Reservoir properties and petrophysical modelling of carbonate sand ...

Geological Society, London, Special Publications published online June 27, 2012 as doi: 10.1144/SP370.6 

Reservoir properties and petrophysical modelling 

of carbonate sand bodies: outcrop analogue study 

in an epicontinental basin (Triassic, Germany) 

DENIS PALERMO 1,2 *, THOMAS AIGNER 1 , BJOERN SEYFANG 1,3 & SERGIO NARDON 2 

1 Department of Geosciences, Centre of Applied Geosciences, 

University of Tübingen, Sigwartstr. 10, D-72076 Tübingen, Germany 

2 Eni S.p.A. – Exploration & Production Division, via Emilia 1, 

20097 San Donato Milanese, Italy 

3 Present address: Centre Scientifique et Technique Jean Feger (CSTJF), 

Avenue Larribau, F-64018 Pau Cedex, France 

*Corresponding author (e-mail: denis.palermo@eni.it) 

Abstract: This paper represents the second part of an integrated study that is focussed on the 

development and distribution of reservoir bodies and properties in epeiric carbonate systems. It 

is based on outcrop analogue data from Triassic ‘layer-cake’ carbonates in the South German 

Basin, which were deposited along an epicontinental, very gently inclined carbonate ramp. 

The reservoir facies consists of skeletal and oolitic carbonate grainstones (F max 23%, K max 

700 mD), which are organized in a pronounced hierarchy of stratigraphic cycles. Based on outcrops, 

cores, gamma ray (GR) logs and thin sections, a high-resolution, geocellular 3D facies 

model was generated, which covers the area of a Middle East giant gas field (25 × 36 km). The 

spatial distribution of reservoir properties was systematically investigated on different scales. 

The lateral distribution of reservoir properties remains in the same order of magnitude for hundreds 

of metres, within in the same stratigraphic position. However, on a kilometre scale, facies bodies, 

diagenetic trends and thus reservoir properties show gradual lateral changes. Vertically, in contrast, 

properties change commonly on a decimetre scale and are largely controlled by stratigraphic 

cycles. Petrophysical modelling enhanced the understanding of key factors and processes controlling 

both reservoir quality and quantity. 

Reservoir heterogeneity remains a significant issue in 

reservoir modelling and prediction of field performance. 

Outcrop analogues are ideal for establishing 

the possible geometries and property distributions 

at the inter-well scale. Quantitative data on carbonate 

rock bodies remain scarce (e.g. Handford 1988; 

Burchette et al. 1990; Grant et al. 1994; Harris & 

Kowalik 1994; Borgomano et al. 2002; Grammer 

et al. 2004; Kostic & Aigner 2004; Ruf & Aigner 

2004; Rankey et al. 2006; Qi et al. 2007; Aigner 

et al. 2007; Palermo et al. 2008). Qualitative outcrop 

studies of geometry (e.g. Burchette et al. 1990; 

Gawthrope & Gutteridge 1990; Azerêdo 1998) and 

quantitative petrophysical studies carried out on 

outcrops at the appropriate scale (e.g. Kittridge 

et al. 1990; Senger et al. 1991; Eisenberg et al. 

1994; Cavallo & Smosna 1997; Jennings 2000; 

Savary & Ferry 2004; Pranter et al. 2005, 2006) 

made useful contributions to the available data. 

Outcrop analogue studies of this nature are 

important in order to condition subsurface reservoir 

models to real data and concepts, thus significantly 

improving field appraisal work and development 

planning. The present work has resulted from a 

joint ENI E&P–University Research Consortium 

on the ‘Geometry of Carbonate Objects’. Within 

this project, the Triassic Upper Muschelkalk carbonates 

in the South-German Basin were studied 

as an analogue to the ‘layer-cake’-type reservoir 

systems of the Middle East. The Upper Muschelkalk 

was deposited on a gently inclined carbonate ramp, 

filling an epicontinental basin, and therefore represents 

an analogue to an important type of ‘nonreefal’ 

skeletal and oolitic carbonate sand reservoirs 

(e.g. Khuff, Hanifa, and Arab in the Middle East). 

A series of excellent quarries and natural outcrops 

in Southern Germany has been used to investigate 

the geometries within high-energy shoal 

water deposits along the margin of the Upper 

Muschelkalk Basin. Close outcrop spacing allows 

for lateral tracing of beds and mapping of lateral 

facies transitions. A hundred years of detailed 

From: Garland, J., Neilson, J. E., Laubach,S.E.&Whidden, K. J. (eds) 2012. Advances in Carbonate Exploration 

and Reservoir Analysis. Geological Society, London, Special Publications, 370, 

http://dx.doi.org/10.1144/SP370.6 # The Geological Society of London 2012. Publishing disclaimer: 

www.geolsoc.org.uk/pub_ethics


D. PALERMO ET AL. 

logging and mapping has resulted in a wellconstrained 

litho- and biostratigraphic framework 

(e.g. Wagner 1913; Vollrath 1938, 1955, 1957, 

1958, 1970; Skupin 1969; Bachmann 1973; Aigner 

1985; Ulrichs & Mundlos 1987, 1990; Hagdorn & 

Simon 1988; Ockert 1988; Geyer & Gwinner 1991; 

Braun 2003). This well-established stratigraphy provides 

the opportunity to transform the so far largely 

descriptive picture into a genetic, process-based 

analysis of the sedimentary bodies and packages. 

A detailed outcrop, core and well logging programme 

was carried out (e.g. GR logs, cores, porosity 

and permeability), allowing an analysis of the 

sedimentology, petrophysical characteristics, internal 

architecture and reservoir properties, within a 

3D geocellular model. Palermo et al. (2010) have 

recently reported on the facies modelling aspects 

of this project. This companion paper focusses on 

reservoir properties and petrophysical modelling. 

Geological setting 

During the Triassic, rifting and disintegration of the 

Pangean supercontinent caused the western extension 

of the Tethys Ocean. This regional crustal extension 

induced the subsidence of a complex network of 

grabens and troughs in Western and Central Europe 

(e.g. Ziegler 1990) and formed the Triassic basins 

including the South German Muschelkalk Basin 

(Fig. 1). This basin extended roughly from Poland 

to the North Sea and from the Alpine foothills to 

Denmark. During the Middle Triassic (≏240– 

231 Ma), the Muschelkalk Basin was covered by an 

epicontinental, semi-enclosed marginal sea that was 

separated by the Vindelician/Bohemian High from 

the open Tethys ocean in the SE. Temporary connections 

existed only through three narrow shifting seaways 

(Ziegler 1990; Dercourt et al. 1993). In the 

early Anisian a relative sea-level rise induced the 

Lower Muschelkalk transgression from the open 

Tethys ocean via the Silesian–Moravian and East- 

Carpathian Gates into the Germanic Basin with 

fully marine conditions. The open communication 

became restricted during the later Anisian, resulting 

in hypersaline conditions and formation of evaporites 

of the Middle Muschelkalk. Finally the fully marine 

carbonates of the Upper Muschelkalk were deposited 

during a transgression in the uppermost Anisian, connecting 

the Germanic Basin with the open Tethys 

realm via the three gates. The Upper Muschelkalk 

succession (Middle/Late Ladinian) represents one 

overall transgressive/regressive third-order sea-level 

cycle (Aigner 1985). The transgressive part is represented 

by an overall fining-upward sequence of 

crinoidal/shelly shoal water carbonates grading into 

Fig. 1. Palaeogeography of Central Europe in the Middle Triassic modified after Ziegler (1990), Hagdorn (1991) and 

Palermo et al. (2010) (AAPG # 2010, reprinted by permission of the AAPG, whose permission is required for further use).


CARBONATE SAND BODIES IN GERMANY 

muddy carbonates and marlstones. The upper regressive 

section is represented by an overall coarseningupward 

sequence, grading from the muddy sediments 

around maximum transgression into shelly/oolitic 

shoal carbonates and muddy backshoal sediments. 

The boundary between the Muschelkalk and the 

overlying, mainly siliciclastic Keuper is considered 

as maximum regression (Aigner et al. 1999). Generally, 

five major facies belts can be distinguished 

in the Upper Muschelkalk: (a) a narrow zone of 

coastal siliciclastics; (b) an irregular backshoal zone 

dominated by (partly dolomitized) peloidal mudand 

wackestones; (c) a high energy belt of skeletal/ 

oolitic pack- to grainstones; (d) shallow-ramp sediments 

dominated by skeletal storm sheets; and (e) a 

deeper ramp characterized by mud- and marlstones. 

