7 synthetic seismic modeling of turbidite...

Synthetic Seismic Modeling of Turbidite OutcropsCopyright ©2007 by The American Association of Petroleum Geologists. DOI: 10.1306/1240891St561510

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7 Synthetic Seismic Modeling of Turbidite Outcrops Mark Chapin and Gottfried TillerShell International Exploration and Production, Inc., Houston, Texas, USA

Figure 1. Flow diagram of modeling process.

Table 1. Modeled outcrops.

Name Formation LocationReservoir

ArchitectureLength Thickness Source

Big Rock Quarry Jackfork Group Arkansas, USA Channels 838 m (2750 ft)

76 m(250 ft)

Cook (1993)

Buena Vista Brushy Canyon Formation

West Texas, USA Channels 1341 m(4400 ft)

61 m(200 ft)

Beauboeuf et al.(1999)

Laga G-H-I Laga Formation Italy Sheets 3450 m(11315 ft)

34 m(110 ft)

Mutti et al.(1978)

Table 2. Rock properties for cases by facies (oil-saturated sands).

Facies Sand (percent) Velocity (ft/sec) Density (g/cc)

Rock Model 1: Shallowly Buried, Pliocene–Pleistocene, Wavelets A and B

Massive Sand 100 5800 2.15

Debris 25 7500 2.20

Laminated Sands 75 6500 2.18

Thin Beds 50 7500 2.20

Mudstone 0 8100 2.25

Rock Model 2: Deeply Buried, Miocene Reservoir 1, Wavelet C


Debris 25 10,000 2.40


Thin Beds 50 9000 2.30

Mudstone 0 10,000 2.40

Rock Model 3: Deeply Buried, Miocene Reservoir 2, Wavelet D


Debris 25 11,000 2.44


Thin Beds 50 10,200 2.34

Mudstone 0 11,000 2.44

1) Construct geologic cross sections from cor-related, measured sec-tions and/or photo pan-els.

2) Digitize cross sec-tions by bed and facies code. Create digital model (proprietary soft-ware).

3) Assign acoustic rock properties to each bed and facies. In these examples we used typi-cal Gulf of Mexico Mio-cene through Pleisto-cene properties.

4) Convolve wavelet with acoustic imped-ance of cross section. We experimented with several different fre-quency spectra from real seismic data sets.

Executive SummarySeismic forward models of turbidite outcrop sections have been created to illustrate how reservoir architecture details may be

expressed within the scale of resolution of commonly available, marine seismic data. These can be instructive for refining seismic interpretations and recognizing uncertainties inherent in those interpretations. Outcrop sections having sufficient length and thick-ness were digitized at a bed scale to generate models (Table 1). These cover a variety of sheet, channel, and mixed architecture styles.

The outcrop sections were derived from various sources (Table 1); additional outcrops are discussed in the full-length version of this paper on the CD-ROM in the back of this book (Chapter 119). The sections were digitized at the finest resolution possible. This usually approximated bed scale; however, very thin, heterolithic laminae were usually lumped together as thin-bed facies. The models are relatively simple, noise-free, normal-incidence, synthetic seismograms. Velocity and density were assigned by facies and given values typical of oil-saturated sands in Gulf of Mexico Tertiary minibasin settings (Table 2). The geometries are, therefore, geologically realistic. However, in real subsurface reservoirs there is likely to be more rock property variation within facies, and also more noise in the seismic response. Shear-wave rock properties or amplitude vs. offset are not considered in the models shown here. The workflow is illustrated in Figure 1.

Testing the seismic response of a single geometry assuming different acoustic contrast and velocity can be instructive. We looked at the seismic response of outcrop sections under different simulated conditions of burial and seismic resolution. Shallowly buried, Pliocene–Pleistocene rocks are significantly slower and less dense (acoustically softer) than more deeply buried Miocene sediments (Table 2). Wavelets were derived from different Gulf of Mexico seismic data sets, which correspond to rock property models as shown in Table 2. Thinner geologic features are better resolved using Pliocene–Pleistocene rock properties compared to those using Miocene rock properties.

The response of the same geological architecture to different seismic wavelets was also investigated (Figure 2). This allows us to see the impact of seismic frequency content on the resolution of geological features. Two display types were considered: 1) typical reflection-coefficient displays where seismic peaks and troughs represent lithology interfaces, and 2) RUNSUM data, a display com-monly used by Shell. RUNSUM is a trace integration similar to a 90°-phase roll. Bed boundaries occur at zero crossings. Seismic traces roughly correspond to a low-frequency, gamma-ray log in cases where acoustically soft sands have thickness in the range of the seismic tuning thickness (also called pseudo-impedance).

