sismoestratigrafia 2013

8/11/2019 sismoestratigrafia 2013

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Whither seismic stratigraphy?

Bruce S. Hart1

Abstract

Here, I provide an historical summary of seismic stratigraphy and suggest some potential avenues for future

collaborative work between sedimentary geologists and geophysicists. Stratigraphic interpretations based on

reflection geometry- or shape-based approaches have been used to reconstruct depositional histories and to

make qualitative and (sometimes) quantitative predictions of rock physical properties since at least the mid-

1970s. This is the seismic stratigraphy that is usually practiced by geology-focused interpreters. First applied to

2D seismic data, interest in seismic stratigraphy was reinvigorated by the development of seismic geomorphol-

ogy on 3D volumes. This type of reflection geometry/shape-based interpretation strategy is a fairly mature sci-

ence that includes seismic sequence analysis, seismic facies analysis, reflection character analysis, and seismic

geomorphology. Rock property predictions based on seismic stratigraphic interpretations usually are qualita-

tive, and reflection geometries commonly may permit more than one interpretation. Two geophysics-based ap-

proaches, practiced for nearly the same length of time as seismic stratigraphy, have yet to gain widespread

adoption by geologic interpreters even though they have much potential application. The first is the use of

seismic attributes for feature detection, i.e., helping interpreters to identify stratigraphic bodies that are

not readily detected in conventional amplitude displays. The second involves rock property (lithology, porosity,

etc.) predictions from various inversion methods or seismic attribute analyses. Stratigraphers can help quality

check the results and learn about relationships between depositional features and lithologic properties of interest.

Stratigraphers also can contribute to a better seismic analysis by helping to define the effects of stratigraphy

(e.g., laminations, porosity, bedding) on rock properties and seismic responses. These and other seismic-related

pursuits would benefit from enhanced collaboration between sedimentary geologists and geophysicists.

Introduction

Seismic stratigraphy is an approach to seismic inter-pretation that is based on principles of stratigraphy. The

science of seismic stratigraphy was first formalized in a

series of papers published in AAPG Memoir 26 in 1977,

although it has older roots (Cross and Lessenger, 1988).

Papers (e.g., Mitchum et al., 1977) published in the

AAPG volume showed how reflection terminations,

continuity, and other qualitative geometry-based

(mostly) analyses of reflections could be used to help

to infer depositional histories and predict lithology in

undrilled areas. This seismic-based approach became

an invaluable tool in the petroleum industrys explo-

ration and development efforts and subsequently

spread to other applied and fundamental geosciences

pursuits. Seismic stratigraphic analyses based on 2D

seismic data were later expanded to application on

3D data sets.

Seismic stratigraphy helped to spawn the develop-

ment of sequence stratigraphy, a science that is now

routinely applied to stratigraphic analyses even if seis-

mic data are not available.Catuneanu (2006)and Miall(2010) summarize these techniques and discuss the

historical development of sequence stratigraphy.

Nevertheless, seismic stratigraphy and sequence stra-

tigraphy are so genetically linked that many geoscient-

ists consider the two to be essentially synonymous.

In this paper and elsewhere (Hart, 2011), I argue for a

somewhat distinct definition of seismic stratigraphy.

Textbooks in sedimentary geology define lithostratigra-

phy as the study of stratigraphic units that are defined

on the basis of their lithology, biostratigraphy as the

characterization and correlation of sedimentary depos-

its based on their fossil content, chemostratigraphy as

the use of inorganic chemistry as a correlation tool, and

so on. In that spirit, seismic stratigraphy should be de-

fined as the study of stratigraphic units that are defined

on the basis of their seismic characteristics. This broad

definition is consistent with the definition ofCross and

Lessenger (1988) who defined seismic stratigraphy as

1Statoil, Houston, Texas, USA. E-mail: [email protected].

Manuscript received by the Editor 23 April 2013; published online 8 August 2013. This paper appears in I NTERPRETATION, Vol. 1, No. 1

(August 2013); p. SA3SA20, 21 FIGS.

http://dx.doi.org/10.1190/INT-2013-0049.1. 2013 Society of Exploration Geophysicists and American Association of Petroleum Geologists. All rights reserved.

tSpecial section: Interpreting stratigraphy from geophysical data

Interpretation / August 2013 SA3


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3) Reflection character analysis focuses on lateral var-

iations in the character of reflections, or groups of

reflections, to predict lateral variations in the stra-tigraphy (lithology, porosity, thickness, etc.). Seis-

mic modeling can play an important role in this

pursuit. Modeling of acoustic responses (i.e., reflec-

tions associated with compressional waves and den-

sity) of stratigraphic features has become less

commonplace, with increased emphasis on model-

ing of elastic responses (reflections associated with

compressional waves, shear waves, and density) be-

cause the latter provide additional constraints on

lithology and physical property predictions.

