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Abstract

The aim of this study is to evaluate what seismic attributes are best able to highlight porous non-stratabound dolostone geobodies set in low porosity limestone. For this purpose three dolostone geobody volume scenarios were defined using outcrop based three-dimensional models to define the range of dimensions of dolostone geobodies and their association with particular fault populations. Three porosity scenarios were created using a global compilation to assign porosities to three lithologies: host limestone, bulk dolostone geobodies, and dolostone geobodies adjacent to faults. The combination of porosity and geobody volume scenarios yielded nine non-stratabound dolostone geobody scenarios. These include models in which the properties of near-fault dolostones were enhanced or degraded relative to the bulk dolostone geobody values. This allows for the effects of processes such as overdolomitization or dissolution to be implicitly explored, since those processes can degrade or enhance near-fault properties such as porosity, although in all scenarios dolostone porosities are greater than host limestone porosity.

Density and compressional velocity (Vp) were assigned to the scenarios based on a global compilation of the density, porosity, and Vp in limestones and dolostones to allow for the calculation of acoustic impedance volumes that are representative of the range of values that could exist at depth. Synthetic seismic cubes and a suite of 14 seismic attributes were generated for each of the nine dolostone scenarios. Each attribute response was evaluated for its potential to highlight porous non-stratabound dolostone geobodies. Attributes that are most sensitive to lateral changes in acoustic properties rank the highest in the evaluation, followed by amplitude attributes, followed in turn by frequency attributes. Continuity attributes rank poorly in this example because fault offset is relatively small and is obscured by dolomitization.

Introduction

There is a global occurrence of carbonate reservoirs with non-stratabound dolostone geobodies, many of which have good porosity (Fig. 1). Many of these occur in low porosity host limestone, which means that the ability to interpret these geobodies on seismic data is critical for estimating hydrocarbon volumes and well and reservoir producibility. A number of seismic attributes (described below) have been reported to be sensitive to porosity in this type of carbonate reservoir (see references in Table 1), but it is not clear what attributes can be used to best interpret non-stratabound dolostone geobodies. The aim of this study is to evaluate what attributes are best able to highlight porous non-stratabound dolostone geobodies set in low porosity limestone by

  1. Creating three-dimensional (3D) models of a well-exposed outcrop occurrence of fault-related non-stratabound dolostone geobodies, including multiple geobody scenarios to account for uncertainty in the volume of dolostone that was removed during erosion of the study area in which the geobodies are exposed.

  2. Comparing the dimensions of those geobodies to a global compilation of geobodies as measured on subsurface and outcrop data to ensure that the dimensions of the geobodies in the studied analogue are similar to what might be expected in the subsurface.

  3. Defining a realistic set of lithologies in the 3D models, to include host limestone, bulk dolostone geobody, and near-fault dolostone. This allows the effects of near-fault processes such as fracturing, overdolomitization, dissolution, or dedolomitization to be included in scenarios of rock properties.

  4. Assigning a realistic range of porosities to each of the lithologies in those models using a global compilation of limestone and dolostone porosities in settings containing porous non-stratabound dolostone geobodies to create a range of porosity scenarios.

  5. Assigning realistic densities and compressional velocities to those porosity scenarios using global compilations from a range of subsurface and outcrop settings.

  6. Generating a range of synthetic seismic volumes using acoustic impedance volumes derived from the previous step (to allow a range of seismic expressions of porous non-stratabound dolostone geobodies to be evaluated).

  7. Generating a set of relevant seismic attributes from each of the synthetic seismic volumes based on a compilation of attributes that have been reported to be useful for interpreting non-stratabound dolostone geobodies, karsting, mounds and other buildups, and a number of morphologically similar examples.

  8. Ranking each of the attributes in each of the synthetic seismic volumes based on how successfully they can be used to distinguish porous dolostone geobodies and tight host limestone.

FIG. 1.

—Figure showing global distribution of sedimentary basins (as defined by Tellus) that have been reported to contain non-stratabound dolostone geobodies based on the references shown in this figure and the compilation of hydrothermal dolomites of Davies and Smith (2006). The location of the Vajont gorge is shown with an arrow. Three types of analogues are shown: (1) Analogues from outcrop and subsurface containing data on geobody dimensions or dolostone geobody and host limestone porosity, Table 1; (2) studies with compressional velocity, density and porosity data from wells, core or outcrop, Table 2; (3) Studies containing descriptions of attributes that were reported to successfully correlate to porosity in a variety of carbonate settings (non-stratabound dolomite, karst, mound/buildups, other structural settings, or other nonstructural settings), Table 3.

FIG. 1.

—Figure showing global distribution of sedimentary basins (as defined by Tellus) that have been reported to contain non-stratabound dolostone geobodies based on the references shown in this figure and the compilation of hydrothermal dolomites of Davies and Smith (2006). The location of the Vajont gorge is shown with an arrow. Three types of analogues are shown: (1) Analogues from outcrop and subsurface containing data on geobody dimensions or dolostone geobody and host limestone porosity, Table 1; (2) studies with compressional velocity, density and porosity data from wells, core or outcrop, Table 2; (3) Studies containing descriptions of attributes that were reported to successfully correlate to porosity in a variety of carbonate settings (non-stratabound dolomite, karst, mound/buildups, other structural settings, or other nonstructural settings), Table 3.

Table 1.

—Compilation of published studies of non-stratabound dolostone reservoirs containing data on dolostone and limestone porosity and/or dolostone geobody dimensions.

RegionCountryFormation nameFormation ageAge of dolomitizationOutcrop, subsurfaceFieldReference
Eastern NorthUSATrenton-Black RiverOrdovicianUnspecifiedOutcropN/ABlack et al. (1981)
AmericaUSATrenton-Black RiverOrdovicianSilurian/DevonianSubsurfaceAlbion-Scipio; Stoney PointHurley and Budros (1990)
USATribe HillsOrdovicianLate OrdovicianOutcropN/ASlater and Smith (2012)
USATrenton-Black RiverOrdovicianLate Ordovician/SilurianSubsurfaceSaybrookSagan and Hart (2006)
CanadaCatocheOrdovicianLate Ordovician to Devonian (?)BothUnnamedBaker and Knight (1993)
CanadaAbenakiMiddle to Late JurassicUnspecifiedSubsurfaceDeep PanukeEnCana (2006)
CanadaAbenakiLate JurassicLate Jurassic/Early CretaceousSubsurfaceDeep PanukeWierzbicki et al. (2006)
CanadaRed Head RapidsLate OrdovicianUnspecifiedBothUnnamedLavoie et al. (2011)
CanadaRomaine, Mingan and TrentonOrdovicianUnspecifiedSubsurfaceUnnamedLynch and Trollope (2001)
CanadaSayabec, Forillon and Indian CoveSilurian and DevonianUnspecifiedOutcropN/AMarcil et al. (2005)
USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultipleNyahay et al. (2006)
Eastern Canada and USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultiplePatchen et al. (2006)
Greater MediterraneanItalyCalcari Grigi GroupEarly JurassicEarly Mesozoic (early), Oligocene to Early Miocene (structurally controlled)OutcropN/ADi Cuia et al. (2011)
ItalyLatemarMiddle TriassicMiddle TriassicOutcropN/ACarmichael et al. (2008), Jacquemyn et al. (2015), Wilson et al. (1990)
ItalyMonte Zugna and MaiolicaEarly Jurassic and Late Jurassic/Early CretaceousTertiaryOutcropN/ARonchi et al. (2012)
ItalyVajontMiddle JurassicLate Paleogene to NeogeneOutcropN/AZempolich and Hardie (1997)
LebanonKesrouaneEarly JurassicEarly Jurassic to Late JurassicOutcropN/ANader and Swennen (2004), Nader et al. (2004)
SpainBenassalEarly CretaceousLate Cretaceous to Early TertiaryOutcropN/AMartin-Martin et al. (2015)
SpainUrgonianEarly CretaceousLate CretaceousOutcropN/ADewit et al. (2012)
SpainUrgonianEarly CretaceousEarly to Late CretaceousOutcropN/ANader et al. (2012)
SpainUrgonianEarly CretaceousEarly CretaceousOutcropN/AShah et al. (2010, 2012)
Middle EastIranSarvakEarly to Late CretaceousEarly to Late CretaceousOutcropN/ALapponi et al. (2011), Sharp et al. (2010)
OmanKhuafiEdiacaranpre-PermianOutcropN/AVandeginste et al. (2014)
OmanSahtan GroupJurassicJurassic and Late CretaceousOutcropN/AVandeginste et al. (2013)
Northern EuropeGermanyMuschelkalkTriassicJurassic (early stratabound), Tertiary (leaching and cementation)OutcropN/AKoehrer et al. (2010)
IrelandNavan GroupLower CarboniferousCarboniferousOutcropN/ABraithwaite and Rizzi (1997)
United KingdomCloud Hill dolostone and Ticknall limestoneLower CarboniferousLower Carboniferous to Permo/TriassicBoth; most samples from quarry, some from a boreholeUnnamedBouch et al. (2004)
United KingdomBalladooleLower CarboniferousCarboniferous (?)OutcropN/AShelton et al. (2011)
SE AsiaIndonesiaTaballarOligocene-MioceneOligocene-MioceneOutcropN/AWilson et al. (2007)
Western North AmericaUSALeadvilleMississippianPennsylvanian to Jurassic (early dolomitization), Cretaceous to Oligocene (saddle dolomite)SubsurfaceLisbonChidsey et al. (2009)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceLadyfernBoreen and Davies (2004)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceClarke LakeLonnee and Machel (2006)
USABootstrapSilurian-DevonianSilurian/Devonian to TertiaryOutcropN/AMorgan (2007)
Western North America (Not fault related)USACisco GroupLate Pennsylvanian-Early PermianLate PermianSubsurfaceReineckeSaller and Dickson (2011)
CanadaWabamun GroupLate DevonianDevonian (early), Unspecified (late)SubsurfaceMultipleSaller and Yaremko (1994)
RegionCountryFormation nameFormation ageAge of dolomitizationOutcrop, subsurfaceFieldReference
Eastern NorthUSATrenton-Black RiverOrdovicianUnspecifiedOutcropN/ABlack et al. (1981)
AmericaUSATrenton-Black RiverOrdovicianSilurian/DevonianSubsurfaceAlbion-Scipio; Stoney PointHurley and Budros (1990)
USATribe HillsOrdovicianLate OrdovicianOutcropN/ASlater and Smith (2012)
USATrenton-Black RiverOrdovicianLate Ordovician/SilurianSubsurfaceSaybrookSagan and Hart (2006)
CanadaCatocheOrdovicianLate Ordovician to Devonian (?)BothUnnamedBaker and Knight (1993)
CanadaAbenakiMiddle to Late JurassicUnspecifiedSubsurfaceDeep PanukeEnCana (2006)
CanadaAbenakiLate JurassicLate Jurassic/Early CretaceousSubsurfaceDeep PanukeWierzbicki et al. (2006)
CanadaRed Head RapidsLate OrdovicianUnspecifiedBothUnnamedLavoie et al. (2011)
CanadaRomaine, Mingan and TrentonOrdovicianUnspecifiedSubsurfaceUnnamedLynch and Trollope (2001)
CanadaSayabec, Forillon and Indian CoveSilurian and DevonianUnspecifiedOutcropN/AMarcil et al. (2005)
USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultipleNyahay et al. (2006)
Eastern Canada and USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultiplePatchen et al. (2006)
Greater MediterraneanItalyCalcari Grigi GroupEarly JurassicEarly Mesozoic (early), Oligocene to Early Miocene (structurally controlled)OutcropN/ADi Cuia et al. (2011)
ItalyLatemarMiddle TriassicMiddle TriassicOutcropN/ACarmichael et al. (2008), Jacquemyn et al. (2015), Wilson et al. (1990)
ItalyMonte Zugna and MaiolicaEarly Jurassic and Late Jurassic/Early CretaceousTertiaryOutcropN/ARonchi et al. (2012)
ItalyVajontMiddle JurassicLate Paleogene to NeogeneOutcropN/AZempolich and Hardie (1997)
LebanonKesrouaneEarly JurassicEarly Jurassic to Late JurassicOutcropN/ANader and Swennen (2004), Nader et al. (2004)
SpainBenassalEarly CretaceousLate Cretaceous to Early TertiaryOutcropN/AMartin-Martin et al. (2015)
SpainUrgonianEarly CretaceousLate CretaceousOutcropN/ADewit et al. (2012)
SpainUrgonianEarly CretaceousEarly to Late CretaceousOutcropN/ANader et al. (2012)
SpainUrgonianEarly CretaceousEarly CretaceousOutcropN/AShah et al. (2010, 2012)
Middle EastIranSarvakEarly to Late CretaceousEarly to Late CretaceousOutcropN/ALapponi et al. (2011), Sharp et al. (2010)
OmanKhuafiEdiacaranpre-PermianOutcropN/AVandeginste et al. (2014)
OmanSahtan GroupJurassicJurassic and Late CretaceousOutcropN/AVandeginste et al. (2013)
Northern EuropeGermanyMuschelkalkTriassicJurassic (early stratabound), Tertiary (leaching and cementation)OutcropN/AKoehrer et al. (2010)
IrelandNavan GroupLower CarboniferousCarboniferousOutcropN/ABraithwaite and Rizzi (1997)
United KingdomCloud Hill dolostone and Ticknall limestoneLower CarboniferousLower Carboniferous to Permo/TriassicBoth; most samples from quarry, some from a boreholeUnnamedBouch et al. (2004)
United KingdomBalladooleLower CarboniferousCarboniferous (?)OutcropN/AShelton et al. (2011)
SE AsiaIndonesiaTaballarOligocene-MioceneOligocene-MioceneOutcropN/AWilson et al. (2007)
Western North AmericaUSALeadvilleMississippianPennsylvanian to Jurassic (early dolomitization), Cretaceous to Oligocene (saddle dolomite)SubsurfaceLisbonChidsey et al. (2009)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceLadyfernBoreen and Davies (2004)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceClarke LakeLonnee and Machel (2006)
USABootstrapSilurian-DevonianSilurian/Devonian to TertiaryOutcropN/AMorgan (2007)
Western North America (Not fault related)USACisco GroupLate Pennsylvanian-Early PermianLate PermianSubsurfaceReineckeSaller and Dickson (2011)
CanadaWabamun GroupLate DevonianDevonian (early), Unspecified (late)SubsurfaceMultipleSaller and Yaremko (1994)

Geological Setting

The Vajont Gorge located in the Southern Venetian Alps, Italy, exposes non-stratabound dolostone geobodies in the Jurassic Vajont Limestone (Fig. 2) described in detail in Bistacchi et al. (2015). This formation was deposited in the Belluno Trough at the northern edge of the Adriatic platform. This part of the Alps represents one of the best preserved passive continental margins of the Tethys Sea existing in the Mediterranean region (Bertotti et al. 1993, Castellarin et al. 2006). The Vajont area was deformed during multiple events, the most important of which for this study are east–west extension during the Mesozoic, followed by Late Cretaceous–Tertiary north–south compression during Alpine orogenesis (Ronchi et al. 2012). Regional N–S striking normal faults related to Jurassic extension defined the borders of large elevated blocks, the orientations of which were controlled in part by deeper inherited Variscan discontinuities (Doglioni 1992).

FIG. 2.

—Annotated outcrop photograph of the Vajont Gorge (view to the northeast). The Vajont Dam is visible at the upper right portion of the image. Bedding in the Vajont Limestone is highlighted in blue, and a non-stratabound dolostone geobody is outlined in red. Prominent faults and fracture zones are highlighted in yellow/green. Note that bedding is generally not preserved in the geobody. A white arrow points to a tunnel and a roadway in the center of the geobody for scale.

FIG. 2.

—Annotated outcrop photograph of the Vajont Gorge (view to the northeast). The Vajont Dam is visible at the upper right portion of the image. Bedding in the Vajont Limestone is highlighted in blue, and a non-stratabound dolostone geobody is outlined in red. Prominent faults and fracture zones are highlighted in yellow/green. Note that bedding is generally not preserved in the geobody. A white arrow points to a tunnel and a roadway in the center of the geobody for scale.

The study area (Fig. 3) is composed of three paleogeographic– structural units, the Trento Platform, the Friuli Platform, and in between the Belluno Basin. The Trento Platform was a Late Triassic elevated block, drowned in the Early Jurassic, and a pelagic plateau with condensed sedimentation during the Late Jurassic (Ronchi et al. 2012). It was bound by the Lombardian Basin at the “Garda escarpment” fault system to the west. This fault system was active during Jurassic and Cretaceous times and has a N–S orientation. The easternmost part of the area consists of the Friuli Platform, a carbonate platform that existed from the Jurassic until the Cretaceous. The Trento Platform and the Friuli Platform were separated by the Belluno Basin (Winterer and Bosellini 1981). The Belluno Basin was essentially a Liassic feature, and its shape and dimensions resemble the present day “Tongue of the Ocean” in the Bahamas, a modern analogue (cf. Schlager et al. 1976, Mullins 1983).

FIG. 3.

—Location maps of the Vajont Gorge study area. A) simplified map of the major tectonic and stratigraphic features of the study area (derived from Winterer and Bosellini 1983 and Ronchi et al. 2012). The study area occurs in the Southern Alps, one of the less deformed regions of the Alps. The Vajont Limestone consists of oolitic deep-water fans deposited in the Belluno Basin in between the Trento and Friuli Platforms. B) Simple geologic map of the Vajont Gorge (faults taken from Bistacchi et al. 2015 based on Riva et al. 1990 and Massironi et al. 2013, and formation contacts are taken from Carulli 2006). Lmst = limestone, Fm = formation.

