Borgomano Et Al 2013

5Journal of Petroleum Geology, Vol. 36(1), January 2013, pp 5 - 42

2013 The Authors. Journal of Petroleum Geology 2013 Scientific Press Ltd

PETROPHYSICS OF LOWER CRETACEOUS PLATFORM

CARBONATE OUTCROPS IN PROVENCE (SE FRANCE):

IMPLICATIONS FOR CARBONATE RESERVOIR

CHARACTERISATION

J. Borgomano1*, J.-P. Masse1, M. Fenerci-Masse1 and F. Fournier1

High resolution petrophysical analyses were carried out on Urgonian (Lower Cretaceous)carbonates from outcrops in Provence, SE France. Porosity and permeability were measured on541 plug samples selected from grain-supported carbonates analogous to those in the age-equivalent Shuaiba and Kharaib Formation reservoirs in the eastern Arabian Plate. The samplingstrategy allowed property heterogeneities from centimetre to kilometre scales to be investigated,as well as correlations between porosity and permeability in several different reservoir rocktypes. Property spatial modelling sensitivity analyses were also undertaken.

The relative abundance of microporosity, grain size and sedimentary-diagenetic anisotropywere the main geological parameters which controlled the petrophysical characteristics of thegrainstones studied. Increasing microporosity decreased permeability but resulted in an increasein the homogeneity of the reservoir rocks and therefore in their predictability. An increase ingrain size, from fine sand to gravel, and in the amount of intergranular pores, enhancedpermeability significantly but resulted in a decrease in the homogeneity (and thereforepredictability) of the reservoir rock.

At a plug scale, poro-perm relationships are very good and can be used predictively for finegrainstones dominated by microporosity; but relationships are moderate to weak for coarserudstones with mixed pore types, including intraskeletal pores. In grainstone units, weaksedimentary anisotropy, such as decametre-scale cross-bedding, did not prevent the predictionof the horizontal property distribution from vertical data over a few hundreds of metres. Inthese units, the lateral correlation of rock properties follows periodic variograms with a 7 mwavelength. The lateral distribution of properties in coarse-grained and heterogeneous rudstoneswith complex pore types and intense sedimentary heterogeneities, such as channel structures,was however more difficult to predict from a vertical data set.

Upscaling poroperm data from plug scale to reservoir scale is linear in the case of grainstoneswith intergranular microporosity, but is non-linear in the case of skeletal rudstones with coarserpore types including skeletal porosity.

1 CEREGE, UMR7330, University of Aix-Marseille, 3 place Victor Hugo, 13331 Marseille Cedex 3, France.* present address: EXPLO/TE/ISS, CSTJF Total SA, Av. Laribeau, 64000 Pau, France.* corresponding author, email:

[email protected]

Key words: Lower Cretaceous, carbonates, Urgonian,Provence, SE France, petrophysics, microporosity,Arabian Plate, Shuaiba Formation, Kharaib Formation,reservoir rock, grainstone.

6 Lower Cretaceous platform carbonates in SE France

INTRODUCTION

Challenges involved in improving hydrocarbonrecovery from carbonate reservoir rocks includeunderstanding the petrophysical and stratigraphicheterogeneities which occur at all scales, andovercoming difficulties associated with the evaluationof rock properties (particularly permeability) in thesubsurface. In this context, efficient fielddevelopments are often based on 3D numerical rockproperty models which allow realistic simulations ofhydrocarbon flow behaviour (e.g. Ravenne, 2002;Amthor et al., 2010). These flow models, which aremostly obtained by geostatistical modelling ofpetrophysical data from wells and seismic profiles,have as their basis pixel-based or object-basednumerical geological models. They can be conditionedby topological and geometrical rules obtained fromknowledge databases or outcrop analogues. The mainrequirements of the geological models are to honourthe subsurface data and also to comply with relevantgeological concepts. However the precise impact ofgeological concepts on a 3D petrophysical model isoften difficult to estimate at inter-well and flow-unitscales (Borgomano et al., 2008). This may be due toa lack of geological data from inter-well locations,together with the scale jump between core plugsand hydraulic (or reservoir) units, and the poorresolution of seismic data at these scales.

Combined geological and petrophysical analysesof outcrop analogues, which are characterized bylateral continuity at the relevant reservoir scales, canhelp to establish and validate geological modellingmethods for reservoir properties. This approach isgenerally based on the acquisition of petrophysicaland geophysical data from the outcrops by means oflaser, radar, seismic and petrophysical loggingcombined with stratigraphic and sedimentologicalobservations (e.g. Adams et al., 2011; Richet et al.,2011).

In this paper, we investigate the petrophysicalcharacteristics and heterogeneity at well and inter-well scales of bioclastic and peloidal grainstones ofBarremian early Aptian age from outcrops in SEFrance. These grainstones are characterised byvariable grain size and pore types. The main objectivesof this study were: (i) to collect a petrophysicaldatabase from outcropping rocks which are closelyanalogous and time-equivalent to Lower Cretaceousgrainstones in reservoir rocks on the eastern ArabianPlate; (ii) to investigate the rock properties (porosityand permeability) of different reservoir rock types(i.e. with different grain sizes and pore types); (iii) toinvestigate the spatial heterogeneity of these rockproperties from core-plug to inter-well scale (10-2 to103 m) in the context of the outcropping sedimentary

system; and (iv) to establish rules for the modellingand upscaling of these rock properties.

Three localities in Provence (SE France) wereinvestigated (Fig. 1): Calissane and Les Fourches inthe La Fare Range; Orgon; and Rustrel. Matrix porositywas in general well preserved at each locality. Thecarbonates sampled are dated as Barremian earlyAptian (Masse and Fenerci-Masse, 2011) and form alow-relief topography which is typical of Provence(Fig.1).

Geological frameworkEarly Cretaceous carbonate platforms developed onthe passive margin of the Alpine (Tethyan) basin undera tropical to subtropical, warm and humid climate(Fig. 2a). During the Early Cretaceous, the platformsystem was up to 100 km wide and 1000 km long,extending from Spain to Switzerland (Masse et al.,2000). Carbonate deposits were isolated fromcontinental influences and there was limitedsiliciclastic input. Four main stages of growth andprogradation of the platform (200-400 m thick) arerecognised and are separated by drowning events (Fig.2b) dated as mid-Valanginian, mid-Hauterivian, lateBarremian and mid-Aptian (Masse and Fenerci-Masse,2011). The end of platform development during theearly Aptian was characterized by a reduction of theplatform area and by a series of high frequencydrowning events (Fig. 2b) (Masse, 1993; Masse andFenerci-Masse, 2011).

The Barremian and lower Aptian carbonatesinvestigated were deposited in high to moderateenergy environments within an outer platform settingor on the distal part of an inner platform (Fig. 2c)(Leonide et al., 2012). They comprise laterallyextensive, cross-bedded carbonate sand bodies (sheets,channels and sand waves), characterised by ahomogenous texture (100% grain-supported) andcontaining a significant content of sand-gradebioclasts; and rudist-bearing deposits with a highcontent of peloids (Fig. 3) (Masse and Fenerci-Masse,2011). The diagenetic modification of thesegrainstones was not investigated in this study but ingeneral includes the precipitation of phreatic calcitecements (marine and freshwater), micritisation andthe leaching/replacement of unstable grains (aragoniteand high magnesium calcite). This aspect, which isvery important for the rock properties, is currentlybeing investigated in a separate study.

Analogies with carbonate reservoir rocksin the Arabian PlateThe carbonate units studied at outcrop are closelyanalogous to Barremian lower Aptian carbonatereservoir rocks on the eastern Arabian Plate, such asthe Kharaib and Shuaiba Formations in terms of both

7 J. Borgomano et al.

Fig. 1. Sampling locations shown on a simplified geological map of the Provence region, SE France,

with first-order structural elements and main Barremian Aptian outcrops.

chronostratigraphy and sedimentology (Masse, 1992,1995; Dercourt et al., 2000). Barremian lowerAptian rudist-rich grainstones and rudstones occuraround the Bab Basin (Yose et al., 2010; van Buchemet al., 2010), and form reservoir rocks at giant oilfieldssuch as Al Huwaisah, Asab, Bab, Bu Hasa,Yibal,Dhulaima, Murban, Safah, Shaybah and Zakum(Harris et al., 1968; Johnson and Budd, 1975; Hassanand Wada, 1981; Litsey et al., 1983; Alsharhan, 1990,

1993; Borgomano et al., 2002; Al Ghamdi and Read,2010; Amthor et al., 2010). Both sets of rocks arecomposed of grainstones and rudstones with mouldicand intergranular porosity, with a typical chalkytexture related to abundant microporosity (Fig. 3),and rock fabrics are similar at macro- and microscopicscales. In thin section, it is not possible visually todistinguish the bioclastic grainstones from Provencefrom those from northern Oman (Fig. 3). This

Orange

Avignon

Nimes

Arles

CavaillonApt

Aix-en-Provence

Marseille

Manosque

Carpentras

Aix

Faul

t Dura

nce

Faul

t

Lam

anon

Gra

ben

.

