
interpreter input to each process.
Interactive multiattribute
analysis
Review of multiattribute display, crossplotting, and geobodies. Principal
component analysis
Statistical multiattribute
analysis
Fundamentals of geostatistics, including kriging, kriging with external
drift, colocated cokriging, sequential Gaussian simulation, and
geostatistical impedance inversion.
Unsupervised
multiattribute
classification
Clustering algorithms including k-means, self-organizing maps (e.g.
Stratimagic's "waveform classification") and generative topographic
maps.
Supervised multiattribute
classification
A simple overview and application of neural networks and support
vector machine algorithms.
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.
Advanced knowledge of seismic theory is not required; this course focuses on
understanding and practice.
Kurt J. Marfurt joined The University of Oklahoma in 2007 where
he serves as the Frank and Henrietta Schultz Professor of Geophysics
within the ConocoPhillips School of Geology and Geophysics.
Marfurt’s primary research interest is in the development and
calibration of new seismic attributes to aid in seismic processing,
seismic interpretation, and reservoir characterization. Recent work
has focused on applying coherence, spectral decomposition, structureoriented filtering, and volumetric curvature to mapping fractures and
karst with a particular focus on resource plays. Marfurt earned a Ph.D. in applied geophysics
at Columbia University’s Henry Krumb School of Mines in New York in 1978 where he also
taught as an Assistant Professor for four years. He worked 18 years in a wide range of
research projects at Amoco’s Tulsa Research Center after which he joined the University of
Houston for 8 years as a Professor of Geophysics and the Director of the Allied Geophysics
Lab. He has received SEG best paper (for coherence), SEG best presentation (for seismic
modeling) and as a coauthor with Satinder Chopra best SEG poster (for curvature) and best
AAPG technical presentation. Marfurt also served as the EAGE/SEG Distinguished Short
Course Instructor for 2006 (on seismic attributes). In addition to teaching and research duties
at OU, Marfurt leads short courses on attributes for the SEG and AAPG.
Results of unsupervised multiattribute classification using generative topographic mapping,
co-rendered with coherence, over a turbidite system, offshore New Zealand. Input attributes
included peak spectral frequency, peak spectral magnitude, curvedness, and GLCM entropy.
(After Zhao et al., 2015; data courtesy of New Zealand Petroleum Ministry).