FIG. 3.

—Location maps of the Vajont Gorge study area. A) simplified map of the major tectonic and stratigraphic features of the study area (derived from Winterer and Bosellini 1983 and Ronchi et al. 2012). The study area occurs in the Southern Alps, one of the less deformed regions of the Alps. The Vajont Limestone consists of oolitic deep-water fans deposited in the Belluno Basin in between the Trento and Friuli Platforms. B) Simple geologic map of the Vajont Gorge (faults taken from Bistacchi et al. 2015 based on Riva et al. 1990 and Massironi et al. 2013, and formation contacts are taken from Carulli 2006). Lmst = limestone, Fm = formation.

During the deposition of the Vajont Limestone, the Belluno Trough was about 50 km wide, 250 km long, and around 1500 to 2000 m deep (Winterer and Bosellini 1981). The trough formed by the rapid subsidence of a basement block, and this block was probably bound on both sides by listric faults (Winterer and Bosellini 1981). In some places the Vajont Limestone Formation has a thickness of over 600 m and is predominantly composed of shallow-water oolitic sand and biogenic skeletal debris that were redeposited by gravity flow processes with deposition taking place in slope and basin environments. Paleogeographic reconstruction suggests that the Vajont Limestone Formation is an eastward-thickening wedge with a depositional area in excess of 100 km by 50 km. The Vajont Limestone thins toward the west and overlaps part of the Trento Platform.

The formation of dolostone geobodies in the Vajont Gorge is related to the fault populations that formed during the polyphase deformation history of the Vajont area. The horizons in the digital outcrop model of Bistacchi et al. (2015) show small, but clear, offsets that are not readily apparent in outcrop due to the dolomitization obscuring original bedding near the faults (Fig. 2). Projecting the horizons from where they are visible to the middle of the geobodies (the presumed location of the fault) indicates offsets of 5 to 10 m (Bistacchi et al. 2015). Moreover, the dolostone geobodies are generally thin and dip parallel to faults observed in the gorge (Fig. 2). There are also plumose geobodies, where the dolomitization preferentially followed an interval with good porosity and permeability (Bistacchi et al. 2015). The geometry of these geobodies is compared with a global compilation of non-stratabound dolostone geobodies below.

Geological Model Building

A significant component of this study was the creation of a geocellular model of the geology at the Vajont Gorge from the digital outcrop model of the same area created by Bistacchi et al. (2015). The Vajont Limestone at the Vajont Gorge contains limestone beds with thicknesses ranging from tens of centimeters up to ~2 m. These beds are separated by very thin layers of marly material (<15 cm). Prominent bedding surfaces in the Vajont Limestone interpreted from the digital outcrop model of Bistacchi et al. (2015) were used as input to ensure that the layering in the geocellular model grid matched the layering in the outcrop (17 bedding surfaces in total).

Fault surfaces created from extrapolation of fault contacts in the digital outcrop model were used as a direct input for the creation of a 3D structural framework. It should be noted that the location and size of the faults are based on two-dimensional (2D) observations, so the 3D surfaces (represented as rectangular planes in this study) are simplifications of the real fault surfaces. Nevertheless, the modeled fault population includes 40 faults and provides a very good representation of all fault sets measured in outcrop in the gorge (Bistacchi et al. 2015).

It is not possible to create a unique model of dolostone geobodies due to the uncertainty about how much dolostone was eroded in the gorge. Based on 2D field observations, 3D interpretations of the dolomite bodies were created. The full 3D extent of the bodies was based on interpolation of the 2D field data and from field measurements of the fault orientations as well as regional geological trends. Consequently, low, middle, and high dolostone geobody volume models (Fig. 4) were generated to try to capture and manage the full range of inherent uncertainties. The dolomite scenarios were developed by connecting dolomite fronts in the digital outcrop model. In addition to the uncertainty due to the erosion of the gorge, there is also uncertainty about the geometries of the dolostone geobodies behind the outcrop surface (although two roadway tunnels did penetrate a dolomite front and therefore provide some “behind the outcrop” control). The low case model was created by conservatively connecting dolomite fronts across the gorge, resulting in a model that is 5% dolomite in volume. The middle case was developed by assuming more connection between dolomite bodies that were disconnected in the low case, resulting in a model that is 8% dolomite in volume. The high case is based the assumption that dolomite was preferentially eroded, and therefore that the current gorge was largely dolostone. This results in a model that is 11% dolomite in volume. These models are shown in Figure 4.

FIG. 4.

—Volumetric perspective of geobodies in the Vajont Gorge based on the digital outcrop model created by Bistacchi et al. (2015). The topographic surface of the gorge is represented by the shaded gray surface in all panels. Three geobody volume scenarios were created honoring the uncertainty associated with the erosion of the gorge. A) Digitized outcrop dolostone geobody contacts, which provide constraints for the geobody volume scenarios. B) The low geobody volume scenario. C) The middle geobody volume scenario. D) The high geobody volume scenario.

FIG. 4.

—Volumetric perspective of geobodies in the Vajont Gorge based on the digital outcrop model created by Bistacchi et al. (2015). The topographic surface of the gorge is represented by the shaded gray surface in all panels. Three geobody volume scenarios were created honoring the uncertainty associated with the erosion of the gorge. A) Digitized outcrop dolostone geobody contacts, which provide constraints for the geobody volume scenarios. B) The low geobody volume scenario. C) The middle geobody volume scenario. D) The high geobody volume scenario.

The range of orientations and close spacing of the faults in the digital outcrop model made it impractical to explicitly include them in a model grid. The faults were implicitly modeled in a simple rectangular grid as properties, using the “Distance from an object” functionality in the property modeling module of the geological modeling program Petrel. Even though the faults do not offset the grid, the horizons show the same fault-related deflection that was observed in the field. Fault offset in the field is generally obscured due to the intensity of dolomitization near the faults (Bistacchi et al. 2015). Dolostones in general were “tighter” around faults or close to fractures, and fractures were generally completely cemented by calcite and dolomite (Lauriks 2013) within the dolomitic bodies observed in the gorge.

Stratigraphic layering in the model was constrained by the bedding surfaces from the digital outcrop model. A thin layer representing the marl layers observed in the gorge was added to the base of each stratigraphic layer in the model.

Ultimately, three models were created, one for each of the dolostone geobody volume scenarios. Each model contains three lithologies: (1) host limestone beds with thin marly layers at their base; (2) bulk dolostone geobodies; (3) near-fault dolostone. The creation of geological entities in a geocellular grid using surfaces taken from the digital outcrop model as input is illustrated in Figure 5. Subsequently, compilations of published data were used to assign properties to the lithologies created in the geocellular models as described in the following section.

FIG. 5.

—Example of how the fault surfaces and dolostone geobody fronts from the digital outcrop model of Bistacchi et al. (2015) were used to create sets of voxets in a geocellular model. A) A volume perspective of the topography of the Vajont Gorge (shaded gray) and fault surfaces (multicolored rectangles) in the digital outcrop model, and B) those faults as sets of voxets in a geocellular grid. C) The topography of the Vajont Gorge with the dolostone geobodies from one of the three dolostone geobody volume scenarios as visualized in the digital outcrop model, and D) the dolostone geobodies as voxets in a geocellular grid. The representation of faults and geobodies as voxets in a geocellular grid allows for property modeling (porosity, density, and velocity).

FIG. 5.

—Example of how the fault surfaces and dolostone geobody fronts from the digital outcrop model of Bistacchi et al. (2015) were used to create sets of voxets in a geocellular model. A) A volume perspective of the topography of the Vajont Gorge (shaded gray) and fault surfaces (multicolored rectangles) in the digital outcrop model, and B) those faults as sets of voxets in a geocellular grid. C) The topography of the Vajont Gorge with the dolostone geobodies from one of the three dolostone geobody volume scenarios as visualized in the digital outcrop model, and D) the dolostone geobodies as voxets in a geocellular grid. The representation of faults and geobodies as voxets in a geocellular grid allows for property modeling (porosity, density, and velocity).

Geobody Dimensions and Porosities

Non-Stratabound Dolomite Geobody Porosity and Dimension Compilation

To ensure that the dimensions of the dolostone geobodies were consistent with global analogues and that the dolostone geobodies were populated with representative porosity values, the dimensions and porosities of non-stratabound dolostone and host limestone were compiled from the literature. Compilations allowed the models to be populated with realistic values of porosity, density, and compressional velocity and to determine how the dimensions of the non-stratabound dolostone geobodies compared with a global compilation of geobodies. Almost all of the analogues are reported to be fault related (Black et al. 1981; Hurley and Budros 1990; Wilson et al. 1990; Baker and Knight 1993; Braithwaite and Rizzi 1997; Zempolich and Hardie 1997; Lynch and Trollope 2001; Boreen and Davies 2004; Bouch et al. 2004; Nader and Swennen 2004; Nader et al. 2004, 2012; Marcil et al. 2005; EnCana 2006; Lonnee and Machel 2006; Nyahay et al. 2006; Patchen et al. 2006; Wierzbicki et al. 2006; Morgan 2007; Wilson et al. 2007; Carmichael et al. 2008; Chidsey et al. 2009; Koehrer et al. 2010; Shah et al. 2010, 2012; Sharp et al. 2010; Di Cuia et al. 2011; Lapponi et al. 2011; Lavoie et al. 2011; Shelton et al. 2011; Dewit et al. 2012; Ronchi et al. 2012; Slater and Smith 2012; Vandeginste et al. 2013, 2014; Jacquemyn et al. 2015; Martin-Martin et al. 2015). Sharp et al. (2010) and Lapponi et al. (2011) describe massive and stratabound limestone in the Zagros in Iran, with porosity as great as 16%. Dolomitization was inferred to be fault related, with shallower dolostone pipes rooting in a deeper massive dolostone. Koehrer et al. (2010) describe a generally stratabound dolostone that was modified by later structural processes, imparting a measure of non-stratabound architecture in an analogue from the Triassic in Germany. Wilson et al. (2007) found that the Taballar Limestone is dolomitized near a regional fault (distances of 0.5–2 km perpendicular to the fault) with porosity up to 20%. Further away from the fault (distances of 4–8 km), the formation is still dolomitized, but porosity only reaches 5%. The geobody is quite large with a size of 4 to 8 km wide and 10 km long.

There are two examples of non–fault-related, non-stratabound dolostone geobodies related to dolomitization of elements of reefs/buildups in Alberta, Canada (Saller and Yaremko 1994), and in Texas, USA (Saller and Dickson 2011). Saller and Dickson (2011) study Pennsylvanian to Permian buildups in west Texas. In areas that are partially dolomitized, dolomite is stratabound, while in areas of pervasive dolomitization it is not. Saddle dolomite is present, and the above mentioned authors conclude that there are no obvious faults controlling the dolomites, but the geobodies show a NW–SE orientation that could be related to fracturing.

A summary of all analogues containing porosity and/or geobody dimensions is shown in Table 1. The compilation of non-stratabound dolostone geobody dimensions and porosities taken from the studies described above is shown in Figures 6 and 7. The location of the analogues included in this compilation is shown in Figure 1. As shown in Figure 6, most of the geobodies measured on subsurface data have aspect ratios between 1:1 and 1:10. The geobodies from the Vajont Gorge plot nicely within geobodies measured on subsurface data, indicating that data generated from models of the Vajont Gorge can serve as analogues for seismic characterization of fields producing from non-stratabound dolo-stone geobodies. Geobodies from outcrop data overlap with geobodies from subsurface data but show a broader range of aspect ratios, with aspect ratios ranging between 1:10 and 1:100 being very common. The maximum axes of these geobodies are typically oriented parallel to faults.

FIG. 7.

—A compilation of non-stratabound dolostone geobodies based on a combination of outcrop and subsurface data. The very large symbols shown at the left of the figure are the porosity values used in one of the three porosity scenarios created in the models in this study. Each study in the compilation is represented by a letter, and the studies are placed in order of descending dolostone porosity. Dolostones are represented in red (subsurface) and pink (outcrop), and limestones are represented in dark blue (subsurface) and light blue (outcrop). Bars show the range of values for dolostone and/or limestone porosity reported in each of the studies. The symbols (diamond or circle for dolostone, square or triangle for limestone) are mean values in each of the studies. No mean value could be calculated for studies that presented only a range (maximum and minimum values), and these studies are represented by error bars without a symbol in this compilation (studies “c” and “g,” for example). Symbols without error bars represent maximum reported values in studies that only reported the maximum value of dolostone or limestone porosity (e.g., “dolostone porosity as great as …” or “limestone porosity up to…”). Examples include studies “f” and “l.” Referenced studies are a = Koehrer et al. (2010); b = Boreen and Davies (2004); c = Lavoie et al. (2011); d = EnCana (2006); e = Sagan and Hart (2006); f = Braithwaite and Rizzi (1997); g =Wilson et al. (2007); h =Nader and Swennen (2004); i = Di Cuia et al. (2011); j = Saller and Dickson (2011); k = Baker and Knight (1993); l =Nyahay et al. (2006); m =Carmichael et al. (2008); n =Lonnee and Machel (2006); o =Bouch et al. (2004); p = Lapponi et al. (2011); q = Zempolich and Hardie (1997); r = Chidsey et al. (2009); s = Saller and Yaremko (1994); t = Marcil et al. (2005); u = Dewit et al. (2012); v =Hurley and Budros (1990); w = Nader et al. (2012); x = Lynch and Trollope (2001); y = Shah et al. (2010); z = Ronchi et al. (2012); aa = Morgan (2007); ab = Martin-Martin et al. (2015).

FIG. 7.

—A compilation of non-stratabound dolostone geobodies based on a combination of outcrop and subsurface data. The very large symbols shown at the left of the figure are the porosity values used in one of the three porosity scenarios created in the models in this study. Each study in the compilation is represented by a letter, and the studies are placed in order of descending dolostone porosity. Dolostones are represented in red (subsurface) and pink (outcrop), and limestones are represented in dark blue (subsurface) and light blue (outcrop). Bars show the range of values for dolostone and/or limestone porosity reported in each of the studies. The symbols (diamond or circle for dolostone, square or triangle for limestone) are mean values in each of the studies. No mean value could be calculated for studies that presented only a range (maximum and minimum values), and these studies are represented by error bars without a symbol in this compilation (studies “c” and “g,” for example). Symbols without error bars represent maximum reported values in studies that only reported the maximum value of dolostone or limestone porosity (e.g., “dolostone porosity as great as …” or “limestone porosity up to…”). Examples include studies “f” and “l.” Referenced studies are a = Koehrer et al. (2010); b = Boreen and Davies (2004); c = Lavoie et al. (2011); d = EnCana (2006); e = Sagan and Hart (2006); f = Braithwaite and Rizzi (1997); g =Wilson et al. (2007); h =Nader and Swennen (2004); i = Di Cuia et al. (2011); j = Saller and Dickson (2011); k = Baker and Knight (1993); l =Nyahay et al. (2006); m =Carmichael et al. (2008); n =Lonnee and Machel (2006); o =Bouch et al. (2004); p = Lapponi et al. (2011); q = Zempolich and Hardie (1997); r = Chidsey et al. (2009); s = Saller and Yaremko (1994); t = Marcil et al. (2005); u = Dewit et al. (2012); v =Hurley and Budros (1990); w = Nader et al. (2012); x = Lynch and Trollope (2001); y = Shah et al. (2010); z = Ronchi et al. (2012); aa = Morgan (2007); ab = Martin-Martin et al. (2015).

Porosity Scenarios in the Geological Model

As discussed earlier, and as mentioned by Bistacchi et al. (2015), three different dolostone geobody volume scenarios for the Vajont Gorge were proposed because of uncertainty in how much of the rock removed during erosion of the gorge was dolostone and how much was limestone. The compilation of non-stratabound dolostone and host limestone porosities enables assignment of realistic porosities to the lithologies in each of the dolostone geobody volume scenarios. As discussed above, there are three lithologies in each of the volume scenarios: host limestone, bulk dolostone geobody, and near-fault dolostone.

The relationship between faults and non-stratabound dolostone geobodies has been long established (see summary in Davies and Smith 2006). Compatible with this, many of the studies in the compilation describe enhanced dolomitization near faults (Braithwaite and Rizzi 1997; Boreen and Davies 2004; Bouch et al. 2004; Marcil et al. 2005; EnCana 2006; Sagan and Hart 2006; Wierzbicki et al. 2006; Wilson et al. 2007; Shah et al. 2010, 2012; Shelton et al. 2011; Ronchi et al. 2012; Martin-Martin et al. 2015). Sagan and Hart (2006) show that high-porosity dolostones are associated with faults in the Trenton-Black River at the Saybrook Field in Ohio, USA. Marcil et al. (2005) describe preferential dolomitization along strike-slip faults as observed both in outcrop and at depth. Wierzbicki et al. (2006) and EnCana (2006) describe the occurrence of dolostone geobodies near a regional fault that act as reservoirs at the Deep Panuke Field. Ronchi et al. (2012) document pervasive dolomitization in the Monte Zugna Formation connected to dolostone breccia bodies in the overlying Maiolica, with faults interpreted to provide pathways for dolomitizing fluids. Shah et al. (2010, 2012) describe a dolostone geobody related to the Ranero Fault, and Martin-Martin et al. (2015) describe a similar fault-related dolostone scenario in the Albian to Aptian Benassal Formation. Bouch et al. (2004) characterize the Cloud Hill Dolostone and Ticknall Limestone Formations in England. In this example, dolomitization tends to destroy porosity, except where beds are dominated by moldic porosity. Dolomitization was found to be most extensive at fault intersections, providing another example of structural control on the occurrence of non-stratabound dolostone geobodies. Shelton et al. (2011) present the results of a study of the Balladoole Formation exposed on the Isle of Man. Braithwaite and Rizzi (1997) document dolomitized limestones of the Navan Group in Navan, Ireland. They describe several episodes of diagenesis including dissolution followed by pore-filling dolomite. Vuggy porosity was found at the center of the geobody, which occurs along a regional fault. Boreen and Davies (2004) describe dolomitization in the Slave Point Formation at Ladyfern field, reporting that the Hay River Fault zone controls patterns of dolomitization and leaching with vertical dolomites breccia pipes of high porosity and permeability occurring preferentially at fault intersections. Morgan (2007) describes a Carlin-type gold deposit in Nevada that contains non-stratabound dolostone geobodies. The greatest porosities are in saddle and zebra dolomites that experienced dissolution, and these types of dolomite occur preferentially near faults.

