4. Spectral Decomposition AASPI Overview of AASPI accomplishments and software development Kurt J. Marfurt Attribute-Assisted Seismic Processing and Interpretation

AASPI 2014 Sponsors AASPI Welcome 2012 Our sponsors as of November 2014.

AASPI Software Package AASPI Welcome 2012 AASPI Software Package Volumetric Attributes geometric attributes spectral decomposition GLCM texture and disorder attributes Prestack and Poststack Data Conditioning pre- and post-stack structure-oriented filtering residual velocity analysis non-stretch NMO footprint suppression acquisition patch-based coherent noise suppression preconditioned least-squares time migration Laplacian of Gaussian fault plane enhancement The AASPI software package runs on both Linux and Windows. Most applications run in parallel, such that if your PC has 16 processors, it will use all of them if you ask it to. The software is broken into four general areas – volumetric attributes, data conditioning, correlation tools, and cluster analysis (i.e. looking for hidden patterns and relationships in the data). Areas in pink are major additions in 2014, though all codes are routinely modified and extended.

AASPI Software Package AASPI Welcome 2012 AASPI Software Package Correlation tools Azimuthal intensity (“fault proximity”) Vector correlation Prestack Analysis tools AVAz Cluster Analysis Self-organizing maps Generative topological maps Principal component analysis Support vector machines Polygonal cluster selection in histogram (SOM) Page 2 of the previous list.

AASPI Software Package Sponsors have full source code Data formats are the same as Stanford’s SEPLib and UT Austin/CSM’s Madagascar Documentation online and accessible from the program. Continue to add details and examples Same applications, GUIs, and scripts run on Linux and Windows Computationally intensive algorithm parallelized, running on multi threaded Windows PCs and across Linux clusters. (One sponsor runs across Windows PCs)

AASPI Welcome 2012 mcee.ou.edu/aaspi documentation We keep all our documentation online under mcee.ou.edu/aaspi . This makes it easy for our sponsors to access, and will also turn up in google-type searches which forms a type of subliminal advertising. In contrast, the software is password protected. Sponsors receive both executables and source code for their use and modification.

Most applications have a flow chart AASPI Welcome 2012 Most applications have a flow chart Here is a representative page from our documentation. Most applications include a flow chart.

Many applications now have gray “Theory” boxes AASPI Welcome 2012 Many applications now have gray “Theory” boxes Many documentation of many applications (and more as we proceed) include a theory section, often in a gray box, to allow the interested user to see exactly how a given code has been implemented. This theory is augmented by publications also found on the same web site, where greater detail and case studies are presented.

AASPI Welcome 2012 Followed by instructions defining parameters and how to complete the GUI The documentation has a step by step instruction, much as I would teach lab exercises in a classroom environment. All software is accessed via graphics user interfaces (GUIs). When the mouse is moved over various parameters, simple help and tip comments will appear.

These are followed by references and examples AASPI Welcome 2012 These are followed by references and examples We also attempt to include case studies and references for further insight as to how the software can be used.

Snapshot of some recent software releases and workflows AASPI Welcome 2012 Snapshot of some recent software releases and workflows Here are some recent releases as of the end of 2014.

Multiattribute Display N 1 Time (s) 1 mi Azimuth of Reflector Convergence modulated by its Magnitude co-rendered with Variance and Amplitude Pinchout Karst Unconf Karst Pinchout Pinchout Fault Grooves Pinchout -180 +180 Opacity (%) 100 Converge Azim (degrees) N S E W Conv Mag Vertical slices and a time slice at t=1.150 s through the azimuth of reflector convergence index plotted against hue as the background and the magnitude of reflector convergence plotted against saturation. Seismic amplitude plotted on the vertical slice, and variance plotted on the time slice against lightness. Note the W-NW trending pinch-out seen in cyan on the vertical slice that can be traced across the time slice. Fault 0.004 100 Opacity (%) Conv Mag 0.0 0.2 0.5 100 Opacity (%) Variance 0.1 0.3 0.4 -2500 +2500 100 Opacity (%) Amp

Correlation of production to seismic attributes: Miss. Lime Kay Co Correlation of production to seismic attributes: Miss. Lime Kay Co., OK ZP (g-ft/cm3-s) 60000 40000 Grayscale Amplitude 1 -1 Pre-stack ZP impedance along Mississippi horizon shown with well bores and cumulative oil production from the Mississippi Lime posted as green circles at the top of the well bore. Amplitude (grayscale) shown in cross-line and inline. Magnitude of oil production is directly related to the size of the circle. Figure 64. Pre-stack ZP impedance along Mississippi horizon shown with well bores and cumulative oil production from the Mississippi Lime posted as green circles at the top of the well bore. Amplitude (grayscale) shown in cross-line and inline. Magnitude of oil production is directly related to the size of the circle.