The facies belts are modified by subtle structural 

trends that define subtle palaeo-highs and palaeolows, 

which are well known in the regional geology 

and can also be recognized by thickness variations 

(isopach maps) in various stratigraphic levels (e.g. 

Geyer & Gwinner 1991). These are probably reactivated 

basement blocks, inducing slight variations in 

differential subsidence. 

Study area, methods and database 

The SW German Hohenlohe area provides numerous 

quarries and natural outcrops in the Upper 

Muschelkalk (Fig. 2). The shoal water carbonates 

represent a major target for the raw-material industry 

in this region. Therefore, the closest spacing of 

quarries can be observed in areas where the clean 

and porous skeletal/oolitic grainstone bodies show 

their thickest development. Furthermore, natural 

exposures along river gorges allow a lateral tracing 

of individual bodies and property transitions. 

The database consists of 50 measured outcrop 

sections, supplemented by six cores and wireline 

logs (1787 m). Thirty-one of these sections (711 m) 

were integrated from previous work (Aigner 1985; 

Kostic 2001; Ruf 2001; Braun 2003; Allgöwer 

2006; Dmitrieva 2006; Looser 2006; Seyfang 

2006). For the investigation of diagenetic and petrophysical 

properties 568 cylinder-shaped plugs were 

extracted from rock samples and cores. The plugging 

focused on potential reservoir rocks, areas 

with visual porosity or dolomite content. The 

plugs have a diameter of four centimetres and 

Fig. 2. Composite map of the study area indicating the data points and the thickness of the Upper Muschelkalk; 

modified after Palermo et al. (2010).



were cut to a depth between 4.5 cm and 3 cm. 

Additionally, four rock columns (1 m × 0.2 m × 

0.2 m) were derived from a block (2.1 m × 

1.1 m × 1.0 m) in order to investigate the distribution 

of permeability within an individual reservoir 

body on different scales. 

Porosity and density measurements were carried 

out with two devices in two separate steps: 

(a) 

(b) 

The determination of net volume (netVol) and 

net density (without pores) was carried out with 

a calibrated helium pycnometer. 

Gross volume (grossVol) of the sample was 

measured with a powder pycnometer. This 

device calculates the rock density and the percentage 

porosity (F) with the formula: 

F = (grossVol − netVol) × 100/grossVol. 

Permeability was measured with a gas minipermeameter. 

Eight measurements were carried 

out on every sample, to provide the mean horizontal 

and the mean vertical permeability. The device is 

controlled by special automatic software, which 

adjusts the contact pressure, regulates the flow rate 

and calculates the permeability from the gas-flow 

rate. The measuring error of the mini-permeameter 

is around 0.5 mD. 

For the reconstruction of the diagenetic history 

and the deeper understanding of reservoir properties, 

451 stained thin sections were investigated. 

For a systematic semi-quantitative analysis of 

several parameters (e.g. amount of components, 

mud content, cement generations, pore types) the 

comparison charts of Flügel (2004) were applied. 

The petrography and cement-types were investigated 

with transmission light and cathodoluminescence 

microscopy. The pore types were categorized 

and analysed by using mainly the concepts of 

Choquette and Pray (1970) and Lucia (1983). In 

order to quantify the results with the porosity and 

permeability measurements, the thin sections were 

commonly derived from the trim ends of the plugs. 

The 3D petrophysical modelling was carried out 

using industry-standard software within a high resolution 

3D facies model described in Palermo 

et al. (2010). 

Facies and diagenesis 

Facies types 

Based on lithology, Dunham texture, particle size, 

sorting, sedimentary structures and environmentindicative 

allochems such as for example ooids, 

oncoids and black pebbles 16 different lithofacies 

types were distinguished in the Upper Muschelkalk 

of the study area. These have been described in 

Palermo et al. (2010) and thus do not need to be 

further discussed here. The facies types are interpreted 

to be deposited within a shallow epeiric carbonate 

ramp setting, characterized by a very gentle 

depositional gradient (between 0.0028 to 0.38). 

The carbonate ramp can be subdivided into three 

major subenvironments (cf. Burchette et al. 1990): 

(1) low energy inner ramp or backshoal; (2) highenergy 

mid-ramp with carbonate sands in shoreline 

detached shoals; and (3) low energy foreshoal 

and outer ramp with mud-dominated successions 

(Fig. 3). Figure 4 shows a generalized depositional 

model with the main reservoir facies types and 

their petrographic composition. 

Diagenetic history 

The diagenetic history of the Upper Muschelkalk 

grainstones has been subject to several previous 

studies (e.g. Bachmann 1973; Kostic 2001; Braun 

2003; Seyfang 2006). Bachmann (1973) differentiated 

two major diagenetic stages with several 

cementation and leaching phases: 

(1) Within the early diagenetic stage under 

shallow burial, isopachous circum-granular 

crust cement (A1 cement; Fig. 5.1) consolidated 

the original sedimentary grains. This 

process was followed by leaching of the 

aragonitic components and the precipitation 

of secondary isopachous crust cement (A2 

cement; Fig. 5.2) of similar appearance, 

grown exclusively on the fringes inside the 

dissolved pores. 

(2) The late diagenetic stage was refined by observations 

of Braun (2003), who recognized 

an additional phase of late diagenetic dog 

tooth cement (B1 cement) grown in irregular 

rims of isopachous prismatic spar, which 

was followed by the precipitation of equant 

drusy calcite-spar (B2 cement; Fig. 5.3). 

Finally, a minor amount of scattered dolomite 

cement precipitated in remnant separate vug 

pore space. 

The present investigation of 442 thin sections 

from different locations and stratigraphic intervals, 

using standard petrographic, cathodoluminescence 

and fluorescence microscopy, resulted in slight revisions 

of some aspects of the so far established late 

diagenetic history (Table 1). 

Discussion: controlling factors of diagenesis 

Facies. Facies types and their primary composition 

seem to have a major impact on diagenesis 

and reservoir development since both leaching 

and dolomitization are selective with respect to 

the mineralogy of components. For instance, the



Fig. 3. Facies types with key attributes, depositional environment and colour code. 

semi-quantitative analysis of 442 thin sections 

(Figs 5 & 6) shows a negative correlation between 

mud content and the intensity of early diagenetic 

A-type cements (correlation coefficient, 20.7; standard 

deviation, 0.19). However, apart from the 

depositional energy and mud content, the growth 

intensity of A1 cements is also controlled by the 

type of associated components. For example, A1 

cements are often pore filling in ooid dominated 

grainstones, while they from only thin seams 

around crinoid ossicles. In general, however, the 

relationship between primary facies and diagenesis 

is complex (see below). 

Stratigraphy. The systematic semi-quantitative 

investigation of thin sections in their stratigraphic 

context showed that diagenetic alteration 

is also controlled by stratigraphic cycles. This is



Fig. 4. Facies distribution and petrographic composition, based on the semi-quantitative analysis of 442 thin sections. 

particularly important for the prediction of systematic 

changes of reservoir properties within individual 

facies bodies. An example for these variations 

within the reservoir facies of one regressive 

medium-scale hemicycle is depicted in Figure 6. 

In particular, the mud content and primary A 

cements change systematically within the cycle. 

Generally, the sum of mud content and A cements 

constitutes a more or less constant petrographic 

volume fraction together (arithmetic mean of mud 

content and A cement, 40.9 Vol%; standard deviation, 

17.3; n ¼ 442 thin sections). As displayed 

in the column ‘Cumulative Vol %’ of Figure 6, 

upwards decreasing mud content is compensated 

by a systematic increase in B-type cements. Both 

A-type and B-type cement combined show a clear 

upward increasing fracture towards the mediumscale 

regressive maximum. 

Maximum values of interparticle and mouldic 

porosity are commonly located around the regressive 

maximums of the medium-scale cycles in shoal 

facies associations, following an upward decreasing 

fraction of matrix mud (Fig. 7b). However, intense 

late diagenetic B cements can partially plug the 

pore space in the upper portion the regressive hemi 

cycles, shifting the maximum porosity and permeability 

values towards the middle part (Fig. 6). 