Using the compilation shown in Figure 7, three porosity scenarios were created:

  1. Porosity scenario one: Host limestone porosity was set to 3%, bulk dolostone geobody porosity was set to 15%, and dolostone porosity near faults was set to 8%.

  2. Porosity scenario two: Host limestone porosity was set to 2%, bulk dolostone geobody porosity was set to 25%, and dolostone porosity near faults was set to 12%.

  3. Porosity scenario three: Host limestone porosity was set to 3%, bulk dolostone geobody porosity was set to 8%, and dolostone porosity near faults was set to 15%.

In the first porosity scenario, porosity of host limestone is low, porosity of the bulk dolostone geobody is in the middle to upper range (of the compiled porosities shown in Fig. 7), and porosity near the fault is reduced (but still greater than host limestone porosity). Such a reduction in porosity could be due to over-dolomitization or calcite cementation (e.g., Koehrer et al. 2010, Di Cuia et al. 2011). An example of porosity degradation due to overdolomitization is shown in a hand sample from the Vajont Gorge in Figure 8. In the second porosity scenario, the porosity of the host limestone is low, the porosity of the bulk dolostone geobody is at the maximum observed in the compilation (Fig. 7), and the porosity near faults is reduced, but is still high compared with host limestone. This porosity scenario results in a large contrast between host limestone and the dolostone geobodies. The porosity values in the third scenario are the same as in the first scenario, but the porosity near the faults is greater than the bulk dolostone porosity. Such a configuration could result from increased fracturing or dissolution near the faults, and examples of both can be found in the compilation (Braithwaite and Rizzi 1997; Boreen and Davies 2004; Bouch et al. 2004; Marcil et al. 2005; EnCana 2006; Sagan and Hart 2006; Wierzbicki et al. 2006; Wilson et al. 2007; Shah et al. 2010, 2012; Ronchi et al. 2012). The combination of three dolostone volume scenarios with three porosity scenarios results in nine models (Fig. 9).

FIG. 8.

—An example of an overdolomitization front in a hand sample from the Vajont Gorge.

FIG. 8.

—An example of an overdolomitization front in a hand sample from the Vajont Gorge.

FIG. 9.

—Map views of horizon slices through the nine models that were generated using three dolostone volume scenarios and three porosity scenarios. Each row represents a volume scenario, and each column represents a porosity scenario. Porosity values and other properties are given in Table 4.

FIG. 9.

—Map views of horizon slices through the nine models that were generated using three dolostone volume scenarios and three porosity scenarios. Each row represents a volume scenario, and each column represents a porosity scenario. Porosity values and other properties are given in Table 4.

Acoustic Properties of Limestone and Dolostone

Dolostone and Limestone Velocity, Density, and Porosity Analogue Compilation

In order to assign realistic density and compressional velocities to the porosity models shown in Figure 9, a distribution of the density, compressional velocity (Vp), and porosity of dolomites and limestones was compiled from three main types of studies: studies based on well measurements, studies based on laboratory measurements of core plugs, and studies based on laboratory measurements of samples collected from outcrop. These properties are needed to calculate acoustic impedance and in turn the synthetic seismic response. A summary of this compilation is shown in Table 2, and key observations are discussed below.

Table 2.

—Compilation of published datasets relating porosity, density, and velocity.

Well, outcrop, coreFormation(s)LocationLithologyRange of porosity (%)Original Fluid(s)Fluids in this compilationReference
WellPekisko Limestone, Wabamun and Leduc DolomitesDavey Field, Alberta, CanadaLimestone and dolostonePekisko, 1-8; Wabamun, <5; Leduc, 0 to ~10Brine or oilRecalculated to dry assuming brine saturationMiller (1992)
WellShuaibaAbu DhabiUnspecified6-26Oil and brineRecalculated to dry and brineFischer et al. (1997)
WellBaturajaSouth Sumatra Basin, IndonesiaLimestone2-30Gas and oilRecalculated to dry and brineNoor Ali (2002)
WellArab DSaudi ArabiaLimestone10-30OilRecalculated to dry and brineAlMuhaidib et al. (2012)
Well/coreUnspecifiedUnspecifiedLimestone and dolostone0 to ~25Unspecified, assumed to be brineAs original for brine, recalculated to dryMarion and Jizba (1997)
CoreUnspecifiedWest TexasLimestone2-20Dry and waterAs originalWyllie et al. (1956)
CoreMission Canyon FormationWilliston Basin, North DakotaLimestone, dolostone, anhydrite0-20Dry and waterAs originalRafavich et al. (1984)
CoreUnspecifiedUnspecifiedLimestone and dolostone0 to ~20GasRecalculated to dry and brineWang (1997)
CoreMiocene to Pleistocene carbonatesGrand Bahama BanksLimestone and dolostone~5-55WaterWater as original, recalculated to dryAnselmetti and Eberli (2001)
CoreShuaiba Formation, Miocene PlatformsMiddle East, Southeast Asia, Marion Plateau, AustraliaUnspecified10-40WaterAs originalWeger et al. (2009)
CoreSarvak FormationIranUnspecified3-30Dry and water saturatedAs originalMisaghi et al. (2010)
OutcropNittany DolomitePennsylvaniaDolostone2-18WaterWater as original, recalculated to dryWyllie et al. (1958)
OutcropBahamas, Maiella, Florida BayBahamas, Italy, FloridaLimestone and dolostoneMaiella and Bahamas, 0 to 60; Florida Bay, 30-60WaterWater as original, recalculated to dryAnselmetti and Eberli (1997)
OutcropMaiellaItalyUnspecified2-22WaterWater as original, recalculated to dryAnselmetti et al. (1997)
OutcropGreat OoliteEngland, UKLimestone3-17Dry and water saturatedAs originalAssefa et al. (2003)
OutcropCap BlancMallorca, SpainLimestone and dolostone5-55Dry and water saturatedAs originalVerwer et al. (2008)
Well, outcrop, coreFormation(s)LocationLithologyRange of porosity (%)Original Fluid(s)Fluids in this compilationReference
WellPekisko Limestone, Wabamun and Leduc DolomitesDavey Field, Alberta, CanadaLimestone and dolostonePekisko, 1-8; Wabamun, <5; Leduc, 0 to ~10Brine or oilRecalculated to dry assuming brine saturationMiller (1992)
WellShuaibaAbu DhabiUnspecified6-26Oil and brineRecalculated to dry and brineFischer et al. (1997)
WellBaturajaSouth Sumatra Basin, IndonesiaLimestone2-30Gas and oilRecalculated to dry and brineNoor Ali (2002)
WellArab DSaudi ArabiaLimestone10-30OilRecalculated to dry and brineAlMuhaidib et al. (2012)
Well/coreUnspecifiedUnspecifiedLimestone and dolostone0 to ~25Unspecified, assumed to be brineAs original for brine, recalculated to dryMarion and Jizba (1997)
CoreUnspecifiedWest TexasLimestone2-20Dry and waterAs originalWyllie et al. (1956)
CoreMission Canyon FormationWilliston Basin, North DakotaLimestone, dolostone, anhydrite0-20Dry and waterAs originalRafavich et al. (1984)
CoreUnspecifiedUnspecifiedLimestone and dolostone0 to ~20GasRecalculated to dry and brineWang (1997)
CoreMiocene to Pleistocene carbonatesGrand Bahama BanksLimestone and dolostone~5-55WaterWater as original, recalculated to dryAnselmetti and Eberli (2001)
CoreShuaiba Formation, Miocene PlatformsMiddle East, Southeast Asia, Marion Plateau, AustraliaUnspecified10-40WaterAs originalWeger et al. (2009)
CoreSarvak FormationIranUnspecified3-30Dry and water saturatedAs originalMisaghi et al. (2010)
OutcropNittany DolomitePennsylvaniaDolostone2-18WaterWater as original, recalculated to dryWyllie et al. (1958)
OutcropBahamas, Maiella, Florida BayBahamas, Italy, FloridaLimestone and dolostoneMaiella and Bahamas, 0 to 60; Florida Bay, 30-60WaterWater as original, recalculated to dryAnselmetti and Eberli (1997)
OutcropMaiellaItalyUnspecified2-22WaterWater as original, recalculated to dryAnselmetti et al. (1997)
OutcropGreat OoliteEngland, UKLimestone3-17Dry and water saturatedAs originalAssefa et al. (2003)
OutcropCap BlancMallorca, SpainLimestone and dolostone5-55Dry and water saturatedAs originalVerwer et al. (2008)

Studies based on well measurements were taken from the Pekisko, Wabamun and Leduc Formations in the Davey Field in Alberta (Miller 1992), the Shuaiba Formation in Abu Dhabi (Fischer et al. 1997), the Baturaja Formation in Indonesia (Noor Ali 2002), the Arab D Formation in Saudi Arabia (AlMuhaidib et al. 2012), and general data from Marion and Jizba (1997). Studies based on core measurements were taken from the Mission Canyon Formation in the Williston Basin in North Dakota (Rafavich et al. 1984), Miocene to Pleistocene carbonates in the Bahamas (Anselmetti and Eberli 2001), the Shuaiba Formation in an unspecified location in the Middle East, an unspecified Miocene platform in Southeast Asia and two Miocene Platforms in the Marion Plateau in Australia (Weger et al. 2009), the Sarvak Formation in Iran (Misaghi et al. 2010), and several unspecified locations (Wyllie et al. 1956, Wang 1997, Marion and Jizba 1997). Studies based on outcrop measurements were taken from the Nittany Dolomite in Pennsylvania, USA (Wyllie et al. 1958); the Bahamas; the Maiella region of Italy; Florida Bay, USA (Anselmetti and Eberli 1997); the Maiella region of Italy (Anselmetti et al. 1997); the Great Oolite Formation of England (Assefa et al. 2003); and the Cap Blanc Formation of Mallorca, Spain (Verwer et al. 2008).

Rafavich et al. (1984), Marion and Jizba (1997), and Wang (1997) show datasets in which the velocity in dolostone is greater than in limestone at any given value of porosity. Anselmetti and Eberli (1997, 2001) and Verwer et al. (2008) show datasets in which velocity is independent of dolomite concentration. One important aspect of Verwer et al. (2008) is that velocities are markedly faster than all other datasets, which was also noted by the authors when compared with their own compilation of existing data. For this reason, results from Verwer et al. (2008) are treated separately from the other datasets here.

Fluid Substitution and Existing Relationships between Porosity and Velocity

Seismic velocity is strongly influenced by the properties of pore-filling fluids. To account for this, all measurements in the studies listed above were converted to both dry and brine-saturated values of velocity if measurements made under those conditions were not available (Table 2). The density and velocity of the pore fluids was calculated based on the method of Batzle and Wang (1992) as modified by Mavko et al. (1998). Following calculation of the pore fluid properties, Gassmann substitution was used to calculate dry and brine-saturated velocities. We do not attempt to account for changes in shear moduli caused by differences in fluid saturation, although Baechle et al. (2009) found that water saturation resulted in changes in the shear modulus of up to approximately 1 GPa in limestones. A major goal of the compilation is to define a range of velocities that can be expected as a function of porosity. The calculation of dry and brine-saturated velocities covers the range of possible velocities, with dry velocities at the low end and brine-saturated velocities at the high end.

There are a number of studies that have presented relationships between some combination of porosity, density, and velocity. Relationships between porosity and velocity have been proposed by Wyllie et al. (1958), Raymer et al. (1980), and a modification of Raymer et al. (1980) to include a relationship between velocity and density proposed by Gardner et al. (1974) as described in Dvorkin and Nur (1998). The terminology of Dvorkin and Nur (1998) is used to simplify reference to the various relationships (Fig. 10). The compiled measurements for both dry and brine-saturated conditions are shown in Figures 10 through 12. The relationships of Gardner et al. (1974) and Mavko et al. (1998) are shown in Figure 12. The locations of the analogues included in this compilation are shown in Figure 1.

FIG. 10.

—Compilation of porosity and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Raymer et al. (1980), Gardner et al. (1974), and Wyllie et al. (1956) are shown for dolomite (RHG Dolo, GGG Dolo, and WGG Dolo in figure) and calcite (RHG Cal, GGG Cal, and WGG Cal in figure) under water-saturated conditions. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956), Wyllie et al. (1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 10.

—Compilation of porosity and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Raymer et al. (1980), Gardner et al. (1974), and Wyllie et al. (1956) are shown for dolomite (RHG Dolo, GGG Dolo, and WGG Dolo in figure) and calcite (RHG Cal, GGG Cal, and WGG Cal in figure) under water-saturated conditions. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956), Wyllie et al. (1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 11.

—Compilation of porosity and bulk density for dolostones (diamonds), limestones (squares), and unspecified carbonates (circles) under dry and saturated conditions. The labeling conventions are as described in Figure 10. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 11.

—Compilation of porosity and bulk density for dolostones (diamonds), limestones (squares), and unspecified carbonates (circles) under dry and saturated conditions. The labeling conventions are as described in Figure 10. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 12.

—Compilation of bulk density and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Gardner et al. (1974) and Mavko et al. (1998) are shown for dolomite and calcite. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 12.

—Compilation of bulk density and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Gardner et al. (1974) and Mavko et al. (1998) are shown for dolomite and calcite. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

Acoustic Properties in the Geological Model

The compilation of porosity, velocity, and density allows realistic velocities and densities to be assigned to each of the nine models. The trends in Figures 10 and 12 were used to assign velocity and density as functions of porosity (Table 4).

Velocity, as a function of porosity, is found to range from approximately 7000 to 2000 m/s with a range of approximately 2000 m/s for any given value of porosity (Fig. 10). The purpose of the compiled data shown in this figure is to define a reasonable minimum and maximum velocity as a function of porosity. Since the porosity of the host limestone as used in this study is 2–3%, the range of possible velocities is relatively small. The range in possible velocities for the extreme porosity geobody scenario (25% bulk dolostone porosity) is quite large. Velocities were assigned by picking a value toward the lower end of the velocity distribution (dominated by the dry values) and a value at the higher end of the velocity distribution (dominated by the saturated values) for each of the porosity scenarios to capture the range in velocity. Since the limestone velocities in both the dry and saturated scenarios are very similar due to the very small porosity values, the acoustic impedance contrast between limestone and dolostone is greater in the low velocity scenario than in the high velocity scenario.

A summary of porosity, density, velocity, and acoustic impedance and reflection coefficients for all scenarios is shown in Table 4. The purpose of this study is not to derive or refine predictive models of carbonate velocities as a function of porosities, but rather to define the range of observed values as compiled from published studies. There is a range of velocities of approximately 2000 m/s (Fig. 10) and densities of approximately 0.2 g/cm3 (Fig. 12) for any given value of porosity. The range of values for limestones and dolomites taken as a whole is roughly bound by the dolomite curve of Raymer et al. (1980) and the calcite curve of Wyllie et al. (1958). The selected dry velocities and densities are toward the lower end of that range, and the selected high velocities are toward the upper end of that range.

Synthetic Seismic Generation and Seismic Attribute Response

Compilation of Seismic Attributes

There are dozens of seismic attributes that can be generated by an interpreter, which can make determining which attributes are most useful in a particular setting difficult. In order to infer which attributes are best suited to interpret porous non-stratabound dolostone geobodies, a selection of attributes that were reported to be sensitive to porosity in a number of settings was created. The settings include non-stratabound dolomite (referred to as hydrothermal dolomite by Sagan and Hart [2006], Hart [2008], Hart et al. [2009], and Ogiesoba [2010]), karst, mounds/buildups, other structural settings, and other nonstructural settings (Fig. 1; Table 3).

Table 3.

—Compilation of published studies that have successfully used seismic attributes to interpret porosity in non-stratabound dolostone reservoirs and related scenarios. ABS is absorption, AMP is amplitude, AMP SUM is sum of amplitude, AZI is azimuth, BPASS is band pass filter 15-20-25-30, COHERENCE is coherence, COS PHASE is cosine of instantaneous phase, CRV is curvature, DER is derivative, DER REF STR is derivative of reflection strength, DIP is dip, DIP AZI is dip-azimuth, DOM FREQ is dominant frequency, ENER HALF TIME is energy half time, ENERGY is energy, ENV is envelope or reflection strength, FREQ is frequency, FREQ ATN is energy absorption or frequency attenuation, INST FREQ is instantaneous frequency including thin bed indicator, INTG ABS AMP is integrated absolute amplitude, INTG TR is integrated trace, PERI is perigram, PHASE is phase, Q is Q factor (attenuation), RMS AMP is RMS amplitude including RMS amplitude of a spectral decomp volume (35 Hz), SEM is semblance, SPEC is a frequency volume from spectral decomposition (20 and 22 Hz) SWT is sweetness, TUNE FREQ is tuning frequency.

