1. 4. Spectral Decomposition
AASPI
2016 AASPI Workplan
Kurt J. Marfurt
Marcilio Matos
Bo Zhang
Brad Wallet
OU AASPI Team
Attribute-Assisted Seismic Processing and Interpretation

2. 4. Spectral Decomposition
AASPI 2015 Workplan
Software Development
Continued construction of “workflows” that link common steps for
Preconditioned least-squares migration Thang Ha
Migration-driven 5D interpolation Thang Ha
Poststack or common angle Q estimation Fangyu Li
Diffraction imaging Yuji Kim
Multispectral fault delineation followed by fault enhancement
Fault skeletonization
Computation of volumetric aberrancy Dania Shaib
Level set object and surface detection Tao Zhao
An AASPI artificial neural network algorithm Yin Zhang

3. 4. Spectral Decomposition
AASPI 2016 Workplan
Diffraction imaging
Time-processed shot gathers
Migration velocities
Diffraction image?
Demigrated shot gathers
CLSM with cosθ obliquity factor
CLSM with (1-cosθ) obliquity factor
Migrated gathers
Workflow for diffraction imaging which holds promise in mapping natural and induced fractures in resource plays. One approach is to migrate the data using 1.0 minus the obliquity factor. Early migration algorithms (e.g. Schneider, 1978) set the obliquity factor to be centered about the vertical where in principal it should be normal to structural dip. The obliquity factor cosθ favors specular reflections while 1-cosθ favors diffractions.
Dip and azimuth
cosθ
stack
dip3d
Yuji Kim

4. Level Sets and Deformable Models (the mathematics of shrink-wrapping)
12. Image Enhancement and Object Extraction
Level Sets and Deformable Models (the mathematics of shrink-wrapping)
Shrink-wrapped boat
Shrink-wrapped beer
Papers on level sets (sometimes called deformable models) can be highly mathematical, with many of the mathematical constructs somewhat ‘ad hoc’. I think of level sets as ‘shrink wrapping’. My boat and boat cover are OU crimson and cream, but this image gives you the idea. I usually drink canned beer in the boat to protect future generations of swimmers, but the image of a shrink-wrapped six-pack gives you the idea of a level set. Physically, we wish to shrink-wrap a feature such as an irregular salt dome (imaged say by low coherence and low energy) within an elastic surface such that almost all voxels with the desired features fall inside the surface while voxels not having such desired properties fall outside the surface. The elastic surface has nice properties such as smooth derivatives in 2 dimensions.
Objective:
Enclose (almost) all the voxels of the volume of interest
Constrain the surface to have constraints on curvature (strain)
Tao Zhao

5. 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”)

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

7. AASPI 2016 Workplan 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, Tengfei Lin, Yin Zhang 19

8. 4. Spectral Decomposition
AASPI 2015 Workplan
Software Development
Velocity vs. Azimuth (VVAz) via correlation Jie Qi
Frequency vs. Azimuth (FVAz) Fangyu Lin
Wave equation least-squares migration for land surveys Bin Lyu
Q-compensation internal to migration Bin Lyu
Wavelet based Radon transforms Tengfei Lin

9. 4. Spectral Decomposition
AASPI 2016 Workplan
Correlation of attributes to “specialty logs”
Finalized license with Pioneer on Permian Basin data (microseismic, image logs,…?) Megan Gunther? Saurab Sinha?
Chesapeake Miss Lime data (image logs, ROP, …?) Joseph Snyder, Xuan Qi, Stephanie Cook, Mohsen Alali
Correlate prestack inversion, MWD, image logs to natural and induced fractures in RedFork Energy Miss Lime survey – Trey Stearns and Mohsen Alali
Will continue work on TOC prediction (Devon data)
Correlation of attributes to production
Obtained license from Devon Energy to perf locations in two Barnett Shale and one Granite Wash survey
Have requested access to perf locations from Chesapeake Miss Lime survey
Correlation of attributes to hydraulic fracturing
May have lost champion at Chevron (John Best) for “time lapse survey”

10. TOC estimation from well logs
Gamma Ray
Low
High
Density
Resistivity
Neutron Porosity
WTOC
Discretized WTOC
Estimated WTOC MD
(ft)
8100
8200
8300
8400
Well A
Discretized WTOC
Low MD
(ft)
8100
8200
8300
Estimated WTOC
High
Well A
8400
6500
6600
6700
Well B
WTOC
Regression WTOC 12

11. 4. Spectral Decomposition
AASPI 2016 Workplan
Seismic geomorphology
Have license to large 3D data volumes from New Zealand and Australia – contains volcanic intrusives, turbidites, FLTs – Lennon Infante and multiple geology students
Will acquire licenses to large 3D volumes from Australia – carbonate banks, buildups, dewatering, … Brad Wallet and multiple geology students
Salt segmentation
Finalizing license with PGS to 3600 mi2 from GOM shelf – Jie Qi and multiple geology students
Attributes from 2D vs. 3D surveys
2D and 3D data from New Zealand - Bryce Hutchinson

12. 4. Spectral Decomposition
AASPI 2016 Workplan
Seismic data processing
Legacy data from Central Basin Platform (CP) – Thang Ha and Gabriel Machado
Perpendicular 3D surveys from Jeju Basin (KIGAM) – Yuji Kim
Ground roll suppression and prestack inversion of Granite Wash (Devon) – Tobi Olorunsola
Ground roll suppression and prestack inversion of Mississippi Lime (Chesapeake) – Mohsen Alali

13. 4. Spectral Decomposition
AASPI 2016 Workplan
Suggestions from the floor?