Seismic stratigraphy offers significant advantages

over the more traditional lithostratigraphic interpreta-

tion of basins, as illustrated in Figure 1. Part (a) shows

an uninterpreted 2D seismic line from the North Slope

of Alaska. The image shows a series of clinoforms that

represent the infill of a Cretaceous foreland basin. In

part (b), some of the more continuous reflections have

been identified (picked). These reflections represent

seismic surfaces in the sense ofBertram and Milton

(1996), i.e., they are laterally continuous reflections

against which other reflections terminate. One of the

fundamental tenets of seismic stratigraphy is that,

within the resolution of the seismic data, reflections

represent depositional timelines. Although there are ex-

ceptions (e.g., Tipper, 1993; Zeng and Kerans, 2003),

this has proven to be a useful starting point for mostseismic stratigraphic interpretations. The seismic surfa-

ces (seismic sequence boundaries) shown in Figure1b

can therefore be used to divide the seismic transect into

units of relative geologic age using the Law of Superpo-

sition; the clinoforms at left represent sedimentary

deposits that are younger than those at right. Biostrati-

graphic data from wells, if any, along the seismic tran-

sect could be used to assign more rigorous ages.

Although not illustrated here, reflection terminations

(onlap, downlap, etc.; Figure2) could be identified on

the image in Figure1and used to designate some of the

seismic surfaces as sequence boundaries or maximum

flooding surfaces3. Instead, I have chosen to illustratehow seismic facies analysis of reflection geometries,

amplitudes, and other characteristics of the seismic

profiles can be integrated with other data sets (wireline

logs, outcrop analogs, etc., not shown) to interpret

probable depositional environments within the seismic

sequences (Figure 1c). Note that even this simple

Figure 1. Seismic reflections approximate timelines and cross lithologic boundaries within the limits of seismic resolution. Thisexample shows a progradational Cretaceous succession in a 2D seismic profile collected by the United States Geological Surveyfrom the North Slope of Alaska. (a) Uninterpreted seismic profile. Note the vertical scale, in meters, has been approximated usinglocal well control. (b) Profile showingseismic surfaces, continuous seismic reflections that can be traced over most of the lengthof the profile. Note the well-developed clinoform geometries with topset, foreset, and bottomset portions to the reflections. The

seismic surfaces are dashed in places in the lower foreset portions where strata have been disrupted by approximately syn-dep-ositional deformation. The blue dots represent the location of the paleo shelf break, the break in slope between the relatively gentlydipping topset reflections and the more steeply dipping foreset reflections. Changes in trajectory of the shelf break (upward,downward, outward) are used to distinguish whether the system is aggrading, prograding, retrograding, or degrading. (c) Profileshowing depositional settings represented by the different portions of the clinoforms. Note that the seismic surfaces cut acrossseveral different types of depositional setting and so must cross lithological boundaries. (d) Lithostratigraphic interpretation of the

profile, wherein the stratigraphic names are assigned on the basis of lithology. The Torok Formation corresponds to marine shalesand deep water clastics, whereas the Nanushuk Group consists of shallow-marine and nonmarine clastics (e.g., Houseknecht andSchenk, 2004). The seismic surfaces cut across lithostratigraphic formation boundaries and so are better for defining depositionalhistories than the diachronous lithostratigraphic units. FromHart (2011). Reprinted by permission of the AAPG, whose permissionis required for further use.

3The SEPMs stratigraphy Web site (http://www.sepmstrata.org )

has mapping and sequence stratigraphy exercises that are based

on this same public-domain Alaska seismic data set.

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seismic stratigraphic interpretation has more predictive

power than the lithostratigraphic interpretation shown

in Figure1d. Integrating the seismic surfaces with the

seismic facies analysis is important because it would

allow the interpreter to evaluate changes in depositio-

nal processes from one seismic sequence to the next.

From a reservoir perspective, it is also important be-

cause the rocks associated with these seismic surfa-

ces can act as barriers or baffles to lateral and

vertical fluid flow; the lateral continuity of the toe-of-slope facies (potentially sandy reservoirs) suggested

by Figure 1c is likely to be more apparent than real.The advent of 3D seismic technology led to a fourth

branch of seismic stratigraphy called seismic geomor-

phology (e.g., Posamentier, 2004). Once 3D data sets

became commercially available, it quickly became ap-

parent that plan-view images derived from 3D seismic

volumes could be used to detect and map stratigraphic

features (e.g.,Brown et al., 1981). Time slices, horizon

slices, proportional slices, and other types of plan-view

images now are routinely used to generate paleogeo-

graphic images from 3D cubes (e.g., Brown, 2004;

Weimer and Slatt, 2004). These images can be very use-ful for fundamental studies of depositional systems

(e.g., Figure3) and for qualitative rock property predic-

tions based on relationships between depositional ele-

ments and properties of interest (see seismic facies

discussion above).

From a seismic sequence perspective, the ability to

view 3D seismic data from many angles is particularly

significant because reflection terminations and configu-

rations are most easily observed from specific angles

with respect to the stratigraphic bodies being imaged.

