Improving reservoir models of Cretaceous carbonates with digital outcropmodelling (Jabal Madmar, Oman): static modelling and simulating

clinoforms

Erwin W. Adams1,3,*, Carine Grélaud2, Mayur Pal1, Anita É. Csoma1,5, Omar S. Al Ja’aidi3 andRashid Al Hinai4

1Shell International Exploration and Production B.V., Kessler Park 1, 2288 GS Rijswijk, The Netherlands2EGID Institute, Bordeaux 3 University, Allée Daguin 1, 33607 Pessac Cedex, France

3Sarawak Shell Berhad, Locked Bag No. 1, 98009 Lutong, Sarawak, Malaysia4Petroleum Development Oman, P.O. Box 81, Muscat, PC 113, Sultanate of Oman

5Current Affiliation: ConocoPhillips, 600N Dairy Ashford, PR3064, Houston TX, 77079, USA*Corresponding author (e-mail: erwin.adams@shell.com)

ABSTRACT: In Jabal Madmar in the Sultanate of Oman, Cretaceous epeiriccarbonate platform architectures were characterized by employing a digitaloutcrop modelling workflow. A framework model for Natih Sequence I(Natih E member) was established, which embeds a meticulously studiedplatform-top incision and shoal complex. Outcrop-scale clinoforms arerecognized in these shoals by hectometre-scale (100 m long) medium tohigh-angle (1–5() inclined stratal surfaces comprising texture-based faciestransitions. These clinoforms are usually beneath the resolution of seismicdata and as such are not easily recognized and correlated between wells.Geologically realistic clinoform models were built using a well-definedstratigraphic model that incorporated inclined surfaces in the model gridand if available, data on lateral facies transitions. Waterflood simulationsdemonstrated improved sweep efficiency in these models. In contrast, simplemodels without clinoform heterogeneities resulted in less efficient piston-likepatterns of sweep. The study presented in this paper demonstrates anoutcome contrary to previous studies, as in this study, barriers to flow areabsent. Complex clinoform models must be considered in reservoir modellingworkflows to correctly derive static and dynamic rock properties. This isbecause outcrop-scale clinoforms have a potential impact on reservoirbehaviour under secondary and tertiary recovery mechanisms.

KEYWORDS: carbonate reservoir heterogeneity, static and dynamic reservoirmodelling, clinoform, outcrop, Oman, Natih Formation

INTRODUCTION

Forecasting responses to secondary and tertiary recoverymechanisms are critically dependent on the accurate predictionand robust incorporation of geobodies into reservoir models.A geobody is considered as a volume of rock material boundedby envelopes that are genetically related to a set of specificgeological processes (Borgomano et al. 2008). Some of themost reliable information on geobodies and their associatedinternal heterogeneities comes from outcrop analogues becausethey allow the examination and sampling of geological varia-bility at all scales up to that of the outcrops themselves (Dreyeret al. 1993; Geehan 1993; White & Barton 1999; Li & White2003; Falivene et al. 2006; Labourdette et al. 2008). Byemploying a digital outcrop modelling workflow, geologicalelements observed in outcrop can be efficiently and accuratelyquantified (Hodgetts et al. 2004; Bellian et al. 2005; McCaffreyet al. 2005; Verwer et al. 2007). Specifically, outcrop-based

geological features can be positioned and recorded spatiallywith digital mapping and surveying techniques in 3D on scalesranging from centimetre (core plug) to kilometre (reservoirscale). Such large amounts of collected data can be assimilatedand visualized by creating a digital outcrop model (DOM)(Bellian et al. 2005). Because a DOM entails a geospatial andnumerical framework it not only improves the collection andvisualization of outcrop geology (and commonly for thisreason indirectly the understanding) but also aids the buildingof pseudo static reservoir models that can be used for a rangeof purposes from reservoir performance to seismic modelling(Janson et al. 2007; Howell et al. 2008a; Adams et al. 2009;Jackson et al. 2009; Enge & Howell 2010; Falivene et al. 2010).

Commonly, the interiors of Middle East carbonate plat-forms are stratigraphically correlated in a layer cake fashionimplying that the platform interior comrised largely flat,extensive and undifferentiated shallow-water environmentscreating wide, laterally continuous facies belts (Droste 2007).

