Local skewness attribute as a seismic phase detector

Application example

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Figure 6. Stratigraphic column of the Fort Worth Basin where the Boonsville dataset is located, after Pollastro et al. (2007). Karstification in the Ellenburger Carbonates has caused local sags in the overlying Barnett and Bend Conglomerate formations, creating reservoir compartmentalization.

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Figure 7. Input data: a section of the Boonsville dataset

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Figure 8. Inverse local skewness as a function of the phase rotation angle, with application to the section from Figure 7. Red colors correspond to high inverse similarity.

The input dataset for our field-data example is the Boonsville dataset from the Fort Worth Basin in North-Central Texas, USA (Hardage et al., 1996b,a). The formations of interest are the Ellenburger Carbonates, the Barnett Shale and the Bend Conglomerates (see Figure 6 for a stratigraphic column). The Ellenburger Carbonates are of Ordovician age. Their karstification due to post-Ellenburger carbonate dissolution and subsequent cavern collapses has created sags in the overlying formations, affecting sedimentation patterns and structures in the overlying Barnett Shales and Bend Conglomerates. The collapse features look like vertical chimneys with roughly circular cross-sections, extending up to 600-760 m above the Ellenburger Carbonates (Hardage et al., 1996a), sometimes even reaching into the Strawn Group above the Bend Conglomerates.

The Barnett Shales are of Mississippian age. They are the target of much current exploitation in Texas as these are tight-shale reservoirs (Pollastro et al., 2007). Zones with karst-induced cavern collapses form a drilling and completion hazard for the mainly horizontal drilling programs in these tight-shale reservoirs and must be mapped. They may affect local fracture densities and thus permeabilities and reservoir drainage positively but can also lead to fluid barriers due to reservoir compartmentalization.

The shallower clastic Bend Conglomerates are of Middle Pennsylvanian (Atokan) age. The formation has a thickness of 300-360 m in this area with depths between 1370 to 1830 m. It was targeted throughout the 1980s and 1990s as it contains several gas and oil-bearing reservoirs in a stacked fashion. Hardage et al. (1996b,a) describe how the karstification has greatly impacted the system tracts and sedimentation patterns in the Bend formation which were characterized by low accommodation space. Resulting reservoir compartmentalization is a significant challenge for this formation and has also affected the reflector character. Reflection near the base of this formation display both reflector weakening and sometimes even polarity reversals in areas depressed due to local sagging. Acquisition and processing of this dataset are described by Hardage et al. (1996a). A stacked section is shown in Figure 7. A zero-phase correction has been applied to the data but has left regions with variable localized phase.

Our processing sequence is similar to the one used in Figure 5. First, we apply a number of phase rotations with different angles and compute local skewness for each rotation. The regularization lengths in this examples were 500 samples or 0.5 s in time and 50 traces in space. The result is displayed in Figure 8. Next, we apply automatic picking with the algorithm described by Fomel (2009) to extract the nonstationary phase rotation that maximizes the local skewness. Finally, the phase correction is applied to the data, with the result displayed in Figure 9. A zoomed-in comparison shows the effects of non-stationary phase correction: rotating major seismic events to zero degrees and improving their continuity. These effects can be useful both for improving structural interpretation and for improved matching of seismic data and well logs.

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Figure 9. Zoomed-in comparison of the data before phase correction (a) and after phase correction (b). Nonstationary phase correction helps in identifying significant horizons and increasing their resolution in time.

We applied the local phase detection to the 3-D volume in a window centered on the target horizon (Figure 10). The estimated local phase variation along the target horizon is shown in Figure 11. Comparing the amplitude before and after phase correction (Figure 12), we observe a noticeable improvement in event continuity. Once the processing and interpretation are done on the zero-phase-corrected volume, it is easy to restore the original phase by applying the inverse phase rotation.

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Figure 10. Boonsville dataset windowed around the target horizon.

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Figure 11. Local phase variation along the target horizon estimated from the data shown in Figure 10.

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Figure 12. Amplitude along the target horizon before and after phase rotation.

Local skewness attribute as a seismic phase detector