25 kmSampling localities

Thrust

Strike-slip fault

Buried fault

LEGEND

Ales

Gra

ben

Vistre

nque

Grab

en

VentouxLure

Mediterranean Sea

Early Aptian

BarremianUndifferentiatedBarremian-Aptian

FRANCE

Orgon

La Fare

Rustrel

43 N

44 N

05 E


Fig. 2A. Palaeogeographic configuration of SE France during the Early Cretaceous (left: late Barremian;

right: early Aptian), with the location of the three sampling locations (1: La Fare-Calissane; 2: Orgon;3: Rustrel) relative to the Urgonian carbonate platform and the Vocontian basin (after Masse and Fenerci-Masse, 2011). B. Stratigraphical architecture of the Early Cretaceous carbonate platform in the Provence area

(Masse and Fenerci-Masse, 2011) with the sample locations. C. Palaeo-environments and facies mosaic of theUrgonian carbonate system (Masse and Fenerci-Masse, 2011), with the three sample locations.

N

MARSEILLE

AvignonAles

Toulon

Nimes

LYON

NICE

GRENOBLE

Mt. Ventoux

MEDITERRANEAN SEA 6E

45N

30 km

LANGUEDOC

DAUPHINE

PROVENCE

VOCONTIAN BASIN

ALPINEBASIN

+

?

? CF

Basin Cevennes fault system

outer platform bioclastic belt

inner platform rudist bearing domain

Carbonate platform

CF

FRANCE

Annecy

RhoneN

MARSEILLE

Avignon

Ales

Toulon

Nimes

LYON

NICE

GRENOBLE

Mt. Ventoux

MEDITERRANEAN SEA 6E

45N

30 km

LANGUEDOC

DAUPHINE

PROVENCE

VOCONTIAN BASIN

ALPINEBASIN

+

?

? CF

Annecy

Rhone

B A

1

2 3

114

112

119

124

12810 km Outer shelf and

hemipelagic basinOuter platform bioclastics

Inner platform rudist bearingcarbonates

TOULONSN

VENTOUX

VOCONTIAN BASIN

MONTS DE VAUCLUSE

SAINT CHAMAS MARSEILLECASSIS

MID HAUTERIVIAN DROWNING

MID VALANGINIAN DROWNING

BEDOULIAN

HAUTERIVIAN

Early

Early

Early

Late

Late

LateMa

Northward mainprogradation phases

VALANGINIAN

BARREMIAN

?

1

1

2

2

3

3

4

4

1

Bipolar progradation

Late Barremian drowning Late Bedoulian drowning Mid-Late Bedoulian drowning

Middle Bedoulian drowning

1 2

3

INNER PLATFORM

INFRA-LITTORAL

CIRCA-LITTORAL

OUTER SHELF BASIN

small requieniids large requieniids

caprinids

corals dasycladales

orbitolinids

brachiopods sponges

echinoderms

gastropods infaunal echinoids serpulids

ammonites

rudstones deep muddy sediments ooids bioclastics

BIOLOGICAL SYMBOLS

SEDIMENTOLOGICAL SYMBOLS

RUSTREL

LA FARE SAINT CHAMAS ORGON

OUTER PLATFORM

1 2 3

A.

B

C


0.5 mm 0.5 mm

0.5 mm 0.5 mm

A

B

C D

E F

Fig. 3. Thin-section photomicrographs of typical grainstone facies investigated in this study and analogousreservoir rocks from the Arabian Plate. Porosity in blue-green.A-B. Orgon grainstone showing dominant intragranular porosity (mainly microporosity) and minor mouldic

porosity surrounded by intergranular calcite cement.C-D. Porous grainstone facies from the Shuaiba Formation, Lekhwair field, Oman. Note the striking similarity

to the Orgon facies in terms of texture, cement and porosity distribution.E. Rustrel rudstone-grainstone with intergranular, mouldic and intragranular porosity.F. Porous grainstone-rudstone facies from the Shuaiba Formation, Al Huwaisah field, Oman. Note the similarity

with the Rustrel facies in terms of texture, cement and porosity distribution.


similarity can be explained by the identical primarymineralogical composition of the carbonates, and bysimilar diagenetic modification comprising leaching andreprecipitation of calcite (Lambert et al., 2006;Fournier et al., 2011). Both sets of grainstones havesimilar porosity and permeability trends (Fig. 4).

MATERIALS AND METHODS

Most of the plug samples were drilled in fresh quarryslabs (Figs 5, 6). One-inch diameter plugs (n = 514)were drilled within correlatable grainstone units atregular spacings. Four locations were sampled:Calissane (386 samples) and Les Fourches (43samples) in the La Fare area; Orgon (46 samples);and Rustrel (40 samples) (locations in Fig. 1). Thenumber of samples per location varied according toboth the sampling strategy and the outcrop dimensions

and apparent rock texture. At Calissane (Fig. 5), thestudied unit is a bioclastic grainstone body > 1 kmlong, in which the 386 samples were drilled at alogarithmic spacing ranging from 0.1 to 500 m indifferent directions and extending over three separatequarries: Les Coussous (Fig. 6), Rocher Rouge andBelvdre. At Les Fourches, which is an extensionto the east of the Belvdre outcrop, the grainstoneunit was sampled with an average spacing of 40 mover a 1600 m long outcrop in only one direction. AtRustrel (Fig. 7) and Orgon, high-resolution samplegrids were used with a 1 m spacing over an area of30 m2. The sample positions were recorded with adecimetre resolution within a local coordinate system(xyz). The locations of natural fractures, bedboundaries and stylolites were carefully surveyed.

Porosity, permeability and grain densitymeasurements were carried out in the Shell Research

Fig. 4. Cross-plot of porosity versus permeability obtained from plug measurements. The rock samples

investigated in this study plot within the range of the porosity-permeability measurements taken from variousBarremian Aptian carbonate reservoir rocks from the eastern Arabian Plate. This corroborates the analogies

between Barremian Aptian carbonates from Provence and the eastern Arabian Plate in terms of rock typeestablished from thin sections (Fig. 3).

Yibal Field - Shuaiba (Litsey et al.,1983) Zakum Field - Thamama B (Alsharhan, 1990) Zakum Field -Thamama C (Hasan et al,1981) Thamama B-C in various Abu Dhabi fields (Johnson & Budd , 1975)

Shuaiba reservoirs in various Oman fields (Borgomano et al., 2002) Bu Hasa South Field - Shuaiba (Russel et al., 2002)

Kharaib reservoirs in UAE (Neilson et al.1998)


1m

1 m

2.5 cm C

Fig. 5. Calissane La Fare outcrop information.

A. General view and stratigraphical cross-section with vertical exaggeration of the sampled carbonate units.The numbers indicate the location of the sampled grainstone strata (Les Coussous, Rocher Rouge, Le

Belvdre).B. Stratigraphic log of the Barremian interval in the Calissane area, showing the position of the Les Coussous

and Les Fourches grainstone units.C. Photographs of the Les Coussous unit outcrop showing the lateral continuity of the cross-beddedgrainstone unit, the good quality of the sampled rock faces, and a few examples of sample plugs.

5 0 0 m1 2 3

20m

F

F

F F

Relative stratigraphic position of the main bioclastic bodies

Coussous transect

Les Coussousquarry

Les Coussousquarry

Rocher Rouge

Rocher Rouge

Chateau Virant

Chateau Virant

Belvedere

Belvedere

LES COUSSOUSUNIT

JAS DALISSONUNIT

LES FOURCHESUNIT

LES COUSSOUSUNIT

JAS DALISSONUNIT

LES FOURCHESUNIT

SW NE

BioclasticLimestoneChertyLimestone

CoralLimestone

RudistLimestone

Fault

Les Coussousquarry Belvedere

BioclasticLimestone

ChertyLimestone

CoralLimestone

RudistLimestone

Facies Legend

Les CoussousUnits 2-3

Les CoussousUnit 1

Jas DAlisson

Les Fourches

200

150

100

50

0m

A

B C


Fig. 6. Les Coussous quarry sampling site (# 1 on Fig. 5A).A. The quarry faces result from several phases of slabbing and do not display artificial fractures or stratigraphicjoints. Note the apparent massive aspect of the grainstone unit over a vertical height of 25 m despite the

presence of metre-thick beds and the scarcity of natural fractures. Horizontal and vertical sampling tracks areindicated by the dotted lines. B. Mapping of the sample location relative to the quarry faces and a point of

origin (NE corner). C. Vertical sample site of the grainstone unit together with the permeability curve andstratigraphical information. Quarrying of the porous grainstone stopped at the upper level of the cemented

and tight rudistid wackestone bed. Note the presence of only three stylolites in the entire grainstone interval,closely associated with thin cemented layers. Grainstone bed sets are in general10-50 cm thick at this location.

Permeability (mD) 0 1 10

5 m

3 cemented beds associated with

stylolites

Rudistid wackestone

Peritidal succession with beach rocks

Cross-bedded bioclastic grainstone-rudstone

A

B

C

25 m


Fig. 7. Rustrel sampling site located on the D30 road, one km to the east of Rustrel village (location on Fig. 1).A. General view of the sampled rudistid rudstone beds along the roadside. Cross-bedding, fining-upwards

patterns and channels with erosive bases characterize this porous unit, described and interpreted by Leonide etal. (2012). B. Lithostratigraphic log of the Aptian interval in the Rustrel area (Masse and Fenerci-Masse, 2011)

showing the position of the sampled rudstone unit (arrow).