Studies 56 CH007_v2.indd 21 10/29/2007 10:19:47 AM

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Big Rock Quarry Outcrop, Pennsylvanian Jackfork Formation, Arkansas, USA

The Big Rock Quarry outcrop section was measured and interpreted by Tim Cook in his 1993 Master’s thesis. The section was digitized and quantitative analysis performed in collaboration with Shell as reported in Cook et al., (1994). The section comprises amalgamated channels with parallel infill in the lower section and inclined, possibly laterally accreting beds near the top. Muddy debris flows are also common near the top of the section. The scour geometries are complex, and many are mud-draped. Complex, possibly disconnected, fluid flow paths would be expected.

The seismic response of this section displays relatively complex, interfering loops with common dip changes, amplitude variation, and discontinuity with higher frequency wavelets (Figure 3B–E). The top and base of the gross sand package is well expressed both on RUNSUM and reflection-coefficient (RFC) displays. However, details of bedding, and even major scour surfaces cannot be uniquely mapped. With the lower frequency wavelet and faster velocities typical of more deeply buried sediments, the interval has a more sheetlike appearance. Seismic character changes are more related to gross thickness variations (Figure 3F, G). When interpreting seismic data, attention should be paid to frequency content and expected resolution of the seismic data sets in relation to rea-sonable expectations of geologic geometries and dimensions.

Figure 3. Big Rock Quarry models. A) Bed-scale architecture of outcrop section. B) Velocity section with synthetic overlay using RUNSUM traces. Plio-cene–Pleistocene rock model and high-frequency, Pliocene–Pleistocene wavelet (rock model 1 and wavelet A) applies to B–E. C) RUNSUM traces with negative loops shaded (acoustically slow/soft). D) Velocity section with reflection coefficient (RFC) display overlay. E) RFC display with negative loops shaded (negative reflection coefficient). F) Velocity display with reflection coefficient display. Deep Mio-cene rock model and wavelet (rock model 3 and wave-let D) applies to F, G. G) RFC display with negative loops shaded (negative reflection coefficient). Undef = undefined (not used); LamSd = laminated sand; ThBed = thin beds.

Figure 2. Seismic wavelets used for comparison. A) Shallowly buried Pliocene–Pleistocene reservoir with high-frequency, whitened wavelet. Note that although higher frequencies are enhanced, there is some loss in the low-frequency range compared to subsequent wavelets. B) Shallowly buried, Plio-cene–Pleistocene reservoir with a lower frequency wavelet than A. C) Deeply buried Miocene reservoir with a high-fre-quency wavelet. D) Deeply buried Miocene reservoir with a lower frequency wavelet.


Synthetic Seismic Modeling of Turbidite Outcrops23

Figure 4. West Texas, Brushy Canyon Formation, Buena Vista outcrop. A) Facies section showing amalgamated channels incising thinner sheetlike sands. B) Velocity section with RUNSUM trace overlay. Pliocene–Pleistocene rock model and high-frequency, Pliocene–Pleistocene wavelet (rock model 1 and wavelet A). C) RUNSUM traces only. Rock model 1 and wavelet A. D) Velocity section with reflection coefficient trace overlay. Rock model 1 and wavelet A. E) Reflection coefficient (RFC) traces only. Rock model 1 and wavelet A. F) RUNSUM traces with Pliocene–Pleistocene rock model and lower frequency wavelet (rock model 1 and wavelet B). G) RUNSUM traces with Miocene rock model and high-frequency, Miocene wavelet (rock model 2 and wavelet C). H) RUNSUM traces with deep Miocene rock model and wavelet (rock model 3 and wavelet D). Undef = undefined (not used); LamSd = laminated sand; ThBed = thin beds.

Buena Vista Outcrop, Permian Brushy Canyon Formation, Texas, USAThe Buena Vista section was measured and interpreted by Beauboeuf et al. (1999). This section was selected to investigate the full

range of possible seismic responses using different rock property models and seismic wavelets. The section displays broad channels incis-ing thinner sheet sands at the margin. The high-frequency wavelet displays (Figure 4B–E) show three main events corresponding to the thin-bedded upper interval, the middle sand, and the lower sand. As in the Big Rock Quarry section, details of scour and bedding surfaces and proportion of sand are not immediately obvious. However, the overall seismic pattern is less chaotic than the Big Rock

Quarry section. An interpreter given just one 2-D seismic section would be hard-pressed to know if there are channels here, or if so, where their edges are. Higher frequency wavelets pick out tops of sandy sections, but could be misinterpreted as having thinner sands. Wavelets with better dynamic range to low frequencies provide better representation of net sand distribution (Figure 4F, G). Lowest frequency and fastest velocity model (Figure 4H) illustrates a rather featureless, apparently sheetlike seismic loop devoid of detail. This observation underscores the importance of understanding seismic frequency and resolution in relation to likely geometries and dimen-sions of geologic features.