SettingAttribute(s)LocationFieldFormationAgeReference
Non-stratabound DolomiteENV, ENER HALF TIME, BPASS, COS PHASE, QOntario, CanadaRochesterTrentonOrdovicianOgiesoba (2010), Hart et al. (2009), Hart (2008)
Non-stratabound DolomiteCRV, RMS AMP, INTG TR, DER REF STR, ENV, PERI, COS PHASEOhio, USASaybrookTrentonOrdovicianSagan and Hart (2006), Hart et al. (2009), Hart (2008)
KarstCOHERENCE, CRVTexas, USAWaddellSan AndresPermianDou et al. (2011)
KarstINTG ABS AMP, ENV, DOM FREQ, COS PHASE, INST FREQIranSirri C/DMishrif, IlamCretaceousFarzadi and Hesthammer (2007)
KarstAMP, SEMAustraliaUnspecifiedUnnamedMioceneRosleff-Soerensen et al. (2012)
KarstCOHERENCE, AZI, RMS AMP, AMP SUM, INST FREQSouth China SeaLiuha 11-1ZhujiangMioceneSun et al. (2013)
Karst and Mounds/buildupsAMP, INST FREQNorwayUnspecifiedBjarmeland GroupPermianColpaert et al. (2007)
Mounds/buildupsAMP, FREQ, SWTIndiaUnspecifiedBasseinEoceneHarilal et al. (2008)
Mounds/buildupsDIPNorwayUnspecifiedBjarmeland and Gipsdalen GroupsCarboniferous to PermianHong and Shipilova (2013), Samuelsberg et al. (2003)
Mounds/buildupsAMP, COHERENCE, DIPGermanyGeothermal wellsMalmUpper JurassicLüschen et al. (2014)
Mounds/buildupsRMS AMP, DIPGermanyGeothermal wellsMalmUpper Jurassicvon Hartmann et al. (2012)
Other, StructuralENERGY, TUNE FREQ, PHASEDenmarkSyd ArneTor and EkofiskCretaceous and PaleoceneChristensen et al. (2006)
Other, StructuralSPEC, COHERENCE, CRVPrecaspian basinUnspecifiedUnspecifiedCarboniferousLi et al. (2010)
Other, StructuralAMP, ABS, COHERENCE, CRVSaudi ArabiaUnspecifiedUnspecifiedUnspecifiedNeves and Triebwasser (2006)
Other, StructuralCOHERENCE, CRVKansas, USADickmanSt. Genevieve, St. Louis, Salem, Spergen, WarsawMississippianNissen et al. (2009)
Other, StructuralCRV, COHERENCEAbu DhabiUnspecifiedUnspecifiedUnknownShibasaki et al. (2006)
Other, StructuralCOHERENCE, CRVOklahoma, USAUnspecifiedHuntonOrdovician-DevonianStaples et al. (2010)
Other, StructuralAMP, FREQ ATN, SEM, DIP AZIBritish Columbia, CanadaBubbles 3D surveySlave PointDevonianStrecker et al. (2004)
Other, NonstructuralINST FREQMaldivesUnspecifiedUnspecifiedEocene to PlioceneBetzler et al. (2011)
Other, NonstructuralRMS AMP, COHERENCE, SPEC, FREQ ATNChinaUnspecifiedUnspecifiedOrdovicianLiu et al. (2011)
Other, NonstructuralDER, DER REF STRENGTH, COS PHASEAlabama, USAAppletonSmackoverJurassicTebo and Hart (2005)
Other, NonstructuralINST FREQBritish Columbia, CanadaSierraKeg RiverDevonianVetrici and Stewart (1996)
SettingAttribute(s)LocationFieldFormationAgeReference
Non-stratabound DolomiteENV, ENER HALF TIME, BPASS, COS PHASE, QOntario, CanadaRochesterTrentonOrdovicianOgiesoba (2010), Hart et al. (2009), Hart (2008)
Non-stratabound DolomiteCRV, RMS AMP, INTG TR, DER REF STR, ENV, PERI, COS PHASEOhio, USASaybrookTrentonOrdovicianSagan and Hart (2006), Hart et al. (2009), Hart (2008)
KarstCOHERENCE, CRVTexas, USAWaddellSan AndresPermianDou et al. (2011)
KarstINTG ABS AMP, ENV, DOM FREQ, COS PHASE, INST FREQIranSirri C/DMishrif, IlamCretaceousFarzadi and Hesthammer (2007)
KarstAMP, SEMAustraliaUnspecifiedUnnamedMioceneRosleff-Soerensen et al. (2012)
KarstCOHERENCE, AZI, RMS AMP, AMP SUM, INST FREQSouth China SeaLiuha 11-1ZhujiangMioceneSun et al. (2013)
Karst and Mounds/buildupsAMP, INST FREQNorwayUnspecifiedBjarmeland GroupPermianColpaert et al. (2007)
Mounds/buildupsAMP, FREQ, SWTIndiaUnspecifiedBasseinEoceneHarilal et al. (2008)
Mounds/buildupsDIPNorwayUnspecifiedBjarmeland and Gipsdalen GroupsCarboniferous to PermianHong and Shipilova (2013), Samuelsberg et al. (2003)
Mounds/buildupsAMP, COHERENCE, DIPGermanyGeothermal wellsMalmUpper JurassicLüschen et al. (2014)
Mounds/buildupsRMS AMP, DIPGermanyGeothermal wellsMalmUpper Jurassicvon Hartmann et al. (2012)
Other, StructuralENERGY, TUNE FREQ, PHASEDenmarkSyd ArneTor and EkofiskCretaceous and PaleoceneChristensen et al. (2006)
Other, StructuralSPEC, COHERENCE, CRVPrecaspian basinUnspecifiedUnspecifiedCarboniferousLi et al. (2010)
Other, StructuralAMP, ABS, COHERENCE, CRVSaudi ArabiaUnspecifiedUnspecifiedUnspecifiedNeves and Triebwasser (2006)
Other, StructuralCOHERENCE, CRVKansas, USADickmanSt. Genevieve, St. Louis, Salem, Spergen, WarsawMississippianNissen et al. (2009)
Other, StructuralCRV, COHERENCEAbu DhabiUnspecifiedUnspecifiedUnknownShibasaki et al. (2006)
Other, StructuralCOHERENCE, CRVOklahoma, USAUnspecifiedHuntonOrdovician-DevonianStaples et al. (2010)
Other, StructuralAMP, FREQ ATN, SEM, DIP AZIBritish Columbia, CanadaBubbles 3D surveySlave PointDevonianStrecker et al. (2004)
Other, NonstructuralINST FREQMaldivesUnspecifiedUnspecifiedEocene to PlioceneBetzler et al. (2011)
Other, NonstructuralRMS AMP, COHERENCE, SPEC, FREQ ATNChinaUnspecifiedUnspecifiedOrdovicianLiu et al. (2011)
Other, NonstructuralDER, DER REF STRENGTH, COS PHASEAlabama, USAAppletonSmackoverJurassicTebo and Hart (2005)
Other, NonstructuralINST FREQBritish Columbia, CanadaSierraKeg RiverDevonianVetrici and Stewart (1996)

Analogues from non-stratabound dolostone reservoirs are from Ontario, Canada (Hart 2008, Hart et al. 2009, Ogiesoba 2010), and Ohio, USA (Sagan and Hart 2006, Hart 2008). Analogues from karsted reservoirs come from the Permian Basin in Texas, USA (Dou et al. 2011); Kansas, USA (Nissen et al. 2009); the Browse Basin in Australia (Rosleff-Soerensen et al. 2012); Iran (Farzadi and Hest-hammer 2007); the South China Sea (Sun et al. 2013); and the Norwegian Barents Sea (Colpaert et al. 2007).

Studies from fields with various sorts of mounds and buildups were included in this compilation since they represent examples of non-stratabound geological bodies. Moreover, as discussed above, portions of mounds/buildups may contain non-stratabound dolostone geobodies, and so they are relevant to a study of the seismic response such as the one presented in this paper. Studies involving mounds and/or buildups are from the Norwegian Barents Sea (Samuelsberg et al. 2003, Colpaert et al. 2007, Hong and Shipilova 2013), the Mumbai Basin in India (Harilal et al. 2008), and from the Molasse Basin in Germany (von Hartmann et al. 2012, Lüschen et al. 2014). Von Hartmann et al. (2012) characterized a reservoir in the Upper Jurassic. Root mean square (RMS) amplitude of a 35 Hz volume was used to define reef seismic facies, and dip maps were used to highlight faults. The porosity of these reefs is often enhanced by dolomitization near faults, thereby making this an example of non-stratabound dolomite as well as an example of mounds/buildups.

Analogues from a variety of structural settings containing structural features that impact porosity are also included in this compilation. These studies are from the Danish North Sea (Christensen et al. 2006); the Precaspian Basin (Li et al. 2010); Saudi Arabia (Neves and Triebwasser 2006); Abu Dhabi (Shibasaki et al. 2006); Oklahoma, USA (Staples et al. 2010), and British Columbia, Canada (Strecker et al. 2004).

This compilation also includes four examples of the use of attributes to predict porosity that cannot be placed in any of the preceding categories. These are from the Maldives (Betzler et al. 2011); the Tarim Basin in China (Liu et al. 2011); Florida, USA (Tebo and Hart 2005); and British Columbia, Canada (Vetrici and Stewart 1996). Vetrici and Stewart (1996) present results from a field producing from the Devonian Keg River Formation in British Columbia, Canada. Instantaneous frequency was used to highlight tectonic features and areas of likely high porosity associated with dolomitization. This example is included in this nonstructural category since the reservoir appears to be similar to the non–fault-related non-stratabound geobodies described elsewhere in western Canada by Saller and Yaremko (1994), in that dolomitization is preferentially occurring at reef margins.

Calculation of Synthetic Seismic Volumes and Seismic Attributes

The assignment of velocity and density to each of the nine models (Fig. 9) enables the calculation of acoustic impedance volumes (Fig. 13). Marly layers between host limestone horizons were also modeled so that the host limestone would not be acoustically transparent. These layers were assigned a velocity of 2100 m/s. The acoustic impedance volumes were differentiated to obtain reflectivity, which was then convolved with a 30 Hz Ricker wavelet (one-dimensional [1D] normal incidence) to yield the synthetic seismic data.

FIG. 13.

—A horizontal and vertical slice through the middle of the synthetic seismic volumes generated from acoustic impedance volumes created for each of the nine dolostone scenarios shown in Figure 9.

FIG. 13.

—A horizontal and vertical slice through the middle of the synthetic seismic volumes generated from acoustic impedance volumes created for each of the nine dolostone scenarios shown in Figure 9.

The ultimate purpose of this study is to determine which attributes should be examined to discriminate porous dolostone geobodies from low porosity host limestone. The attributes described in each of the studies in the preceding section were grouped with closely related attributes that could be generated using available software packages. This grouping was done to ensure that attributes that are sensitive to continuity/fault offset (e.g., dip), lateral changes in acoustic properties (e.g., semblance), amplitude (e.g., envelope), and frequency (e.g., dominant frequency) could be evaluated. A total of 14 attributes were generated for each of the nine synthetic seismic cubes. No offset-dependent attributes were generated, since the relatively simple synthetic seismic data resulting from 1D convolution do not exhibit angle dependency. The attributes (Table 5) that were generated are

  1. Cosine of instantaneous phase

  2. Original amplitude

  3. RMS amplitude

  4. Envelope

  5. Reflection intensity

  6. Derivative

  7. Derivative of reflection intensity

  8. Sweetness

  9. Instantaneous frequency

  10. Dominant frequency

  11. Ant tracking

  12. Curvature

  13. Dip

  14. Semblance

Table 4.

—Properties of host limestone, bulk dolostone geobody, and near-fault dolostone for the three porosity scenarios. AI is acoustic impedance, RC is reflection coefficient.

 Bulk dolostone geobodyNear-fault dolostoneHost limestone
Porosity scenario 1   
Porosity (%)1583
Vp, dry (m/s)428451555721
Vp, saturated (m/s)490058006000
Density, dry (g/cm3)2.292.472.60
Density, saturated (g/cm3)2.602.732.66
AI, dry, 107 (kg/m3) (m/s)0.981.271.49
AI, saturated, 107 (kg/m3) (m/s)1.271.581.60
RC, dolo geobody, host lmst, dry 0.21 
RC, dolo geobody, host lmst, saturated 0.11 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Porosity scenario 2   
Porosity (%)25122
Vp, dry (m/s)304046575871
Vp, saturated (m/s)400053006100
Density, dry (g/cm3)2.032.372.63
Density, saturated (g/cm3)2.412.652.67
AI, dry, 107 (kg/m3) (m/s)0.621.101.54
AI, saturated, 107 (kg/m3) (m/s)0.971.401.63
RC, dolo geobody, host lmst, dry 0.43 
RC, dolo geobody, host lmst, saturated 0.26 
RC, dolo geobody, near-fault dolo, dry 0.28 
RC, dolo geobody, near-fault dolo, saturated 0.18 
Porosity scenario 3   
Porosity (%)8153
Vp, dry (m/s)515542845721
Vp, saturated (m/s)580049006000
Density, dry (g/cm3)2.472.292.60
Density, saturated (g/cm3)2.732.602.66
AI, dry, 107 (kg/m3) (m/s)1.270.981.48
AI, saturated, 107 (kg/m3) (m/s)1.581.271.60
RC, dolo geobody, host lmst, dry 0.08 
RC, dolo geobody, host lmst, saturated 0.005 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
 Bulk dolostone geobodyNear-fault dolostoneHost limestone
Porosity scenario 1   
Porosity (%)1583
Vp, dry (m/s)428451555721
Vp, saturated (m/s)490058006000
Density, dry (g/cm3)2.292.472.60
Density, saturated (g/cm3)2.602.732.66
AI, dry, 107 (kg/m3) (m/s)0.981.271.49
AI, saturated, 107 (kg/m3) (m/s)1.271.581.60
RC, dolo geobody, host lmst, dry 0.21 
RC, dolo geobody, host lmst, saturated 0.11 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Porosity scenario 2   
Porosity (%)25122
Vp, dry (m/s)304046575871
Vp, saturated (m/s)400053006100
Density, dry (g/cm3)2.032.372.63
Density, saturated (g/cm3)2.412.652.67
AI, dry, 107 (kg/m3) (m/s)0.621.101.54
AI, saturated, 107 (kg/m3) (m/s)0.971.401.63
RC, dolo geobody, host lmst, dry 0.43 
RC, dolo geobody, host lmst, saturated 0.26 
RC, dolo geobody, near-fault dolo, dry 0.28 
RC, dolo geobody, near-fault dolo, saturated 0.18 
Porosity scenario 3   
Porosity (%)8153
Vp, dry (m/s)515542845721
Vp, saturated (m/s)580049006000
Density, dry (g/cm3)2.472.292.60
Density, saturated (g/cm3)2.732.602.66
AI, dry, 107 (kg/m3) (m/s)1.270.981.48
AI, saturated, 107 (kg/m3) (m/s)1.581.271.60
RC, dolo geobody, host lmst, dry 0.08 
RC, dolo geobody, host lmst, saturated 0.005 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Table 5.

—Attributes used in this study, the number of times they were applied in the compiled studies discussed in the text, and their definition. Note that the count includes the named attribute as well as analogous or closely related attributes. For example semblance includes a number of different algorithms, all of which are grouped together in this table. Similarly, frequency attenuation attributes are grouped with instantaneous frequency. Groups of attributes that are similar are marked with like symbols.

Attribute used in this studyAnalogous attributes used in other studiesCount of how often attribute was used in compilation of seismic attributesDefinition of attributes
Original amplitude*Unspecified amplitude including amplitude of volumes generated by spectral decomposition9Amplitude measured on the input seismic volume
RMS amplitude*RMS amplitude, including RMS amplitude of volumes generated by spectral decomposition4Root mean square amplitude over a specified window
Reflection intensity†Integrated absolute amplitude, Integrated trace, sum of amplitude6Average amplitude over an interval multiplied by the sample interval
Envelope†Envelope, energy, reflection strength, perigram3Reflection strength measured as total energy of the seismic trace
Sweetness† 1Envelope divided by the square root of instantaneous frequency
Instantaneous frequency‡Instantaneous frequency, thin bed indicator, Q factor, frequency attenuation, energy absorption Energy half time9Time derivative of the phase, sensitive to attenuation
Cosine of instantaneous phase‡Cosine of instantaneous phase and phase5Cosine of instantaneous phase
Dominant frequency**Dominant frequency, unspecified frequency4Maximum of the amplitude spectrum over a small window around the time sample
Derivative § 1Rate of change of amplitude
Derivative of reflection intensity § 2Rate of change of reflection intensity
Ant tracking¶ 1A discontinuity highlighting algorithm developed by Schlumberger. Semblance used as input in this example.
Curvature¶ 7The rate of change in the dip of a seismic reflector
Dip¶Dip, azimuth, or dip-azimuth5Magnitude of the dip of a reflector
Semblance¶Coherence or semblance11A measure of the lateral similarity of seismic traces
Attribute used in this studyAnalogous attributes used in other studiesCount of how often attribute was used in compilation of seismic attributesDefinition of attributes
Original amplitude*Unspecified amplitude including amplitude of volumes generated by spectral decomposition9Amplitude measured on the input seismic volume
RMS amplitude*RMS amplitude, including RMS amplitude of volumes generated by spectral decomposition4Root mean square amplitude over a specified window
Reflection intensity†Integrated absolute amplitude, Integrated trace, sum of amplitude6Average amplitude over an interval multiplied by the sample interval
Envelope†Envelope, energy, reflection strength, perigram3Reflection strength measured as total energy of the seismic trace
Sweetness† 1Envelope divided by the square root of instantaneous frequency
Instantaneous frequency‡Instantaneous frequency, thin bed indicator, Q factor, frequency attenuation, energy absorption Energy half time9Time derivative of the phase, sensitive to attenuation
Cosine of instantaneous phase‡Cosine of instantaneous phase and phase5Cosine of instantaneous phase
Dominant frequency**Dominant frequency, unspecified frequency4Maximum of the amplitude spectrum over a small window around the time sample
Derivative § 1Rate of change of amplitude
Derivative of reflection intensity § 2Rate of change of reflection intensity
Ant tracking¶ 1A discontinuity highlighting algorithm developed by Schlumberger. Semblance used as input in this example.
Curvature¶ 7The rate of change in the dip of a seismic reflector
Dip¶Dip, azimuth, or dip-azimuth5Magnitude of the dip of a reflector
Semblance¶Coherence or semblance11A measure of the lateral similarity of seismic traces

Attribute Evaluation

A number of papers relate the use of seismic attributes to create constrained porosity models in fields with non-stratabound dolostone geobodies. In an example from a field with fault-related dolomitization in Ontario, a combination of attributes was related to log porosity using a neural network with a correlation coefficient between the combined attributes and porosity of 0.74 to 0.78 (Ogiesoba 2010). EnCana (2006) reports that a porosity model created using undefined seismic attributes at the Deep Panuke Field in Nova Scotia has a correlation coefficient of 0.8 when compared with measured porosity. Tebo and Hart (2005) report the results of a characterization of a field producing from the Jurassic Smackover Formation in Alabama, USA. A combination of attributes was used to model porosity. The final model based on a combination of four attributes results in a relationship with porosity that has a correlation coefficient of 0.93. Christensen et al. (2006) describe a field in chalk in the Danish North Sea in which a porosity model created using four attributes as input for a neural network was validated by results from four post-model wells. Those authors also used curvature to map subtle faults that could impact porosity. Li et al. (2010) used a combination of three attributes to deduce structures interpreted to control the formation and/or later dissolution of dolomite in an example from the Precaspian Basin. This is likely an example of non-stratabound dolostone geobodies, but this cannot be verified.