For example, Figure4shows mutually orthogonal slices

through a 3D seismic cube that are viewed from differ-

ent angles. The data set images a Tertiary shelf-phase

Figure 2. Two different representations of stratal termina-tions that might be visible in seismic data, well-log cross sec-tions, or sometimes exceptional outcrops. (a) Reflectionterminations as defined by Exxon workers in AAPG Memoir26 (Mitchum et al., 1977). Reprinted by permission of the

AAPG, whose permission is required for further use. (b) Re-flection terminations as defined by BP in 1996 (Redrawn andused with permission from Bertram and Milton, 1996).

Figure 3. Seismic amplitude map of the Cadotte Member, aCretaceous-age clastic strandplain deposit from Alberta, Can-ada. (a) Uninterpreted map showing prominent amplitudetrends that strike approximately eastwest, with other trendsmore northsouth in orientation. Note the acquisition geometryis oblique to these trends, suggesting that the amplitudes are not

acquisition artifacts. (b) Interpreted map. The eastwest-strikingamplitude lineations are interpreted to indicate strandplain ori-entation (i.e., they represent paleoshorelines), whereas the ap-

proximately northsouth trends are related to: (1) Cretaceouschannels that cut through the Cadotte shoreline, (2) Cretaceouschannels in the stratigraphic unit immediately above the Cadotte(i.e., the Paddy Member) that interfere with the Cadotte ampli-tudes, or (3) poor-data areas beneath a modern river floodplain.Seismic modeling, log-based stratigraphic interpretations, andcomparison of the amplitude map to modern surficial featureswere used to constrain the amplitude interpretation. SeeMcCul-lagh and Hart (2010)for further details. Reprinted by permissionof the AAPG, whose permission is required for further use.

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delta. Reflection terminations showing toplap, down-

lap, and erosional truncation are more clearly seen in

some orientations than in others. Horizontal slices

through clinoforms allow true progradation directions

to be determined. The 3D seismic visualization software

allows the viewer to pick these or other orientations,

whereas an interpreter working with 2D seismic data

would be constrained to view the data in the orientation

in which they were collected.

The 3D seismic data sets should also be used by in-terpreters to help make the conceptual link between the

expression of seismic facies seen on vertical transects

and in plan (map) views. This exercise can help inter-

preters to more confidently interpret 2D data. The 3D

seismic interpretation packages also permit inter-

preters to detect and visualize stratigraphic features

in various ways, such as volume (opacity) rendering

and geobody detection (Figure 5). These types of ex-

tractions and visualizations need to be undertaken in

the context of a fit-for-purpose seismic stratigraphic

analysis.

Some of the large 2D and 3D (covering several1000 km2) seismic data sets collected offshore provide

unparalled opportunities for visualizing entire deposi-

tional systems (e.g., Saller et al., 2004). Conversely,

small land surveys (covering areas of several km2 to

several tens of km2) can be much smaller than the

sequences or systems tracts being imaged. In these

small data sets, it is commonly impossible to see the

reflection termination patterns that are necessary to

identify seismic-scale systems tracts. As such, they

need to be integrated with regional data sets consisting

of longer 2D seismic lines and/or log-derived correla-

tions (Hart et al., 2007).

Figure 4. The seismic expression of stratigraphic features ina 3D seismic cube depends on which way the data volume issliced. (a) Vertical transect and (b) time-slice images througha prograding deltaic system. Compare the seismic expressionof the clinoforms and the incised valley from one image to theother. The clinoforms show toplap and downlap in the verticaltransect, but the time slice shows them as lineations that canbe used to determine the shoreline orientation. The incision inthe vertical transect is seen to have a meandering geometry inthe time slice. Area of timeslice covers 96 km2 (38 squaremiles). From Hart (2011). Reprinted by permission of the

AAPG, whose permission is required for further use.

Figure 5. A simple example of geobody detection. (a) A dis-continuous trough (red) is observed in a vertical transect andused as a seed point by the software to track the featurethrough the 3D data set, only a portion of which is shown here.The software traces the body as a series of connected voxelsthrough the data set (using user-defined thresholds) and, inthis case, identifies a channel feature (b and c). From Hart(2011). Reprinted by permission of the AAPG, whose permis-sion is required for further use.



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Seismic stratigraphy was originally conceived using,

and applied to, compressional wave seismic data. How-

ever, the methods of seismic stratigraphy also can be

applied to multicomponent data, a subdiscipline re-

ferred to as elastic wavefield seismic stratigraphy

byHardage and Aluka (2006).Shear-wave seismic data can sometimes detect

stratigraphic features that are invisible to compres-

sional waves. For example, Figure 6 shows two differ-

ent time slices through some Cretaceous clastics of the

Western Canada Sedimentary Basin in Alberta. The

image on the left shows a conventional seismic image

obtained by recording (and then processing) P-wave

reflections. A channel corresponding to the producing

wells (black dots) is, at best, possibly visible in the data.