24 Studies in Geology 56

Laga Formation Outcrop, Miocene, ItalyThe Laga Formation represents a continuous package of sheet sands (Figure 5). Mutti et al. (1978) published detailed cross sections

of several sand packages. Shell workers field-checked these correlations, and the G, H, and I sand sections were digitized for this analysis. The basal part of the section has thinner sand beds ranging from lamination scale to approximately 1 m (3 ft) in thickness. The upper part of the section is dominated by beds thicker than 1 m (3 ft). The package and recognizable beds are correlated across a lateral distance of 3450 m (11,315 ft). The seismic response of this very continuous package is also very continuous, with only gradual changes in loop geometry and amplitude, and little dip change. This provides a marked difference to the channel package of Big Rock Quarry.

The loop geometry of the RUNSUM data mimics the thickening-upward pattern of the bedding, showing greater negative ampli-tude in the upper portion of the section, responding to the higher sand percent there (Figure 5B, C). The reflection-coefficient display (Figure 5D, E) shows a strong top reflector and weaker basal doublet corresponding to the thin-bed package.

Comparison of outcrop architecturesWith sufficient seismic frequency and rock property contrast between sand and mud, channel vs. sheet sands clearly have different

seismic character channels being more chaotic with rapid amplitude and dip changes (Figure 6). However, as acoustic contrast between sand and shale diminishes and frequency diminishes, it can become very difficult to distinguish channel from sheet sand internal archi-tectures (Figures 3G, 4H). Even where seismic-frequency content is good, the internal details of potentially significant scour surfaces are rarely mapped with confidence, and individual bed geometry is, of course, well below resolution. Three-dimensional seismic data can help enormously. Seemingly minor features in cross section, such as subtle dip or amplitude changes, can sometimes be mapped into clearly recognizable channelform features in map view. However, even where such features are mapped in 3-D, the nature of the critical connections between adjacent channels or between channel and nonchannel sands is almost always below seismic resolution. A com-mon observation in the channel sections is that the number of scours would tend to be underestimated from seismic data whereas their depth is overestimated. Outcrops tend to show more numerous, shallower scours than can be typically interpreted on seismic. Seismic data may indicate where the channel fairways potentially lie, but not their detailed internal architecture or stacking pattern. Outcrop studies are important for filling in the details of potential architectural scenarios where seismic resolution and sparse well control leave significant room for alternative interpretations.

Figure 5. Laga G-H-I models. A) Bed-scale architecture of the outcrop section. B) Velocity section with synthetic overlay, RUNSUM traces using rock model 1 and wavelet A. C) RUNSUM traces with negative loops shaded (acoustically slow/soft). D) Velocity section with reflection coefficient (RFC) display overlay. E) RFC display with negative loops shaded (negative reflection coefficient).

Figure 6. Comparison of seismic geometries of three different outcrop architectures using the same scale, wavelet, and rock proper-ties. Each model uses rock model 1 and wavelet A and displays reflection-coefficient traces. A) Channel architecture from Big Rock Quarry, Arkansas, USA. B) Mixed channel-sheet architecture of the Buena Vista outcrop, West Texas, USA. C) Sheet architecture from the Laga Formation, Italy.


Synthetic Seismic Modeling of Turbidite Outcrops25

ReferencesBeaubouef, R. T., C. Rossen, F. B. Zelt, M. D. Sullivan, D. C. Mohrig, and D. C. Jennette, 1999, Deep-water sandstones, Brushy

Canyon Formation, West Texas, Field Guide for AAPG Hedberg Conference April 15–20, 1999: AAPG Continuing Education Course Note Series 40, variously paginated.

Cook, T. W., 1993, Facies architecture of deepwater channel deposits, Brushy Canyon Formation, West Texas, and Jackfork Group, Arkansas, M.S. Thesis, Louisiana State University, Baton Rouge, LA, USA, 108 p.

Cook, T. W., A. H. Bouma, M. A. Chapin, and H. Zhu, 1994, Facies architecture and reservoir characterization of a submarine fan channel complex, Jackfork Fm., Arkansas, in P. Weimer, A. H. Bouma, and B. F. Perkins, eds., Submarine fans and turbidite sys-tems: Sequence stratigraphy, reservoir architecture, and production characteristics-Gulf of Mexico and International: 15th Annual Gulf Coast Section Society of Economic Paleontologists and Mineralogists Research Conference, Houston.

Mutti, E., T. H. Nilsen, and F. Ricci Lucchi, 1978, Outer fan depositional lobes of the Laga Formation (Upper Miocene and Lower Pliocene), east-central Italy, in D. J. Stanley and G. Kelling, eds., Sedimentation in submarine canyons, fans and trenches: Strouds-burg, PA, USA, Dowden, Hutchinson and Ross, Inc., p. 210–223.


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