The upward decreasing mud content reflects the 

vertically increasing depositional energy of a prograding 

shoal complex and seems to be a major 

controlling factor in the reservoir properties. The 

carbonate mud in the lower portion of the regressive



Fig. 5. Thin section photographs. (1) Thin section Crailsheim, parallel Nichols, stained: blue indicates porosity. 

Pel-oolitic grainstone: the peloids are surrounded by A1 cement (A1) forming typical circum-granular crusts of fibrous 

to bladed calcite crystals. Note the preserved primary interparticle porosity between the grains (Ip). The black opaque 

inclusions within the peloids are probably pyrite crystals (Py). (2) Thin section Sattelorf-Bölgental, parallel Nichols, 

stained: blue indicates porosity. Crinoidal grainstone: shape-preserving micritic envelope of a dissolved aragonitic 

bioclast (M). A1 cement (A1) forms a more regular seam of fibrous to bladed crystals compared with the more irregular 

A2 cement (A2) grown inside the biomouldic pore (Bm). (3) Thin-section Steinbächle, parallel Nichols. Bioclastic 

grainstone: a mosaic of drusy, equant B2 cement (B2) overgrows the earlier generation of B1-‘dog tooth’ cement (B1). 

(4) Thin-section Crailsheim, parallel Nichols, stained: blue indicates porosity. Oolitic grainstone: the dolomitization of 

ooids and coated grains occurs preferentially by replacing the cortical layers with a sub- to anhedral crystal mosaic (D). 

Note the undisturbed primary A1 cement (A1) surrounding the grains. Note the preserved primary porosity (Ip). (5a, b) 

Thin-section Steinbächle, parallel Nichols (1); crossed Nichols (2), stained: blue indicates porosity. Oolitic grainstone: 

patchy saddle dolomite cement with typical curved crystal faces (S) and sweeping extinction (E). (6) Thin section 

Satteldorf Bölgental, parallel Nichols, stained: blue indicates porosity. Crinoidal grainstone: limonitic crusts of leached 

saddle dolomites (LC), and partly leached dolomite crystals (LD) with rusty limonitic fractures. Remaining A1 cement 

has grown in the mould of an early diagenetic leached possibly aragonitic coated grain-core (A1). The cortical layers 

were dolomitized and subsequently leached during the second leaching phase (L2). 

hemicycles reduced the primary porosity and the 

circulation of diagenetic fluids, which enhanced 

the porosity in later diagenetic stages. However, in 

cases with intense B cementation at the top of the 

regressive hemicycles, the best reservoir properties 

tend to be located below the regressive maxima 

(Fig. 6). 

Reservoir properties 

In order to quantify the reservoir properties, 442 thin 

sections and 570 porosity and permeability plugs 

from outcrop samples and cores were analysed. 

Porous intervals are generally restricted to discrete 

reservoir bodies, composed of high-energy shoal 

and shoal fringe facies types. However, the internal 

petrophysical characterization of these reservoir 

bodies turned out to be complex. During diagenesis, 

several cementation and leaching phases led to a 

stepwise alteration of the pore-space, which resulted 

in a complex relationship between reservoir properties 

and reservoir-facies types. Nevertheless, 

owing to the selective character of the diagenetic 

processes, the initial sediment composition and



Table 1. Overview of the diagenetic history and the impact on pore-space 

Diagenetic stage Diagenetic phase Impact on pore space 

Early marine, shallow 

Circum granular crust Protection and reduction of primary porosity 

burial diagenesis 

cement (A1 cement) 

First leaching phase Creation of biomouldic porosity owing to 

selective leaching of aragonite 

Circum granular crust 

cement (A2 cement) 

Slight reduction of complete plugging 

of biomouldic porosity 

Late marine, deeper 

burial cementation 

Dog tooth cement 

(B1 cement) 


of biomouldic and primary porosity 

Patchy calcite spar 

cement (B2 cement) 


of biomouldic and primary porosity 

Late diagenetic, deep 

burial dolomitization 

Patchy dolomitization 

and recrystallization 


of biomouldic and primary porosity, 

dolomitization of ooids, coated grains 

and crinoidal columnar plates 

Uplift and meteoric alteration Second leaching phase Creation of Oo- and crino-mouldic porosity 

mineralogy remains one of the most important controlling 

factors. The investigated Upper Muschelkalk 

reservoir bodies have average reservoir 

properties of 11.5% porosity (standard deviation, 

4.9) and 30.8 mD permeability (standard deviation 

72.4) with a correlation coefficient of 0.41. 

Pore-types 

Lucia (1983) developed commonly used concepts 

for reservoir characterization of carbonate rocks by 

subdividing the pore-space into the three major poretype 

classes: (a) touching vug porosity; (b) separate 

vug porosity; and (c) interparticle porosity. The 

Upper Muschelkalk is dominated by separate vug 

porosity (SV, 60%). Touching vug (TV) and interparticle 

porosity (IP, 20%) constitute the minor 

part, but are important factors controlling permeability. 

However, the touching vugs observed in 

the Upper Muschelkalk are smaller (up to several 

millimetres; Fig. 8) than those described by Lucia 

(1983). 

Using the fabric-selective concept for the classification 

of carbonate pores after Choquette and Pray 

Fig. 6. Diagenesis and the resulting petrography are controlled by stratigraphic cycles. Generally, mud content and 

A cements constitute a more or less constant petrographic volume fraction, as displayed in the column ‘Cumulative 

Vol %’; upwards decreasing mud content is compensated by a systematic increase of A cement.



Fig. 7. (a) Cross section through two reservoir bodies (yellow/orange) along the depositional gradient, highlighting 

facies distribution, sedimentology and reservoir properties. Within the individual facies bodies, the lateral porosity and 

permeability distribution is mostly marked by gradational transitions. (b) Cross section highlighting the qualitative 

distribution of cement types and mud content. The vertical sections depict the cumulative petrographic composition 

of the shoal bodies, combined with petrophysical properties. Both properties show laterally gradational transitions and 

are strongly influenced vertically by stratigraphic cycles. 

(1970), biomouldic porosity comprises the largest 

pore-fraction. Most of the investigated samples 

belong to one of the following combined pore-types: 

(1) biomouldic (BM, 27%); 

(2) biomouldic and interparticle (BM + IP, 42%); 

(3) biomouldic, interparticle and Oomouldic 

(BM + IP + OM, 23%); 

(4) biomouldic, and oomouldic (BM + IP + OM, 

8%). 

Porosity and permeability relationships 

Pore types after Choquette and Pray (1970) v. 

porosity and permeability (Fig. 9a). Generally, the 

samples with pure mouldic porosity show less



Fig. 8. Thin section photographs, stained: blue indicates porosity, parallel Nichols: typical pore-types in the Upper 

Muschelkalk reservoir bodies. Pore-types after Lucia (1983): touching vugs, TV; separate vugs, SV; interparticle 

porosity, IP. Pore-types after Choquette & Pray (1970): biomouldic porosity, BM; oomouldic porosity, OM. (a) Oolitic 

grainstone with preserved primary interparticle porosity. (b) Bioclastic grainstone showing two pore generations of 

biomouldic porosity. Upper left corner: leached dolomite recrystallization. Middle right corner: early diagenetic 

biomouldic pore with several cement generations. (c) Bioclastic pack- to grainstone with touching vugs owing to 

leached dolo-cement. The dissolution enlarged touching vugs of the Upper Muschelkalk reservoir bodies are much 

smaller (several millimetres) than those described by Lucia (1983). Furthermore, remnants of collapsed biomouldic 

porosity are visible in the upper middle part. (d) Oolitic grainstone. Typical oomouldic separate vug porosity, enhanced 

by a large touching vug of leached dolo-cement and small amounts of interparticle porosity. (e) Bioclastic grainstone: 

oomouldic porosity of leached dolomite recrystallizations combined with early diagenetic biomouldic separate vug 

porosity and large interparticle pores. (f) Bioclastic grainstone: early diagenetic biomouldic separate vug porosity. 

permeability than samples where both, mouldic- and 

interparticle porosity are combined. Regarding the 

pore type distribution within the Lucia classes, 

samples with biomouldic, oomouldic and interparticle 

porosity tend to class 2, while samples with biomouldic 

and interparticle porosity plot slightly more 

towards Lucia’s class 1. 