The converted-wave data4 (right side of Figure6) more

clearly delineate the extent of the channel because the

channel-filling deposits respond to shear waves more

clearly than the compressional waves. Despite the ap-

parent utility of shear-wave seismic data for seismic

stratigraphic analyses, a variety of logistical and cost

reasons prevent them from being more widely em-

ployed (e.g., Harris and OBrien, 2008).

Because of seismic resolution limits and the ambigu-

ity associated with seismic facies (i.e., any one seismic

facies can be associated with several types of strati-

graphic deposits), seismic stratigraphic analyses should

be integrated with wireline logs, core, and other data

types wherever possible. Figure 7 shows an exampleof this approach. In this Cretaceous clastics example,

log-based correlations were ambiguous because of

the highly channelized nature of the deposits. Con-

versely, some important stratigraphic surfaces could

not be defined in the seismic data because of resolution

issues and, perhaps, imaging problems in this P-wave

data set. Integration of the log and seismic data, and

a modern analog, reduced the ambiguity in the seismic

stratigraphic correlations and led to more confident

lithology predictions in interwell areas.

These geometry-based approaches tend to be most

commonly applied by interpreters with geologic

backgrounds. Vertical transects (e.g., 2D seismic data)

continue to be used in exploration settings to help de-

fine lithologies and depositional histories in areas lack-

ing well control (e.g.,Bachtel et al., 2004; Gregersen and

Skaarup, 2007). Diagnostic combinations of seismic

facies and reflection terminations have been advanced

for a variety of different depositional settings

(e.g., Handford and Loucks, 1993; Weimer and Slatt,

2004; Figure 8). However, the approach has some

pitfalls, including: (1) nonuniqueness of the seismic re-

sponse due to resolution problems, interference effects

Figure 6. In some cases, stratigraphic features that are invisible to P-waves can be detected using S-waves, as shown by theseimages from the Cretaceous section of Alberta. (a) Time slice through P-P data (conventionalseismic data P-wave down andP-wave up). The black dots running from top to bottom toward the left side of the image indicate the location of oil wells that

produce from channel sands. There is only a faint indication from this P-wave image that a channel might be present. (b) A cor-responding time slice through a P-S data volume (i.e., mode-converted S-waves recorded). This image much more clearly indicatesthe presence of a channel connecting the productive oil wells. From Margrave et al. (1998).

4Converted-wave seismic data generally involve using a compres-

sional-wave source to generate downgoing seismic energy but record-

ing upgoing shear waves that were produced by mode conversion at

stratigraphic boundaries. SeeMargrave et al. (1998)for more details.

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or other phenomena (Figure 9); (2) nonuniqueness

of the genetic relationships between depositional proc-

ess, external form/seismic facies and lithology, (3) non-

uniqueness of the relationship between depositional

facies (e.g., turbidite sheets) and physical properties

of interest (e.g., porosity and permeability are

affected by the degree/type of diagenesis, which is

not readily predicted from depositional systems ap-

proaches), and (4) nonquantitative output. Finally,

although some slices/attribute extractions show geo-morphic features that are readily identifiable as depo-

sitional features, in some cases, these views show

ambiguous patterns that can be interpreted in several

different ways.

Seismic attributes and feature detectionNot all stratigraphic features of interest are readily

apparent in amplitude data. Various seismic attributes

have shown to be useful for detecting stratigraphic fea-

tures in the data in the same way that some attributes

are useful for identifying structural features such as

faults. Unfortunately, attribute-based analyses are not

Figure 7. Integration of a vertical transect, stratal slice, wireline logs, and suspected modern analog to define meandering fluvialpoint bar deposits. (a) Seismic transect showing key seismic stratigraphic surfaces mapped through the integration of seismic andlog data. (b) The shingled reflections in the yellow oval correspond to shalier-upward successions in gamma-ray logs (c). A slicethrough the data at this level (d) shows crescentic amplitude patterns that are similar in scale and planform morphology to scrollbars of a modern meandering river system (e). See Sarzalejo and Hart (2006) for fuller discussion.



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typically used by geology-based interpreters (i.e., see

papers published in the sedimentary geology or petro-

leum geology literature). I explore the application of

seismic attributes to seismic stratigraphy in this section.

I defer discussion of inversion results to a later section,

although these derived volumes are sometimes referred

to as physically significant attributes.