Neves and Triebwasser (2006) use both principal component analysis and a neural network using amplitude, absorption, coherence, and curvature to define two seismic facies and to interpret faults from a field in Saudi Arabia. Of the wells hitting facies 2, some 65% were good producers (96 well penetrations), and 74% of wells hitting facies 1 were poor producers (35 well penetrations). Production is stated to be directly or indirectly related to structure, due either to the presence of fracturing or structurally controlled alteration. Liu et al. (2011) studied a field in the Tarim Basin, China, finding that a principal component analysis using RMS amplitude, coherence, and a 22 Hz volume could be used to define wet reservoir, hydrocarbon-charged reservoir, and nonreservoir.

Shibasaki et al. (2006) used two attributes (curvature and coherence) to map subtle faults inferred to enhance reservoir properties in a field in Abu Dhabi, and, similarly, Staples et al. (2010) used those same attributes to predict areas of enhanced fracturing in an example from Oklahoma, USA. Strecker et al. (2004) characterize a field producing from the Slave Point Formation in the province of British Columbia, Canada. Lineation mapping was used to define fracture and fault trends, and frequency attenuation was used to identify portions of those trends that were gas saturated. The authors report that energy absorption (frequency attenuation) highlights gas-charged vs. tight areas along faults. Those authors speculate that patterns of energy absorption could be used to infer fault sealing behavior: areas of high energy absorption should indicate areas with open fractures, while areas of low energy absorption should indicate tighter rocks.

Each of the attributes used in this study was evaluated in each of the models based only on how well they allowed for recognition of dolostone and limestone. Because the porosity models were simple ternary models with constant values assigned in each of the three lithologies (host limestone, bulk dolostone geobody, and near-fault dolostone) no attempt was made to quantitatively relate attribute response to porosity magnitude. Rather, each of the attributes was evaluated for how well it might allow for recognition of porous dolostone geobodies set in a tight host limestone (referring to the setting for the analogues in the dolostone and limestone porosity compilation shown in Fig. 7).

The attributes were evaluated for how well their response could be used to differentiate tight limestone and porous dolostone by plotting model porosity against the median attribute response in each of the three lithologies. In addition to the median value, the 25th and 75th percentile values were also calculated to define a range of attribute responses in each of the lithologies (Fig. 14). The fraction of the dolostone response that is distinct from the host limestone response is calculated as A/B as shown in Figure 14. The attributes were ranked by summing the calculated values of A/B in each of the nine synthetic seismic cubes. The ranked attributes from greatest to least discriminative are cosine of instantaneous phase, amplitude, semblance, derivative, derivative of reflection intensity, reflection intensity, RMS amplitude, sweetness, envelope, dominant frequency, instantaneous frequency, ant tracking, curvature, and dip. The scenarios and the ranking are shown in Table 6. A discussion of the ranking of attributes, the main result of this study, can be found in the next section.

FIG. 14.

—An example of the evaluation and ranking of attribute responses. Fourteen attributes were created in each of the nine synthetic seismic cubes and evaluated for how well their response could be used to differentiate limestone and dolostone. Limestone is shown in blue, lower porosity dolostone in pink, and higher porosity dolostone in red. Note that in two scenarios the higher porosity limestone occurred in the bulk geobody and the lower porosity dolostone occurred near faults, while that relationship was inverted in the third scenario. Symbols represent median values (diamond for limestone, square for lower porosity dolostone, triangle for higher porosity dolostone) and error bars cover the 25th and 75th percentiles. The range of the attribute response of the dolostones is represented by “B,” and the portion of that range that plots distinctly from the host limestone response is represented by “A.” The fraction of the dolostone response that is distinct from the host limestone response is calculated as A/B. The attributes were ranked by summing the calculated values of A/B in each of the nine synthetic seismic cubes. These are summarized in Table 6.

FIG. 14.

—An example of the evaluation and ranking of attribute responses. Fourteen attributes were created in each of the nine synthetic seismic cubes and evaluated for how well their response could be used to differentiate limestone and dolostone. Limestone is shown in blue, lower porosity dolostone in pink, and higher porosity dolostone in red. Note that in two scenarios the higher porosity limestone occurred in the bulk geobody and the lower porosity dolostone occurred near faults, while that relationship was inverted in the third scenario. Symbols represent median values (diamond for limestone, square for lower porosity dolostone, triangle for higher porosity dolostone) and error bars cover the 25th and 75th percentiles. The range of the attribute response of the dolostones is represented by “B,” and the portion of that range that plots distinctly from the host limestone response is represented by “A.” The fraction of the dolostone response that is distinct from the host limestone response is calculated as A/B. The attributes were ranked by summing the calculated values of A/B in each of the nine synthetic seismic cubes. These are summarized in Table 6.

Table 6.

—Summary of attributes in all geobody volume and porosity scenarios. The value shown is the fraction of the attribute response in dolostone (bulk geobody and near fault) that is distinct from host limestone. A value of 1.0 indicates that the dolostone response is completely distinct from the limestone response, and a value of 0.0 indicates complete overlap between the response of dolostone and limestone. The ranking is achieved by summing all of the fractional overlaps and sorting from greatest to least. The greatest sum is assigned a rank of 1. COS PHASE is cosine of instantaneous phase, AMP is original amplitude, SEM is semblance, DER is derivative of original amplitude, DER REF INT is derivative of reflection intensity, REF INT is reflection intensity, RMS AMP is RMS amplitude, SWEET is sweetness, ENV is envelope, DOM FREQ is dominant frequency, INST FREQ is instantaneous frequency, ANT is ant tracking, CRV is curvature, and DIP is dip.

 High vol base poroHigh vol high poroHigh vol high fault poroMid vol base poroMid vol high poroMid vol high fault poroLow vol base poroLow vol high poroLow vol high fault poroSumRank
COS PHASE1.00.91.00.91.01.00.91.01.08.71
AMP1.00.81.00.80.81.00.70.81.07.82
SEM0.70.80.71.01.00.81.01.00.77.73
DER0.80.90.80.80.90.70.80.90.77.34
DER REF INT0.80.71.00.70.80.80.70.80.87.05
REF INT0.70.70.90.60.80.80.60.80.96.86
RMS AMP0.70.70.90.60.80.90.60.80.86.77
SWEET0.60.70.80.60.80.70.60.80.76.28
ENV0.60.60.80.60.80.70.60.80.76.19
DOM FREQ0.60.60.80.60.60.70.50.70.75.810
INST FREQ0.50.50.70.50.60.60.50.60.65.111
ANT0.50.00.00.40.20.20.70.30.63.012
CRV0.20.20.10.30.20.00.10.20.01.313
DIP0.20.00.00.00.00.00.00.00.00.214
 High vol base poroHigh vol high poroHigh vol high fault poroMid vol base poroMid vol high poroMid vol high fault poroLow vol base poroLow vol high poroLow vol high fault poroSumRank
COS PHASE1.00.91.00.91.01.00.91.01.08.71
AMP1.00.81.00.80.81.00.70.81.07.82
SEM0.70.80.71.01.00.81.01.00.77.73
DER0.80.90.80.80.90.70.80.90.77.34
DER REF INT0.80.71.00.70.80.80.70.80.87.05
REF INT0.70.70.90.60.80.80.60.80.96.86
RMS AMP0.70.70.90.60.80.90.60.80.86.77
SWEET0.60.70.80.60.80.70.60.80.76.28
ENV0.60.60.80.60.80.70.60.80.76.19
DOM FREQ0.60.60.80.60.60.70.50.70.75.810
INST FREQ0.50.50.70.50.60.60.50.60.65.111
ANT0.50.00.00.40.20.20.70.30.63.012
CRV0.20.20.10.30.20.00.10.20.01.313
DIP0.20.00.00.00.00.00.00.00.00.214

Discussion

As shown in Figure 6, the non-stratabound dolostone geobodies at the Vajont Gorge plot within the range of sizes for non-stratabound dolostone geobodies compiled from subsurface examples. Most subsurface dolostone geobodies have aspect ratios between 1:1 and 1:10, as do the geobodies from the Vajont Gorge. The similarity between size and aspect ratio (Fig. 1) makes it easier to apply results from the synthetic seismic modeling to the subsurface, since the dimensions and geometries are similar to what could be expected in the subsurface. With that in mind, it is worth reemphasizing that the goal of this study is not to attempt to explicitly model what the seismic expression of a single occurrence of non-stratabound dolostone geobodies might be. The goals of this study are to take advantage of global compilations of the properties of non-stratabound dolostones to estimate which attributes are most likely to help interpret porous dolostone geobodies set in low porosity limestone.

No attempt was made to account for the effects of varying fluid saturations in the synthetic seismic volumes, and all attribute evaluations were based on synthetic seismic cubes generated using the velocity and density values for dry samples. This provides a base model for future evaluation of the effects of fluid saturation. The values for both dry and brine-saturated samples are included in the compilation to show the range of possible velocities and densities as a function of porosity. The velocities used in the dry model plot toward the lower end of the envelope of the velocities shown in the compilation in Figure 10. Although that lower end is dominated by dry samples, there are a number of saturated samples that plot there too. Similarly, the upper end of the envelope is dominated by brine-saturated samples. Since fluid composition influences the acoustic impedance contrast between limestone and dolostone, it is possible that the attribute ranking may change in some settings. A range of reflection coefficients for the dolostone geobodies in each of the scenarios is given in Table 4. In the dry scenario, the reflection coefficient between bulk dolostone geobody and host limestone ranges from 0.08 in porosity scenario 3 (8% bulk dolostone porosity, 3% host limestone porosity) to 0.43 in scenario 2 (25% bulk dolostone porosity, 2% host limestone porosity). In the brine-saturated scenario the reflection coefficients for those models range from 0.005 to 0.26. Reflection coefficients for gas and oil-saturated samples are bracketed by the dry and brine-saturated values. The scenario used to generate synthetic seismic data in this study is closer to what might be expected in gas reservoirs. Under brine-saturated conditions, dolostones with a porosity of 8% (the lowest value used in this study) would be much more difficult to detect. The reflection coefficient under oil-saturated conditions would be greater than under brine-saturated conditions, but less than under dry conditions. The attribute ranking described in this study therefore represents a best possible scenario for detecting porous non-stratabound dolostone geobodies on reflection seismic data. This is further enhanced by the fact that noise is ignored. Finally, a comparison of images generated from 1D convolution and from 3D modeling found that the primary difference between the two is that the spatial “blurring” effect of imaging on 3D seismic data is not present in the convolution-based images (van Hoek and Salomons 2006). This means that geobody margins on the synthetic 3D seismic volumes in this study will most likely be sharper than observed in subsurface data but that the magnitude of the attribute responses will be similar.

Reflecting on the ranked attributes listed in the previous section, the following observations can be made:

  • Attributes that are sensitive to the lateral change in acoustic properties (cosine of instantaneous phase, semblance) come out near the top of the list.

  • The second best attributes are amplitude related (original amplitude, RMS amplitude, reflection intensity, derivative, envelope, sweetness, which is also related to frequency) and pick up acoustic impedance contrasts between dolostone and host limestone.

  • Frequency-dependent attributes (instantaneous frequency, dominant frequency, and sweetness, which is also related to amplitude) come in third position. There is significant overlap between host limestone and dolostone (bulk geobody and near fault) with these attributes, but there is one example of better performance of instantaneous and dominant frequency. Those attributes show clear separation between bulk dolostone and host limestone in the high-porosity scenario (scenario 2) and may therefore be useful for highlighting dolostone geobody boundaries. Note that the bulk dolostone porosity in scenario 2 is 25%, on the high end of the data compilation; therefore, frequency attributes will have limited usefulness in all but the most favorable configurations. In the lower porosity scenarios the changes in frequency caused by the transition from dolostone to limestone are too small to aid interpretation. Since sweetness (which ranks higher than frequency attributes) is a function of both amplitude and instantaneous frequency it is a “poor cousin” of spectral decomposition. It is logical for sweetness to fall in between amplitude and frequency attributes, since it is calculated from both amplitude and frequency. Taken as a whole, the ranking of the frequency-related attributes suggests a more effective way to interpret frequency data, which also leverages the benefits of amplitude and phase discrimination, could be the use of spectral decomposition attributes (Christensen et al. 2006, Colpaert et al. 2007, Li et al. 2010, Liu et al. 2011, von Hartmann et al. 2012).

  • Continuity attributes (curvature, ant tracking, dip) ranked poorly in this study. There are a number of studies that successfully used curvature and coherence to interpret higher porosity features (Neves and Triebwasser 2006, Shibasaki et al. 2006, Nissen et al. 2009, Staples et al. 2010, Li et al. 2010, Dou et al. 2011). The poor performance of these attributes in this study can be explained by the negligible offset of the faults that are associated with dolostone geobodies in the Vajont Gorge (Bistacchi et al. 2015). As discussed earlier, the offset on those faults is greatly obscured because the dolomitization process destroyed bedding. This translates to a lack of obvious offset in reflectors in the synthetic seismic volumes. Fields with larger faults displacements are more susceptible to characterization using attributes that are sensitive to trace continuity.

It is interesting to ponder the case of the ranking of the semblance attribute. Classically, semblance is considered a continuity attribute, and as such, may have been expected to rank low on our list given the relatively poor performance of other structural attributes. The likely explanation for this apparent inconsistency is that semblance is sensitive to lateral changes in rock properties such as the transition from tight limestone to porous dolostone, as occurs in this study. This will induce a lateral change in seismic waveform, to which semblance is sensitive.

The statistical ranking of attributes in this study is best suited for the identification of which attributes are best suited to distinguish porous dolostone from tight limestone, and not to quantitative prediction of porosity. A more complicated statistical characterization would be warranted if more complicated porosity models than in the present study are used. The compilation of attribute studies shows a large number of examples of using combinations of attributes to create a porosity model (Strecker et al. 2004, Tebo and Hart 2005, Christensen et al. 2006, EnCana 2006, Neves and Triebwasser 2006, Sagan and Hart 2006, Hart 2008, Hart et al. 2009, Li et al. 2010, Ogiesoba 2010). There are a number of ways in which combination of the ranked, validated attributes might be used to highlight the presence of porous non-stratabound dolostone geobodies in the subsurface. These include principal component analysis to define a facies or porosity model (Neves and Triebwasser 2006), a neural network based on the creation of a facies or porosity model (Christensen et al. 2006, Ogiesoba 2010), or RGB (Red Green Blue) blending to create false color images. Owing to the simple porosity structure of the input models used to generate the synthetic seismic volumes, a simpler way to combine the attributes was appropriate in this case. This rule-based technique relies on normalizing each of the highly ranked attributes by assigning a threshold value that separates the attribute responses of limestone and dolostone and assigning everything that plots on the dolostone side of the threshold a value of one and everything that plots on the limestone side a value of zero. Multiple normalized attribute responses can be summed to help highlight dolostone geobodies. Figure 15 shows an example of how the three normalized attribute responses could be combined to define geobody volumes instead of explicitly predicting porosity.

FIG. 15.

—Example of how multiple normalized attribute volumes with good differentiation between limestone and dolostone can be combined to improve interpretation of non-stratabound dolostone geobodies. A) One of the porosity scenarios shown in Figure 9 (the high geobody volume model with porosity scenario 1). B) The summation of three normalized attribute maps (RMS amplitude, semblance, and cosine phase). Each of those attributes are normalized by specification of a threshold value separating the attribute response of limestone from the attribute response of dolostone. All values on the dolostone side of the threshold are assigned a value of 1, while all values on the limestone side are assigned a value of 0. Avalue of three (indicated by a red color) on the summed attributes map indicates an area that was flagged as dolostone on all three attributes.