Reservoir rock types and classes using Lucia (1983) 

classification (Fig. 9b). Several studies on the 

Upper Muschelkalk carbonates have shown that 

porosity–permeability relationships show strong 

scattering (e.g. Braun 2003; Kostic & Aigner 

2004; Ruf & Aigner 2004; Dmitrieva 2006; 

Seyfang 2006). However, Lucia (1999) was able 

to categorize porosity–permeability relationships 

in discrete petrophysical classes by the definition 

of rock-fabric types, which are a combination of 

both Dunham texture and pore-type. His rockfabric 

petrophysical classes were mainly developed 

for non-vuggy carbonates with interparticle porosity. 

As the fraction of vuggy porosity within the 

investigated reservoir bodies amounted to 80%, 

the Lucia classes could only be applied to a 

certain extent. Most of the porosity and permeability 

values plot in class 2 of Lucia, followed by 

class 1. The values above 3 mD permeability 

show a relatively linear trend within a certain 

range of scatter. The values plot relatively close 

together between Lucia classes 1 and 2. As



Fig. 9. Porosity and permeability relationships keyed to (a) pore type according to (a) Choquette & Pray (1970), 

(b) Lucia (1983), (c) approximated pore-throat size measured in thin sections and (d) pore size. 

expected, samples with only separate vugs have 

generally lower permeability. When touching-vug 

porosity is present, permeability is higher. The 

highest porosity and permeability values occur 

when all pore types are present (Table 2). 

However, biomouldic pores compose the largest 

part of porosity and occur, independent of Dunham 

texture, in all reservoir rock types. This fact implies 

that the distribution of biomouldic porosity could 

be an additional key controlling factor for the 

reservoir properties. 

Pore-throat size v. poro-perm (Fig. 9c). The porethroat 

diameters presented in this study have been 

measured in 2D thin sections and represent therefore 

just a limited approximation to the real 3D 

pore-throat distribution. Nevertheless, Figure 9c 

depicts a clear increase of both permeability and 

porosity with larger pore-throats (Table 4). The 

remaining data scatter could be due to the fact that 

pore-throat diameters were measured in 2D. 

Pore size v. porosity and permeability (Fig. 9d). 

Despite considerable scattering, permeability generally 

increases with an increasing pore-size (Table 2). 

Relationship facies association: reservoir 

properties 

As can be observed in many carbonate systems, 

facies and reservoir properties of the Upper 

Muschelkalk do not show a direct relationship. 

This is mainly due to the susceptibility of carbonates 

to diagenetic alteration, but also to the higher 

complexity of primary sedimentary parameters in 

addition to measuring inaccuracies. Nevertheless, 

the primary lithofacies plays a fundamental role 

for the distribution of reservoir properties in the 

Upper Muschelkalk, since porous shoal bodies are 

exclusively formed of high energy shoal and shoalfringe 

facies types. Furthermore, the diagenetic 

leaching and dolomite cementation phases proceeded 

very selectively according to the mineralogy 

of the components, a parameter that is in turn mainly 

facies controlled. 

Therefore, despite considerable data scattering, a 

certain relationship between facies association and 

reservoir properties can be observed. 

Shoal-fringe facies association (pel-ooidal packto 

grainstone, oncoidal pack- to grainstone, intraclastic 

packstone). The facies types forming the 

shoal-fringe facies association show a strong scattering, 

in particular within the range below 3 mD 

permeability and 3% porosity (Fig. 10a, Table 3). A 

possible explanation for the strong data scatter 

could be a scale-dependent measuring inaccuracy, 

which occurs specifically with the mini-permeameter. 

Vugs and large bioclastic moulds may act as 

direct flow-connection through the plug-sample. 

Intraclastic packstones have low porosities, predominantly 

in the form of large vugs. This facies



Table 2. Correlation coefficients, standard deviation and arithmetic mean between porosity/permeability and 

selected parameters (pore size, pore throat size and pore types), based on the semi-quantitative analysis of 

thin sections 

Correlation coefficient 

Poro (%) Micro-pores (%) 

Poro/micro Mean 12.0 2.2 20.07 

n ¼ 222 Standard deviation 4.6 1.8 

Perm H Meso-pores (%) 

Perm H/meso Mean 32.5 6.5 0.17 


Perm H Macro-pores (%) 

Perm H/macro Mean 32.5 3.2 0.55 


Poro (%) TV (%) 

Poro/TV Mean 12.0 2.6 0.63 


Poro (%) SV (%) 

Poro/SV Mean 12.0 7.1 0.47 


Poro (%) IP (%) 

Poro/IP Mean 12.0 2.2 0.50 


Perm H (mD) TV (%) 

Perm H/TV Mean 32.5 2.6 0.58 


Perm H (mD) SV (%) 

Perm H/SV Mean 32.5 7.1 0.00 


Perm H (mD) IP (%) 

Perm H/IP Mean 32.5 2.2 0.30 


Poro (%) Pore-throats (mm) 

Poro/pore-throats Mean 12.0 177.8 0.57 


Perm H (mD) Pore-throats (mm) 

Perm H/pore-throats Mean 32.5 177.8 0.52 


type shows the most extreme scatter combined 

with the poorest reservoir properties. In contrast, 

peloidal- and oncoidal pack- to grainstones have 

additionally small amounts of regularly distributed 

primary porosity and commonly smaller vugs. 

The limited reservoir properties (F max ¼ 13%; 

K max ¼ 12 mD) of the shoal-fringe facies types 

could be explained by the combination of a high 

mud content, occluding the primary interparticle 

pore space and the dominance of mostly calcitic, 

relatively leaching-resistant components (e.g. oncoids, 

peloids, intraclasts), and preventing the creation 

of vuggy porosity (Fig. 10a). 

Mid-shoal facies association (crinoidal pack- to 

grainstone, poorly sorted pack- to grainstone, amalgamated 

packstone). These facies types constitute 

the largest part of the reservoir bodies and show relatively 

similar porosities and permeability trends 

Within this group, amalgamated packstones have 

the best correlation between porosity and permeability 

(Fig. 10b, Table 4). Amalgamated packstones 

are dominated by biomouldic porosity; the high 

mud content plugs the potential interparticle porespace. 

Therefore, permeability is probably due to the 

occurrence of solution-enlarged touching vugs. Consequently, 

diagenetic leaching is the most important 

controlling factors on pore space within this facies 

type. In contrast, crinoidal pack- to grainstones have 

additionally substantial amounts of interparticle porosity. 

They show the overall best reservoir properties 

(F max ¼ 25%; k max ¼ 710 mD), but also a strong 

scattering of data points. The scattering within the 

porosity and permeability relationship is thus mainly 

controlled by the interplay of different pore-systems 

(interparticle and vuggy). Nevertheless, also scaledependent 

measuring inaccuracy (see above) can be 

observed within the poorly sorted pack- to grainstone 

facies, where vugs of large shells are common, dispersing 

the values towards higher permeability.



Fig. 10. (a) Porosity–permeability cross-plot depicting the reservoir properties of shoal fringe facies types. Strong 

scattering towards low permeability is mainly interpreted as scale-dependent measuring inaccuracy, since vugs of large 

shells may act as direct flow connection through the plug sample. (b) Porosity–permeability cross-plot depicting the 

reservoir properties of mid-shoal facies types. Strong scattering towards low permeability within the poorly sorted packto 

grainstone facies is interpreted as scale-dependent measuring inaccuracy. Amalgamated packstones are dominated by 

vuggy porosity and show an excellent fit into the main cluster, whereas crinoidal pack- to grainstones tend to a stronger 

scattering, but overall better reservoir properties. The stronger scattering can be explained by two different interacting 

pore-systems owing to a combination of interparticle and vuggy porosity. (c) Porosity–permeability cross-plot 

depicting the reservoir properties of inner-shoal facies types. Facies types of the mid-shoal facies association have a 

higher fraction of oomouldic porosity and fewer solution enlarged touching vugs, which results in a scattering towards 

higher porosities. 

