acoustic impedance as a sequence stratigraphic tool in structurally complex deepwater settings

1072 The Leading Edge September 2010

SPECIAL SECTION: S e i s m i c i n t e r p r e t a t i o nS e i s m i c i n t e r p r e t a t i o n

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Many deepwater areas of the world are structurally complex

and challenging for hydrocarbon exploration; the lack of well control coupled with difficult seismic ties between sub-basins make the gener-ation of a consistent stratigraphic framework challenging. The develop-ment of the framework is critical before any attempt of multibasinal depositional systems analysis can be performed.

The objective of this study was to develop a new methodology which would enhance our ability to develop a sequence stratigraphic framework and depositional systems interpretation in frontier deepwater environments. Four quite diverse areas of the world have now been utilized to test the concepts.

Ultimately, the ideal method for solving the correlation problem in such structurally complex areas would be to drill multiple wells in each minibasin and use the well data as an additional tool to constrain seismic correlations. However, drilling wells in offshore deep water is prohibitively expensive and, consequently, another approach was required.

In deepwater and slope environ-ments, the condensed sections are nearly equivalent to sequence boundar-ies (SB) (Loutit et al., 1988), especially at seismic resolution. Based on the se-quence stratigraphic model developed by Mitchum (Figure 1, personal com-munication), we have taken advantage of the regionally extensive character of the condensed sections (Armentrout et al., 1996; Prather et al., 1998) to aid SB identification and correlation across adjacent sub-basins.

In order to overcome the lack of well control and structural complexity across minibasins, we developed a new methodology based on the generation of acoustic impedance inversions from seismic data, stratal slicing of the AI data, and creation of acoustic impedance (AI) pseudologs over individ-ual sub-basins, followed by correlation across contiguous sub-basins utilizing an enhanced version of the condensed section

ARTURO J. CONTRERAS and REBECCA B. LATIMER, Chevron Energy Technology Company

SPECIAL SECTION: S e i s m i c i n t e r p r e t a t i o n

Figure 1. Idealized sequence stratigraphic model for deepwater settings from R. Mitchum (personal communication). Sandier sediments of the next lowstand systems tract, including the basin floor fan, the slope fan, and the distal prograding complex overlie the condensed sections shown in green.

Figure 2. Well-log and crossplot analysis from the calibration well showing relatively low Vsh (<0.4 � sands) associated with high AI values.

concept (patent pending). Once the sequence stratigraphic framework was established, further depositional system anal-ysis based on horizon mapping could be performed with a higher level of confidence.

The main assumption in the approach is that condensed

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September 2010 The Leading Edge 1073

S e i s m i c i n t e r p r e t a t i o n

taken into consideration in seismic facies analysis include reflection amplitude, dominant reflection frequency, re-flection polarity, interval velocity, reflection continuity, re-flection configuration, abundance of reflections, geometry of seismic facies unit, and relationships with other units. From our combined seismic/AI facies analysis, the seismic facies we have identified are almost certainly sand-prone. Distinct isochron changes and depositional mounding can be clearly seen on the cross-sectional profiles tied to the map patterns. Having evidence of sand-prone facies from a variety of techniques provides confidence in the interpre-tation (Figure 3).

• Analysis of AI anomalies at a well location. The objective of this portion of the study was to analyze sediment dis-tribution and depositional patterns on the seismic data, compare the results to the AI data through 3D visualiza-tion, and use the available well data for calibration. In the first test area, we examined one well drilled updip of the target basins. Analysis of the impedance data in 3D shows a relationship between acoustic impedance and deposi-tional architecture (sands versus shales in the locations where they were expected based on our known sequence stratigraphic concepts). A variety of sand-rich elements were identified on the seismic and impedance data based on their character (isolated channel complexes, distribu-tary lobes containing channel, channelized-sheet and sheet elements, and confined sheets). These elements are asso-

sections can be readily differentiated from surrounding sedi-ments based on their distinctive acoustic properties; accord-ingly, pseudologs of acoustic impedance (AI) could poten-tially be used to facilitate identification of condensed sections and subsequent seismic correlation.

Lithology sensitivity analysisWe performed a lithology sensitivity analysis to determine the relationship between sand- and shale-based AI. The anal-ysis involved:

• Well-log and cross-plots analysis (GR, Vsh, AI). In order to determine the existence of a relationship between acoustic impedance and lithotypes (sand/shale) in the study area, we performed crossplot analysis of the well data. The pres-ence of sands (or a slight decrease in the volume of shale) is characterized by an increase in P-wave velocity and densi-ty, and therefore it is associated with relatively high values of acoustic impedance in (Figure 2).