Several definitions of seismic attributes exist,

but generally they can be thought of as quantitative

measures derived from the seismic data or interpreta-

tions. They include simple amplitude extractions (e.g.,

Figure3, complex-trace attributes, other mathematical

manipulations of the seismic trace (e.g., integration and

derivative of the seismic trace; Figure 10), and mea-

sures derived from interpretations (e.g., curvature;

Figure11). Summaries and examples of seismic attrib-

utes are presented by Brown (2004),Chopra and Mar-

furt (2007),Hart (2011), and others.Meta-attributesare

Figure 9. As demonstrated in this example,geometry-based seismic stratigraphic inter-

pretations can be ambiguous because of seis-mic resolution problems, interference effects,or other factors. Top row shows a simple geo-logic model composed of layers having differ-ent acoustic properties. Image at top rightshows how the stratigraphy can be subdividedinto three packages: (1) a lowermost unit con-

sisting of folded/dipping layers (yellow), (2) amiddle unit consisting of a divergent basin fill(green), and (3) an undisturbed upper unitconsisting of horizontally layered strata(blue). An unconformity (dashed red line) sep-arates the uppermost unit from the underlyingtwo units. The middle row shows the stratig-raphy as imaged using a 75 Hz Ricker wavelet.The reflections in this model generally showthe structural/stratigraphic geometries of themodel, and the image allows the three strati-graphic units to be identified (middle right).However, reflections appear to converge inthe middle unit (might be interpreted as strati-graphic pinchouts?) and there appears to be

some subtle relief associated with the uncon-formity (might be interpreted as differentialerosion of harder/softer layers?). The Lower-most row shows the stratigraphy as imagedusing a 25 Hz Ricker wavelet. In this image,it would be possible to interpret the presenceof a sequence-bounding unconformity (reddashed line, lower right) that has truncationbelow and onlap above (reflection polaritywould need to be considered). Only twostratigraphic units might be inferred from thisinterpretation. Based on seismic modeling

presented in Hart (2000).

Figure 8. Seismic lapout geometries andfacies common to carbonate platform depos-its. Labeled features are: (1) karst-relatedtruncations, (2) shelf mounds, (3) land-ward migrating clinoforms (rimmed shelves),(4) bioherms (rimmed shelves), (5) steepdepositional slopes (>angle of repose),(6) downlapping clinoforms at toe-of-slope,(7) alternating downlap/onlap, (8) conver-gence of clinoform reflections, (9) shelf edgeincision, and (10) incision within sequences.

Redrawn from Handford and Loucks (1993).Reprinted by permission of the AAPG, whose

permission is required for further use.



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Figure 10. Poststack attributes used for detection of stratigraphic features based on simple seismic modeling. (a) Geologic modelof a prograding succession of clinoforms. High-velocity sands (yellow) grade down into low-velocity shale (brown and gray).(b) Predicted amplitude response (variable density display with wiggle overlay) of the clinoforms using a 60 Hz Ricker wavelet.(c) Integrated trace (relative acoustic impedance) attribute derived from the amplitude data shown in (b). The sands correspond torelatively high impedance (green) and the shales to low impedance (red). Wiggle trace overlay shows the original seismic am-

plitudes. The clinoform geometry is apparent. (d) Predicted amplitude response (variable density display with wiggle overlay) ofthe clinoforms using a 30 Hz Ricker wavelet. Subtle changes in amplitude are produced by the changes in sand thickness, butthe clinoform geometry is not clearly visible. (e) Integrated trace (relative acoustic impedance) attribute derived from the am-

plitude data shown in (d). The upper sandy portion of the succession corresponds to relatively high impedance (green) and theshales to low impedance (red), but the clinoform geometry is not apparent. Wiggle trace overlay shows the original seismicamplitudes. (f) Second derivative attribute derived from the amplitude data shown in (d). This attribute is capturing subtle changesin waveform that suggest the presence of the clinoforms. From Hart (2008b).



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attributes derived by the combination of more than one

attribute (de Rooij and Tingdahl, 2002; Figure 12).

Qualitative analyses of reflection amplitude and fre-

quency, in addition to geometric analyses, originally

formed part of seismic facies analyses (Mitchum et al.,

1977). Taner and Sheriff (1977) are generally creditedwith introducing the seismic stratigraphy community

to complex trace attributes. Other workers sub-

sequently incorporated complex trace attributes into

seismic stratigraphic analyses (e.g., Fontaine et al.,1987) but published examples are few. Similarly, rela-

tively few attempts have been made to define seismic

attributes that capture reflection geometries such as

parallelism, convergence, or downlap (e.g., Barnes,

2000;van Hoek et al., 2010).The seismic response is frequency-dependent.

Although some analyses have examined changes in re-

flection configuration in vertical transects as a function

of frequency (e.g.,Zeng, 2013), most published analyses

have focused on map view slices through 3D volumes.

Spectral decomposition methods break the seismic sig-

nal down into its component frequencies. The resultant

images commonly bring out stratigraphic (or structural)features that are not readily apparent in the original

broadband data (Castagna and Sun, 2006; Chopraand Marfurt, 2007). Different approaches are possible

for analyzing the results, but one common approach

is to optically stack different frequency bands (e.g.,

Figure 13). Frequency-dependent tuning observed in

these analyses can be used to predict the thickness

of stratigraphic features.