Laboratory (Rijswijk, Netherlands) according toindustrial standards. Thin-section petrographicinvestigations were carried out on 98 samples bypoint-counting and observation with an opticalmicroscope. Grain size and grain composition referredto the following grain types: micritic grains (pelletsand micritized components); sparry grains (grainscomposed of sparry calcite, originally aragonitic); andskeletal grains (foraminifera, dascycladacean algae andrudist fragments). Mean size was measured on thinsections by using the ribbon sampling method (Plas,in Flgel, 1982). Intragranular, intergranular andmouldic pore types and non-porous grains wereidentified. A few samples were investigated with SEM.

The petrophysical and petrographical data obtainedwere used for statistical and geostatistical analyses.Simple statistical analyses were performed on eachgroup of samples, and spatial trend analyses andvariography (Goovaerts, 1997) were carried out onthe Calissane and Les Fourches data sets. Principalcomponent analysis (Roux, 1993; Lebart et al., 1995)and bivariate cross-plots were applied to the entiredata set.

RESULTS

In the following section, the principal results arepresented. These include the results of petrographicanalyses (optical microscopy and SEM); an evaluationof the statistical nature of the petrophysical

parameters; multivariate analyses combining bothpetrographic and petrophysical parameters; andgeostatistical data taken from the La Fare data set.These results are then interpreted in terms of ageological-petrophysical model (taking account ofproperty trends and heterogeneity) in the followingsection.

Rock texture and sedimentary componentsThe six groups of samples (Belvdre, Les Coussous,Rocher Rouge, Les Fourches, Orgon and Rustrel)are all composed of grain-supported carbonates (Figs.3, 8, 9) which were classified as grainstones orrudstones depending on the dominant grain size: veryfine sand grade at Orgon, Roucher Rouge andBelvedere; fine sand grade at Les Fourches; mediumsand grade at Les Coussous; and coarse sand to gravelat Rustrel (Fig. 10).

The standard deviation of the grain size is stronglycorrelated (r2 = 0.93) with the average grain size forall samples (Fig. 10). This is consistent with acommon characteristic of bioclastic carbonates,namely that sorting decreases with increasing grainsize at a sample scale (d = 10-2 m) (Friedman et al.,1992). At a larger scale (d = 10-1 to 102 m), grain sizeheterogeneity is more significant in the relativelycoarse-grained carbonates at Les Coussous andRustrel, where grain size trends (fining or coarseningupwards) are common. This kind of texturalheterogeneity, which is a result of hydrodynamic

Fig. 8. Thin section photomicrograph of a typical

grainstone facies from the Les Coussous unit. Mostof the thin section is occupied by intergranular orintragranular calcite cement. Porosity corresponds

to the blue colour resulting from the blue epoxy.The main visible pore types are: (1) intragranular

(mainly microporosity) in micritic grains; and(2) intergranular. Most of the bioclastic grains,

identified by micritic envelopes (3) have beenleached and replaced by calcite. Few benthicforaminifera are still recognizable (4). Porosity: 18.3

% bv; permeability: 3.65 mD.

1

1

2

2

2

1

3

4


factors associated with the depositional environment,controls the petrophysical parameters of the rocktypes (Lucia, 1995).

Petrographic analyses drew attention to the varietyof grain types present, which include micritic grains,ooids, sparry grains and bioclastic grains. Some grainstake the form of micritic envelopes surrounding a void(Fig. 3E). Micritic grains, foraminifera anddascycladacean algae are dominant in the finer-grained

carbonates at Orgon and Belvdre, whereas rudistfragments are more abundant in the coarser-grainedfacies at Rustrel.

Diagenetic features and pore typesDiagenetic modification mainly consists of aragonitedissolution, high-Mg calcite replacement by low-Mgcalcite, and calcite cementation (Figs. 3, 8, 9). Cementfabrics observed include: (i) isopachous rims of

Fig. 9. SEM photomicrograph of a typical Orgon grainstone (polished thin section) showing the large amountof intergranular calcite cement in white and the intragranular microporosity (in rounded micritic grains) inblack. Sample # Orgon 1; porosity: 22 % bv; permeability: 4.56 mD.

Fig. 10. Cross-plot of average versus standard deviation of grain size for the main sampled grainstone units.Each of the sampled locations and units is characterized by a specific grain size. The graph shows a general

increase of standard deviation, indicating decreasing sorting, with increasing average grain size.

1.40

1.20

1.00

0.80

0.60

0.40

0.20

0.001.000.800.600.400.200.00

y=1.3015 x 0.475

R2 = 0.9328

Arit

hmet

ic a

vera

ge (m

m)

Standard deviation (mm)

5

6

4

32

1

1 Rocher Rouge 0.01 0.172 Belvedere 0.06 0.233 Orgon 0.02 0.244 Fourches 0.13 0.455 Coussous 0.24 0.796 Rustrel 0.89 0.31

Standard deviation (mm) Average (mm)


fibrous to prismatic cements surrounding grains; (ii)blocky calcite cements; and (iii) syntaxial cementssurrounding echinoderm fragments. Dissolution ofaragonite results in intragranular and mouldic porespaces. High-Mg calcite recrystallisation resulted inthe development of intercrystalline microporosity.Microporosity is defined here as pore spaces of lessthan 10 m generally enclosed within micrite (Figs 3and 11). Minor leaching of low-Mg calcite micriteresulted in enhanced intragranular microporosity.

Intragranular microporosity together withintergranular and mouldic pore types were identifiedin thin section (Fig. 12) and relative frequencies wereestimated by point counting. The dominant pore typeis microporosity within micritic grains. This pore typerepresents on average 70 % of the pore volume, withan absolute value that may reach 20% of the bulkvolume (Appendix 1, see page 40-41). Intergranularpores are less abundant, comprising on average 7 %of the pore volume with a maximum absolute value

Fig. 11. SEM photomicrographs of the main micrite microfabrics of the micritic grains from the studiedUrgonian limestones (see text for explanation).

(A) Sample RU 10, Microfabric 1 (MF1): fine-grained (12 m), tight anhedral compact micrite withcoalescent crystals.(B-C) Samples LCY 34 and ORG 1, Microfabric 2 (MF2): subhedral-euhedral crystals with punctic-serrate

contacts and high intercrystalline porosity.(D-E) Samples ORG 13 and Be 129, Microfabric 3 (MF3): poorly sorted, punctic to serrate micrite, showing

irregular crystals with euhedral overgrowths (arrows).(F) Sample LCY 35, Microfabric 4 (MF4): loosely packed subrounded crystals with punctic contacts and highintercrystalline porosity.

10m

20m

A B

D

F E

C

10m


of 6.5 %. The abundance of mouldic porosity iscomparable to that of intergranular porosity. Therelative abundance of intragranular microporosityincreases with decreasing grain size and withincreasing amounts of micritic grains (for example atOrgon: Fig. 11c, d). In the coarser-grained carbonatesat Rustrel, intergranular and mouldic pore spaces arein general more abundant.

Characterisation of microporosity andintragranular micriteThe relative proportion of microporosity associatedwith micritic grains is a significant parameter incarbonate reservoir rocks (Fournier and Borgomano,2009; Fournier et al., 2011), as discussed below. SEMinvestigation allowed four intragranular micritemicrofabrics to be distinguished on the basis of crystalshape, sorting and contacts (terminology after Loreau,1972) (Fig. 11):

(i) Microfabric 1 (MF1): subhedral tight mosaicmicrite; (ii) Microfabric 2 (MF2): serrate subhedral/euhedral micrite; (iii) Microfabric 3 (MF3): puncticto serrate subhedral/euhedral micrite, showing sub-rounded crystals with subhedral/euhedralovergrowths; and (iv) Microfabric 4 (MF4): punctic,loosely packed and locally coalescent subroundedmicrite.

Fournier et al. (2011) interpreted the tight mosaicmicrofabric (MF1) and the serrate subhedral/euhedralmicrofabric (MF2) to result from various stages ofcementation of a micritic precursor without significantcompaction. Microporous euhedral/subhedral micrites(MF3) may result from recrystallisation. A later phaseof micrite recrystallisation in a meteoric setting could

have protected the micrite from further compactionduring burial. Finally, the subrounded micrite MF4was interpreted to result from a later phase of leachingof low-Mg micrites.

PETROPHYSICAL DATA

Statistical data concerning porosity and permeabilityform the basis for the geostatistic analyses andgeological interpretations which are discussed below.The data-set constitute a statistical reference forfurther comparison to subsurface reservoiranalogues, for reservoir property modelling, and forproperty upscaling from core data to the numericalmodel. The statistical parameters are presented forthe entire data set first and then for each group ofsamples (i.e. Calissane, Les Coussous, Orgon, LesFourches and Rustrel).