• Combined seismic/AI facies analysis. The main goal of this study is the identification of sand intervals through seis-mic facies analysis of seismic amplitude data and of AI-inverted seismic data. Conventional seismic facies analysis uses seismic character or pattern analysis to identify strati-graphic relationships. A seismic facies unit can be defined as a sedimentary unit that differs from adjacent units in its seismic character or pattern. Parameters that should be

Figure 3. Example of seismic amplitude data (left) versus acoustic impedance inversion products (right) from interval of interest. Seismic facies analysis, from the seismic amplitude data, shows distinct isochron thickening and depositional mounding. These characteristics are considered to have a high probability of being sand-prone. The sand-prone facies correlate to the high-AI anomaly (blue) in section and map views.

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Figure 4. Analysis of the AI anomalies at the well location. (a) A stratal slice of the top of the sequence (+44 ms) at an oblique angle so that the structure of the minibasins is apparent. The dark blue (high AI) impedance data within the slice is interpreted to be channel lobe complexes. (b) has been rotated flat with labels showing the variety of depositional elements.

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ciated with relatively high acoustic impedance values in this test area (Figure 4). The channel geometries in this first test area are easily recognizable due to the high AI fill (in contrast to surrounding low AI values); however, there are bypassed, shale-filled channel elements with lower AI fill which we interpret as plugged or abandoned channels. Additionally, we often observe relatively low AI values as-sociated with channel overbank/levee deposits. Our ob-servations support the crossplot analysis discussed earlier

which suggested that high impedance was more likely to indicate sand-prone intervals. Note that these rock prop-erties are somewhat unique. The relationship between AI and lithology should be investigated for each area and can often have an opposite relationship. In areas with “softer” sands, lower velocity and density, the channels will have low AI sandy fill.The calibration well penetrated shales within the analyzed

interval and was surrounded by relatively low values of acous-

Figure 5. AI-pseudolog methodology. (a) Develop the stratigraphic framework and generate stratal slices. (b) Average AI value computed for each stratal slice. (c) Comparison with trace stacking.

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tic impedance, which also is consistent with the observation above. This observation has been consistently corroborated in deeper intervals. Analysis of the surrounding area indicates that the sand-prone depositional architecture tends to cor-relate with higher impedance, at least higher than the imped-ance values found in the condensed section.

It should be noted that by increasing porosity or adding hydrocarbons to the system, a situation might exist in which the sand-prone facies might not have the same relationship with acoustic impedance. The impedance would likely de-crease due to the increase in porosity or hydrocarbons (oil and especially gas), making it more difficult to “see” the sands on AI alone. We have taken the added precaution of utilizing all sources of traditional data (seismic character, pattern analysis, coherence detection, and seismic amplitudes) to verify the in-terpretation.

AI pseudologs generationIn order to interpret the sequence stratigraphic framework for the study area, we had to determine the ages of the sedi-ments across minibasins. This was a major difficulty due to the lack of well data. As pointed out earlier, the condensed sections should be laterally extensive in deep water (unless eroded) and thus can be used as correlation markers. All con-ventional approaches were utilized (surface comparison, loop tying, stratal terminations, and seismic facies analyses). De-spite these efforts, there was still a great deal of uncertainty. A new and novel methodology for identification and corre-lation of sequence boundaries between adjacent minibasins was developed utilizing pseudowells extracted from the AI data.

Based on our crossplot analysis, acoustic impedance data in this area correlate with rock properties; low-acoustic im-pedance data indicate soft shales and high-impedance data correlate with the siltier, harder section. We therefore utilized a band-limited acoustic impedance inversion to assist in gen-erating a set of pseudo-acoustic impedance logs. Acoustic impedance data are the natural link between well logs and seismic data. The condensed sections showed up as consistent and mappable low-acoustic impedance events. This allowed us to easily identify the condensed sections (shales) and had the potential of allowing us to correlate between basins.

Acoustic impedance, being a layer property, complements the standard seismic amplitude data interpretation (inter-face property) because changes in value (color) may reflect changes in lithology as it appears to in the area around the well location.

