3D Seismic Attributes for Prospect Identification and Reservoir Characterization (5-day) 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.
Transcript
3D Seismic Attributes for Prospect Identification and Reservoir Characterization (5-day)
Kurt J. MarfurtProfessor 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 how seismic attributes fit within modern interpretation workflows.
Complex trace, horizon, and formation attributes
Theory, definition, and limitations of attribute based on the analytic (or complex trace) such as envelope and instantaneous frequency. Definition and use of attributes computed from a horizon, such as dip magnitude and horizon-based curvature as well as formation attributes computed between horizons, such as RMS amplitude and thickness.
Multiattribute display
Definition and interrelationship between RGB, CMY, and HLS color models. Best practices for multiattribute display. Definition and use of horizon slices, phantom horizon slices, stratal slices, and Wheeler slices.
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, amplitude and structural curvature, reflector shapes, reflector rotation, reflector convergence, and GLCM texture attributes.
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 refector 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 terrains.
Attribute expression of shallow stratigraphy and drilling hazards
Attribute expression of mass transport complexes, glide tracks, outrunner blocks, pock marks, glacial keel marks,and shale "dewatering" (syneresis) features, many of which when gas- or water-charged may become drilling hazards.
Attribute expression of igneous extrusive and intrusive rocks
Attribute expression of volcanic mounds, sills, fractured basement, and lacoliths which can serve as or give rise to reservoirs. Impact of overlying igneous rocks on seismic data quality.
Impact of acquisition and processing on seismic attributes
Value of long-offset, wide-azimuth, and dense seismic surveys in seismic data quality and attribute analysis.
Poststack seismic data conditioning
Spectral balancing, structure-oriented filtering and footprint suppression of poststack data volumes.
Prestack seismic data conditioning
Prestack structure-oriented filtering, nonhyperbolic moveout, and correction of NMO/migration stretch. Preconditioned least-squares migration and 5D interpolation.
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.
Image enhancement and object detection
Algorithms that enhance faults and channels to generate computer "objects". Lay-person's explanation of modren ant-tracking, skeletonization, and level set algorithms that indicate the future of computer-assisted seismic interpretation.
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.
Attributes and hydraulic fracturing of shale reservoirs
Review of the microseismic method and the relationship of microseismic events to surface seismic measurements. The use of prestack impedance inversion in predicting brittleness. Calibration using microseismic events and production logs.
Attribute applications to the Mississippi Lime
Recent work in mapping the unconventional Mississippi Lime play in OK and KS. Synthesizes previous sections on prestack impedance inversion, curvature, texture analysis and Self-organizing maps.
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, 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 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).
Phantom horizon slices 20 ms above the top Viola limestone through amplitude vs. azimuth (AVAz) anisotropy strike Ψazim modulated by its value Baniso . Most-
positive curvature is plotted against a gray scale and shows subtle faults. The survey in the NW has been hydraulically fractured while that in the SE has not. Note the compartmentalization of azimuth in the upper left survey, where curvature acts as fracture barriers. Note the stronger anisotropy (brighter colors) in the SE survey which had not yet been hydraulically fractured. (Image courtesy of Shiguang Guo, OU).