Automated seismic facies analyses use a variety of

computer-based techniques to characterize seismic

trace shape. Thereafter, it is assumed (or hoped) thateach facies can be related to lateral variations in lithol-

ogy, rock properties, and/or fluid content of the strati-graphic bodies being imaged. Several different

approaches have been applied to this task, including

artificial intelligence-based methods such as neural

networks (Colou et al., 2003; Figure 14). Originally

applied to a narrow time window defined with respect

to a single reflection, 3D applications of seismic facies

analysis also have been developed (e.g., Farzadi and

Hesthammer, 2007;Gao, 2007). These automated seis-mic facies can be very useful; however, knowledge of

depositional systems is required to: (1) help define geo-

logically meaningful windows for the facies analyses,

and (2) ensure that the stratigraphic features inter-

preted to be revealed by the analyses make geo-logic sense.

References presented in this section about seismic

attributes are generally from the geophysical literature.

Despite the potential applications to sedimentary geol-

ogy, seismic attribute studies have yet to become a

mainstream part of that disciplines toolkit.

Physical properties predictionWhile indirect methods for predicting physical prop-

erties from seismic data (i.e., exploiting relationships

Figure 11. Example of horizon curvature for stratigraphicanalysis. (a) Time-structure map of the top of a leveed-channelcomplex. (b) Subtle structural and stratigraphic features areemphasized when dip curvature is overlain over the surfaceand directional lighting is applied. (c) Expanded and rotated

view of the dip curvature overlay showing some fine-scalemorphological features that are emphasized by curvature

visualization. Note the changes in sharpness of the channelmargin (green and blue) along its length, and the presenceof an incised inner channel (shown by red hues indicating neg-ative curvature) in the upper portion of this image. Repro-duced with permission from Hart and Sagan (2007).



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near base of each well display) is not readily apparent in

the original seismic data. The unconformity becomes

readily apparent after the inversion. Clearly the imped-

ance version of the data would be much more useful for

seismic stratigraphic analyses than the original ampli-

tude volume.

Prestack inversion methods solve for P-impedance,

S-impedance, and density. These products can then

be manipulated in various ways to predict elastic prop-

erties (Youngs modulus, Poissons ratio, VP/VS ratio,etc.) that can be related to lithology, porosity, fluid

saturation, etc. Given the current interest in unconven-

tional reservoirs (e.g., shale plays), there is much

interest in using inversion products to predict areas

of better reservoir properties (porosity, hydrocarbon

saturation) and to design hydraulic fracture treatments

(e.g., Close et al., 2012; Figure 16).

Although seismic inversion products have become

widely used in the petroleum industry, there are several

ambiguities and limitations that need to be kept in mind.

These problems include: (1) dependence of the result on

the low-frequency model, (2) wavelet estimation, (3)

nonuniqueness of the inversion solution, and (4) non-

uniqueness of the relationship between elastic/acoustic

properties and lithologic properties of interest to seis-

mic stratigraphers. Geophysicists have fully embraced

Figure 14. Sample automated seismic facies classificationresults from Marroquin et al. (2009). (a) Devonian pinnaclereefs and (b) a probable Jurassic tidal channel (siliciclastic).

Although different seismic facies (and lithologies) are presentat the two stratigraphic levels, the software has used the samecolor palette for both classification exercises.

Figure 15. Example showing how a simple model-basedacoustic impedance inversion was used to help with a strati-graphic interpretation (i.e., mapping a significant unconform-ity). (a) The original seismic amplitude data. Well controlindicates the presence of a significant unconformity nearthe base of the wells that separates Cretaceous clastics fromunderlying Devonian carbonates (black line and star). Theunconformity does not correspond to a prominent reflectionin the data. (b) Model-based inversion result showing thesame profile presented in (a). The unconformity is clearly vis-ible as an upward transition between high-impedance carbon-

ates (purple) and the overlying clastics (blue, red, yellow,green). A high-impedance layer (purple) above the uncon-formity corresponds to carbonate-cemented sandstone. Wire-line logs in both cases are sonic logs. From Hart (2011).Reprinted by permission of the AAPG, whose permission isrequired for further use.

Figure 16. Prestack elastic inversion-based prediction of thedistribution of brittleand ductile rocks in the CretaceousEagle Ford Formation. Map area covers approximately80 km2. Modified from Treadgold et al. (2011).

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inversion methods for predicting rock properties (includ-

ing fluid content) but the sedimentary geology commu-

nity has been slow to adopt these methods for studying

depositional systems (but see Contreras and Latimer,

2010). Sedimentary geologists could play a key role in

inversion studies by helping to define which results

are geologically/stratigraphically possible.

Another approach to physical property prediction

was termed the data-driven methodology by Schultzet al. (1994). This approach exploits empirically derived

relationships between seismic attributes and log-de-

rived properties of interest to predict those properties

away from well control (e.g., Hampson et al., 2001).

