3D Seismic Attributes for Prospect Identification and Reservoir Characterization

by Kurt Marfurt

Duration: Two days

Summary:
Each participant 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 how 'independent' seismic attributes are coupled through geology.

Course Description:

• Introduction
• Complex Trace, Horizon, and Formation Attributes
• Multi-attribute Display
• Spectral Decomposition
• Geometric Attributes
• Attribute Expression of Geology
• Impact of Acquisition and Processing on Attributes
• Attributes Applied to Offset- and Azimuth-Limited Volumes
• Structure-Oriented Filtering and Image Enhancement
• Inversion for Acoustic and Elastic Impedance
• Multiattribute Analysis Tools
• Reservoir Characterization Workflows
• 3D Texture Analysis

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 high, differential compaction seen incurvature and edges seen in coherence).
• Recognize acquisition footprint on seismic attribute time and horizon slices.
• Apply attributes to azimuth-limited impedance volumes to identify fracture trends.
• 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

Method of Assessment: In class group activity

Instructor Biography:
Kurt Marfurt