Inner-shoal facies association (oolitic grainstone, 

well-sorted pack- to grainstone). These facies 

types, in particular oolitic grainstones, show a 

tendency towards higher porosities (Fig. 10c, 

Table 5) and comparably low permeability. Vugs 

and larger bioclastic moulds which interconnect



Table 3. Correlation coefficients, arithmetic mean and standard deviations of shoal-fringe facies types 

Facies type Porosity Permeability Correlation coefficient 

Oncoidal WP Mean 7.6 4.4 0.34 


Pelo-ooidal P Mean 5.5 7.4 0.14 


Intraclastic P Mean 3.9 0.6 20.25 


the pores are rare in this grain-dominated, commonly 

well sorted facies association. Thus, this 

facies association is characterized by relatively 

high amounts of oomouldic porosity, which is, 

however, not accompanied by higher permeability 

values. If biomouldic pores are present, they are 

commonly surround by thick cement crusts. These 

cements are commonly not affected by selective 

diagenetic alteration and are an additional reason 

for reduced pore connectivity. As observed in thin 

sections, solution-enlarged touching vug porosity 

is present but not very abundant, which could be 

the result of the more stable pore-framework preventing 

the circulation of late diagenetic fluids and 

associated porosity creation. 

Distribution of reservoir properties 

on different scales 

The final aim of a reservoir characterization is to 

predict the spatial distribution of reservoir properties 

on a field scale. In this outcrop study, the geometries 

of the facies bodies show a remarkable lateral 

continuity on a kilometre-scale (for details see 

Palermo et al. 2010). In contrast to one-dimensional 

well data, the outcrops have the advantage that the 

individual facies bodies and the presence of porosity 

can be traced laterally in a qualitative way, since 

millimetre- to centimetre-sized mouldic macroand 

meso-pores can be observed easily with a 

hand lens. In order to calibrate these observations, 

plug measurements and thin sections were taken 

along vertical sections. Different scales of reservoir 

heterogeneities in one selected shoal body were 

investigated in a hierarchical way (for details see 

Seyfang 2006). 

Decimetre scale (tens of centimetres) 

In order to investigate the distribution of permeability 

on a decimetre scale, four rock columns 

from the central part of a major shoal body were 

cut from a 2.1 × 1.1 m-sized block from the 

quarry Neidenfels. The investigated shoal portion 

consists of the poorly sorted pack- to grainstone 

facies and shows a variable mud content. The twodimensional 

permeability distribution was measured 

with a mini-permeameter by covering each 

rock column (1 × 0.2 m) with a grid of 7 × 50 

data points spaced at 2 cm intervals (Fig. 11). The 

porosity values of the investigated samples range 

between 15 and 23%. The values were interpolated 

with the software Surfer TM using a kriging algorithm 

and a logarithmic colour scheme. Sedimentary 

structures are not recognizable at this scale (Fig. 

12) and the facies appeared as a moderately bioturbated, 

massive unit; therefore the interpolation 

was carried out without preferential direction. The 

resulting permeability is rather patchily distributed, 

which can be partly related to unidirectional algorithm. 

Nevertheless, the values show a clear upward 

increasing trend in all four rock columns. The values 

follow a small-scale regressive hemicycle with an 

upward decreasing fraction of matrix mud. The permeability 

ranges in the order of 10 mD (green) in the 

Table 4. Correlation coefficients, arithmetic mean and standard deviations of mid-shoal facies types 


Crinoidal PG Mean 14.2 63.2 0.26 


Poorly sorted PG Mean 10.5 28.2 0.51 


Amalgamated P Mean 11.8 38.7 0.60 

n ¼ 63 Standard deviation 3.4 77.7



Table 5. Correlation coefficients, arithmetic mean and standard deviations of inner-shoal facies types 


Oolitic G Mean 12.9 7.9 0.45 


Well sorted PG Mean 13.1 25.9 0.42 


lower part and reaches up to several 100 mD (orange 

to red) in the top part (Fig. 12). Thin sections document 

that this trend is combined with an upwards 

increase in interparticle porosity. 

Metre scale 

For the determination of the metre-scale heterogeneities 

in permeability, a 2.1 × 1.1 m-sized block from 

the quarry Neidenfels was investigated for sedimentary 

structures. The positions of the above-described 

rock columns within the block are marked in 

Figure 13. The individual rock columns appeared as 

massive units, whereas the entire block reveals 

some subtle, irregular low-angle cross-beds. They 

consist of crudely graded and shell-rich laminae 

above an erosive base and are intercalated into moderately 

bioturbated poorly sorted pack- to grainstones. 

Generally, slight differences in texture, sorting and 

mud content seem to follow the orientation of the 

cross bedding on this larger scale. The photograph 

of the block (Fig. 13) depicts the predominant inclination 

of the sets to the left, traced with yellow lines. 

The heterogeneities in permeability are mainly 

due to variations in the amount of mouldic porosity 

and the shape of the leached components. Portions 

with leached shells make a better contribution 

to permeability than those with ooids and crinoidal 

columnar plates. The shells are often connected by 

solution-enlarged touching vugs and build an interconnected 

framework with primary interparticle 

pores. Based on the observed sedimentary structures, 

the interpolation of permeability values 

between the four measured columns was carried 

out with a directional kriging algorithm, characterized 

by an elevated horizontal range. The resulting 

permeability distribution is displayed as an overlay 

with logarithmic colour gradient (Fig. 13). An 

important result is the systematic upward increase 

in permeability that follows a small-scale regressive 

hemicycle and the upward decreasing mud content. 

This systematic vertical trend results in a laterally 

continuous highly permeability zone in the upper 

portion of the block. Note also that the lowpermeability 

lower part is laterally continuous, as 

well as some high permeability streaks in the 

middle part, which seem to be controlled by the 

sedimentary structures. The permeability distribution 

shows that the highly permeable areas are 

often vertically interconnected. 

Tens of metres scale. For the determination of porosity 

and permeability trends within the shoal body on 

a tens of metre scale, two vertical sections were 

investigated along a 25 m-long outcrop wall that 

was previously mapped for facies. The outcrop 

wall panel, log positions and the corresponding 

porosity and permeability histograms are depicted 

in Figure 14. The reservoir body can be subdivided 

into four layers (A–D) consisting of alternating 

well-sorted pack- to grainstone (Lft 2e) and 

poorly sorted bioclastic pack- to grainstone (Lft 2e) 

Fig. 11. Measurements of the two-dimensional permeability distribution with the mini-permeameter. Each rock 

column is covered (1× 0.2 m) with a grid of 7× 50 data points.



Fig. 12. Rock-columns from the central part of a major shoal body were derived for a 2.1× 1.1 m sized slab which was 

previously mapped for sedimentary structures. Permeability shows a patchy distribution but also a clear upward 

increasing trend in all four rock columns. 

facies types. The well-sorted pack and pack- to 

grainstones (A) of the bottom part have about 

10% porosity and low permeability, which can be 

explained by comparably high mud content, occluding 

parts of potential primary interparticle porespace. 

In contrast, the upper well-sorted grainstone 

sheet (C) has lower mud content and higher 

amounts of interparticle porosity, resulting in better 

porosity and permeability values. The poorly sorted 

bioclastic pack- to grainstones also show textural 

differences, but are additionally controlled by the 

amount of biomodic porosity of leached shells. 

Generally, the porosities in the investigated poorly 

sorted pack- to grainstone beds (B, D) range 

between 11.3 and 18.2%. The moderate permeability 

in bed B ranges between 7.6 and 32.9 mD, while 

the average permeability in the upper bed (D) 

varies between 90.1 and 202.0 mD. Thus, lateral 

changes in porosity and permeability within an individual 

facies body remain in the same order of 

magnitude within a similar stratigraphic position. 

These changes seem to correspond generally to the 

vertical textural changes that seem to follow the 

observed stratigraphic cycles. Moreover, also the 

lateral differences in porosity and permeability 

within the individual layers seem to be accompanied 

by slight lateral variations in the Dunham texture. 

The general occurrence of laterally continuous 

reservoir bodies is commonly controlled by 

medium-scale cycles, since porous units occur 

preferentially around their regressive maxima. In 

contrast, internal porosity and permeability variations 

are often controlled by small-scale cycles. 

The best reservoir properties are found around 

small-scale regressive maximum. This effect is 

most likely the result of stratigraphically controlled 

variations in mud content, and additional complex 

diagenetic overprints of cementation and selective 

leaching (see previous section). 

