EAGE Conference & Exhibition

Integration of seismic stratigraphy and seismic geomorphology for prediction of lithology applications and workflows H. W. Posamentier, A. S. Madof, S. C. Lang, K. D. Ehman, O. Bakare

Introduction The stratigraphic interpretation of high-quality 3D seismic data has significantly improved our ability to predict the subsurface. Through the integration of section and plan view images (i.e., stratigraphic and geomorphologic, respectively), we are currently able to create robust interpretations of stratigraphic architecture and associated lithology in three dimensions. The investment of billions of dollars to acquire and process 3D seismic data drives the need to extract the maximum value of information. Seismically-derived geologic interpretations can have significant impact on exploration and production. Specifically, enhanced geologic and geophysical understanding encompasses the following: Geology: 1) prediction of lithology, 2) prediction of compartmentalization, 3) development of depositional analogs, and 4) enhanced understanding of geologic processes. Through a variety of visualization and interpretation techniques, it is possible to image significant portions of depositional systems. From the interpretation of smaller scale components (i.e., channels, patch reefs, etc.), we are then able identify and predict lithology, as well as their stratigraphic compartmentalization. Geophysics: 1) depositional context for geophysical analyses (e.g., reservoir properties from seismic, inversion, etc.), and 2) quality control for geophysical processing. Providing geologic context can provide a “reality check” for evaluating geophysical data for rock and fluid property analyses. In addition, iteratively evaluating the effects of geophysical processing is critical to maximizing the value of seismic data (i.e., not processing out relevant geologic features).

Workflows Five effective workflows facilitate the seismic identification of geologic patterns: 1) Volume slicing (Figure 1) - Slicing through a seismic volume (i.e., using time slices, dipping planar slices, and horizon (i.e., stratal) slicing) is a critical first step to interpreting any 3D seismic dataset. The selection of these workflows should relate to the degree of structural deformation characterizing the data. With minimal structuring, time slices suffice. With uniformly dipping reflections, dipping planar slices are sufficient. Horizon-parallel slicing (i.e., horizon slicing) yields the best results where deformation is significant, yet should be limited only to intervals where reflections are approximately parallel. 2) Creative datuming (Figures 2 & 3) - A workflow informally referred to as “creative datuming” is advised when neither datuming nor slicing is sufficient. This workflow requires interpreting a horizon, not “snapped” to a peak, trough, or zero crossing, that is approximately parallel to regional reflections within the interval of interest. After the interpolated surface is draped with amplitude, the slice can be vertically shifted to better image the geology in three dimensions. 3) Optical stack (Figure 4) - Building on the creative datuming procedure, a subsequent workflow using opacity rendering, is recommended. This workflow involves analysis of a stack of slices (herein referred to as an optical stack), usually ranging from 20-30 slices (comprising a thickness of 80-120 milliseconds in data sampled at 4 milliseconds) in thickness. The opacity is set to render the stack (typically in plan view) completely transparent except for extreme amplitude values, which are

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rendered opaque. Hence a high-amplitude stratigraphic feature such as a channel element or channel complex which may be “porpoising” through the optical stack, can be entirety imaged (Figure 4). 4) Channel chasing (Figure 5) - Another useful stratigraphic workflow is one we refer to as “channel chasing”. This technique involves the use of an auto-picker focused over a small area. The interpreter leverages their stratigraphic understanding to “chase” the trend of a channel element, or other depositional element(s) over a large distance. 5) Attribute analyses (Figure 6) - In addition to slicing through data, analyzing horizon and interval attribute maps can provide critical insights. The most common horizon attributes include a) dip magnitude, b) dip azimuth, c) curvature, d) roughness, and e) simple illumination. A broad range of interval attributes can also yield significant stratigraphic and geophysical insights. Interval attributes such as root mean square (RMS), maximum or minimum amplitude, or total amplitude (positive and negative) can also yield patterns that have stratigraphic significance. Seismic Pattern Recognition All the workflows outlined above are designed to better image seismic patterns that have geologic significance. As such, these workflows represent a means to an end: the intermediate product (seismic visualization) being the elucidation of meaningful patterns, and the end product (seismic interpretation) being an architectural and lithologic prediction prior to drilling. In addition to workflows, seismic data types and attribute volumes also can play a critical role. Near vs. far vs. full offset stack volumes can provide useful and unique insights, which highlight both geologic and geophysical phenomenon. Coherence volumes are especially suited to identify edges of depositional elements, whereas different processing streams can be leveraged to highlight stratigraphic patterns. The application of seismic stratigraphy and geomorphology hinges on the identification of reflection patterns and consequently the interpreter’s ability to leverage those patterns to predict lithology. Patterns can be expressed both in the section or stratigraphic domain, as well as in the plan view or geomorphic domain. Thus, a feature recognized in section view that is suspected to have stratigraphic significance must be corroborated and confirmed in the associated plan view, and vice versa. Summary and Conclusions Predicting lithology prior to drilling centers on seismic pattern recognition, a process that involves the iterative interpretation of seismic section and plan view images. Efficient workflows, involving slicing through seismic volumes, are critical to rapidly and accurately bringing such patterns to light. Recommended workflows include 1) time slicing, 2) horizon slicing, 3) and “creative datuming”, where no discrete continuous reflections are available. Horizon attributes such as 1) dip magnitude, 2) dip azimuth, 3) curvature, 4) roughness, and 5) simple illumination also can yield critical insights that can be interpreted geologically. However, workflows and attribute extractions require the careful understanding of patterns, which will ultimately lead to the three dimensional interpretations of subsurface lithologies.


Figure 1 Examples of different slicing styles through 3D seismic volumes. A) Time slice of deep-water turbidite fan. Note that only a small portion of depositional system is imaged, as the section is not horizontal. B) Dipping plane showing same system as in A). This technique is more effective, as the stratigraphy uniformally dips to the lower right. C). Horizon slice of same system. The picked reflection, located at the base of the system, has been shifted upwards.

Figure 2 Summary of “creative datuming” workflow. A and B) Cross sections showing a manual pick (yellow line) along the base of a submarine canyon. Note that surface is not “snapped” to a particular reflection. C) Plan view with additional lines (colored), picked in 3D to create a skeleton grid. D) The skeleton grid is interpolated (yellow polygon). E) Amplitude draped onto the interpolated polygon.

Figure 3 Shifted horizon slices from Figure 2E. A) Figure 2E, “creative datum”. B) Slice shifted -40 ms (shallower). C) Slice shifted -80 milliseconds (shallowest).


Figure 4 Optical stack. A) Plan view of slice stack, with positive amplitudes rendered transparent. Note that high amplitude negative values “porpoise” through the data. B and C) Magnified areas from A). Note that opacity rendering illuminates multiple channelized turbidites, with negative amplitudes.

Figure 5 “Channel chasing”. A) Auto-picker is focused over a small vertical area. Note that the black reflection is seeded. B) Plan view image of surface created from auto-picker. C and D) Subsequent steps in picking the deep water turbidite channelized complex. The system is “chased” towards the bottom of the image with a propagation brush.

Figure 6 Horizon attributes calculated for a deep water turbidite channel. A) Illuminated surface relief. B) Amplitude. C) Dip azimuth. D) Structure. E) Curvature. F) Roughness. G) Dip magnitude. H) Oblique 3D perspective.