Petroleum Geoscience, Vol. 17 2011, pp. 309–332 1354-0793/11/$15.00 � 2011 EAGE/Geological Society of LondonDOI 10.1144/1354-079310-031

Nevertheless, high-resolution three-dimensional (3D) seismicdata from the subsurface of Oman and United Arab Emirates(UAE) revealed complicated stratigraphic architectures includ-ing the presence of platform-top incisions and progradingclinoforms delineating platform margins (Droste & Van Steen-winkel 2004; Grélaud et al. 2006; Yose et al. 2006). Althoughhigh-resolution 3D seismic data provides information on theperipheral dimensions and geometries of these Cretaceousplatform interior geobodies, the internal make up remainslargely unknown.

In this study, digital outcrop modelling was used in theCretaceous Natih Formation outcrops in Jabal Madmar,Sultanate of Oman, in order to depict an incision geobody andgrainstone shoal geobodies and carefully characterize theirrelated internal heterogeneities. The effectiveness of the digitaloutcrop modelling workflow is demonstrated by using theincision characterization as an example. Outcrop-scale clino-forms were recognized in shoal geobodies by hectometre-scale(100 m long) medium-angle to high-angle (1–5() inclined sig-moidal stratal surfaces comprising texture-based facies transi-tions. The clinoform geometries as well as the nature of verticaland lateral facies transitions have been meticulously studiedand mapped using a digital outcrop modelling workflow. Thisstudy compares simple with more advanced facies modellingmethods and accordingly outlines a workflow whereby geologi-cally realistic clinoforms can be incorporated into static models.Finally, waterflood simulations demonstrate the impact ondynamic behaviour, including better sweep in models embed-ding clinoforms. These outcrop-based static models can beused to produce virtual reality (VR) training datasets by usingthe DOM as a template.

GEOLOGICAL BACKGROUND ANDPREVIOUS WORK

Geological setting of the Natih Formation

During the Permian and Mesozoic, after the break-up ofGondwana, several extensive carbonate platforms developedon the Arabian plate (Murris 1980). By and large, theseplatforms were bordered to the SW by the Arabian Shieldand to the NE by the Tethys Ocean. Stable platform con-ditions ended in the Late Cretaceous (Turonian) with anAlpine episode of compression and obduction of oceanic crustassociated with the collision between Eurasia and the Arabianplate (Warburton et al. 1990). From the Late Cretaceous toMiocene, carbonate sedimentation and stable conditionsreturned, after which a second Alpine phase of continent–continent collision affected the region again (Loosveld et al.1996).

During the Cretaceous, Oman was located on the easternmargin of the Arabian plate where a 1200 m thick and up to1000 km wide carbonate platform developed (Fig. 1) (Murris1980; Droste & Van Steenwinkel 2004). This carbonate plat-form unconformably overlies Jurassic and older strata (seeBase Cretaceous Unconformity in Fig. 1). From the Berriasianto Hauterivian, a phase of progradation created a carbonateplatform and adjacent slope and basin deposits; a main phaseof carbonate deposition in a shelf interior setting occurredduring a phase of aggradation from the Barremian to Turo-nian (Pratt & Smewing 1993; van Buchem et al. 2002a; Droste& Van Steenwinkel 2004). During this period, several phasesof subaerial exposure and clastic input suspended carbonate

Fig. 1. Geological cross-section and stratigraphic column illustrating the Cretaceous carbonate succession of the Sultanate of Oman (figure fromDroste & Van Steenwinkel 2004, reprinted by permission of the AAPG whose permission is required for further use). The line in the inset satelliteimage locates the cross section; the box outlines the image of Figure 2. The Cretaceous Natih Formation (Late Albian–Early Turonian) representsa carbonate succession that was deposited in a shelf interior setting comprising carbonate platforms and intrashelf basins. Jabal Madmar, part ofthe Adam Foothills, is located in the central part of the Cretaceous carbonate platform.