Tebo and Hart (2005)and Sagan and Hart (2006)dem-onstrate how this approach could be used to study

porosity development in two different carbonate set-

tings, and Sarzalejo Silva and Hart (2013) show how

the approach could be used to evaluate a siliciclastic

heavy oil reservoir. Although once relatively widelyused, the popularity of this empirical approach has

waned as geophysicists attention has turned to focus

on rock physics-based inversion approaches described

previously. The sedimentary geology community did

not embrace attribute-based property predictions.

Stratigraphic controls on seismic responseDespite much overlap in topics of interest, there has

been relatively little collaboration between the sedi-

mentary geology community, rock physicists, and geo-

physicists in terms of understanding the relationships

between depositional/diagenetic processes and seismic

responses. This, despite the fact that sediment prov-

enance (i.e., the source of the sediment), depositional

processes, and diagenesis are the only controls on min-

eralogy, porosity, fabric, and other properties that, in

turn, are fundamental controls on density, elastic

moduli, anisotropy, etc.

Rock physicists have developed many different

mathematical treatments for predicting properties of in-terests based on theoretical mixes of grain sizes, poros-

ity, mineralogy, etc., in siliciclastic successions (e.g.,

Mavko et al., 1998;Avseth et al., 2005). However, there

are few studies that specifically link depositional proc-

esses (e.g., debris flows, tractive sediment transport) or

Figure 17. Two stacked

coarsening upward successions

(parasequences) exposed in Cretaceous clastics of the BookCliffs, Utah. Each succession is shaley at the base with sand-stone beds generally becoming thicker and more abundantupward. People at lower right for scale. Note that these para-sequences would be below the resolution of most petroleumindustry seismic data sets.

Figure 18. Outcrop photo of the Eagle FordFormation, west of Del Rio, Texas, fromHartet al. (this issue). The formation is not a litho-logically homogeneous unit. Instead, differentlithologies of different physical properties(porosity, Poissons ratio, Youngs modulus,etc.) are arranged into laminae, beds, bedsets(shown), and members, all of which areshorter than a seismic wavelet. Do maps ofseismically derived physical properties, suchas the one shown in Figure16, represent aver-age properties over the entire interval or arethey the product of some other phenomenon,such as changes in stratigraphic stacking pat-terns that affect the AVO response (e.g., Fig-ure 20) upon which the physical property

predictions are based?



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diagenetic processes (e.g., effects of chlorite rims on

porosity development) to an appropriate physical

model (but see, for example, Avseth et al. [2005],

p. 8390). Eberli et al. (2003) and Weger et al. (2009)

examined relationships between carbonate porosity

types (a function of depositional and diagenetic proc-

esses) and rock physical properties.

Closer integration between sedimentary geologists

and rock physicists might allow more realistic modeling

of mineral textures and would almost certainly help to

ensure that appropriate rock physics models are se-

lected for property prediction in seismic modeling stud-

ies, i.e., certain mineral textures (e.g., clay-supported

sandstones) are more likely to develop in some deposi-

tional facies than others. For example, Hart et al. (this

issue) state that mathematical/conceptual models de-

veloped for clay-rich shales are inappropriate for

source-rock reservoirs (shale plays) because the latter

are commonly clay poor. In fine-grained systems

(shales), there has been considerable work undertaken

to examine the origins and effects of anisotropy at

the particle scale (e.g., Sayers, 2005;Day-Stirrat et al.,

2010). Layering imparts a vertical axis of symmetry,

making sedimentary rocks vertically transverse

isotropic media5. The anisotropic parameters are de-

rived from measurements on core plugs and need to

be upscaled mathematically (e.g.,Backus, 1962;Berry-

man, 2008) to predict seismic-scale properties that

cannot readily be measured otherwise. Sedimentary

geologists could help to define realistic stratigraphic

model parameters for this type of upscaling work.

Sedimentary rocks are typically bedded, with

collections of beds forming bedsets or parasequences

(Figures 17 and 18). Seismic modeling has been usedsuccessfully to predict seismic responses (amplitudeand other attributes) for various types of stratigraphic

successions (e.g., Meckel and Nath, 1977; Hart and

Chen, 2004; Figure19), typically with a focus on predict-

ing poststack character. However, variations in strati-

graphic layering also are known to affect amplitude

variation with offset responses that would be visible in

prestack analyses (Figure 20). The effects of stratigraphic

Figure 19. Simple 1D acoustic models showing the predictedseismic response of various stratigraphic stacking patterns(blocky, fining upward, coarsening upward) as a functionof thickness. Each block shows an acoustic impedance profile(increasing to right) and the calculated seismic response usinga simple Ricker wavelet. From Hart (2008b).

Figure 20. Amplitude-variation-with-offset (AVO) responsesfor various stratigraphic stacking patterns. Although the samechange in physical properties is present in each case, the AVOresponses are markedly different. Modified fromLindsay and

Van Koughnet (2001).

Figure 21. Example of how seismic geometries can helpguide log correlations. In this simple case, gamma ray logsfrom a series of wells (top row) can be correlated in at leasttwo different ways (middle row). A seismic transect from thisarea (bottom) shows clinoforms that would support theshingled correlation option of the middle row. ModifiedfromHart (2011) and reprinted by permission of the AAPG,whose permission is required for further use.