Regarding the entire flow unit, the lateral pattern 

of the permeable layers along the same stratigraphic 

cycles indicates that the observed centimetre-scale 

heterogeneities owing to sedimentary structures 

play a subordinate role.



Fig. 13. A 2.1×1.1 m sized block sampled from a major shoal-body in quarry Neidenfels with subtle low-angle cross 

bedding (1) and the resulting permeability distribution (2). Permeability was interpolated from the rock columns along 

the major sedimentary structures. Note the systematic upward increase of permeability following a small-scale 

regressive hemicycle and the resulting lateral continuity of the highly permeable zone in the upper portion of the block. 

Kilometre scale 

Three sections through two shoal bodies, with a 

lateral spacing of 1 km each, were investigated for 

the distribution of facies, diagenetic changes and petrophysical 

properties along the depositional gradient. 

The cross-section in Figure 7a displays the geometries 

of facies-bodies combined with porosity and 

permeability values of medium- and small- scale 

cycles. Within the individual facies bodies, the 

porosity and permeability distribution shows gradual 

lateral changes. The cross-section of Figure 7b 

depicts the gradational lateral changes of mud 

content and cement-types, which are held as key 

indicators for the relationship between the diagenetic 

overprint and the resulting reservoir properties. The 

overall distribution of petrographic trends and reservoir 

properties shows similar patterns. Both properties 

show laterally gradational transitions and are 

strongly influenced by stratigraphic cycles. The vertical 

distribution of reservoir properties is mainly 

controlled by medium-scale cycles, whereas the 

smaller internal variations seem to be controlled by 

small-scale cycles. 

Petrophysical modelling 

Three-dimensional geological modelling software 

offers a wide range of different possibilities for the 

distribution of petrophysical properties. However, 

in contrast to the subsurface, outcrop analogue 

studies allow a direct determination of the spatial 

distributions of reservoir properties. Furthermore, 

outcrop data can be used to constrain different conditioning 

factors and algorithms that are applied 

in the modelling process. As documented above, 

the lateral distribution of reservoir properties

Fig. 14. Outcrop wall panel with stratigraphic cycles, sedimentology and the reservoir properties. Lateral changes in porosity and permeability within the individual facies bodies 

remain in the same order of magnitude within a similar stratigraphic position. The red arrows indicate a common trend in the Dunham texture (mud content) and the reservoir property 

values, following the stratigraphic cycles. (Dunham Textures: G, grainstone; PG, pack- to grainstone; P, packstone; W, wackestone; M, mudstone. Particle size: L, lutite; S, siltite; 

A, arenite; R, rudite.) 


Geological Society, London, Special Publications published online June 27, 2012 as doi: 10.1144/SP370.6



Fig. 15. Fence diagram of the 3D facies model (200× vertical exaggeration), which was used as the trend for the spatial 

distribution of the reservoir properties. 

within a facies body, in the same stratigraphic position, 

remains in the same order of magnitude for 

hundreds of metres. On a kilometre scale, facies 

bodies, reservoir properties and diagenetic trends 

show gradual lateral changes, whereas the mostly 

stratigraphic cycle-controlled vertical differences 

vary commonly on a decimetre scale. 

These observations within this particular setting 

were considered for the construction of the 

geological model. The detailed, deterministic geomodel 

of Palermo et al. (2010) was used as a main 

input for geostatistical data analysis and the distribution 

of reservoir properties. It covers an area 

on the scale of a Middle East giant gas-field 

(25 × 36 km) and provides both (a) high resolution 

sequence stratigraphic reservoir layering and (b) a 

detailed facies distribution (Fig. 15). It is composed 

of 619 layers and 3.5 million cells. This high vertical 

resolution provides the possibility to model detailed 

variations of reservoir properties within individual 

reservoir bodies. Furthermore, these observations 

and the data distribution suggest a deterministic 

approach for modelling reservoir properties. With 

respect to the palaeogeographic positions and the 

resulting differences in facies distribution, the 

well spacing in this study is commonly similar or 

smaller than the minimum dimension (commonly 

dip width) of the major reservoir bodies (cf. Kerans 

& Tinker 1997). 

Input data 

The investigation of thin sections showed that preservation 

and creation of pore space is restricted to 

high-energy shoal and shoal-fringe facies types 

and is modified by the selective diagenetic history. 

Therefore, the primary facies types can be clearly 

subdivided into reservoir and non-reservoir facies. 

Furthermore, structural influences (e.g. fractures) 

on the reservoir properties are generally negligible. 

These observations and the software-dependent 

necessity for a constant sampling rate led to the following 

steps in preparing the dataset for 3D modelling: 

after the insertion of the plug measurements as 

point values, all portions of reservoir facies without 

corresponding plug-data were set to ‘undefined’ 

(2999.2). Subsequently, the porosity and permeability 

values of all non-reservoir facies types were 

set to zero. The step of data preparation was furthermore 

used for a quality control of the dataset. The 

upscaling process transfers porosity and permeability 

plug measurements to the grid cells neighbouring



Table 6. Results from the sample-variogram analysis for porosity and permeability, which are the main input 

parameters for petrophysical distribution algorithms (sequential Gaussian simulation and kriging) 

Results from sample variograms Shoal fringe Mid-shoal Inner shoal 

Porosity (F) 

Variogram type Gaussian Gaussian Gaussian 

Nugget (random values) 27% 27% 29% 

Vertical range 0.6 m 1.5 m 1.8 m 

Major direction 2938 2888 2158 

Range 3991 m 4702 m 1930 m 

Bandwidth 2211 m 2612 m 1101 m 

Number of lags 10 15 15 

Search radius 9000 m 10230 m 6580 m 

Minor direction 2038 1988 1258 

Range 2029 m 2843 m 1732 m 

Bandwidth 1624 m 1004 m 


Search radius 6000 m 6292 m 5070 m 

Permeability (K) 

Variogram type Gaussian Gaussian Spherical 

Nugget (random values) 53% 24% 36% 

Vertical range 1.1 m 0.8 m 3.2 m 

Major direction 1168 2238 1498 

Range 920.7 m 4552.3 m 4721 m 

Bandwidth 1121.7 m 1975 m 2585 


Search radius 4254 m 12844.4 m 14070 

Minor direction 268 1258 598 

Range 728 m 1732 m 2568 

Bandwidth 1121 m 1610 m 1858 


Search radius 4096 8310.4 m 8000 

the wells. In contrast to porosity, permeability tends 

to change exponentially and requires a different averaging 

method for the upscaling process: 

(1) Porosity: arithmetic average of point data 

upscaled on neighbour cells. 

(2) Permeability: geometric average (e.g. Warren 

& Price 1961; Jensen 1991) of point data 

upscaled on neighbour cells. 

However, owing to the extraordinary high vertical 

resolution (average 0.1 m) of the 3D grid, which 

corresponds to the sampling rate of the plug 

measurements, the upscaling had no considerable 

impact on the quality of the model. 

Geostatistical data analysis 

Variograms are geostatistical models for the 3D distribution 

of reservoir heterogeneity and fundamental 

input parameters for the most common modelling 

algorithms, for example sequential Gaussian simulations 

and kriging. Variograms are defined as the 

semi-variance of the difference between field values 

at two locations across realizations of the field 

(Cressie 1993). In order to establish the necessary 

input parameters, a geostatistical variogram analysis 

(Gringarten & Deutsch 2001) was carried out on the 

complete dataset with the original log data spaced 

between 10 m and a few kilometres. Based on the 

observations described above, the reservoir properties 

were analysed for each facies type and each 

association with various lag-sizes, ranges, and so 

on. A reasonable variogram model could only be 

found for the facies associations, by using relatively 

large bin sizes and large ranges that are in the order 

of the observed facies changes (Palermo et al. 

2010). The results and the used parameters are summarized 

in Table 6. The vertical semi-variance was 

determined within decimetre-sized lags and the 

resulting ranges for porosity result in 0.6 m for shoalfringe, 

1.6 m for mid-shoal and 1.8 m for inner-shoal 

facies associations. The lateral semi-variance of porosity 

results in Gaussian variogram models for all 

three facies associations with high random values 

(Nugget effect, after Eisenberg et al. 1994) slightly 

below 30% in all three cases. The lateral ranges are 

remarkably high and reach values in the major direction 

of 1930 m for the inner-shoal, 3991 m for shoal



fringe and 4702 m for mid-shoal facies associations. 