FIG. 15.

—Example of how multiple normalized attribute volumes with good differentiation between limestone and dolostone can be combined to improve interpretation of non-stratabound dolostone geobodies. A) One of the porosity scenarios shown in Figure 9 (the high geobody volume model with porosity scenario 1). B) The summation of three normalized attribute maps (RMS amplitude, semblance, and cosine phase). Each of those attributes are normalized by specification of a threshold value separating the attribute response of limestone from the attribute response of dolostone. All values on the dolostone side of the threshold are assigned a value of 1, while all values on the limestone side are assigned a value of 0. Avalue of three (indicated by a red color) on the summed attributes map indicates an area that was flagged as dolostone on all three attributes.

Conclusions

The aim of this study was to evaluate what attributes are best able to highlight porous non-stratabound dolostone geobodies set in low porosity limestone. Evaluation of the response of seismic attributes against model porosity leads to creation of a list of validated attributes that can be combined to define non-stratabound dolostone geobodies as highlighted in this work.

The range of porosities used in these models covers the range of porosities found in a global compilation of non-stratabound dolostone geobodies, and the porosity scenarios include models in which the properties of near-fault dolostones were enhanced or degraded relative to the bulk dolostone geobody values. This allows for the effects of processes such as overdolomitization or dissolution to be implicitly explored, since those processes can degrade or enhance near fault properties, although in all scenarios dolostone porosities are greater than host limestone porosity.

The methodology described in this study provides a means to interpret/evaluate 3D seismic cubes for the occurrence of non-stratabound dolomite geobodies in exploration, appraisal, and production settings. In settings where porous non-stratabound dolo-stone geobodies provide the dominant type of reservoir rock, the ability to accurately interpret their volume from 3D seismic data makes it easier to estimate gross rock volume and therefore hydrocarbon volumes. Dolostone geobodies may also be equivalent to flow units, and so the ability to accurately interpret them can also help constrain well and field development plans. Interpreting porous non-stratabound dolostone geobodies is most straightforward when those geobodies are saturated with gas. There is still an acoustic impedance contrast between limestone and dolostone when those lithologies are oil saturated. For the higher porosities used in this study (>12%), the acoustic impedance contrast is in the same range as with the acoustic impedance contrasts for the dry scenarios. There is very little acoustic impedance contrast between brine-saturated dolostone with a porosity of 8% and brine-saturated limestone with a porosity of 3%.

While this study describes the results from nine models of an occurrence of non-stratabound dolostone geobodies, the application of this methodology is not limited to non-stratabound dolostone geobodies. Indeed, the same attributes that were highly ranked in this study (e.g., cosine of instantaneous phase, semblance, amplitude, derivate of amplitude or reflection intensity) have been successfully applied in other settings with other sorts of non-stratabound geobodies (e.g., karsting, leaching) as discussed earlier. The workflow for validating seismic attributes described in this study can be applied to other types of carbonate reservoirs as well.

The global occurrence of dolostone and non-dolostone geobodies and their significance in hydrocarbon exploration requires careful characterization with all available data. This study demonstrates that a number of seismic attributes and combinations thereof are well suited to enhance the prediction and characterization of carbonate reservoirs containing non-stratabound dolostone geobodies.

Acknowledgments

Acknowledgments

The authors thank Shell Global Solutions International, B.V., for permission to publish this study. The authors thank Paul Wagner and Stephane Gesbert for many conversations and a review that helped to improve the manuscript. We greatly appreciate reviews by an anonymous reviewer, Ralf Weger, and the lead volume editor Alex MacNeil, which helped to refine the manuscript.

The authors thank Shell Global Solutions International, B.V., for permission to publish this study. The authors thank Paul Wagner and Stephane Gesbert for many conversations and a review that helped to improve the manuscript. We greatly appreciate reviews by an anonymous reviewer, Ralf Weger, and the lead volume editor Alex MacNeil, which helped to refine the manuscript.

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Figures & Tables

FIG. 1.

—Figure showing global distribution of sedimentary basins (as defined by Tellus) that have been reported to contain non-stratabound dolostone geobodies based on the references shown in this figure and the compilation of hydrothermal dolomites of Davies and Smith (2006). The location of the Vajont gorge is shown with an arrow. Three types of analogues are shown: (1) Analogues from outcrop and subsurface containing data on geobody dimensions or dolostone geobody and host limestone porosity, Table 1; (2) studies with compressional velocity, density and porosity data from wells, core or outcrop, Table 2; (3) Studies containing descriptions of attributes that were reported to successfully correlate to porosity in a variety of carbonate settings (non-stratabound dolomite, karst, mound/buildups, other structural settings, or other nonstructural settings), Table 3.

FIG. 1.

—Figure showing global distribution of sedimentary basins (as defined by Tellus) that have been reported to contain non-stratabound dolostone geobodies based on the references shown in this figure and the compilation of hydrothermal dolomites of Davies and Smith (2006). The location of the Vajont gorge is shown with an arrow. Three types of analogues are shown: (1) Analogues from outcrop and subsurface containing data on geobody dimensions or dolostone geobody and host limestone porosity, Table 1; (2) studies with compressional velocity, density and porosity data from wells, core or outcrop, Table 2; (3) Studies containing descriptions of attributes that were reported to successfully correlate to porosity in a variety of carbonate settings (non-stratabound dolomite, karst, mound/buildups, other structural settings, or other nonstructural settings), Table 3.

FIG. 2.

—Annotated outcrop photograph of the Vajont Gorge (view to the northeast). The Vajont Dam is visible at the upper right portion of the image. Bedding in the Vajont Limestone is highlighted in blue, and a non-stratabound dolostone geobody is outlined in red. Prominent faults and fracture zones are highlighted in yellow/green. Note that bedding is generally not preserved in the geobody. A white arrow points to a tunnel and a roadway in the center of the geobody for scale.

FIG. 2.

—Annotated outcrop photograph of the Vajont Gorge (view to the northeast). The Vajont Dam is visible at the upper right portion of the image. Bedding in the Vajont Limestone is highlighted in blue, and a non-stratabound dolostone geobody is outlined in red. Prominent faults and fracture zones are highlighted in yellow/green. Note that bedding is generally not preserved in the geobody. A white arrow points to a tunnel and a roadway in the center of the geobody for scale.

FIG. 3.

—Location maps of the Vajont Gorge study area. A) simplified map of the major tectonic and stratigraphic features of the study area (derived from Winterer and Bosellini 1983 and Ronchi et al. 2012). The study area occurs in the Southern Alps, one of the less deformed regions of the Alps. The Vajont Limestone consists of oolitic deep-water fans deposited in the Belluno Basin in between the Trento and Friuli Platforms. B) Simple geologic map of the Vajont Gorge (faults taken from Bistacchi et al. 2015 based on Riva et al. 1990 and Massironi et al. 2013, and formation contacts are taken from Carulli 2006). Lmst = limestone, Fm = formation.

FIG. 3.

—Location maps of the Vajont Gorge study area. A) simplified map of the major tectonic and stratigraphic features of the study area (derived from Winterer and Bosellini 1983 and Ronchi et al. 2012). The study area occurs in the Southern Alps, one of the less deformed regions of the Alps. The Vajont Limestone consists of oolitic deep-water fans deposited in the Belluno Basin in between the Trento and Friuli Platforms. B) Simple geologic map of the Vajont Gorge (faults taken from Bistacchi et al. 2015 based on Riva et al. 1990 and Massironi et al. 2013, and formation contacts are taken from Carulli 2006). Lmst = limestone, Fm = formation.

FIG. 4.

—Volumetric perspective of geobodies in the Vajont Gorge based on the digital outcrop model created by Bistacchi et al. (2015). The topographic surface of the gorge is represented by the shaded gray surface in all panels. Three geobody volume scenarios were created honoring the uncertainty associated with the erosion of the gorge. A) Digitized outcrop dolostone geobody contacts, which provide constraints for the geobody volume scenarios. B) The low geobody volume scenario. C) The middle geobody volume scenario. D) The high geobody volume scenario.

FIG. 4.

—Volumetric perspective of geobodies in the Vajont Gorge based on the digital outcrop model created by Bistacchi et al. (2015). The topographic surface of the gorge is represented by the shaded gray surface in all panels. Three geobody volume scenarios were created honoring the uncertainty associated with the erosion of the gorge. A) Digitized outcrop dolostone geobody contacts, which provide constraints for the geobody volume scenarios. B) The low geobody volume scenario. C) The middle geobody volume scenario. D) The high geobody volume scenario.

FIG. 5.

—Example of how the fault surfaces and dolostone geobody fronts from the digital outcrop model of Bistacchi et al. (2015) were used to create sets of voxets in a geocellular model. A) A volume perspective of the topography of the Vajont Gorge (shaded gray) and fault surfaces (multicolored rectangles) in the digital outcrop model, and B) those faults as sets of voxets in a geocellular grid. C) The topography of the Vajont Gorge with the dolostone geobodies from one of the three dolostone geobody volume scenarios as visualized in the digital outcrop model, and D) the dolostone geobodies as voxets in a geocellular grid. The representation of faults and geobodies as voxets in a geocellular grid allows for property modeling (porosity, density, and velocity).

FIG. 5.

—Example of how the fault surfaces and dolostone geobody fronts from the digital outcrop model of Bistacchi et al. (2015) were used to create sets of voxets in a geocellular model. A) A volume perspective of the topography of the Vajont Gorge (shaded gray) and fault surfaces (multicolored rectangles) in the digital outcrop model, and B) those faults as sets of voxets in a geocellular grid. C) The topography of the Vajont Gorge with the dolostone geobodies from one of the three dolostone geobody volume scenarios as visualized in the digital outcrop model, and D) the dolostone geobodies as voxets in a geocellular grid. The representation of faults and geobodies as voxets in a geocellular grid allows for property modeling (porosity, density, and velocity).

FIG. 7.

—A compilation of non-stratabound dolostone geobodies based on a combination of outcrop and subsurface data. The very large symbols shown at the left of the figure are the porosity values used in one of the three porosity scenarios created in the models in this study. Each study in the compilation is represented by a letter, and the studies are placed in order of descending dolostone porosity. Dolostones are represented in red (subsurface) and pink (outcrop), and limestones are represented in dark blue (subsurface) and light blue (outcrop). Bars show the range of values for dolostone and/or limestone porosity reported in each of the studies. The symbols (diamond or circle for dolostone, square or triangle for limestone) are mean values in each of the studies. No mean value could be calculated for studies that presented only a range (maximum and minimum values), and these studies are represented by error bars without a symbol in this compilation (studies “c” and “g,” for example). Symbols without error bars represent maximum reported values in studies that only reported the maximum value of dolostone or limestone porosity (e.g., “dolostone porosity as great as …” or “limestone porosity up to…”). Examples include studies “f” and “l.” Referenced studies are a = Koehrer et al. (2010); b = Boreen and Davies (2004); c = Lavoie et al. (2011); d = EnCana (2006); e = Sagan and Hart (2006); f = Braithwaite and Rizzi (1997); g =Wilson et al. (2007); h =Nader and Swennen (2004); i = Di Cuia et al. (2011); j = Saller and Dickson (2011); k = Baker and Knight (1993); l =Nyahay et al. (2006); m =Carmichael et al. (2008); n =Lonnee and Machel (2006); o =Bouch et al. (2004); p = Lapponi et al. (2011); q = Zempolich and Hardie (1997); r = Chidsey et al. (2009); s = Saller and Yaremko (1994); t = Marcil et al. (2005); u = Dewit et al. (2012); v =Hurley and Budros (1990); w = Nader et al. (2012); x = Lynch and Trollope (2001); y = Shah et al. (2010); z = Ronchi et al. (2012); aa = Morgan (2007); ab = Martin-Martin et al. (2015).

FIG. 7.

—A compilation of non-stratabound dolostone geobodies based on a combination of outcrop and subsurface data. The very large symbols shown at the left of the figure are the porosity values used in one of the three porosity scenarios created in the models in this study. Each study in the compilation is represented by a letter, and the studies are placed in order of descending dolostone porosity. Dolostones are represented in red (subsurface) and pink (outcrop), and limestones are represented in dark blue (subsurface) and light blue (outcrop). Bars show the range of values for dolostone and/or limestone porosity reported in each of the studies. The symbols (diamond or circle for dolostone, square or triangle for limestone) are mean values in each of the studies. No mean value could be calculated for studies that presented only a range (maximum and minimum values), and these studies are represented by error bars without a symbol in this compilation (studies “c” and “g,” for example). Symbols without error bars represent maximum reported values in studies that only reported the maximum value of dolostone or limestone porosity (e.g., “dolostone porosity as great as …” or “limestone porosity up to…”). Examples include studies “f” and “l.” Referenced studies are a = Koehrer et al. (2010); b = Boreen and Davies (2004); c = Lavoie et al. (2011); d = EnCana (2006); e = Sagan and Hart (2006); f = Braithwaite and Rizzi (1997); g =Wilson et al. (2007); h =Nader and Swennen (2004); i = Di Cuia et al. (2011); j = Saller and Dickson (2011); k = Baker and Knight (1993); l =Nyahay et al. (2006); m =Carmichael et al. (2008); n =Lonnee and Machel (2006); o =Bouch et al. (2004); p = Lapponi et al. (2011); q = Zempolich and Hardie (1997); r = Chidsey et al. (2009); s = Saller and Yaremko (1994); t = Marcil et al. (2005); u = Dewit et al. (2012); v =Hurley and Budros (1990); w = Nader et al. (2012); x = Lynch and Trollope (2001); y = Shah et al. (2010); z = Ronchi et al. (2012); aa = Morgan (2007); ab = Martin-Martin et al. (2015).

FIG. 8.

—An example of an overdolomitization front in a hand sample from the Vajont Gorge.

FIG. 8.

—An example of an overdolomitization front in a hand sample from the Vajont Gorge.

FIG. 9.

—Map views of horizon slices through the nine models that were generated using three dolostone volume scenarios and three porosity scenarios. Each row represents a volume scenario, and each column represents a porosity scenario. Porosity values and other properties are given in Table 4.

FIG. 9.

—Map views of horizon slices through the nine models that were generated using three dolostone volume scenarios and three porosity scenarios. Each row represents a volume scenario, and each column represents a porosity scenario. Porosity values and other properties are given in Table 4.

FIG. 10.

—Compilation of porosity and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Raymer et al. (1980), Gardner et al. (1974), and Wyllie et al. (1956) are shown for dolomite (RHG Dolo, GGG Dolo, and WGG Dolo in figure) and calcite (RHG Cal, GGG Cal, and WGG Cal in figure) under water-saturated conditions. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956), Wyllie et al. (1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 10.

—Compilation of porosity and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Raymer et al. (1980), Gardner et al. (1974), and Wyllie et al. (1956) are shown for dolomite (RHG Dolo, GGG Dolo, and WGG Dolo in figure) and calcite (RHG Cal, GGG Cal, and WGG Cal in figure) under water-saturated conditions. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956), Wyllie et al. (1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 11.

—Compilation of porosity and bulk density for dolostones (diamonds), limestones (squares), and unspecified carbonates (circles) under dry and saturated conditions. The labeling conventions are as described in Figure 10. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 11.

—Compilation of porosity and bulk density for dolostones (diamonds), limestones (squares), and unspecified carbonates (circles) under dry and saturated conditions. The labeling conventions are as described in Figure 10. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 12.

—Compilation of bulk density and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Gardner et al. (1974) and Mavko et al. (1998) are shown for dolomite and calcite. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 12.

—Compilation of bulk density and compressional velocity for dolostone, limestone, and unspecified carbonates under both dry and saturated conditions. Dolostone is represented by diamonds, limestone by squares, and unspecified carbonates by circles. Very large symbols represent the values assigned to lithologies in one of the nine models created in this study. The relationships of Gardner et al. (1974) and Mavko et al. (1998) are shown for dolomite and calcite. The dataset of Verwer et al. (2008) is colored in green as velocities from that study are markedly faster than data from any other study. Data compiled from Wyllie et al. (1956, 1958), Rafavich et al. (1984), Miller (1992), Anselmetti and Eberli (1997), Anselmetti et al. (1997), Fischer et al. (1997), Marion and Jizba (1997), Wang (1997), Anselmetti and Eberli (2001), Assefa et al. (2003), Verwer et al. (2008), Misaghi et al. (2010), AlMuhaidib et al. (2012).

FIG. 13.

—A horizontal and vertical slice through the middle of the synthetic seismic volumes generated from acoustic impedance volumes created for each of the nine dolostone scenarios shown in Figure 9.

FIG. 13.

—A horizontal and vertical slice through the middle of the synthetic seismic volumes generated from acoustic impedance volumes created for each of the nine dolostone scenarios shown in Figure 9.

FIG. 14.

—An example of the evaluation and ranking of attribute responses. Fourteen attributes were created in each of the nine synthetic seismic cubes and evaluated for how well their response could be used to differentiate limestone and dolostone. Limestone is shown in blue, lower porosity dolostone in pink, and higher porosity dolostone in red. Note that in two scenarios the higher porosity limestone occurred in the bulk geobody and the lower porosity dolostone occurred near faults, while that relationship was inverted in the third scenario. Symbols represent median values (diamond for limestone, square for lower porosity dolostone, triangle for higher porosity dolostone) and error bars cover the 25th and 75th percentiles. The range of the attribute response of the dolostones is represented by “B,” and the portion of that range that plots distinctly from the host limestone response is represented by “A.” The fraction of the dolostone response that is distinct from the host limestone response is calculated as A/B. The attributes were ranked by summing the calculated values of A/B in each of the nine synthetic seismic cubes. These are summarized in Table 6.