Correlation of AVAz and curvature? AASPI Welcome 2012 Correlation of AVAz and curvature? AVAz k1 Vector correlation This image shows the results of a new algorithm that correlates two vectors, amplitude vs. azimuth and the strike and magnitude of curvature each of which may be correlated to natural fractures and horizontal stress. This example comes from Osage Co., OK. Top Miss Lime Osage Co., OK Data courtesy of Spyglass LLC

Why is coherence so discontinuous? Seismic Attributes Pop-up structure (1:1 scale) D D’ -500- k1 k1 -750- k2 Fault Fault k2 k1 -1000- 0.4 Amp Time(ms) pos -1500- -0.4 k2 neg 0.4 -1750- This image comes from Onur Mutlu’s M.S. thesis analyzing the unconventional Chicontepec reservoir of Mexico, where he shows that the lack of fault lineaments seen in coherence is due to overprinting of the image by strong, coherent interbed multiples. All figures are displayed with 1:1 ratio. Positive curvature anomalies are observed on the upthrown blocks, whereas negative curvature anomalies are observed on the downthrown blocks. -2000- -0.4 -2250- 1 km Why is coherence so discontinuous? 28

Poststack structure-oriented filtereing AASPI Welcome 2012 Poststack structure-oriented filtereing … (1, 9) trace , 9 x 9 covariance matrix k k+1 k+2 k-1 k-2 time Inline crossline slice dk We have also made considerable progress in data conditioning. This image shows the computational stencils for post-stack single window structure-oriented filtering. Bo Zhang has worked on extending this algorithm to prestack migrated data. Figure 1a.

Poststack structure-oriented filtereing AASPI Welcome 2012 Poststack structure-oriented filtereing 9 x 9 covariance matrix “eigenmap” v1 Obtain α by cross correlating with slice dk PCA=α v1 The workflow of poststack structure-oriented filtering. In program sof3d, we compute a covariance matrix about each window oriented along structural dip, compute eigenvectors, and project them onto the data slice resulting in a principal-component filtered data volume. Such filtering preserves amplitude and suppresses cross-cutting noise. In actual implementation, we would chose the most coherent of 9 overlapping windows (for this stencil) to perform our filter. Figure 1b.

Prestack structure-oriented filtereing offset m-1 k k+1 k+2 k-1 k-2 time Inline crossline slice dk offset m offset m+1 … (1, trace , 27) 27 x 27 covariance matrix In prestack structure-oriented filtering we filter not only along structural dip for a given offset and azimuth, but also across adjacent offsets and azimuths. Several filters are provided, with non-linear LUM and alpha-trimmed mean filters working better for data with spikes in them. Figure 2a.

“eigenmap” v1 to obtain the signal component for slice dk Poststack structure-oriented filtereing 27 x 27 covariance matrix “eigenmap” v1 to obtain the signal component for slice dk Obtain α by cross correlating with slice dk PCA=α v1 Figure 2b.

Build sample vectors for offset m at analysis Structure oriented filtered gathers Migrated seismic gathers Build sample vectors for offset m at analysis index k along local reflector’s orientation Is there a significant discontinuity at analysis index k? Yes No Obtain filtered amplitude using “eigenmap” Stacking Estimate the reflectors orientation Stacked Volume Coherence Inline dip Crossline dip Estimate the discontinuity More traces or samples It looks complicated, but this is what is internal to prestack SOF. A workflow GUI drives the process.

Time-migrated gather (Barnett Shale) AASPI Welcome 2012 Time-migrated gather (Barnett Shale) t0 (s) Offset - x(m) 2100 4200 0.5 1.0 2.0 1.5 0.0 -4 -2 4 2 Amp An example of a common reflection point gather from the Fort Worth Basin (data courtesy of Devon Energy). Figure 4a.

After prestack SOF Figure 4b. AASPI Welcome 2012 t0 (s) Offset - x(m) 2100 4200 0.5 1.0 2.0 1.5 0.0 -4 -2 4 2 Amp The same data after prestack SOF using 3 inlines, 3 crosslines, and 3 offsets (i.e. a 27x27 covariance matrix) Figure 4b.

Rejected noise Figure 4c. AASPI Welcome 2012 t0 (s) Offset - x(m) 2100 2100 4200 0.5 1.0 2.0 1.5 0.0 -1 1 Amp Here is the rejected noise plotted at the same scale. So ends a short review of some of the effort presented at the end of 2014. Much greater detail can be found in the documentation, theses, dissertations, and published papers and abstracts. Figure 4c.