The semi-variance of the permeability is generally 

characterized by higher random values in the shoal 

fringe (53%) and the inner shoal (36%) facies types, 

whereas the random factor of the mid-shoal (24%) 

is comparably low. Compared with the porosity, the 

computed permeability ranges are significantly 

lower for the shoal fringe, similar for the mid-shoal 

and higher for the inner-shoal facies associations. 

The results of the variogram analysis for both porosity 

and permeability are summarized in Table 6. 

Effect of input data 

As described in the sections above, the major portion 

of the effective porosity within the system was 

created at an early stage and is mostly linked to 

either primary porosity or cycle-controlled early 

diagenetic leaching. In the upper described qualitative 

and quantitative outcrop observations, two major 

scales of lateral heterogeneities could be observed: 

(a) small-scale heterogeneities on centimetre to decimetre 

scale; and (b) large-scale, smooth transitions 

in the order of the lateral facies association extensions. 

A reasonable variogram model could only be 

found for the largest scale. The intermediate scale, 

tens to hundreds of metres and the centimetre scale 

did not provide enough data to make a sufficient 

variogram analysis. According to the previous 

observations, the remarkably high lateral porosity 

ranges can be related to the subtle gradational transitions 

of mud content along the depositional slope, 

reflecting the depositional energy of a flat and very 

gently inclined epeiric carbonate ramp. 

Owing to the continuous character of the porosity 

that can be traced visually along the outcrop 

walls, the predominantly Gaussian semi-variogram 

models and the resulting ranges were considered 

as the most probable scenario. However, the possibility 

of other scenarios cannot be generally 

excluded. Follow-up sampling as performed by, 

for example, Pranter et al. (2005) is planned to 

better quantify the small-scale heterogeneities. 

Conditioning and modelling algorithms 

In order to compare the impact of stratigraphy and 

facies (Palermo et al. 2010) on the distribution of 

reservoir properties, three different approaches 

were used for the petrophysical modelling. 

(1) Unconditioned. The grid used here is a relatively 

simple 3D cube that was subdivided 

into 180 conformable layers. Only the top 

and base of the model were defined by stratigraphic 

surfaces. Unrelated to facies or stratigraphy, 

the modelled reservoir properties 

were directly interpolated between the data 

points using different algorithms. 

(2) Conditioned to stratigraphic cycles. In this 

approach, the petrophysical properties were 

conditioned to the zones based on stratigraphic 

cycles. Each reservoir bearing zone 

was subdivided into eight layers and the 

average vertical resolution was around 10 cm. 

Therefore, vertical variations of reservoir properties 

within single reservoir units could also 

be displayed with a high level of detail. 

(3) Conditioned to stratigraphic cycles and facies 

associations. In this scenario the entire information 

from the geological model (Fig. 17) 

was used to model the spatial distribution of 

reservoir properties. Each reservoir facies 

association was modelled individually, and 

tight facies types were set to zero. To avoid 

unrealistically sharp contacts between the 

lateral facies boundaries the properties were 

slightly smoothened. 

Three different modelling algorithms were tested 

for the distribution of the reservoir properties in 

the upper described scenarios and compared with 

the outcrop observations (Table 7): 

(1) Moving average. This interpolation algorithm 

is based on the average values of the input data 

and weights the distribution according to the 

distance from data points without a spatial 

preference (Chou 1975). 

(2) Kriging. This is a deterministic interpolation 

method developed by Matheron (1963) and 

D. G. Krige. The algorithm distributes a property 

with a spatial preference that has to be 

defined by an input variogram. The estimated 

variables are then calculated by the linear 

combination of the input data points. In contrast 

to the moving average, the kriging algorithm 

uses a weighting function and honours 

both distance and direction. 

(3) Sequential Gaussian simulation (SGS). This 

variogram based stochastic simulation technique 

is based on kriging and allows multiple 

equiprobable realizations of a property 

(Alabert 1987; Deutsch & Journel 1992). 

Kriging calculates the weighted mean and 

the standard deviation at each estimated 

point. SGS, however, represents the variable 

as a random deviate from the Gaussian 

normal distribution, which represents the 

input data and trends better than most of the 

kriging algorithms. 

Porosity distribution with sequential 

Gaussian simulation (Fig. 16) 

Porosity – unconditioned. According to the randomity 

of the algorithm, the distribution of porosity 

appears very patchy and the general geological

Table 7. Parameters and algorithms used for the spatial distribution of reservoir properties 

Moving average Vertical range Point weighting Direction Distribution Trends 

Porosity 1 m Exponent: 4 Follow layers normal Facies 

Permeability 1 m Exponent: 4 Follow layers Logarithmic Facies, porosity 

Sequential Gaussian simulation Variogram settings Transformations Distribution Trends 

Porosity Parameters from variogram analysis Normal score Linear Facies 

Permeability Parameters from variogram analysis Normal score Logarithmic Facies, porosity 

Kriging Variogram settings Transformations Distribution Trends 

Porosity Parameters from variogram analysis Normal score Linear Facies 

Permeability Parameters from variogram analysis Normal score Logarithmic Facies, porosity 


Geological Society, London, Special Publications published online June 27, 2012 as doi: 10.1144/SP370.6



Fig. 16. Different porosity distributions with the Sequential Gaussian simulation algorithms conditioned to: (a) a 

simple layering – the porosity values are vertically scattered and form laterally simple bulls eye patterns; (b) 

stratigraphic cycles – vertical distribution matches better with the outcrop observations, although unconnected bulls 

eyes are still present; (c) stratigraphic cycles and facies – the smoothened distribution shows the best match with the 

outcrop continuities. 

pattern is not recognizable. Additionally, the reservoir 

bodies are relatively small with low lateral continuity. 

Both kriging and moving average provided 

better results than SGS in this scenario. 

Porosity – conditioned to stratigraphic cycles. The 

resulting reservoir bodies follow the stratigraphic 

trends and have acceptable dimensions. However, 

compared with the results produced with the 

moving average algorithm, many artefacts are 

visible, especially in the tight middle part. 

Porosity – conditioned to cycles and facies associations. 

The modelled porosities follow the stratigraphic 

trends and the outline of the facies bodies. 

Compared with the results with the moving average 

Fig. 17. Different permeability distributions with the Sequential Gaussian simulation algorithms conditioned to (a) a 

simple layering – the porosity values are vertically scattered and laterally isolated flow units, (b) stratigraphic cycles – 

more realistic vertical connectivity, but the flow units are laterally still isolated, and (c) stratigraphic cycles and facies – 

the smoothened distribution shows a good match with the outcrop observations.



algorithm and kriging, the reservoir bodies modelled 

with a slightly smoothed sequential Gaussian Simulation 

show a better connectivity and more reasonable 

dimensions. Analysing this final scenario, the 

resulting net/gross ratio of the modelled Upper 

Muschelkalk area amounts to 7%, with a theoretical 

storage capacity of 419 490 000 m 3 . 

Permeability distribution with sequential 

Gaussian simulation (Fig. 17, Fig. 18) 

Permeability – conditioned to porosity. Like the 

porosity, the distribution of permeability appears 

patchy and the general geological pattern is not 

recognizable. The reservoir bodies are comparably 

small and highly compartmentalized. 

Permeability – conditioned to stratigraphic cycles 

and porosity. Comparable to the distribution of porosity, 

the resulting flow units follow the general 

stratigraphic trends. Although there are no artefacts 

in the middle part visible, the distribution of permeability 

remains patchy, especially in the upper part. 

Permeability – conditioned to cycles, facies associations 

and porosity. The modelled permeability 

follows the stratigraphic trends and the outline of 

the facies bodies. Both dimensions and connectivity 

of the flow units seem to be more reasonable in 

this case. 

Modelling results 

The outcrop analogue studies of Palermo et al. 