Porosity and permeability histograms,averages and standard deviationThe average (arithmetic) porosity for the total dataset is 17.5 %, with minimum and maximum valuesof 6.7 % and 36.2 % (range = 29.5 %) and a standarddeviation of 3.7 % (Table 1). The porosity histogramis not symmetric and shows negative skewness (Fig.13). This could be the result of sampling bias whichis significant in locations with porosity close to theaverage (e.g. Calissane), and less so in locations withporosity close to the maximum (e.g. Rustrel). Thiscould be explained by the fact that outcrops withaverage porosity (e.g. Les Coussous) are in generalmore favourable for spatial investigation of rockproperties; for example there is less weathering, little

Fig. 12. Cartoons of the main pore types and their relationships with typical end-member grain sizes: Orgon for

the minimum and Rustrel for the maximum. In the Orgon type, porosity is dominated by intragranularmicroporosity associated with micritic grains and minor mouldic pores. In the Rustrel type, porosity is

dominated by mouldic, intergranular and perigranular pore types.

1 mm

Rustrel Type

Orgon Type


or no karst, and the outcrops are of larger extent.Therefore a relatively large number of samples wererecovered.

The arithmetic average of permeability is 10.02mD with minimum and maximum values of 0.04 and299.65 mD (the range is close to 300 mD); thestandard deviation is 32 mD (Table 1). Thepermeability distribution appears more symmetric thanthe porosity distribution (Fig. 13). This is in spite ofthe greater variability, which is related to the significantpermeability heterogeneity of these carbonate rocksat the plug scale.

As with previous studies of subsurface carbonatereservoirs, the high permeability variability (with bothlow and high extreme values), and the apparent normaldistribution, illustrate a significant support effect.This phenomena, which was first described byMatheron (1967), is characterised by a strongdependence between measurement variance andsample dimensions (the support of themeasurement). The concept applies to permeabilitymeasurements of heterogeneous and coarse carbonatereservoir fabrics with small samples, such as plugs(Anguy et al., 1994; Goovaerts, 1997).

i. CalissaneThe arithmetic average of porosity values for the datafrom Calissane (the Les Coussous, Rocher Rouge andBelvdre quarries) is 16.89 %; minimum andmaximum values are 8.7 % and 28.1 % (range = 19.4%) (Table 1) and the standard deviation is 3.2 %. Theporosity histogram is symmetrical and shows a normaldistribution with a moderate spread (Fig. 13),indicating a lack of significant bias during sampling.This normal distribution and moderate dispersion isconsistent with the apparent homogeneity of reservoirproperties at an outcrop scale over the 1 km transect(Fig. 5).

The arithmetic average of permeability values is4.1 mD; the minimum and maximum values are 0.08and 17.7 mD; and the standard deviation is 3.3 mD(Table 1). The permeability distribution is symmetricwith a moderate dispersion, similar to the porosityhistogram. The support effect is minimised comparedto the total data set because the rock properties arehomogeneous at the sample scale as a result of thevery fine to medium sand texture (Fig. 13).

Property histograms for the Les Coussous quarrysamples, a subset of the Calissane data-set, allow acomparison to be made of property distributions inthe vertical and horizontal directions (Fig. 6). At thisquarry, 208 samples were collected in a horizontaldirection along a 100 m transect, just above the baseof a 20 m thick grainstone unit. In addition, 139samples were collected in a vertical direction at fourlocations intersecting the horizontal transect andcovering the total unit thickness. The mean samplespacing was 0.1 m (Fig. 6).

All the histograms display a similar average, asymmetric distribution and a moderate dispersion(Fig. 13). The most striking feature is the closesimilarity between the vertical and horizontalpermeability statistics, which are respectively: 5.62and 5.76 mD for the arithmetic average, 3.39 and 3.8mD for the harmonic average, and 3.29 and 3.61 mDfor the standard deviation (Table 1).

In spite of the presence of large-scale cross-bedding and associated variations in grain size, thisresult is not surprising given the lack of significantdiagenetic heterogeneity (fractures, stylolites, karsts,vugs and cemented layers) in the rock volumesampled. Thus the apparent layering of this 20 m thickcross-bedded grainstone does not introduce significantdifferences to the reservoir properties in the verticaland horizontal directions. These results will have animportant bearing on 3D property modelling from a

Table 1. Petrophysical averages for the data sets from the different sample locations.

Abbreviations: All: complete data set; Caliss. : Calissane; Cous H: Les Coussous horizontal sampling;Cous. V: Les Coussous vertical sampling; Four.: Les Fourches.

Sample Set All Caliss. Cous. H Cous. V Four. Orgon Rustrel

Samples N 629 386 208 139 40 46 40MIN POR % 6.7 8.7 11.02 9.57 7.1 6.7 6.7MAX POR % 36.2 28.1 23.3 24.3 18.9 23.2 36.2St. Dev. % 3.78 3.23 2.7 2.87 2.87 4.03 6.6Ar. Ave % 17.5 16.89 18.03 18.6 13.62 19.64 20.6

MIN PER mD 0.04 0.08 0.55 0.2 0.04 0.18 0.6MAX PER mD 299.65 17.7 17.7 29 57.49 5.57 299.6St. Dev. mD 32.15 3.35 3.29 3.61 9.36 1.53 95.1

Ar. Ave. mD 10.02 4.1 5.76 5.62 4.77 3.38 96.78Geo. Ave. mD 3.39 2.68 4.8 4.65 1.85 2.7 34.55Har. Ave. mD 1.49 2.08 3.8 3.39 0.69 1.56 5.27


Fig. 13. Frequency histograms of porosity and permeability for different groups of samples:total data set (top row); Calissane (row 2); Les Coussous horizontal sampling (row 3); Les Coussous vertical

sampling (row 4); and Les Fourches (bottom row) (see text for explanation).

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directional data set, such as that derived from verticalor horizontal wells.

ii. Les FourchesThe arithmetic average of porosity values for the LesFourches data-set is 13.6 %, with minimum andmaximum values of 7.1 % and 18.9 % and a standarddeviation of 2.8% (Table 1). The 40 samples werecollected within a 5 m thick continuous grainstoneunit over a 1600 m long transect with an averagesample spacing of 50 m. The porosity histogram isnot symmetrical and shows moderate dispersion withpositive skewness (Fig. 13). This indicateshomogeneous average porosity at the scale of thesedimentary unit, with a lack of highly cemented low-porosity layers or patches and the presence ofenhanced porosity zones. Permeability values rangefrom 0.04 to 57 mD, with an arithmetic average of4.7 mD and a standard deviation of 9.3 mD. Thepermeability distribution is almost symmetrical witha high dispersion. These characteristics probablyindicate a support effect, related to the coarse rockfabric, with the introduction of extreme values ofpermeability (high and low), whereas the porositymeasurements are less sensitive to a support effect.

iii. OrgonFor the Orgon samples, porosity values range from6.7% to 23.2% with an arithmetic average of 19.6 %and a standard deviation of 4%. Some 46 sampleswere collected here, with a spacing of 1 m over arectangular grid of 28 m2 (2 * 14 m) from a well-bedded grainstone unit belonging to the stratotype ofthe Urgonian limestone (Fig. 2). The porosity

histogram is not symmetric and shows a highdispersion with clear negative skewness (Fig. 14).This is related to overall high porosity in themicroporous very fine grained grainstones, withoccasional low porosity and thin, cement-rich bedsassociated with stylolites. Permeability values rangefrom 0.18 to 5.57 mD, with an arithmetic average of3.3 mD and a standard deviation of 1.53 mD. Thepermeability distribution is not symmetrical and has avery low dispersion and a negative skewness. Porosityand permeability histograms are very similar (Fig.14) indicating the close correlation between theseparameters at a sample scale (see below).

iv. RustrelProperty histograms for the Rustrel samples (Fig. 14)indicate a very heterogeneous rock at all scales, fromplug samples to sedimentary unit (Fig. 7). Fortysamples were collected at a regular spacing of 1 mwithin a grid of 25 m2 covering a continuous unitwithin a bioclastic rudstone channel (Fig. 7A).Porosity values range from 6.7% to 36.2% with anarithmetic average of 20.6 %, and a standard deviationof 6.6% (Table 1). The porosity histogram is notsymmetrical but tends to a bimodal distribution andshows significant dispersion (Fig. 14). This is relatedto the heterogeneity of the pore spaces in the Rustrelrock units combined with a high support effect, whichis a consequence of the small size of the plug samplesrelative to the porosity network. Permeability valuesrange from 0.6 to 299.6 mD, with an arithmeticaverage of 96.78 mD and a standard deviation of95.1 mD. The permeability histogram is characterisedby a high dispersion and an irregular distribution

Fig. 14. Frequency histograms of porosity and permeability for the Rustrel and Orgon data sets (seeexplanation in the text).

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which is difficult to analyse. These characteristicsindicate a high support effect and probable under-sampling of this reservoir rock unit, relative to thecoarse rock fabric and the complex porosity structure.

A preliminary conclusion which can be drawn fromthis investigation is that limited and random plugsamples are probably not representative of theeffective reservoir properties of these rudist-richrudstones. Larger samples than plug sample(exceeding 10 cm diameter and length) are needed tomeasure the effective reservoir properties.