The methodology consisted of generating AI pseudologs for individual minibasins and then comparing and correlat-ing the pseudologs across the minibasins. Analogous to the trace-stacking concept, our pseudolog approach consists of computing the mean value of acoustic impedance from indi-vidual stratal slices over an area and interval of interest; this process enhances the property and accentuates the condensed section character, allowing a more obvious correlation marker with the condensed section (Figure 5). In other words, events with the same value of acoustic impedance (allowing for a

more obvious high) get enhanced. Areas where the AI varies or changes laterally across a stratal slice are diminished, caus-ing the condensed sections to be accentuated when averaged.

In order to generate the AI pseudolog curves, we inter-preted an initial and basic stratigraphic framework for each minibasin. The initial framework consisted of the major (more continuous) horizons in each sub-basin. The surfaces do not necessarily need to correlate across basins because it is the correlation that we are trying to determine. From this interpretation, we generated a set of stratal slices from the AI volume. We then computed the average AI value from each individual stratal slice and plotted it against its stratigraphic microlayer number. We have called the resulting curve an “AI cyclicity curve” or “AI pseudolog.” Figure 6 shows 27 stratal slices for the area from the top to the base of the target zone. The slices with low average AI values (yellows and reds) are lower on the cycle curve; slices with higher values are higher on the curve. These have been shown to represent times of sand input into the basin or cyclicity, which can be compared from basin to basin.

The pseudologs can also be extracted from an AI strati-graphic volume (directly out of the volume as you would do to create a pseudowell) and directly plotted in time for spatial comparison with seismic data. The most important result is that condensed sections (~sequence boundaries in deepwa-ter settings) can be readily identified on the pseudologs as AI spikes because they have been enhanced by the averaging process. We validated the results with biostratigraphic and wireline data at the available well location (Figure 7).

Assumptions and complicationsMultiple factors affect the final shape (curve motif and am-plitude) of averaged AI pseudologs, which could result in problematical SB identification and correlation if they are not taken into account. The following analyzes the main fac-tors affecting AI pseudologs:

Averaging area. The extent of the area over which the pseudolog was calculated can alter the quality of the results. We performed a sensitivity analysis on the relationship of the quality of the extraction versus the area used for AI-pseudolog averaging to visualize changes in value and in the pseudolog shape with increasing area for averaging. Multiple averaging regions were selected and AI pseudologs generated accord-ingly. We compared the enhancement achieved when utiliz-ing 3D data versus 2D data as well as the size of the basin. Although the AI amplitude decreases when increasing the averaging area, there is a clear enhancement of the relative difference of the condensed sections (high-amplitude spikes) versus the material between the condensed sections (contain-ing a variety of sediments, both low- and high-impedance sands and shales) even on 2D data. The main benefit of the proposed approach is to enhance the condensed sections so that they can be utilized for correlation between basins (Fig-ure 8).

One should always keep in mind the size of the area of investigation when using AI contrast to identify condensed sections and candidate SBs, because an event appearing to

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Figure 6. AI values were plotted against the stratigraphic microlayer number, generating AI-pseudologs/cyclicity curve.

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be significant within one sub-basin may not have regional expression. The converse, that regionally significant surfaces may not be readily identifiable in all sub-basins, is also true; for example, a higher-order SB may have been eroded or lo-cally removed, thus suppressing any impedance contrast.

Acoustic impedance contrast (lithology and/or fluid contacts). High-amplitude seismic reflections occur at the interface be-tween two continuous layers with a significant impedance contrast. The magnitude of the acoustic impedance contrast is based on the relative difference in acoustic properties (P-

Figure 7. AI-pseudologs/cyclicity curve/condensed sections identification. The pseudolog generated from the mean value of the microlayers is plotted horizontally on both diagrams (a) and (b). Sequence boundaries are shown on diagram (b). These are derived from the time scale of the seismic data at the well location. Note the excellent correlation between AI spikes and the condensed sections immediately below the sequence boundaries.

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Figure 8. Sensitivity analysis on the relationship of the area used for AI-pseudolog averaging and the quality of the extraction. (a) Multiple averaging regions of increasing area were selected within the analyzed sub-basin and AI-pseudologs generated accordingly. Although the AI amplitude increases when decreasing the averaging area, there is a clear enhancement of the relative difference between arcally significant events (high-amplitude spikes) as shown in (b). Enhancing of condensed sections AI response is one of the main goals of the proposed approach.

velocity and density) of the two layers. Accordingly, a signifi-cant impedance contrast is required for the seismic inversion algorithm to be able to resolve differentiable layers from seis-mic into impedance.