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sedimentation with tectonic movements of the Arabian Shieldand/or eustatic sea level variations being the expected causes(Sharief et al. 1989). One of the largest falls in relative sea leveloccurred during the late Aptian and created a regional uncon-formity in the Middle East (Harris et al. 1984). In Oman, thisevent was associated with karstification and erosion and wasfollowed by clastic input of the Nahr Umr Formation (seeBase Nahr Umr Unconformity in Fig. 1). The Natih Forma-tion (Late Albian to Early Turonian) conformably overlies theNahr Umr Formation and was deposited on a very wide andextensive (more than 800 km) carbonate platform (Alsharhan& Nairn 1988, 1993; van Buchem et al. 1996). In Oman, theNatih Formation is characterized by platform-interior sedi-mentary cycles ranging from several tens to some 150 m thickmixed carbonate–shale sediments at the base, grading intocarbonate deposits (van Buchem et al. 1996, 2002b). Platformdevelopment terminated in the Turonian due to a regionalphase of uplift causing emersion, as indicated by karstificationand local incisions (van Buchem et al. 2002b). This uplift isthought to be related to the formation of a peripheral bulge tothe south of a foreland basin that developed due to obductionof oceanic crust in North Oman during the first Alpine phase(Warburton et al. 1990; Terken 1999). The emersion lasted afew million years and was followed by deepening in theConiacian–Santonian and shale deposition of the ArumaGroup in the developing foreland basin (see Base ArumaUnconformity in Fig. 1).

Sequence stratigraphy of the Natih Formation

In the subsurface, the Natih Formation can be subdivided,based on log signature (mainly gamma ray), into seven litho-stratigraphic members coded A to G from top to base (HughesClarke 1988; Droste & Van Steenwinkel 2004). High-resolution sequence stratigraphic models have been built andthree third order sequences (Sequence I to Sequence III)defined for the Natih Formation (van Buchem et al. 1996,2002b; Schwab et al. 2005; Grélaud et al. 2006; Homewoodet al. 2008). Sequence I corresponds more or less to Natih G,F, and E members, Sequence II to Natih D and C members,and Sequence III to Natih B and A members. Each sequenceshows a similar depositional evolution, with a mixed carbonate-clay ramp system at the base, followed by a carbonate-

dominated platform system in the upper part (van Buchemet al. 1996, 2002b). Organic-rich intra-shelf basins developed inthe transgressive parts of Sequence I (Lower Natih E) andSequence III (Upper Natih B). Similar deposition did notoccur in Sequence II, probably because of a greater influx ofclay and deposition under shallower water conditions (vanBuchem et al. 2002b). The top of each third order sequence (i.e.top Natih E, top Natih C and top Natih A) corresponds to aphase of platform emersion, sometimes associated with thedevelopment of incisions (Al-Ja’aidi et al. 2002; Grélaud et al.2006).

Architectural elements of the Natih Formation

The high-resolution sequence stratigraphic models describedabove have been built from integrated studies of outcrops inNorth Oman (Oman Mountains and Adam Foothills; see Figs2 and 3) and subsurface data including high-resolution 3Dseismic data and well logs (van Buchem et al. 1996, 2002b;Schwab et al. 2005; Grélaud et al. 2006; Homewood et al.2008). These detailed studies of the Natih Formation in Omandemonstrated the presence of a highly differentiated internaltopography and complex architectural elements (Droste & VanSteenwinkel 2004; Grélaud et al. 2006). Figure 3 shows aneast–west stratigraphic transect of Natih Formation SequenceI (i.e. Natih G, F, and E). It illustrates a large-scale (tens-of-kilometres) progradational carbonate platform that isbordered by an intra-shelf basin and truncated by incisionsthat developed during times of relative sea-level falls andplatform emergence. Abundant progradational inclined stratalgeometries with variable dips, so-called clinoforms, as well asincision geometries have been recorded for the Natih Forma-tion in Oman (Droste & Van Steenwinkel 2004; Grélaud et al.2006).

Clinoforms observed in Sequence I (Natih E member)demonstrate different directions of carbonate platform progra-dation (Droste & Van Steenwinkel 2004). These clinoformdepositional profiles, although of low angles, demonstrate acomplicated internal stratigraphic architecture and the shortdistances over which facies can change. Generally, clinoformscontain facies partitioning following the clinoform deposi-tional profile with coarse-grained textures found at the top ofclinoforms that grade into fine-grained facies types downslope. Early diagenetic overprint, resulting in either dissolutionor cementation, can occur at topset beds during exposure,whilst hardgrounds can develop along the entire depositionalprofile or only downslope, at condensed intervals. The scalesand dip of clinoforms merit some attention (Fig. 3). Twosubdivisions can be made and range from:

1. Low-angle (0.1–0.3() up to 10 km long ‘seismic’-scale clino-forms have been extensively documented and exert a signifi-cant influence on regional-scale correlatability of units(Droste & Van Steenwinkel 2004; Grélaud et al. 2006).However, their presence and hence impact on a field scale,has been questioned (see Homewood et al. 2008 on theexample of the Fahud Field, Oman). Generally, at thisscale, the clinoforms are modelled in a subhorizontal fash-ion. Notwithstanding, interwell stratigraphic interpreta-tions (including inclined correlations) have to be consideredbecause of their potential impact on internal facies parti-tioning and gradual facies transitions. As mentioned above,seismic-scale clinoforms have been investigated in greatdetail in other studies (Droste & Van Steenwinkel 2004;Masaferro et al. 2004; Yose et al. 2006).

2. Medium- to highangle (1–5(), hectometre (100 m long) or‘outcrop’-scale clinoforms have been observed in Jabal

Fig. 2. A satellite image of North Oman locating Al Jabal Al Akhdarand the Adam Foothills (Fig. 1 shows location). The Adam Foothillscomprise, from east to west, Jabal Madar, Jabal Madmar, JabalSalakh, Jabal Nadah, and Jabal Qusaybah. A cross-section, which hasbeen reconstructed using the Adam Foothills outcrops as well as aseismic survey (indicated by bracket), is indicated by the black line andshown in Figure 3.

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Madmar, Oman, and are the focus of this study. This scaleof clinoform is usually beneath the resolution of seismicdata and as such is not easily recognized and correlatedbetween wells. Nevertheless, there is a key potential impactto interwell-scale modelling and reservoir behaviour if sec-ondary and tertiary recovery mechanisms are adopted. Adetailed geological description and the quantification andmodelling of these outcrop-scale clinoforms is presented insubsequent sections of this paper.

High-angle foresets, with angles up to 30–35(, are com-monly observed in outcrop and frequently interpreted fromlogs (dipmeter and micro image). These are interpreted asthe products of depositional processes that demonstrate themigration of bedforms and are not considered as part of thisstudy.

GEOLOGICAL SETTING OF THE NATIHFORMATION JABAL MADMAR OUTCROPS

In Jabal Madmar, the complete Natih Formation is exposed(Sequence I to III or A to G members) and has been describedby Philip et al. (1995) and Homewood et al. (2008). JabalMadmar is part of the Adam Foothills in North Oman (Figs1–3). The origin of the Adam Foothills is interpreted as beingeither related to diapiric structures (the case for Jabal Madar)

or to basement-involved compressional structures (Mountet al. 1998). Jabal Madmar is related to the second interpreta-tion, i.e. an anticline developed in response to a small amountof slip over a reverse fault, which extends into the basement. Apublished field guide provides detailed information on out-crops exposing the Natih Formation in northern Oman includ-ing Jabal Madmar (Homewood et al. 2008). The structuralsetting of Jabal Madmar, including characterization of faultsand style plus scale of fractures, have been extensively studied(De Keijzer et al. 2007). The reader is referred to these studiesfor detailed geological background on Jabal Madmar. Theoutcrop exposures of the Natih Formation in Jabal Madmarare regarded as analogues to oilfields in the subsurface vicinity(Homewood et al. 2008; Hollis et al. 2010). For example, theFahud Field is located geographically about 150 km west fromJabal Madmar (Fig. 2) and is considered to be part of the sameregional structural domain where faults and fractures havesimilar orientations (De Keijzer et al. 2007). In terms of thepalaeogeographic setting, Jabal Madmar is located in a slightlymore proximal part of the carbonate platform compared to theFahud Field (Homewood et al. 2008).

DIGITAL OUTCROP MODELLING

Historically, high quality, detailed and integrated outcropstudies have been used to provide constructive analogue

Fig. 3. East–west stratigraphic cross section of Natih Formation Sequence I (i.e. Natih G, F, and E members) integrating outcrop and subsurfacedata (modified figure from Grélaud et al. 2006; reprinted by permission of SEPM (Society for Sedimentary Geology) whose permission is requiredfor further use). It illustrates (1) the presence of a large-scale (tens-of-kilometres) carbonate platform which prograded into an intra-shelf basin tothe west and (2) subaerial exposure surfaces highlighted by incisions (IS1 and IS2) that developed during times of relative sea-level falls. The locationof the cross-section is shown in Figure 2. The dotted box illustrates the setting and location of the Jabal Madmar study area.