FIG. 14.

—An example of the evaluation and ranking of attribute responses. Fourteen attributes were created in each of the nine synthetic seismic cubes and evaluated for how well their response could be used to differentiate limestone and dolostone. Limestone is shown in blue, lower porosity dolostone in pink, and higher porosity dolostone in red. Note that in two scenarios the higher porosity limestone occurred in the bulk geobody and the lower porosity dolostone occurred near faults, while that relationship was inverted in the third scenario. Symbols represent median values (diamond for limestone, square for lower porosity dolostone, triangle for higher porosity dolostone) and error bars cover the 25th and 75th percentiles. The range of the attribute response of the dolostones is represented by “B,” and the portion of that range that plots distinctly from the host limestone response is represented by “A.” The fraction of the dolostone response that is distinct from the host limestone response is calculated as A/B. The attributes were ranked by summing the calculated values of A/B in each of the nine synthetic seismic cubes. These are summarized in Table 6.

FIG. 15.

—Example of how multiple normalized attribute volumes with good differentiation between limestone and dolostone can be combined to improve interpretation of non-stratabound dolostone geobodies. A) One of the porosity scenarios shown in Figure 9 (the high geobody volume model with porosity scenario 1). B) The summation of three normalized attribute maps (RMS amplitude, semblance, and cosine phase). Each of those attributes are normalized by specification of a threshold value separating the attribute response of limestone from the attribute response of dolostone. All values on the dolostone side of the threshold are assigned a value of 1, while all values on the limestone side are assigned a value of 0. Avalue of three (indicated by a red color) on the summed attributes map indicates an area that was flagged as dolostone on all three attributes.

FIG. 15.

—Example of how multiple normalized attribute volumes with good differentiation between limestone and dolostone can be combined to improve interpretation of non-stratabound dolostone geobodies. A) One of the porosity scenarios shown in Figure 9 (the high geobody volume model with porosity scenario 1). B) The summation of three normalized attribute maps (RMS amplitude, semblance, and cosine phase). Each of those attributes are normalized by specification of a threshold value separating the attribute response of limestone from the attribute response of dolostone. All values on the dolostone side of the threshold are assigned a value of 1, while all values on the limestone side are assigned a value of 0. Avalue of three (indicated by a red color) on the summed attributes map indicates an area that was flagged as dolostone on all three attributes.

Table 1.

—Compilation of published studies of non-stratabound dolostone reservoirs containing data on dolostone and limestone porosity and/or dolostone geobody dimensions.

RegionCountryFormation nameFormation ageAge of dolomitizationOutcrop, subsurfaceFieldReference
Eastern NorthUSATrenton-Black RiverOrdovicianUnspecifiedOutcropN/ABlack et al. (1981)
AmericaUSATrenton-Black RiverOrdovicianSilurian/DevonianSubsurfaceAlbion-Scipio; Stoney PointHurley and Budros (1990)
USATribe HillsOrdovicianLate OrdovicianOutcropN/ASlater and Smith (2012)
USATrenton-Black RiverOrdovicianLate Ordovician/SilurianSubsurfaceSaybrookSagan and Hart (2006)
CanadaCatocheOrdovicianLate Ordovician to Devonian (?)BothUnnamedBaker and Knight (1993)
CanadaAbenakiMiddle to Late JurassicUnspecifiedSubsurfaceDeep PanukeEnCana (2006)
CanadaAbenakiLate JurassicLate Jurassic/Early CretaceousSubsurfaceDeep PanukeWierzbicki et al. (2006)
CanadaRed Head RapidsLate OrdovicianUnspecifiedBothUnnamedLavoie et al. (2011)
CanadaRomaine, Mingan and TrentonOrdovicianUnspecifiedSubsurfaceUnnamedLynch and Trollope (2001)
CanadaSayabec, Forillon and Indian CoveSilurian and DevonianUnspecifiedOutcropN/AMarcil et al. (2005)
USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultipleNyahay et al. (2006)
Eastern Canada and USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultiplePatchen et al. (2006)
Greater MediterraneanItalyCalcari Grigi GroupEarly JurassicEarly Mesozoic (early), Oligocene to Early Miocene (structurally controlled)OutcropN/ADi Cuia et al. (2011)
ItalyLatemarMiddle TriassicMiddle TriassicOutcropN/ACarmichael et al. (2008), Jacquemyn et al. (2015), Wilson et al. (1990)
ItalyMonte Zugna and MaiolicaEarly Jurassic and Late Jurassic/Early CretaceousTertiaryOutcropN/ARonchi et al. (2012)
ItalyVajontMiddle JurassicLate Paleogene to NeogeneOutcropN/AZempolich and Hardie (1997)
LebanonKesrouaneEarly JurassicEarly Jurassic to Late JurassicOutcropN/ANader and Swennen (2004), Nader et al. (2004)
SpainBenassalEarly CretaceousLate Cretaceous to Early TertiaryOutcropN/AMartin-Martin et al. (2015)
SpainUrgonianEarly CretaceousLate CretaceousOutcropN/ADewit et al. (2012)
SpainUrgonianEarly CretaceousEarly to Late CretaceousOutcropN/ANader et al. (2012)
SpainUrgonianEarly CretaceousEarly CretaceousOutcropN/AShah et al. (2010, 2012)
Middle EastIranSarvakEarly to Late CretaceousEarly to Late CretaceousOutcropN/ALapponi et al. (2011), Sharp et al. (2010)
OmanKhuafiEdiacaranpre-PermianOutcropN/AVandeginste et al. (2014)
OmanSahtan GroupJurassicJurassic and Late CretaceousOutcropN/AVandeginste et al. (2013)
Northern EuropeGermanyMuschelkalkTriassicJurassic (early stratabound), Tertiary (leaching and cementation)OutcropN/AKoehrer et al. (2010)
IrelandNavan GroupLower CarboniferousCarboniferousOutcropN/ABraithwaite and Rizzi (1997)
United KingdomCloud Hill dolostone and Ticknall limestoneLower CarboniferousLower Carboniferous to Permo/TriassicBoth; most samples from quarry, some from a boreholeUnnamedBouch et al. (2004)
United KingdomBalladooleLower CarboniferousCarboniferous (?)OutcropN/AShelton et al. (2011)
SE AsiaIndonesiaTaballarOligocene-MioceneOligocene-MioceneOutcropN/AWilson et al. (2007)
Western North AmericaUSALeadvilleMississippianPennsylvanian to Jurassic (early dolomitization), Cretaceous to Oligocene (saddle dolomite)SubsurfaceLisbonChidsey et al. (2009)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceLadyfernBoreen and Davies (2004)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceClarke LakeLonnee and Machel (2006)
USABootstrapSilurian-DevonianSilurian/Devonian to TertiaryOutcropN/AMorgan (2007)
Western North America (Not fault related)USACisco GroupLate Pennsylvanian-Early PermianLate PermianSubsurfaceReineckeSaller and Dickson (2011)
CanadaWabamun GroupLate DevonianDevonian (early), Unspecified (late)SubsurfaceMultipleSaller and Yaremko (1994)
RegionCountryFormation nameFormation ageAge of dolomitizationOutcrop, subsurfaceFieldReference
Eastern NorthUSATrenton-Black RiverOrdovicianUnspecifiedOutcropN/ABlack et al. (1981)
AmericaUSATrenton-Black RiverOrdovicianSilurian/DevonianSubsurfaceAlbion-Scipio; Stoney PointHurley and Budros (1990)
USATribe HillsOrdovicianLate OrdovicianOutcropN/ASlater and Smith (2012)
USATrenton-Black RiverOrdovicianLate Ordovician/SilurianSubsurfaceSaybrookSagan and Hart (2006)
CanadaCatocheOrdovicianLate Ordovician to Devonian (?)BothUnnamedBaker and Knight (1993)
CanadaAbenakiMiddle to Late JurassicUnspecifiedSubsurfaceDeep PanukeEnCana (2006)
CanadaAbenakiLate JurassicLate Jurassic/Early CretaceousSubsurfaceDeep PanukeWierzbicki et al. (2006)
CanadaRed Head RapidsLate OrdovicianUnspecifiedBothUnnamedLavoie et al. (2011)
CanadaRomaine, Mingan and TrentonOrdovicianUnspecifiedSubsurfaceUnnamedLynch and Trollope (2001)
CanadaSayabec, Forillon and Indian CoveSilurian and DevonianUnspecifiedOutcropN/AMarcil et al. (2005)
USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultipleNyahay et al. (2006)
Eastern Canada and USATrenton-Black RiverOrdovicianLate Ordovician?SubsurfaceMultiplePatchen et al. (2006)
Greater MediterraneanItalyCalcari Grigi GroupEarly JurassicEarly Mesozoic (early), Oligocene to Early Miocene (structurally controlled)OutcropN/ADi Cuia et al. (2011)
ItalyLatemarMiddle TriassicMiddle TriassicOutcropN/ACarmichael et al. (2008), Jacquemyn et al. (2015), Wilson et al. (1990)
ItalyMonte Zugna and MaiolicaEarly Jurassic and Late Jurassic/Early CretaceousTertiaryOutcropN/ARonchi et al. (2012)
ItalyVajontMiddle JurassicLate Paleogene to NeogeneOutcropN/AZempolich and Hardie (1997)
LebanonKesrouaneEarly JurassicEarly Jurassic to Late JurassicOutcropN/ANader and Swennen (2004), Nader et al. (2004)
SpainBenassalEarly CretaceousLate Cretaceous to Early TertiaryOutcropN/AMartin-Martin et al. (2015)
SpainUrgonianEarly CretaceousLate CretaceousOutcropN/ADewit et al. (2012)
SpainUrgonianEarly CretaceousEarly to Late CretaceousOutcropN/ANader et al. (2012)
SpainUrgonianEarly CretaceousEarly CretaceousOutcropN/AShah et al. (2010, 2012)
Middle EastIranSarvakEarly to Late CretaceousEarly to Late CretaceousOutcropN/ALapponi et al. (2011), Sharp et al. (2010)
OmanKhuafiEdiacaranpre-PermianOutcropN/AVandeginste et al. (2014)
OmanSahtan GroupJurassicJurassic and Late CretaceousOutcropN/AVandeginste et al. (2013)
Northern EuropeGermanyMuschelkalkTriassicJurassic (early stratabound), Tertiary (leaching and cementation)OutcropN/AKoehrer et al. (2010)
IrelandNavan GroupLower CarboniferousCarboniferousOutcropN/ABraithwaite and Rizzi (1997)
United KingdomCloud Hill dolostone and Ticknall limestoneLower CarboniferousLower Carboniferous to Permo/TriassicBoth; most samples from quarry, some from a boreholeUnnamedBouch et al. (2004)
United KingdomBalladooleLower CarboniferousCarboniferous (?)OutcropN/AShelton et al. (2011)
SE AsiaIndonesiaTaballarOligocene-MioceneOligocene-MioceneOutcropN/AWilson et al. (2007)
Western North AmericaUSALeadvilleMississippianPennsylvanian to Jurassic (early dolomitization), Cretaceous to Oligocene (saddle dolomite)SubsurfaceLisbonChidsey et al. (2009)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceLadyfernBoreen and Davies (2004)
CanadaSlave PointMiddle DevonianLate Devonian-MississippianSubsurfaceClarke LakeLonnee and Machel (2006)
USABootstrapSilurian-DevonianSilurian/Devonian to TertiaryOutcropN/AMorgan (2007)
Western North America (Not fault related)USACisco GroupLate Pennsylvanian-Early PermianLate PermianSubsurfaceReineckeSaller and Dickson (2011)
CanadaWabamun GroupLate DevonianDevonian (early), Unspecified (late)SubsurfaceMultipleSaller and Yaremko (1994)
Table 2.

—Compilation of published datasets relating porosity, density, and velocity.

Well, outcrop, coreFormation(s)LocationLithologyRange of porosity (%)Original Fluid(s)Fluids in this compilationReference
WellPekisko Limestone, Wabamun and Leduc DolomitesDavey Field, Alberta, CanadaLimestone and dolostonePekisko, 1-8; Wabamun, <5; Leduc, 0 to ~10Brine or oilRecalculated to dry assuming brine saturationMiller (1992)
WellShuaibaAbu DhabiUnspecified6-26Oil and brineRecalculated to dry and brineFischer et al. (1997)
WellBaturajaSouth Sumatra Basin, IndonesiaLimestone2-30Gas and oilRecalculated to dry and brineNoor Ali (2002)
WellArab DSaudi ArabiaLimestone10-30OilRecalculated to dry and brineAlMuhaidib et al. (2012)
Well/coreUnspecifiedUnspecifiedLimestone and dolostone0 to ~25Unspecified, assumed to be brineAs original for brine, recalculated to dryMarion and Jizba (1997)
CoreUnspecifiedWest TexasLimestone2-20Dry and waterAs originalWyllie et al. (1956)
CoreMission Canyon FormationWilliston Basin, North DakotaLimestone, dolostone, anhydrite0-20Dry and waterAs originalRafavich et al. (1984)
CoreUnspecifiedUnspecifiedLimestone and dolostone0 to ~20GasRecalculated to dry and brineWang (1997)
CoreMiocene to Pleistocene carbonatesGrand Bahama BanksLimestone and dolostone~5-55WaterWater as original, recalculated to dryAnselmetti and Eberli (2001)
CoreShuaiba Formation, Miocene PlatformsMiddle East, Southeast Asia, Marion Plateau, AustraliaUnspecified10-40WaterAs originalWeger et al. (2009)
CoreSarvak FormationIranUnspecified3-30Dry and water saturatedAs originalMisaghi et al. (2010)
OutcropNittany DolomitePennsylvaniaDolostone2-18WaterWater as original, recalculated to dryWyllie et al. (1958)
OutcropBahamas, Maiella, Florida BayBahamas, Italy, FloridaLimestone and dolostoneMaiella and Bahamas, 0 to 60; Florida Bay, 30-60WaterWater as original, recalculated to dryAnselmetti and Eberli (1997)
OutcropMaiellaItalyUnspecified2-22WaterWater as original, recalculated to dryAnselmetti et al. (1997)
OutcropGreat OoliteEngland, UKLimestone3-17Dry and water saturatedAs originalAssefa et al. (2003)
OutcropCap BlancMallorca, SpainLimestone and dolostone5-55Dry and water saturatedAs originalVerwer et al. (2008)
Well, outcrop, coreFormation(s)LocationLithologyRange of porosity (%)Original Fluid(s)Fluids in this compilationReference
WellPekisko Limestone, Wabamun and Leduc DolomitesDavey Field, Alberta, CanadaLimestone and dolostonePekisko, 1-8; Wabamun, <5; Leduc, 0 to ~10Brine or oilRecalculated to dry assuming brine saturationMiller (1992)
WellShuaibaAbu DhabiUnspecified6-26Oil and brineRecalculated to dry and brineFischer et al. (1997)
WellBaturajaSouth Sumatra Basin, IndonesiaLimestone2-30Gas and oilRecalculated to dry and brineNoor Ali (2002)
WellArab DSaudi ArabiaLimestone10-30OilRecalculated to dry and brineAlMuhaidib et al. (2012)
Well/coreUnspecifiedUnspecifiedLimestone and dolostone0 to ~25Unspecified, assumed to be brineAs original for brine, recalculated to dryMarion and Jizba (1997)
CoreUnspecifiedWest TexasLimestone2-20Dry and waterAs originalWyllie et al. (1956)
CoreMission Canyon FormationWilliston Basin, North DakotaLimestone, dolostone, anhydrite0-20Dry and waterAs originalRafavich et al. (1984)
CoreUnspecifiedUnspecifiedLimestone and dolostone0 to ~20GasRecalculated to dry and brineWang (1997)
CoreMiocene to Pleistocene carbonatesGrand Bahama BanksLimestone and dolostone~5-55WaterWater as original, recalculated to dryAnselmetti and Eberli (2001)
CoreShuaiba Formation, Miocene PlatformsMiddle East, Southeast Asia, Marion Plateau, AustraliaUnspecified10-40WaterAs originalWeger et al. (2009)
CoreSarvak FormationIranUnspecified3-30Dry and water saturatedAs originalMisaghi et al. (2010)
OutcropNittany DolomitePennsylvaniaDolostone2-18WaterWater as original, recalculated to dryWyllie et al. (1958)
OutcropBahamas, Maiella, Florida BayBahamas, Italy, FloridaLimestone and dolostoneMaiella and Bahamas, 0 to 60; Florida Bay, 30-60WaterWater as original, recalculated to dryAnselmetti and Eberli (1997)
OutcropMaiellaItalyUnspecified2-22WaterWater as original, recalculated to dryAnselmetti et al. (1997)
OutcropGreat OoliteEngland, UKLimestone3-17Dry and water saturatedAs originalAssefa et al. (2003)
OutcropCap BlancMallorca, SpainLimestone and dolostone5-55Dry and water saturatedAs originalVerwer et al. (2008)
Table 3.

—Compilation of published studies that have successfully used seismic attributes to interpret porosity in non-stratabound dolostone reservoirs and related scenarios. ABS is absorption, AMP is amplitude, AMP SUM is sum of amplitude, AZI is azimuth, BPASS is band pass filter 15-20-25-30, COHERENCE is coherence, COS PHASE is cosine of instantaneous phase, CRV is curvature, DER is derivative, DER REF STR is derivative of reflection strength, DIP is dip, DIP AZI is dip-azimuth, DOM FREQ is dominant frequency, ENER HALF TIME is energy half time, ENERGY is energy, ENV is envelope or reflection strength, FREQ is frequency, FREQ ATN is energy absorption or frequency attenuation, INST FREQ is instantaneous frequency including thin bed indicator, INTG ABS AMP is integrated absolute amplitude, INTG TR is integrated trace, PERI is perigram, PHASE is phase, Q is Q factor (attenuation), RMS AMP is RMS amplitude including RMS amplitude of a spectral decomp volume (35 Hz), SEM is semblance, SPEC is a frequency volume from spectral decomposition (20 and 22 Hz) SWT is sweetness, TUNE FREQ is tuning frequency.