(2010) could show that the shoal facies associations 

show remarkable lateral extensions of up to several 

tens of kilometres whereas vertical facies changes 

range within the order of decimetres. Another part 

of the study shows that lateral facies changes are 

characterized by very subtle and gradual transitions 

within the range of up to a few kilometres. Therefore, 

the Palermo et al. (2010) study suggests 

Fig. 18. Filtered permeable reservoir bodies (cut-off 1 mD) distributed with sequential Gaussian simulation, 

conditioned to stratigraphic cycles and facies and processed with a smoothing algorithm. All three hierarchies of 

stratigraphic cycles had an impact on quality and presence of the flow units: (a) large scale cycle – controls the lateral 

extension, retro- and progradation, (b) medium scale cycles – control the stratigraphic presence of a flow unit, and 

(c) small-scale cycles – control the body internal vertical heterogeneities. Furthermore the distribution of palaeohighs 

and -lows is important for the general localization of the reservoir bodies.



encoding facies as a continuous rather than discrete 

property. The thin section observations and petrophysical 

measurements confirm theses observations. 

Mud content in the matrix changes laterally and vertically 

in a gradual way and reflects the depositional 

energy of the epeiric system with a very gentle 

depositional gradient. The outcrop observations 

above show that mud content has a significant impact 

on the presence of primary porosity. However, early 

diagenetic mouldic porosity also seems to increase 

with increasing matrix mud content. The petrophysical 

investigations on multiple scales showed two 

major scales of reservoir heterogeneities: 

(1) Small-scale heterogeneities on centimetre to 

several decimetre scales mostly controlled by 

sedimentary structures and diverse mouldic 

pore types from different components were 

observed. Similar small-scale heterogeneities 

have been described by several studies (e.g. 

Jennings et al. 1998; Pranter et al. 2005) 

However, the quantity of samples in this 

study was insufficient to perform a solid variogram 

analysis that could quantify the 3D lateral 

ranges of the small-scale heterogeneities. 

(2) On a large scale, relatively smooth transitions 

on the order of a few kilometres were 

observed and could be confirmed by the variogram 

analysis. However, the resulting lateral 

long range features are significantly higher 

compared with the dolomitized platform 

carbonates investigated by Jennings et al. 

(1998), which can reach up to 800 m. Apart 

from the different depositional settings and 

diagenetic histories, which make a direct 

comparison difficult, the possibility of additional 

nested heterogeneities as observed and 

described by Pranter et al. (2005) cannot be 

excluded. At least one smaller heterogeneity 

scale is most likely hidden within the high 

random effect and could be an interesting 

subject for further investigations. Extensive 

lateral sampling is foreseen for a future 

study to investigate the presence of additional 

meso-scale heterogeneities. 

Conclusions 

This outcrop analogue study documents the reservoir 

properties in the Upper Muschelkalk formation 

as an outcrop analogue for epeiric carbonates. 

Average porosity is 9–23%, and average permeability 

is 21–700 mD. The net/gross ratio of 

the Upper Muschelkalk amounts to 7%, with a 

theoretical storage capacity of 419 490 000 m 3 . 

Laterally, units with high porosity and permeability 

show a kilometre-scale lateral continuity 

with subtle gradational transitions. The main part 

of the porosity in the shoal bodies has been 

created at an early stage and is mostly linked to 

either cycle-controlled (a) primary porosity triggered 

by a systematically changing mud content or 

(b) early diagenetic leaching around maximum 

regressions. 

Two major scales of lateral heterogeneities could 

be observed: (a) small-scale heterogeneities on 

centimetre to decimetre scale, mainly related to 

sedimentary structures and different mouldic pore 

types which are represented by high random 

values (30%) within the geostatistical variogram 

model; and (b) large-scale heterogeneities of up to 

4700 m lateral range. The remarkably high lateral 

porosity and permeability ranges can be partly 

related to subtle gradational transitions of mud 

content along the depositional slope, which reflect 

the depositional energy of a flat and very gently 

inclined epeiric carbonate ramp. 

Different petrophysical modelling scenarios 

could show the impact and necessity of geological 

constraints for the geostatistical distribution of porosity 

and permeability. The following factors help 

in predicting and modelling the spatial distribution 

of reservoir properties within the Upper Muschelkalk, 

which may be transferred to similar epeiric 

carbonate reservoirs (Fig. 15): 

(1) Facies associations. The reservoir properties 

show a close relationship to the lithofacies 

and the associated matrix mud content. 

Porous facies types are restricted to the highenergy 

shoal facies, whereas both muddy 

inner and outer ramp facies are commonly 

tight since the muddy matrix protects the sediment 

from diagenetic fluids and associated 

mouldic porosity creation. Reservoir heterogeneities 

within the shoal bodies can be 

mainly related to the mud content and early 

diagenesis. 

(2) Statigraphic cycles. All three hierarchies of 

stratigraphic cycles have an impact on quality 

and presence of the flow units: (a) large-scale 

cycle – controls the lateral extent, as well as 

retro- and progradation of the reservoir 

bodies; (b) medium-scale cycles – control the 

stratigraphic presence of the reservoir facies 

associations; and (c) small-scale cycles – 

control the mud content and early mouldic 

porosity creation in facies types with primary 

porosity. which are considered as key drivers 

for systematic vertical changes in porosity 

and permeability within the reservoir bodies. 

(3) Palaeo-relief. Gross volume and dimensions 

of the reservoir bodies seem to be mainly controlled 

by the combination of both stratigraphic 

cycles and a subtle palaeo-relief,



Authors 

induced by slight differential subsidence of 

inherited structural grains. In particular, 

palaeo-highs are important for the presence 

of reservoir facies. 

Denis Palermo studied geology at the University of 

Tübingen and completed his MSc thesis in 2004 on 

carbonate reservoir sedimentology at the Nederlandse 

Aardolje Matschapij (NAM) in the Netherlands. 

He then joined the Sedimentary Geology 

Group at the University of Tübingen to do a Ph.D. 

on outcrop analogue modelling and reservoir characterization 

in cooperation with Eni E&P. In 

2007 he joined Eni E&P as sedimentologist and 

member of the Eni E&P carbonate research team. 

Thomas Aigner studied geology at the universities 

of Stuttgart, Tübingen, Reading and Miami. 

After working for 6 years with Shell Research, he 

has been a professor and head of the sedimentary 

geology group at the University of Tübingen since 

1991. His research group focuses on reservoir 

geology, carbonate reservoir characterization and 

3D modelling. He was an AAPG European Distinguished 

Lecturer in 1996. In 2007–2008 he spent 

a sabbatical with Petroleum Development Oman 

and Qatar Shell. 

Björn Seyfang studied geology at the University 

of Tübingen and finished his M.Sc. on outcrop analogue 

studies for carbonate shoal reservoirs in 2006. 

In 2009, he completed his Ph.D. on facies and reservoir 

modelling at the Sedimentary Geology Group 

of Tübingen University, initiated by GDF-SUEZ 

and conducted in cooperation with ExxonMobil, 

Wintershall and RWE-Dea. In September 2009, he 

joined Total as an operations geologist. 

Sergio Nardon graduated in geology at the University 

of Trieste, Italy, in 1982 and joined Eni 

(former Agip) in 1984 as clastic sedimentologist. 

His experience spans from fractured reservoir 

characterization to carbonate sedimentology and 

he has worked on several projects in Middle East, 

Europe and North Africa. His main interests are 

the integration of carbonate architecture, outcrop 

data and geological concepts into the 3D environment. 

He is currently technical leader of the research 

and development team at the geology department of 

Eni E&P. 

This work is part of an integrated research and development 

project funded by Eni E&P. We want to express our 

thanks to Eni Management for their support and the permission 

to publish. We are grateful for discussions with 

many colleagues working on the German Muschelkalk, 

notably H. Hagdorn, T. Simon, M. Urlichs and 

R. Borkhataria. Assistance was provided by members 

of the Sedimentary Geology group at the University of 

Tübingen (A. Allgöwer, E. Dmitrieva, M. Looser, A. 

Schmid-Röhl, C. Schneider, M. Zeller) and A. Satterley, 

B. White and K. Mair from Eni E&P. P. Jeiseke produced 

the thin sections. Several quarry companies, for 

example Schön & Hippelein, Hohenloher Schotterwerke, 

Schotterwerk Schuhmann, J. Heumann and SHB Schotterwerke 

Hohenlohe generously allowed access to their quarries. 

The Geological Survey of Baden-Württemberg and 

Bayern supplied us with borehole cores and well data. 

We wish to thank A. Glocke and Schlumberger for 

the access to Petrel (TM of Schlumberger) and to 

A. Henriette (ALT) for access to WellCAD. 

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