5I.e., properties such as lithology, porosity, Poissons ratio, and

others change more rapidly from bed to bed, rather than along beds.

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variability on AVO responses, or physical property pre-

dictions (e.g., elastic inversion) derived from AVO

analyses, are seldom documented explicitly.

ConclusionsThe field of seismic stratigraphy is a mature science

that has proven to be useful in a variety of exploration,

development, and fundamental studies in sedimentary

geology. The four main elements of seismic stratigraphyare seismic sequence analysis, seismic facies analysis,

reflection character analysis, and seismic geomorphol-

ogy. The first three were developed for use on 2D seis-

mic data and can be transferred to 3D volumes for use

on vertical transects. Seismic geomorphology evolved

from map-view analyses of 3D seismic volumes and

has since expanded to incorporate a variety of visuali-

zation and analysis techniques. Unfortunately, a variety

of factors can lead to nonunique interpretations, and

physical property predictions are typically qualitative

or, at best, probabilistic in nature. Perhaps for these

reasons, the physics-based quantitative output of seis-

mic inversion methods has become favored for rockproperty prediction in the petroleum industry. On the

other hand, seismic data availability and quality prob-

lems preclude the application of inversion methods

in all settings.

The field of seismic stratigraphy, as practiced by

sedimentary geologists, could benefit from more rou-

tine integration of seismic attribute studies and seis-

mic-based physical property predictions. To do so,

sedimentary geologists will need to learn the physics

behind these methods. The sedimentary geology com-

munity has embraced physics before, such as applica-

tions in sediment transport and bedform development

(e.g., Middleton and Southard, 1984) or basin analysis(e.g., Allen and Allen, 2005), and perhaps will again.

I have tried to highlight a variety of geoscience sub-

disciplines where the methods and interests of sedimen-

tary geologists and geophysicists overlap. Here, I

present several reasons why enhanced collaboration

between these two groups could be mutually advan-

tageous.

Enhanced fundamental understanding of deposi-

tional systems. As described above, the sedimen-

tary geology community has been very slow to

use/accept seismic analyses that are based on

(qualitative) attribute studies or (quantitative)physical property predictions. This is unfortunate

because these analyses can provide clear images

of stratigraphic features and quantitative mea-

sures that would be very useful for fundamental

studies of depositional systems. Miall (2002),

for example, derived a variety of quantitative mor-

phologic parameters from meandering fluvial

systems imaged in time slices through a 3D

seismic amplitude volume.Wood and Mize-Span-

sky (2009) did likewise for a deepwater leveed-channel system. Enhanced seismic stratigraphic

analyses, for example using volume visualization

on attribute volumes, might provide new informa-

tion about other depositional systems that cannot

be adequately imaged using amplitude displays. Better predictive capabilities in exploration set-

tings. The high-quality 3D seismic data needed

to apply prestack elastic inversion methods are

not available everywhere. The results of any given

inversion study can be used to provide analog

data for risk assessment in other areas but shouldbe employed only if appropriate analogs can be

identified. For example, it would be inappropriate

to use data from a sand-prone submarine fan sys-

tem to assess risk in a mud-prone system. Sedi-

mentary geologists play an important role in

selecting appropriate analogs. Improved geology-based interpretations. Geo-

logic interpretations based on subsurface data

(wireline logs, core) have an inherent degree of

ambiguity because of data availability (or lack

thereof) and, typically, data that can be inter-

preted in more than one way. Seismic data can

provide interwell information that can be criticalfor assembling a meaningful geologic story (e.g.,

Figure 21). For example, log-based interpreta-

tions are problematic for some deep shale ba-

sins that are essentially undrilled. Seismic data

also can provide useful analog dimensional andmorphology data for features that cannot be accu-

rately mapped with available subsurface control. Improved geophysics-based interpretations. Geo-

physics-based rock property predictions (e.g., in-

version) are likewise associated with ambiguity

because of reasons such as data quality, data

availability, assumptions made during the inver-

sion (e.g., wavelet estimation), etc. Stratigraphicanalyses of seismic-based rock property predic-

tions should always be undertaken to ensure that

those predictions are geologically reasonable. For

example, it would be appropriate to ask whether

the predicted distribution of brittle and ductile

rocks shown in Figure 16makes geologic sense,

given what is known about Eagle Ford stratigra-

phy (e.g., Figure18) and depositional processes. Subseismic prediction of rock properties. Many

reservoirs contain internal baffles, thief zones,

variations in porosity, or other stratigraphic

heterogeneities that can have important eco-

nomic impacts but are below seismic resolution(e.g., Figures10, 17, and18). Seismic data can pro-

vide the structural and stratigraphic context in

these cases, but stratigraphic knowledge plays

an important role in predicting the presence

and distribution of these features.

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