POROSITY-PERMEABILITY RELATIONSHIPS

An important step in carbonate reservoircharacterisation is to obtain reliable permeabilitymeasurements from core samples which can be usedfor further permeability prediction in a numericalreservoir model (Borgomano et al., 2008). Upscalingof permeability data from cores to numerical grids isgenerally based on a description of the permeabilityas a logarithmic function of porosity (poro-permtransform). This requires specific geologicalattributes interpreted from core and well dataincluding rock types (Archie, 1952), pore types(Choquette and Pray, 1970; Focke and Munn, 1987;Lonoy, 2006), carbonate reservoir rock types (Lucia,1983), flow units (Amaefule et al., 1993),stratigraphic units (Kerans and Tinker, 1997), seismo-diagenetic units (Fournier and Borgomano, 2007), ordepositional units (Borgomano et al., 2002).

All these approaches are based on the early workof Kozeny (1927) and Carman (1937, 1956) whodescribed permeability as function of porosity andspecific surface area (the total area of the grainframework in contact with the pore fluid), at the scaleof a laboratory rock sample (cm). They identified theimportance of the geometry and topology of the porespaces for predicting permeability from porosity. Evenif this upscaling process is associated with non-linearmathematical issues (Amaefule et al., 1993;Mortensen et al., 1998; Prasad, 2003; Fabricius etal., 2007), the integration of poro-perm transformsin reservoir characterisation is widespread as porosity,or density, can be measured in the subsurface atdifferent scales (seismic, wells and cores) with littleuncertainty and by a variety of geophysical methods(acoustic, nuclear and electric logs). By contrast thedirect subsurface measurement of permeability is stillrestricted to flow tests in wells (Borgomano et al.,2008).

In this section, poro-perm transforms arepresented for the entire data-set and then separatelyfor each locality (Calissane, Orgon, Les Fourches andRustrel) and for different geological attributes suchas grain size and pore type.

i. The entire data setPorosity-permeability cross-plots for the total data-set show a similar variation to measurements fromanalogous reservoir rocks from the eastern ArabianPlate (Fig. 4). The reservoir rock types of Lucia (1995)can be applied to this data-set, as the samples aredominated by non-vuggy grainstones (Lucias class1). Fig. 15 shows however that most data points fallwithin the poro-perm trend of Lucias classes 2-3,corresponding to mud-supported fabrics. This isprobably due to the presence of abundantmicroporosity in the bioclastic grains, as samples withdominant intercrystalline microporosity correspondto Lucias class 3. This preliminary observationemphasizes the significance of microporosity (< 10microns) in understanding the reservoir rockproperties of carbonate grainstones (c.f. Lonoy, 2006;Fournier and Borgomano, 2007).

ii. OrgonThe porosity-permeability cross-plot for the Orgongrainstone samples (Fig. 3) shows a power-lawrelationship (K = 3*10-5Por3.8138) with a correlationcoefficient (R2) of 0.96 (Fig. 15b). This trendgenerally characterises mudstones with uniformmicroporosity (Lonoy, 2006). In the Orgongrainstones, complete occlusion of the intergranularpore spaces by sparry calcite cements, combined withthe dominance of intercrystalline microporositydeveloped within micritic grains (Figs. 3 and 9), hasresulted in this mudstone poro-perm transform.Porosity changes are related both to changes in micriticgrain content and to changes in grain microporosity(Fournier et al., 2011).

iii. CalissaneThe poro-perm transforms (power-law, exponentialand linear) established for the Calissane grainstonesamples are characterised by correlation coefficientsof 0.66-0.79 (Fig. 16). The best fit is given by apower-law relationship (K=4*10-6Por4.7419). Thiscorresponds to relationships within Lucias class 2-3, and is very similar to the relationship establishedby Lonoy (2006) for uniform interparticlemesoporosity. Most of the intergranular pore spaceis filled with calcite cement. Despite its grainstonetexture and its coarse grain size, the porosity-permeability relationships associated with this rocktype is mainly controlled by the microporosity withinthe grains. The presence of mouldic and intergranularpore types explains the change from the relationshipestablished for the Orgon grainstones. Significantly,the poro-perm transforms for the samples selectedalong vertical and horizontal directions (i.e.perpendicular and parallel to strata boundaries) areidentical. The data-sets cover nearly 50 m of section


in both directions within a single stratigraphic unit.The similarity of the two transforms indicates thatthe porosity-permeability relationships are notdependent on the sample direction, implying that onevariable can be estimated from the other using thesame transform. This is consistent with the apparentlack of petrophysical anisotropy in the sampledstratigraphic unit. This type of poro-perm transformcan be applied to microporous-dominated coarsegrainstones with minor intergranular and mouldicpore types.

iv. RustrelThe porosity-permeability cross-plot for the Rustrelrudstone samples (Fig. 3F) displays a power-lawrelationship (K=10-4Por4.003) with a correlationcoefficient (R2) of 0.72 (Fig. 17). Pore types aredominated by intergranular mouldic porosity and

intragranular microporosity. This corresponds torelationships for reservoir rocks within Lucias class2, whereas this coarse grainstone facies shouldcorrespond to Lucias class 1; the difference isinterpreted to be due to the presence of intragranularmicroporosity, although the change is difficult toquantify. This poro-perm transform correspondstenuously to the relationship established by Lonoy(2006) for interparticle macroporosity. The diversityof pore type and pore size with increasing grain size(up to gravel grade), together with the heterogeneityof this rock compared to the Orgon chalkygrainstones, may explain the larger scatter of the dataand the moderate correlation coefficient of therelationship.

In addition, a support effect (cf. Matheron, 1970)may have influenced the poro-perm regression functionand the larger scatter in the data, as a result of the

Fig. 15. Porosity versus permeability cross-plots for the total data set (A) and the Orgon samples (B). Luciasclasses 1, 2 and 3 (Lucia, 1995) are indicated for the total data set (A) by the dashed lines. Most of the samples

fall within the poro-perm trend of Luciaclasses 2-3 corresponding to mud-supported fabrics, despite theirgrain-supported texture and non-vuggy pore types (Lucia class 1). This shift from class 1 to class 3 is caused bythe presence of microporosity in the grainstones. The Orgon poro-perm relationship (B), represented by a

power-law function, is characterized by a high correlation coefficient, 0.96. These fine grainstone samples aredominated by microporosity.

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Fig. 16. Porosity versus permeability cross-plots for the Calissane total data set (A), the Coussous horizontal

sampling data set (B) and the Coussous vertical sampling data set (C). For the fine-coarse grainstones of theCalissane total data set (A), which are dominated by microporosity and minor intergranular and mouldic pore

types, the best fit is given by a power-law relationship (K=4*10-6Por4.7419). The Les Coussous coarse grainstones(B) are characterized by similar poro-perm relationships in both vertical and horizontal sampling directions(see text for explanation).

Fig. 17. Porosity and permeability cross-plot for the Rustrel data set. These rudstones, dominated by complex

and coarse pore types, are characterized by a weak poro-perm relationship following the trend of Lucias Class1 (see text).

RUSTREL

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small size of the plug samples relative to the porenetwork dimensions. Based on geometricconsiderations, this effect occurs at 2 mm grain/poresize for 25 mm diameter plugs, and 5 mm for 50 mmdiameter plugs (Anguy et al., 1994). Anguy et al.(ibid.) investigated this phenomenon in detail, usingFourier transforms of thin sections to identifymicrostructures that span core samples. Theyconcluded that the sample size needs to be at leastfour times the wavelength of the microstructurecomponents to be reliable.

Fig. 18. Effects of grain size on porosity-permeability relationships and permeability of fine-coarse grainstonesamples (Calissane and Orgon). A. Cross-plot of average grain size and permeability for selected samples

from the Calissane and Orgon data-sets, showing the lack of a clear relationship between these two variables.B. Cross-plot of porosity versus permeability for the Calissane and Orgon samples. The different sampling sub-localities (Les Fourches, Les Coussous, Le Belvdre, Le Rocher Rouge and Orgon) correspond to specific grain

sizes and facies. The poro-perm transforms for each of these groups are shifted slightly to higher permeabilitywith increasing grain size.

As a consequence, in the case of the coarser-grained samples from Rustrel, the porosity andpermeability measurements on small plug samples areprobably not fully representative of the effectivepermeability of the reservoir rock at a larger scale.

Controls on poro-perm variationsNinety-eight samples, representative of the total dataset, were analysed with a petrographic microscope toinvestigate controls on porosity and permeabilityvariations. The grain-supported texture of the rock

y = 0.0267e0.2325x R = 0.9473

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y = 0.113e0.2042x R = 0.7416 Coussous

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samples and the lack of vuggy porosity made themsuitable for the application of Lucias classificationof carbonate reservoir rocks (Lucia, 1983). It wasnoted above that the porosity-permeability cross-plotsmoved from Lucias class 1-2 to class 2-3 as a resultof the abundance of intragranular microporosity, asshown in Fig. 18A. Not surprisingly, there is no clearrelationship between average grain size andpermeability. The poro-perm cross-plot in Fig. 18B

indicates that, for a given porosity value between 5and 25 %, permeability increases by one order ofmagnitude with increasing average grain size from0.17 mm (Rocher Rouge Calissane) to 0.79 mm(Les Coussous Calissane).

