3D Seismic Attributes for Prospect Identification and Reservoir Characterization (2-Days)

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.
The impact of acquisition, processing on attributes / Attribute response to of acquisition footprint, velocity pull-up and push-down, and other limitations in seismic data
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.
Data integration using interactive multiattribute analysis / Review of multiattribute display, crossplotting, and geobodies. Principal component analysis
Data integration using statistical multiattribute analysis / Fundamentals of geostatistics, including kriging, kriging with external drift, colocated cokriging, sequential Gaussian simulation, and geostatistical impedance inversion.
Data integration using unsupervised multiattribute classification / Clustering algorithms including k-means, self-organizing maps (e.g. Stratimagic's "waveform classification") and generative topographic maps.
Data integration using 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.

About the instructor:

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, structure-oriented 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 eight years as a Professor of Geophysics and the Director of the Allied Geophysics Lab. He has received the SEG best paper (for coherence), SEG best presentation (for seismic modeling), as a coauthor with Satinder Chopra best SEG poster (one on curvature, one on principal component analysis) and best AAPG technical presentation, and as a coauthor with Roderick Perez Altimar, SEG/AAPG Interpretation best paper (on brittleness) awards. Marfurt also served as the EAGE/SEG Distinguished Short Course Instructor for 2006 (on seismic attributes). He will go on tour again in 2018 for the SEG, presenting a one-day course on “Seismic attributes as the framework for data integration throughout the oil field lifespan”. In addition to teaching and research duties at OU, leading short courses on attributes for the SEG and AAPG, Marfurt currently serves as Editor in Chief of the AAPG/SEG Journal Interpretation.

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).