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information for subsurface studies. In the last ten years, digitalsurveying technologies and digital outcrop modelling havebeen increasingly invoked. These provide valuable, quantitat-ive and accurate assessments of carbonate depositional sys-tems, carbonate geobodies, and partitioning of carbonatefacies and lithology within a deterministic framework (Adamset al. 2005, 2009; Bellian et al. 2007; Janson et al. 2007; Phelpset al. 2008; Verwer et al. 2009; Adams & Hasler 2010). It islikely that in the coming decades this trend will develop toprovide field geologists with a reliable and efficient techniquefor assembling numerical geological outcrop data.

Methods and workflow

In order to quantify geobodies and associated heterogeneitiesobserved in Jabal Madmar, a digital outcrop modelling work-flow was employed, which comprises four steps. These stepsare described below and result in a DOM that defines theframework of Natih Sequence I (Natih E member). It alsodescribes the assimilation of all collected data with which moredetailed geobody modelling studies were carried out (for amore comprehensive description and background of a similarworkflow the reader can refer to Verwer et al. 2007). Finally,the characterization of a platform-top incision is used asexample for demonstrating the workflow.

Step 1: Outcrop selection

Jabal Madmar provides a reservoir-scale outcrop (Figs 4 and5). The outcrop is nearly 90% exposed; scree with limitedvegetation covers recessive units, but uncovered exposures canbe found. Weathering varnish is present but does not obscurefabric and textural observations.

The Jabal Madmar anticline is oriented ENE–WSW and isroughly 15 km long, 5 km wide, and 500 m high (Fig. 4).Several deep canyons cut perpendicular into the anticline toprovide pseudo 3D exposure. The research area is primarilyfocused in and around Madmar 3 Canyon (Figs 4 and 5) whereSequence I is exceptionally exposed (Fig. 3). A WNW–ESE-trending reverse fault, with a maximum throw of about 5 mand for which the north block is the hanging wall, is the onlyrelevant structural feature present in the study area. It does nothamper the reconstruction of geobodies (see Fig. 4B). Figure 6illustrates a composite stratigraphic section measured atMadmar 3 Canyon. It shows the further subdivision ofSequence I (I-3 to I-7; Natih E member E4 to E1) including asequence stratigraphic interpretation by Homewood et al.(2008). Figures 7 and 8 show the outcrop expression ofSequence I and important stratigraphic surfaces. An area ofapproximately 1000 m by 1000 m was selected for constructionof a DOM that constrained the framework of Sequence Iand embedded the incision geobody (Fig. 4B). A high-energyshallow carbonate shoal complex comprising ‘outcrop’-scaleclinoforms of the Natih E3 member was studied in detail in anarea of 200 m by 150 m (Fig. 4B).

Step 2: Digital field geology and data collection

In this study, geological data was positioned and collected withreal-time kinematic global positioning systems (RTK GPS)and LiDAR (light detection and ranging) that was subse-quently integrated with a digital elevation model (DEM) andhigh-resolution satellite imagery. A Quickbird satellite image

Fig. 4. Quickbird satellite images of Jabal Madmar. A Quickbirdsatellite image has a pixel resolution of about 0.7 m. (A) Imageshowing Jabal Madmar anticline. (B) Enlargement of image showingthe area around Madmar 3 Canyon. A reverse fault is represented bythe black dashed line.

Fig. 5. (A) Digital elevation model (DEM) of the area around Madmar3 Canyon (for location see Fig. 4). The DEM was used as a referencesurface or base map for digital outcrop modelling. For Jabal Madmara Quickbird satellite image with pixel resolution of about 0.7 m wasdraped over a DEM with a resolution of approximately 90 m. Theimage is displayed with 2 times vertical exaggeration. (B) Photographof Madmar 3 Canyon showing approximately the same area as theDEM in A. White arrows point at Madmar 3 Canyon.

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with 0.7 m pixel resolution and Shuttle Radar TopographyMission (SRTM) DEM with 90 m grid resolution were used(Figs 4 and 5). SRTM is an international research effort thatpossesses digital elevation models on a near-global scale andcan be downloaded freely over the internet. Indian RemoteSensing (IRS) satellite imagery with 5.8 m pixel resolution wasalso available.