The importance of microporosity in predictingpermeability from porosity for these reservoir rocksis shown in Fig. 19. The poro-perm trends are clearlyseparate for the different types of pore systems, and

Fig. 19. Cross-plots between permeability and different types of porosity for selected grain-supported rocksamples (Calissane, Orgon and Rustrel). A. Poro-perm plot according to dominant pore types. Samples

dominated by microporosity or intergranular porosity fall on distinct poro-perm trends. The shift frommicroporosity to intergranular pore types corresponds to a decreasing specific surface area of the porousmedia and an increasing permeability for a given porosity value. B. Relationship between intragranular and

intergranular porosities and permeability. The percentage of the different pore types have been normalizedaccording to thin-section analyses. Intragranular porosity, formed by microporosity of micritic grains, displays

the best trend (see text for explanation).

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the samples with dominant and uniform microporosityfall on a clear regression line. It can also be assumedthat the specific surface area of the pore system isgreater with uniform microporosity than with amixture of mouldic and intergranular macroporosity.In addition, for a given porosity, the permeability of asample with intergranular porosity is greater by oneorder of magnitude than that of a purely microporoussample. By contrast, the impact of mouldic porositydevelopment on permeability is relatively low. Thelocation of microporosity within the grains explainsthe unusually greater influence of intragranular porosityon permeability, as opposed to intergranular porosity(Fig. 19B).

Petrophysical geostatisticsOne of the main objectives of the study was to collectand analyse petrophysical data at an inter-well scale(0.1-1000 m) in apparently homogeneous andcorrelatable grainstone units. For example, in thesubsurface (i.e. in a producing hydrocarbon field),the Calissane grainstone unit would have been sampledby a maximum of 3-4 wells over a distance of 2 km.A maximum of 30 plugs would have been cut from a10 m long vertical core recovered randomly from oneof the wells. The porosity and permeability 3D modelwould have been based on this limited petrophysicaldata set and stratigraphic well correlations(Borgomano et al., 2008).

In the studied outcrops at the Calissane, Orgonand Rustrel locations, a total of 630 plugs were drilledat regular spacings (0.1, 1, 10, 100, 1000 m), both inlateral and vertical directions (Figs. 5, 7). Particularattention during sampling occurred at the Calissanelocation (Les Coussous quarry), where horizontalsampling took place parallel to a stratigraphic datum-line which was crossed by four vertical sections(covering a total of 35 m).

These outcrop investigations explored the spatialdistribution of properties at variable scales, includingthe relationships between distance and property aswell as the potential structure of heterogeneities atintermediate scales between core samples and inter-well distances. A mathematical tool often used tosupport such analyses is the variogram, which ingeneral displays the variance of a property valueplotted against the distance between samples(separation distance or lag) in a specific samplingdirection (Gringarten and Deutsch, 2001; Frykman,2001). The property simulations were carried out withGocad on 2D stratigraphic grids.

Similar methods have been applied to outcroppingPermian carbonates in the USA (Eisenberg et al., 1994;Grant et al., 1994; Kerans et al., 1994; Pranter et al.,2005). Although the stratigraphic, sedimentologicaland diagenetic context of these studies are not

comparable to the present investigation, some of theresults have been integrated in the general discussionbelow. A description of the geostatistical investigationsat the three outcrop locations (Calissane, Rustrel,Orgon) is followed by a general discussion.

CalissaneAt this location, the permeability spans three ordersof magnitudes (0.1-30 mD) over a distance of 1200m. There is a possible long-range periodicity of 600m which could not however be confirmed bycomplementary outcrop investigations (Fig. 20). Atany location, within 100 m distance, the permeabilityvaries by only two orders of magnitude. Zooming inon the Les Coussous quarry data-set allows a preciseanalysis of the lateral variability of this propertybetween distances of 0.1 m and 100 m. The firstobservation concerns the presence of an apparentshort-range periodicity in the permeablity data ofaround 10 m with a variability of one order ofmagnitude. This variability is not correlated with thepresence of faults and fractures plotted on the graphs(Fig. 20). Further zooming allows the periodicity tobe estimated at 7 m. Given the excellent correlationbetween porosity and permeability (Fig. 16), the almostperfect tracking between the porosity and permeabilitycurves is not surprising (Fig. 21).

The horizontal variograms of both porosity andpermeability illustrate the periodicity of the lateralvariation (Fig. 21). For the Les Coussous horizontalpermeability data-set, the variogram analysis indicatesthat only 20 % of the variance is represented by arandom component (nugget). This suggests thatthe remaining 80 % corresponds to a combination ofa short-range correlation structure (with a range of4 m) and the longer range cycles of 7-10 mwavelength. Such variogram cycles are attributed tothe hole effect described by Journel and Huijbregts(1978), which implies the existence of periodicgeological structures controlling the property variance.Similar observations can be described for the porosityvariation (Fig. 21). Only a small portion of the variance(20 %) is random and not structured by the shortrange and periodic geological components. Similartrends have been observed in Mississippian carbonatesof the Madison Formation (Pranter et al., 2005) andPermian carbonates in the San Andres Formation(Jennings et al., 2000) in Texas, with a noticeablegreater proportion of random component. Dynamicsimulations suggest that the long-range periodicpermeability structure could have a significantinfluence on fluid flow, even when short-rangevariability comprises most of the petrophysicalvariance (Jennings et al., 2000).

In a vertical direction (Fig.6), the lowestpermeability picks (< 1 mD) seems to correlate with


the occurrence of thin cemented beds and stylolites,especially towards the top of the unit. The mean grainsize varies between 0.6 and 0.8 mm, except in theuppermost part where it reaches 1.7 mm. An overalldecreasing-upwards permeability trend can beobserved over the 25 m interval, from 20 to 5 mD

(one order of magnitude). The high frequencypermeability variations, within the same order ofmagnitude, correspond to the apparent bed thicknessranging between 0.5 and 3 m. The lowestpermeabilities occur in thin streaks, whereas the higherpermeabilities characterise thicker packages. It seems

Fig. 20. Lateral variations of permeability at different scales in the Les Coussous grainstone unit, Calissanelocation (Fig. 5). A. Long-range (1200 m) permeability variation between the Les Coussous quarry and theBelvdre location. The positions of the three faults with minor displacement which cross the transect are

indicated on the upper part of the graph. A possible long-range cyclic variation of permeability is indicated bythe dotted line. B. Medium-range (70 m) and (C) short-range permeability variations in the Les Coussous

quarry (Fig. 6), showing possible cyclic variations, between 1 and 10 mD, of 5-7 m wavelength. The presence ofopen fractures (Fig. 6B) was recorded accurately, and had no apparent influence on the permeability trendsand rock fabrics.

Les Coussous Horizontal data

0,1

1,0

10,0

100,0

10000,0 20000,0 30000,0 40000,0 50000,0 60000,0 70000,0 80000,0Distance (mm)

Per

mea

bil

ity

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)

Fractures

Petrophysics outcrops Les Coussous Total + Les Fourches Thin sections Profile Grain Size Photo Poutcrops

Strata-conformed Permeability Bioclastic grainstone bed

0.10

1.00

10.00

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0 200000 400000 600000 800000 1000000 1200000 1400000Distance (dm)

Perm

eabi

lity

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)

1.00

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100.00

68.0 70.0 72.0 74.0 76.0 78.0 80.0

Distance (m)

2000 4000 6000 8000 10000 12000 14000 (0.1 m)

Les Coussous Le Rocher Rouge Le Belvedere Main faults

Fractures

Perm

eabi

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(mD

)

A

B

C


that low permeability is correlated with the proximityof bed boundaries, expressed by a whiter colouron the quarry faces (Fig. 6). The porositymeasurements confirm that these low-permeabilitystreaks are related to the lowest porosities (< 15 %).Thin-section point-counting showed that the lowporosity in these streaks is related to a high contentof intergranular calcite cement which could beresponsible for the whiter colouration on the quarryfaces.

Outcrop and thin section observations confirm thatthe porosity and permeability characteristics reflectthe sedimentary bedding as expressed by subtlevariations in grain size and diagenetic transformations.Large-scale cross-bedding has probably had thegreatest effect on these cyclic property variations. Itcan be assumed that the vertical and horizontalproperty curves are spatially correlated, given an

average angle for the cross-bedding of 15-20 and astretching of the vertical height by 3.5 (Fig. 21). Bothproperty curves and variograms in the vertical andhorizontal directions show similar cyclic trends, withsignificant spatial correlation of low and high values.In an analogue subsurface situation, this would meanthat the vertical variogram can be used to model thelateral distribution of the property given the angle ofthe cross-bedding.

The correlation between the vertical and horizontalvariograms has been applied to 2D modelling of theporosity and permeability of the Les Coussous unitby Sequential Gaussian Simulation (SGS) (Fig. 22).SGS integrates the variogram and the histogram ofthe property, and is well adapted to the simulation ofcontinuous variables (Deutsch and Journel, 1998).The simulations were carried out in Gocad on a highresolution stratigraphic grid. For the Les Coussous

Fig. 21. Porosity and permeability variograms for the Les Coussous grainstone unit in the Les Coussous quarry

(Fig. 6). A. Horizontal permeability variogram showing a cyclic correlation. B. Horizontal porosity variogramshowing a cyclic correlation, tracking the permeability variogram. C. Stretched vertical permeability

variogram, apparently matching the horizontal variogram. This correlation between the vertical and horizontalvariograms could be the result of the large-scale cross-bedding in the Les Coussous grainstone units (see text

for explanation).