When two different lithotypes (sand/shale) are in contact as expected at the base of a sequence (lowstand systems tract on top of condensed section), we frequently obtain a high-amplitude and continuous reflection, and, consequently,

we get a high-amplitude negative spike followed by a high-amplitude positive spike on the AI pseudologs. If different sub-basins present different sedimentation patterns (or even if facies change laterally within a single basin), we will expect differences in the magnitude and shape of the AI pseudologs, which must be considered when interpreting and correlating the logs. An analogous situation is expected when consid-ering sand intervals saturated with different type of fluids,

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Figure 9. 3D application. AI-pseudolog methodology over two contiguous sub-basins. (a) Seismic data from the basins. (b) AI pseudologs extracted from each area (turned sideways) using the process discussed in Figure 5. (c) The two pseudologs on top of each other.

mainly due to changes in the density of fluids (hydrocarbons versus water). For the case of “high-impedance” sands, an oil-saturated sand on top of a condensed section will have relatively lower imped-ance contrast (and lower-amplitude re-flection) than a water-saturated sand on top the same condensed section.

Stratigraphic pattern. One of the main assumptions during the AI pseu-dolog generation process is that the “default” stratigraphic pattern is “pro-portional to top and base.” If a different stratigraphic pattern such as truncations, onlaps, toplaps, and downlaps occurs, or an erroneous horizon interpretation is used, the proportional microlayers of our ideal stratigraphic model almost cer-tainly will cut across reflections. Conse-quently, the microlayers will not honor the true stratigraphy and the averaged pseudolog will contain a small fraction of erroneous information.

ApplicationsWe applied the AI pseudolog meth-odology to identify and correlate SB across two contiguous sub-basins using an inverted AI volume from 3D seis-mic data. Using initially mapped hori-zons, we generated individual pseudo-logs for sub-basins A and B (Figure 9). The upper and middle panels of Figure 9 show the seismic and extracted pseu-dologs from each sub-basin; the lower panel compares the two basins on top of each other. Note that the high corre-lation between pseudologs reflects the similarity in sedimentation patterns for both minibasins, and suggests a simi-lar sediment source feeding both mini-basins through time. The pseudologs can be resized (stretched or squeezed) if necessary due to changes in deposi-tional input into a basin, although this was not necessary in this case.

Figure 10 shows the results of the AI pseudolog methodology over sub-basins A and B. The upper section shows AI pseudologs displayed on the seismic and a suggestion for a potential correlation. This was done as a blind test. The lower section shows the results with the correct interpretation, and the results are virtually identical. Without the well data or the new approach, the correlation is not obvious.

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Conclusions and implicationsFor exploration purposes, a consistent sequence stratigraphic framework is necessary for mapping depositional systems, es-pecially in structurally complex, deepwater settings. Acous-tic impedance modeling can be implemented as an addition-al tool for sequence stratigraphy, by taking advantage of the areally extensive character of condensed sections and their distinctive acoustic properties. A novel approach based on AI pseudologs has been developed and successfully applied in four deepwater basins around the world.

The proposed methodology can be modified to suit spe-cific settings but general considerations should be followed, including: (1) calibration from nearby well-log data is critical for an accurate interpretation of lithofacies in terms of AI; (2) combined seismic/AI facies analysis is recommended for im-proved interpretation; (3) detailed sensitivity analysis affect-

Figure 10. 3D application. AI-pseudolog methodology over the sub-basins analyzed in Figure 9. (a) AI pseudologs displayed on the seismic and a suggestion of a potential correlation. This was performed as a blind test. (b) The results with the known interpretation displayed.

ing the shape of AI pseudologs, especially the averaging area, must be performed for quality control purposes.

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Prather, B. E., J. R. Booth, G.S. Steffens, and P. A. Craig, Classifica-tion, lithologic calibration and stratigraphic succession of seismic facies of intraslope basins, deep-water Gulf of Mexico: American Association of Petroleum Geologists, Bulletin, 82, 701–728.

Smith, R., 2004. Turbidite systems influenced by structurally induced topography in the multi-sourced Welsh Basin, in S. A. Lomas and P. Joseph, eds., Confined turbidite systems: Geological Society, London, Special Publication 222, 209–208.

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Corresponding author: [email protected]

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acoustic impedance as a sequence stratigraphic tool in structurally complex deepwater settings

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