SettingAttribute(s)LocationFieldFormationAgeReference
Non-stratabound DolomiteENV, ENER HALF TIME, BPASS, COS PHASE, QOntario, CanadaRochesterTrentonOrdovicianOgiesoba (2010), Hart et al. (2009), Hart (2008)
Non-stratabound DolomiteCRV, RMS AMP, INTG TR, DER REF STR, ENV, PERI, COS PHASEOhio, USASaybrookTrentonOrdovicianSagan and Hart (2006), Hart et al. (2009), Hart (2008)
KarstCOHERENCE, CRVTexas, USAWaddellSan AndresPermianDou et al. (2011)
KarstINTG ABS AMP, ENV, DOM FREQ, COS PHASE, INST FREQIranSirri C/DMishrif, IlamCretaceousFarzadi and Hesthammer (2007)
KarstAMP, SEMAustraliaUnspecifiedUnnamedMioceneRosleff-Soerensen et al. (2012)
KarstCOHERENCE, AZI, RMS AMP, AMP SUM, INST FREQSouth China SeaLiuha 11-1ZhujiangMioceneSun et al. (2013)
Karst and Mounds/buildupsAMP, INST FREQNorwayUnspecifiedBjarmeland GroupPermianColpaert et al. (2007)
Mounds/buildupsAMP, FREQ, SWTIndiaUnspecifiedBasseinEoceneHarilal et al. (2008)
Mounds/buildupsDIPNorwayUnspecifiedBjarmeland and Gipsdalen GroupsCarboniferous to PermianHong and Shipilova (2013), Samuelsberg et al. (2003)
Mounds/buildupsAMP, COHERENCE, DIPGermanyGeothermal wellsMalmUpper JurassicLüschen et al. (2014)
Mounds/buildupsRMS AMP, DIPGermanyGeothermal wellsMalmUpper Jurassicvon Hartmann et al. (2012)
Other, StructuralENERGY, TUNE FREQ, PHASEDenmarkSyd ArneTor and EkofiskCretaceous and PaleoceneChristensen et al. (2006)
Other, StructuralSPEC, COHERENCE, CRVPrecaspian basinUnspecifiedUnspecifiedCarboniferousLi et al. (2010)
Other, StructuralAMP, ABS, COHERENCE, CRVSaudi ArabiaUnspecifiedUnspecifiedUnspecifiedNeves and Triebwasser (2006)
Other, StructuralCOHERENCE, CRVKansas, USADickmanSt. Genevieve, St. Louis, Salem, Spergen, WarsawMississippianNissen et al. (2009)
Other, StructuralCRV, COHERENCEAbu DhabiUnspecifiedUnspecifiedUnknownShibasaki et al. (2006)
Other, StructuralCOHERENCE, CRVOklahoma, USAUnspecifiedHuntonOrdovician-DevonianStaples et al. (2010)
Other, StructuralAMP, FREQ ATN, SEM, DIP AZIBritish Columbia, CanadaBubbles 3D surveySlave PointDevonianStrecker et al. (2004)
Other, NonstructuralINST FREQMaldivesUnspecifiedUnspecifiedEocene to PlioceneBetzler et al. (2011)
Other, NonstructuralRMS AMP, COHERENCE, SPEC, FREQ ATNChinaUnspecifiedUnspecifiedOrdovicianLiu et al. (2011)
Other, NonstructuralDER, DER REF STRENGTH, COS PHASEAlabama, USAAppletonSmackoverJurassicTebo and Hart (2005)
Other, NonstructuralINST FREQBritish Columbia, CanadaSierraKeg RiverDevonianVetrici and Stewart (1996)
SettingAttribute(s)LocationFieldFormationAgeReference
Non-stratabound DolomiteENV, ENER HALF TIME, BPASS, COS PHASE, QOntario, CanadaRochesterTrentonOrdovicianOgiesoba (2010), Hart et al. (2009), Hart (2008)
Non-stratabound DolomiteCRV, RMS AMP, INTG TR, DER REF STR, ENV, PERI, COS PHASEOhio, USASaybrookTrentonOrdovicianSagan and Hart (2006), Hart et al. (2009), Hart (2008)
KarstCOHERENCE, CRVTexas, USAWaddellSan AndresPermianDou et al. (2011)
KarstINTG ABS AMP, ENV, DOM FREQ, COS PHASE, INST FREQIranSirri C/DMishrif, IlamCretaceousFarzadi and Hesthammer (2007)
KarstAMP, SEMAustraliaUnspecifiedUnnamedMioceneRosleff-Soerensen et al. (2012)
KarstCOHERENCE, AZI, RMS AMP, AMP SUM, INST FREQSouth China SeaLiuha 11-1ZhujiangMioceneSun et al. (2013)
Karst and Mounds/buildupsAMP, INST FREQNorwayUnspecifiedBjarmeland GroupPermianColpaert et al. (2007)
Mounds/buildupsAMP, FREQ, SWTIndiaUnspecifiedBasseinEoceneHarilal et al. (2008)
Mounds/buildupsDIPNorwayUnspecifiedBjarmeland and Gipsdalen GroupsCarboniferous to PermianHong and Shipilova (2013), Samuelsberg et al. (2003)
Mounds/buildupsAMP, COHERENCE, DIPGermanyGeothermal wellsMalmUpper JurassicLüschen et al. (2014)
Mounds/buildupsRMS AMP, DIPGermanyGeothermal wellsMalmUpper Jurassicvon Hartmann et al. (2012)
Other, StructuralENERGY, TUNE FREQ, PHASEDenmarkSyd ArneTor and EkofiskCretaceous and PaleoceneChristensen et al. (2006)
Other, StructuralSPEC, COHERENCE, CRVPrecaspian basinUnspecifiedUnspecifiedCarboniferousLi et al. (2010)
Other, StructuralAMP, ABS, COHERENCE, CRVSaudi ArabiaUnspecifiedUnspecifiedUnspecifiedNeves and Triebwasser (2006)
Other, StructuralCOHERENCE, CRVKansas, USADickmanSt. Genevieve, St. Louis, Salem, Spergen, WarsawMississippianNissen et al. (2009)
Other, StructuralCRV, COHERENCEAbu DhabiUnspecifiedUnspecifiedUnknownShibasaki et al. (2006)
Other, StructuralCOHERENCE, CRVOklahoma, USAUnspecifiedHuntonOrdovician-DevonianStaples et al. (2010)
Other, StructuralAMP, FREQ ATN, SEM, DIP AZIBritish Columbia, CanadaBubbles 3D surveySlave PointDevonianStrecker et al. (2004)
Other, NonstructuralINST FREQMaldivesUnspecifiedUnspecifiedEocene to PlioceneBetzler et al. (2011)
Other, NonstructuralRMS AMP, COHERENCE, SPEC, FREQ ATNChinaUnspecifiedUnspecifiedOrdovicianLiu et al. (2011)
Other, NonstructuralDER, DER REF STRENGTH, COS PHASEAlabama, USAAppletonSmackoverJurassicTebo and Hart (2005)
Other, NonstructuralINST FREQBritish Columbia, CanadaSierraKeg RiverDevonianVetrici and Stewart (1996)
Table 4.

—Properties of host limestone, bulk dolostone geobody, and near-fault dolostone for the three porosity scenarios. AI is acoustic impedance, RC is reflection coefficient.

 Bulk dolostone geobodyNear-fault dolostoneHost limestone
Porosity scenario 1   
Porosity (%)1583
Vp, dry (m/s)428451555721
Vp, saturated (m/s)490058006000
Density, dry (g/cm3)2.292.472.60
Density, saturated (g/cm3)2.602.732.66
AI, dry, 107 (kg/m3) (m/s)0.981.271.49
AI, saturated, 107 (kg/m3) (m/s)1.271.581.60
RC, dolo geobody, host lmst, dry 0.21 
RC, dolo geobody, host lmst, saturated 0.11 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Porosity scenario 2   
Porosity (%)25122
Vp, dry (m/s)304046575871
Vp, saturated (m/s)400053006100
Density, dry (g/cm3)2.032.372.63
Density, saturated (g/cm3)2.412.652.67
AI, dry, 107 (kg/m3) (m/s)0.621.101.54
AI, saturated, 107 (kg/m3) (m/s)0.971.401.63
RC, dolo geobody, host lmst, dry 0.43 
RC, dolo geobody, host lmst, saturated 0.26 
RC, dolo geobody, near-fault dolo, dry 0.28 
RC, dolo geobody, near-fault dolo, saturated 0.18 
Porosity scenario 3   
Porosity (%)8153
Vp, dry (m/s)515542845721
Vp, saturated (m/s)580049006000
Density, dry (g/cm3)2.472.292.60
Density, saturated (g/cm3)2.732.602.66
AI, dry, 107 (kg/m3) (m/s)1.270.981.48
AI, saturated, 107 (kg/m3) (m/s)1.581.271.60
RC, dolo geobody, host lmst, dry 0.08 
RC, dolo geobody, host lmst, saturated 0.005 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
 Bulk dolostone geobodyNear-fault dolostoneHost limestone
Porosity scenario 1   
Porosity (%)1583
Vp, dry (m/s)428451555721
Vp, saturated (m/s)490058006000
Density, dry (g/cm3)2.292.472.60
Density, saturated (g/cm3)2.602.732.66
AI, dry, 107 (kg/m3) (m/s)0.981.271.49
AI, saturated, 107 (kg/m3) (m/s)1.271.581.60
RC, dolo geobody, host lmst, dry 0.21 
RC, dolo geobody, host lmst, saturated 0.11 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Porosity scenario 2   
Porosity (%)25122
Vp, dry (m/s)304046575871
Vp, saturated (m/s)400053006100
Density, dry (g/cm3)2.032.372.63
Density, saturated (g/cm3)2.412.652.67
AI, dry, 107 (kg/m3) (m/s)0.621.101.54
AI, saturated, 107 (kg/m3) (m/s)0.971.401.63
RC, dolo geobody, host lmst, dry 0.43 
RC, dolo geobody, host lmst, saturated 0.26 
RC, dolo geobody, near-fault dolo, dry 0.28 
RC, dolo geobody, near-fault dolo, saturated 0.18 
Porosity scenario 3   
Porosity (%)8153
Vp, dry (m/s)515542845721
Vp, saturated (m/s)580049006000
Density, dry (g/cm3)2.472.292.60
Density, saturated (g/cm3)2.732.602.66
AI, dry, 107 (kg/m3) (m/s)1.270.981.48
AI, saturated, 107 (kg/m3) (m/s)1.581.271.60
RC, dolo geobody, host lmst, dry 0.08 
RC, dolo geobody, host lmst, saturated 0.005 
RC, dolo geobody, near-fault dolo, dry 0.13 
RC, dolo geobody, near-fault dolo, saturated 0.11 
Table 5.

—Attributes used in this study, the number of times they were applied in the compiled studies discussed in the text, and their definition. Note that the count includes the named attribute as well as analogous or closely related attributes. For example semblance includes a number of different algorithms, all of which are grouped together in this table. Similarly, frequency attenuation attributes are grouped with instantaneous frequency. Groups of attributes that are similar are marked with like symbols.

Attribute used in this studyAnalogous attributes used in other studiesCount of how often attribute was used in compilation of seismic attributesDefinition of attributes
Original amplitude*Unspecified amplitude including amplitude of volumes generated by spectral decomposition9Amplitude measured on the input seismic volume
RMS amplitude*RMS amplitude, including RMS amplitude of volumes generated by spectral decomposition4Root mean square amplitude over a specified window
Reflection intensity†Integrated absolute amplitude, Integrated trace, sum of amplitude6Average amplitude over an interval multiplied by the sample interval
Envelope†Envelope, energy, reflection strength, perigram3Reflection strength measured as total energy of the seismic trace
Sweetness† 1Envelope divided by the square root of instantaneous frequency
Instantaneous frequency‡Instantaneous frequency, thin bed indicator, Q factor, frequency attenuation, energy absorption Energy half time9Time derivative of the phase, sensitive to attenuation
Cosine of instantaneous phase‡Cosine of instantaneous phase and phase5Cosine of instantaneous phase
Dominant frequency**Dominant frequency, unspecified frequency4Maximum of the amplitude spectrum over a small window around the time sample
Derivative § 1Rate of change of amplitude
Derivative of reflection intensity § 2Rate of change of reflection intensity
Ant tracking¶ 1A discontinuity highlighting algorithm developed by Schlumberger. Semblance used as input in this example.
Curvature¶ 7The rate of change in the dip of a seismic reflector
Dip¶Dip, azimuth, or dip-azimuth5Magnitude of the dip of a reflector
Semblance¶Coherence or semblance11A measure of the lateral similarity of seismic traces
Attribute used in this studyAnalogous attributes used in other studiesCount of how often attribute was used in compilation of seismic attributesDefinition of attributes
Original amplitude*Unspecified amplitude including amplitude of volumes generated by spectral decomposition9Amplitude measured on the input seismic volume
RMS amplitude*RMS amplitude, including RMS amplitude of volumes generated by spectral decomposition4Root mean square amplitude over a specified window
Reflection intensity†Integrated absolute amplitude, Integrated trace, sum of amplitude6Average amplitude over an interval multiplied by the sample interval
Envelope†Envelope, energy, reflection strength, perigram3Reflection strength measured as total energy of the seismic trace
Sweetness† 1Envelope divided by the square root of instantaneous frequency
Instantaneous frequency‡Instantaneous frequency, thin bed indicator, Q factor, frequency attenuation, energy absorption Energy half time9Time derivative of the phase, sensitive to attenuation
Cosine of instantaneous phase‡Cosine of instantaneous phase and phase5Cosine of instantaneous phase
Dominant frequency**Dominant frequency, unspecified frequency4Maximum of the amplitude spectrum over a small window around the time sample
Derivative § 1Rate of change of amplitude
Derivative of reflection intensity § 2Rate of change of reflection intensity
Ant tracking¶ 1A discontinuity highlighting algorithm developed by Schlumberger. Semblance used as input in this example.
Curvature¶ 7The rate of change in the dip of a seismic reflector
Dip¶Dip, azimuth, or dip-azimuth5Magnitude of the dip of a reflector
Semblance¶Coherence or semblance11A measure of the lateral similarity of seismic traces
Table 6.

—Summary of attributes in all geobody volume and porosity scenarios. The value shown is the fraction of the attribute response in dolostone (bulk geobody and near fault) that is distinct from host limestone. A value of 1.0 indicates that the dolostone response is completely distinct from the limestone response, and a value of 0.0 indicates complete overlap between the response of dolostone and limestone. The ranking is achieved by summing all of the fractional overlaps and sorting from greatest to least. The greatest sum is assigned a rank of 1. COS PHASE is cosine of instantaneous phase, AMP is original amplitude, SEM is semblance, DER is derivative of original amplitude, DER REF INT is derivative of reflection intensity, REF INT is reflection intensity, RMS AMP is RMS amplitude, SWEET is sweetness, ENV is envelope, DOM FREQ is dominant frequency, INST FREQ is instantaneous frequency, ANT is ant tracking, CRV is curvature, and DIP is dip.

 High vol base poroHigh vol high poroHigh vol high fault poroMid vol base poroMid vol high poroMid vol high fault poroLow vol base poroLow vol high poroLow vol high fault poroSumRank
COS PHASE1.00.91.00.91.01.00.91.01.08.71
AMP1.00.81.00.80.81.00.70.81.07.82
SEM0.70.80.71.01.00.81.01.00.77.73
DER0.80.90.80.80.90.70.80.90.77.34
DER REF INT0.80.71.00.70.80.80.70.80.87.05
REF INT0.70.70.90.60.80.80.60.80.96.86
RMS AMP0.70.70.90.60.80.90.60.80.86.77
SWEET0.60.70.80.60.80.70.60.80.76.28
ENV0.60.60.80.60.80.70.60.80.76.19
DOM FREQ0.60.60.80.60.60.70.50.70.75.810
INST FREQ0.50.50.70.50.60.60.50.60.65.111
ANT0.50.00.00.40.20.20.70.30.63.012
CRV0.20.20.10.30.20.00.10.20.01.313
DIP0.20.00.00.00.00.00.00.00.00.214
 High vol base poroHigh vol high poroHigh vol high fault poroMid vol base poroMid vol high poroMid vol high fault poroLow vol base poroLow vol high poroLow vol high fault poroSumRank
COS PHASE1.00.91.00.91.01.00.91.01.08.71
AMP1.00.81.00.80.81.00.70.81.07.82
SEM0.70.80.71.01.00.81.01.00.77.73
DER0.80.90.80.80.90.70.80.90.77.34
DER REF INT0.80.71.00.70.80.80.70.80.87.05
REF INT0.70.70.90.60.80.80.60.80.96.86
RMS AMP0.70.70.90.60.80.90.60.80.86.77
SWEET0.60.70.80.60.80.70.60.80.76.28
ENV0.60.60.80.60.80.70.60.80.76.19
DOM FREQ0.60.60.80.60.60.70.50.70.75.810
INST FREQ0.50.50.70.50.60.60.50.60.65.111
ANT0.50.00.00.40.20.20.70.30.63.012
CRV0.20.20.10.30.20.00.10.20.01.313
DIP0.20.00.00.00.00.00.00.00.00.214

Contents

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