Horizontal porosity vario. (spherical)

Var: 7.3

Var: 10.8

Nug

get

Sill

Range: 4 m

Cycle

Short Range correlation

Cyclic long range correlation

Vertical perm vario. spherical (stretched x 3.5)

Cyclic long range correlation

A

B

C

Var

ianc

e

m

m

x 3.5 m


Fig. 22. High resolution 2D property modelling of the Les Coussous grainstone unit in the Les Coussous quarry.

Input data are represented by the spheres. A. Porosity model (SGS) based on an exponential variogram withan ellipsoid that reflects the anisotropy of the sedimentary structure (dipping layers) and the correlation

between the vertical and the horizontal measurements. B. Permeability model realized by applying a poro-perm transform, obtained from plug data (Fig. 16B), to the SGS porosity model. Input data and variogram notused in this case. C. Permeability model (SGS) based on an exponential variogram with an ellipsoid that

reflects the anisotropy of the sedimentary structure (dipping layers) and the correlation between the verticaland the horizontal measurements (see discussion in text).

Porosity SGS

Permeability SGS

mean: 4.5 Var: 8.4 Std d.: 2.9

mean: 5.7 Var: 11.9, Std d.: 3.4

mean: 18.3 Var: 7.7 Std d.: 2.8 5 m

Permeability from poroperm transform

25 % 10

15 mD 0.2

A

B

C


data, the ellipsoid of the modelled variogram reflectsthe strong anisotropy of the properties in relation tothe cross-bedding (Fig. 22). Two workflows tosimulate the permeability were tested: (i) directsimulation from the conditioning outcrop permeabilitydata; and (ii) transformation of the porosity simulationby applying the poro-perm relationship establishedfrom the outcrop samples. The permeability modelresulting from the direct SGS is characterized byhigher values of average and variance (respectively,5.7-11.9 versus 4.5-8.4), a greater proportion of thehigh permeability zones and an apparent higher spatialheterogeneity. The poro-perm derived permeabilitymodel is in general smoother and reflects the porositymodel (Fig. 22). The differences between these twopermeability models and their impact on reservoirmodelling are discussed below, following a discussionof permeability modelling at Rustrel and Orgon.

RustrelAt the Rustrel location, only 30 plug samples weredrilled with a regular spacing within a 16 m2 rectanglecovering part of a bioclastic rudstone channel complex(Figs 7 and 23). The application of simple statisticaltests to this data-set showed that the properties arehighly heterogeneous at a plug scale, in relation to thecoarse-grained rock fabrics associated with thecomplex pore network, resulting in a moderatecorrelation (R2=0.72) between porosity andpermeability at this scale (Fig. 17). Porosity andpermeability respectively range from 6.7 to 36.2 %and from 0.6 to 299.6 mD (Fig. 17). The lowestproperty values are related to plug samples dominatedby cemented coral pebbles, whereas the highest valuescorrespond to porous rudist grainstone-rudstones.Fining-upwards trends and cross-bedding affect theindividual channel bodies which are marked by erosivebases (Fig. 7).

The experimental horizontal variogram of thepermeability can be divided into three intervals (Fig.23D):

(i) at the origin, the nugget effect is absent andthe variogram exhibits Gaussian behaviourrepresenting a smoothly varying property over a shortdistance (< 1 m), corresponding to 35 % of thevariance;

(ii) the second interval represents a 3 m intervalwith linear behaviour and a moderate correlation (20% of the variance); and

(iii) the third interval exhibits linear behaviour overa 2 m interval representing a longer-range correlationwith 45 % of the variance. This experimentalvariogram indicates that, despite their great varianceand dispersion (Fig. 14), the properties are notdistributed randomly; also, their distribution is probablycorrelated to geological structures. Given that the

stratigraphic layering is oblique, and assuming that agreat proportion of the permeability variability iscorrelated to the grain size and texture, an interpretationof the horizontal variogram could be as follows (Fig.23). The short-range strong correlation and smoothvariation are the result of gradual changes in grainsize and texture over short distances within channelbodies (e.g. fining-upwards trends). Medium-rangeweak correlations a over 3 m interval reflect the almostrandom variation at the scale of individual channelbodies; and longer-range correlations correspond tothe stacking of several channel bodies with differentgrain sizes and textures.

The porosity SGS was based on the plug data,their histogram and a modelled exponential variogramwith an ellipsoid that reflects the anisotropy of thesedimentary structure (dipping layers). The overallstratigraphic architecture is represented by the porosityrealization, given that the limits between sedimentarybodies are not marked by systematic porositycontrasts. The permeability was modelled accordingto the methods used previously for the Calissaneoutcrop data, i.e. a direct SGS and the application ofthe poro-perm transform to the porosity model. Thepermeability model resulting from the direct SGS ischaracterized by higher values of average and variance(respectively 44.2 - 7396 versus 126.2 - 30504), agreater proportion of high permeability zones, and anapparent higher spatial heterogeneity. The poro-permderived permeability model is smoother overall andreflects less anisotropy and heterogeneity than theporosity model (Fig. 23).

OrgonAt the Orgon location, 46 plug samples were drilledwith a regular spacing within a 78 m2 rectanglecovering a small part of a fine, bioclastic grainstoneunit (Fig. 24). The application of simple statistics onthis data set showed that the properties are highlyhomogeneous at a plug scale, in relation to the fine-grained rock fabrics associated with a simple networkof microporosity, thus resulting in an excellentcorrelation (R2 = 0.96) between porosity andpermeability at this scale (Fig. 15B). Porosity andpermeability respectively range from 6.7 to 23.2 %and 0.18 to 5.57 mD (Fig. 14). The lowest propertyvalues are related to thin layers of highly calcite-cemented grainstones, whereas the highest valuescorrespond to thicker beds of less cementedgrainstones.

The experimental horizontal variogram of thepermeability can be divided into three intervals (Fig.24D). At the origin, the nugget effect represent only10 % of the total variance, corresponding to a weakrandom component, and the variogram exhibits linearbehaviour (exponential or spherical), suggesting a


Fig. 23. High resolution 2D property modelling of the Rustrel rudstone unit (Fig. 7). Input data are representedby the black dots. A. Porosity model (SGS) based on an exponential variogram with an ellipsoid that reflects

the anisotropy of the sedimentary structure (dipping layers and channels). B. Permeability model realized byapplying a poro-perm transform, obtained from plug data (Fig. 17), to the SGS porosity model. Input data and

variogram not used in this case. C. Permeability model (SGS) based on an exponential variogram with ellipsoidthat reflects the anisotropy of the sedimentary structures (see discussion in text). D. Horizontal permeabilityvariogram; the A-C intervals are discussed in the text to interpret this variogram that reflects the

sedimentary structures (dipping layers and lens). E. Outcop photo showing the sample locations relative to themain sedimentary structures.

9

Permeability SGS mean: 126.2 Var: 30504 Std d.: 174.6

Porosity SGS 29 % mean: 21.7 Var: 58.7 Std d.: 7.6

1 m

103

Modelled Exponential Horizontal Variogram for the Permeability

A B

C Variance

0.5 300 mD mean: 44.2 Var: 7396 Std d.: 86.0

555555

m

Varia

nce

1 m

A

B

C

D

E



strong spatial correlation of the variance over shortdistances (< 3.5 m) corresponding to nearly 90 % ofthe variance. The second zone represents a 9 minterval at the maximum of the variance (seal). Thethird zone corresponds to a hole effect that representa longer range correlation of the variance. Thisexperimental variogram could be interpreted in termsof the presence of a 9 m long homogeneous intervaldominated by average permeability (zone B), separatedby 3-4 m long intervals of increasing or decreasingpermeability (zones A and C). Thin-sectionobservations indicate that these structures correspondto diagenetic transformations: calcite cementationtrends (zones A and C), and porous or cemented lenses

of homogeneous permeability (zone B), whichconform to the horizontal layering.

