1. 4. Spectral Decomposition
AASPI
2017 AASPI Workplan
Kurt J. Marfurt
Marcilio Matos
Bo Zhang
Brad Wallet
OU AASPI Team
Attribute-Assisted Seismic Processing and Interpretation

2. Continued graphical data interaction and display
To better build the link from seismic attributes to different facies, we now see the attribute responses of four key facies. All attribute are normalized using z-score. As expected, similar facies (colors) on the SOM map have similar attribute responses. We see the sample vector from the sand-filled channel deposits, has a similar response to that of facies 3, which is the sandy overbank complexes. The inter-channel overbank complex and the mud-filled sinuous channel complex are in similar facies (blue to purple colors). We can also see the difference in seismic amplitudes of the multistoried channel (more chaotic) and older sand filled channel (more flat and higher amplitude).
Tao Zhao, Thang Ha, Yin Zhang 19

3. Aberrancy and the 3rd derivatives of surfaces
The objective
Delineate flexures
But what are the other three roots?
(in kinematics, we have location, velocity, acceleration, and “jolt” or “jerk”)

4. Spectral decomposition and anisotropy analysis
Software development and comparison of VVAz vs. AVAz Azimuth analysis – Jie Qi
Frequency vs. incident angle to better estimate thickness – Fangyu Li
Frequency vs. Azimuth to estimate fractures and stress anisotropy – Fangyu Li
3D near-offset volumetric Q estimation (soft sediments) – Fangyu Li
Variational mode decomposition (difficulty – how to interpret the results!) – Fangyu Li

5. Data condition and texture analysis
Compare poststack kx-ky vs. space-wavenumber CWT footprint suppression workflows – Mohsen Alali
Use space-wavenumber CWT algorithm to quantify periodic geologic features (syneresis, polygonal faulting, dunes and sand bars,… – Mohsen Alali

6. Facies analysis and clustering
Design workflows to help choose the best attributes to discriminate undefined facies – Tao Zhao
Apply weighted attribute workflow to discriminate facies in both conventional and unconventional reservoirs – Tao Zhao
Define software and workflow to allow identification of small facies populations within the seismic data – Tao Zhao
An AASPI artificial neural network algorithm - David Lubo
An AASPI Gaussian mixture model algorithm – Bob Hardesty
Correlation of image logs to seismic facies – Tao Zhao and Stephanie Cook

7. Development of an AASPI neural network algorithm to compare to PSVM15

8. Diffraction imaging, least-squares migration and data interpolation
Separate specular from non specular reflections using 3D estimates of volumetric dip – Mohsen Alali
Compare 5D interpolation computed in unmigrated common midpoints to migration-driven interpolation – Thang Ha
Merge orthogonal narrow azimuth surveys using least-squares migration – Yuji Kim
Quantify the value of least-squares migration on prestack impedance inversion – Thang Ha
Quantify the value of least-squares migration on AVAz analysis – Thang Ha

9. Fault and Horizon Analysis
Correlate water injection wells to fault proximity – Gabriel Machado
Evaluate Wu and Hale fault slip estimation algorithm – Thang Ha
Implement a geochronostratigraphy algorithm – Bo Zhang

10. 4. Spectral Decomposition
AASPI 2017 Workplan
Suggestions from the floor?