Over the past two decades, seismic attributes have become crucial for mapping structure, stratigraphy, and quantifying reservoir properties. Our research group focuses on refining poststack and prestack data, calibrating attributes to geological and engineering standards, and utilizing advanced analysis techniques for unconventional reservoirs. We leverage comprehensive datasets including 3D surveys, production data, well logs, and more.
Our goal is to improve the accuracy of reservoir characterization and hydrocarbon estimation by addressing acquisition, processing, and imaging impacts on seismic attributes. We provide research reports and algorithm source code to sponsors for internal use and client services
Next Annual meeting:
January 29-30, 2026
Univeristy of Oklahoma campus
When well-log and production data are properly aligned, broadband 3D seismic data becomes pivotal in delineating reservoir heterogeneity and compartmentalization. We've found that modern seismic attributes greatly enhance our ability to visualize stratigraphic and tectonic features, even surpassing classical seismic resolution limits. Notably, attribute images computed on limited offset and azimuth volumes from North and West Texas exhibit higher lateral resolution. Additionally, we've observed variations in feature illumination with offset and azimuth, particularly in land surveys rich in azimuths.
Our research focuses on four main goals: mapping reservoir compartments and fractures, optimizing seismic processing workflows for improved resolution, contextualizing seismic attributes with tectonic deformation and geomorphology, and developing predictive tools for guiding reservoir completion programs.
We believe in hands-on understanding of technology through application to the sponsor's own data. Our deliverables include:
Source code, executables, scripts, and graphical user interfaces for new and existing algorithms. Documentation is accessible on this website and through the Help tab for each application. Our algorithms cover a range of functions, including filtering, normal moveout, edge detection, coherence analysis, spectral decomposition, curvature analysis, aberrancy detection, and machine learning tools like PCA, K-means, SOM, GTM, and PSVM.
Copies of all AASPI thesis proposals, posters, preprints, expanded abstracts, and technical papers.
Upon request, generation of geometric attributes or data analysis on proprietary data, adhering to OU tax exempt status guidelines, at time and materials cost.
Assistance with software installation, training, and algorithm utilization at time and materials cost.
Questions & IT contact: aaspi@ou.edu
Check out our hands-on short course material:
For short courses, we use derivatives that we created from data provided by New Zealand Petroleum & Minerals:
Potential sponsors contact us at aaspi@ou.edu for a demo license!
Contact us at aaspi@ou.edu for a demo license!