The porosity SGS (Fig. 24A) was based on theplug data, their histogram and a modelled exponentialvariogram with an ellipsoid that reflects the anisotropyof the stratigraphic structure. The overall diageneticarchitecture is represented by the porosity realization,given that the limit between diagenetic bodies is notsharp and is marked by gradual porosity changes.Overall, the realization is dominated by a backgroundof values close to the average but with lenses ofextreme values. The permeability was modelledaccording to the methods used previously for theCalissane and Rustrel outcrop data, namely: direct SGS

Fig. 24. High resolution 2D property modelling of the Orgon grainstone unit. Input data are represented bythe black dots. A. Porosity model (SGS) based on an exponential variogram with an ellipsoid that reflects theanisotropy of the sedimentary structure (horizontal layers and diagenetic lens). B. Permeability model realized

by applying a poro-perm transform, obtained from plug data (Fig. 15B), to the SGS porosity model. Input dataand variogram not used in this case. C. Permeability model (SGS) based on an exponential variogram with

ellipsoid that reflects the anisotropy of the sedimentary structures structure (horizontal layers and diageneticlens). D. Horizontal permeability variogram; the A-C intervals are discussed in the text to interpret this

variogram that reflects the sedimentary structures (horizontal layers and diagenetic lens).

mean: 2.9 Var: 2.2 Std d.: 1.5

mean: 18.9 Var: 14.9 Std d.: 3.8

mean: 2.7 Var: 5.2 Std d.: 2.2

10 mD 0.01

Exponential horizontal variogram (permeability)

Total Variance

Nugget (10 % of variance) m

A B C

A B 3.5 12

Hole effect

Permeability SGS

Porosity SGS 29 % 9 1 m

2.5 D

C

B

A



and application of the poro-perm transform to theporosity model. The two permeability models are verysimilar with almost identical averages and variances(2.7 - 5.2 versus 2.9 - 2.2 respectively), but with alower proportion of low permeability zones in theSGS model (Fig. 24B, C).

DISCUSSION

In this section, three topics will be discussed on thebasis of the outcrop petrophysical survey which waspresented above, namely (i) comparison of the intrinsicproperties of the different types of carbonate rocks;(ii) rock property predictability of carbonates usingsparse core samples from distant wells; and (iii)implications for the static modelling of an analoguesubsurface reservoir.

Intrinsic propertiesThe petrophysical characteristics of the Calissane,Rustrel and Orgon carbonates are summarized in Fig.25. The histograms of the properties, in horizontaland vertical directions, give clear indications ofpetrophysical heterogeneity and anisostropy. Thus theCalissane grainstone is very homogeneous withmoderate anisotropy; that at Rustrel is heterogenouswith significant anisotropy; and that at Orgon is veryhomogeneous with weak anisotropy related to thepresence of layering and thin cemented beds. Thehomogeneity of the Calissane and Orgon carbonatesis primarily controlled by the grainstone texture andthe dominant pore type (microporosity). The presenceof diagenetic changes (cemented lenses and beds)explains the differences between the properties in thehorizontal and vertical directions at Orgon. The strongheterogeneity in the Rustrel carbonates results fromthe very coarse grain size and poor sorting of therudstones combined with the presence of variousdifferent pore types. The absence of diageneticstructures may explain the moderate anisotropy, as inthe case of the Calissane carbonates. The heterogeneityrecorded at Rustrel may in fact result from the smalldimensions of the plug (i.e. a support effect) relativeto the rock and pore structures. Increasing the sizeof the samples by a factor of 10 would probably havesuppressed the dispersion of the property histogram.

The interpretation of the variograms confirms theseinitial interpretations which were based on thehistograms and sedimentary characteristics. Verticaland horizontal variograms of similar dimensions arecomparable and proportional, with hole effects (inthe case of the Calissane and Rustrel data) reflectingthe large-scale cross-bedding and the channelstructures. The presence of diagenetic lenses or bedsat Orgon may explain the vertical cycles (hole effect)associated with a more exponential correlation in the

horizontal direction (within the homogeneous layers).The vertical variograms of the Calissane and Rustreldata are representative of the 3D spatial distributionof the properties given the geometry of thesedimentary structures. Diagenetic transformationsat Orgon do not affect the apparent homogeneity ofthe properties but introduce vertical anisotropy.

The poro-perm relationships are very different forthese three carbonate rocks. In the case of the Orgoncarbonates, the almost perfect relationship betweenporosity and permeability (R2= 0.96) is the result ofthe fine-grained chalky texture (no support effect)combined with the presence of microporosity withinthe grains. The increasing porosity trend isproportional to the amount of calcite cement. For theCalissane grainstones, the introduction of intergranularporosity combined with a coarser rock texture mayexplain the inferior poro-perm relationship. For theRustrel rudstones, the very coarse rock texture(strong support effect) combined with the complexityof the pore network may explain the weak poro-permrelationship. Increasing the sample size would probablyhave improved this relationship.

Prediction of the propertiesOne of the main challenges in subsurface reservoirgeology is the prediction of rock properties from alimited data set, for example comprising as few asten plug samples taken from far-distant wells whichmay be one km or more apart. This challenge can beaddressed on the basis of the carbonate outcropstudies reported here. Key questions for the Calissane,Rustrel and Orgon limestones are:

(i) are ten plug samples representative of intrinsicrock properties such as facies, rock type, pore types,porosity and permeability?

(ii) are ten samples taken from two locations onekm apart representative of the spatial propertydistribution? This issue can be addressed usingmathematical methods, such as analysis of thevariance relative to the number of samples (Giles,1997), the dimensions of samples (Anguy et al., 1994)and the distance between samples (Gringarten andDeutsch, 2001). However it is necessary to estimatethe structure (geometry, topology) of the property,and the scale of the property heterogeneity relative tothe sample dimensions and spacing. In brief, is itpossible to estimate the intrinsic geological andpetrophysical characteristics of the Calissane, Rustreland Orgon limestones from this limited data set? Thisis a typical challenge faced by subsurface reservoirgeologists, and it implies the integration of a prioriknowledge, as described in carbonate reservoirmodelling workflows (Borgomano et al., 2008).

In the case of the Orgon limestones, the 10 randomsamples are representative of the intrinsic properties


of the reservoir. The support effect is nil, the poretype and facies are homogeneous at all scales(microporosity and fine grainstone), and therelationship between porosity and permeability fallson a perfect curve. Additional outcrop surveys in thisarea (Masse and Fenerci-Masse, 2011) indicated thatthese reservoir characteristics can be correlated inthe same stratigraphic unit over a distance of morethan several kilometres. But the vertical anisotropydue to the thin tight lenses would be impossible toestablish from samples which are separated by morethan a few metres. In the subsurface, thestratigraphical well correlation concept of thesediagenetic transformations, based on a prioriknowledge, would be critical (Borgomano et al.,2008). The overall homogeneity of the rock and themicroscale of its pore network make the propertiesof the Orgon limestones predictable from a limitednumbers of plug samples. The short-range, tight andflat diagenetic lenses are not predictable from distantdata, and only their relative low proportion in thevertical direction can be established. Thesecharacteristics could be different in susbsurfaceanalogue reservoir as a result of hydrocarbon-relateddiagenetic transformations

In the case of the Rustrel limestones, 10 randomsamples are not representative of the intrinsicproperties of the reservoir. The support effect is toostrong, the pore type and the facies are complex andheterogeneous from micro- to decimetric scales, andthe relationship between porosity and permeability isvery weak. The strong anisotropy due to the cross-bedding and the channels cannot be predicted fromsuch a limited data set, but the intrinsic complexity ofthis reservoir rock can be deduced from these fewsamples. Additional outcrop studies in this area (Masseand Fenerci-Masse, 2011; Leonide et al., 2012)indicate that these reservoir characteristics can becorrelated in the same stratigraphic unit over distancesof more than several hundred metres, but that theshort-range channels are not predictable from distantdata. The petrographic identification of this complexrock type associated with the coarse rudstone texture,in combination with the interpretation of the cross-bedding from borehole image and core data, maysupport the upscaling of the properties. For example,upscaling the porosity and permeability of such rocktypes may require specific averaging methods whichare discussed below. The poro-perm relationshipestablished from 10 plug samples is not realistic; onlymeasurements on whole core samples could providevalid property estimates (Ehrenberg, 2007).

For the Calissane limestones, the 10 randomsamples are representative of the intrinsic propertiesof the reservoir. The support effect is weak, the porenetwork is dominated by microporosity with minor

intergranular porosity, the facies is homogeneous fromcentimetric to hectometric scales, and the relationshipbetween porosity and permeability is excellent. Theporo-perm relationships and the property histogramsestablished from plug samples taken in the verticaland horizontal directions are identical. Vertical andhorizontal property variograms are correlated, allowingthe property anisotropy and the cyclic structureresulting from the cross-bedding to be predicted; thesecould also be interpreted from core and borehole dataimaging. In addition, the poro-perm relationshipestablished from 10 random plug samples would alsobe realistic.

Implications for reservoir modellingThe volume of a plug sample is typically ten milliontimes smaller than the volume of a grid cell in asubsurface geological model. To use a set of plugmeasurements effectively, they need to be averagedinto grid cell properties, a process often described asproperty upscaling (e.g. Borgomano et al., 2008).In general, scalar properties such as porosity can beaveraged by the arithmetic mean independent of thedirection of sampling and of short-range anisotropyor heterogeneity (Goovaerts, 1997; Deutsch, 2002).However permeability, as a tensor property, isdependent on the direction of measurement. In areservoir, permeability is controlled by the directionof flow relative to the anisotropy of the reservoir (e.g.Noettinger, 1994; Romeu and Noettinger, 1995;Renard and De Marsily, 1997). In a typical reservoirworkflow, upscaling permeability from plug data isdependent on the structure of the reservoirheterogeneity, and in particular on the dimensions ofthe heterogeneity spatial correlation (Jennings et al.,2000).

Two approaches have been used to modelcarbonate reservoir permeability from well data(Borgomano et al., 2008). The first approach usesporo-perm relationships established for stratigraphicunit

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Borgomano Et Al 2013