LiDAR images were taken in Madmar 3 Canyon (Fig. 9Aand B). A LiDAR dataset contains thousands of individual x,y, z points including backscatter intensity (Fig. 9B) (Bellianet al. 2005). RTK GPS was used to collect ground controlpoints in order to georeference the LiDAR dataset (Fig. 9C).The cumulative error in spatial positioning is in the order of 15cm and results from merging the RTK GPS and LiDARdatasets into one single coordinate system. LiDAR allowsextraction of data points from inaccessible parts of an outcrop.As such, polylines can be digitized representing fractures orstratigraphic horizons (Fig. 9D).

Spatial point data of stratigraphic horizons were recordedwith RTK GPS by walking along stratal contacts and, for

characterizing internal heterogeneity, simultaneously taggedwith additional geological information (for example, grain sizeor facies type observed above or below the bed contact; Fig.9C). Stratigraphic sections were georeferenced and incorpor-ated into the dataset as pseudo-wells.

Step 3: Data integration and visualization

The combination of the Quickbird satellite image and DEMwas used as a base map. Spatial point data collected with RTKGPS, digitized data from LiDAR, and the digital base mapwere loaded and visualized in Petrel. Locally, the base map wasrefined by using the collected spatial point data (both fromRTK GPS and LiDAR). For example, for the Natih E3member clinoform study area, a DEM with a horizontal gridspacing of 1 m was acquired manually across the outcropsurface topography using RTK GPS.

Fig. 6. Schematic stratigraphic section logged in Madmar 3 Canyon.Nine stratigraphic surfaces were constructed using either GPS (g) orLiDAR (l) data (l); one stratigraphic surface was modelled (m); redlines indicate incision surfaces (IS1 and IS2).

Fig. 7. Outcrop photographs illustrating mapped stratigraphic surfacesin Madmar 3 Canyon from surface Base E to surface IS1. (A)Photograph taken at a location in the centre of incision IS1. The treein the lower right above label ‘Base E’ is roughly 3 m high. (B)Photograph taken outside incision IS1. A person is standing in theupper left corner next to label ‘B’. Note the difference in exposedstratigraphy with Figure A having IS1 being incised into Sequence I-5grainstones and B into Sequence I-6 floatstones only (see also Fig. 6).

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Step 4: Construction of geospatial context

Interrelating and connecting recorded data points was the finalstep in the digital outcrop modelling workflow. To confine aframework for geobodies, first, a DOM was constructed forthe complete Natih E member. For this purpose, the focus wasmainly on constructing surfaces representing stratigraphichorizons (Fig. 9E). Figure 4B indicates the size of the areafor which a DOM was built. Figures 6, 7 and 8 show thestratigraphic surfaces that were mapped for establishing theframework of the model. Several surfaces were directlyrecorded with RTK GPS in the field (Fig. 9C); other surfaceswere obtained by digitizing polylines on LiDAR data (Fig.9D); some surfaces were modelled by assuming constantthicknesses of some of the stratigraphic units in the area ofinvestigation (Fig. 6). The resulting DOM (Figs 9E and 10)confined the framework of Natih Sequence I or E member andassimilated all collected data with which more detailed geo-body analysis was carried out.

Digital outcrop modelling of incision IS1

In Madmar 3 Canyon, incision IS1 can be observed, mappedand modelled in detail (see Figs 3, 6, 9 and 10). The observedgeometries and filling history of the incision are complex(Grélaud et al. 2006). The incision cuts either into float/rudstones of Sequence I-6 or even deeper into cross-beddedgrainstones of Sequence I-5 (Fig. 6). The complex fill (i.e.Sequence I-7) is recognized by a basal packstone or rudstonelag deposit, followed by low-energy confined wackestone in thelower part and high-energy pack/floatstone in the upper part.The wackestone deposits are commonly dolomitized (Fig. 8).

To model the external geometry of IS1, first, the incisionsurface itself was mapped with RTK GPS by physicallywalking-out the contact (Fig. 9C). At steep cliff faces, no RTKGPS measurements were taken and the LiDAR imagery wasused to digitize additional datapoints (Fig. 9D). Next, thesurface representing the top of the fill was recorded byphysically walking the contact with RTK GPS.

An isopach map was computed from the incision surfaceand top fill horizon, resulting in a thickness model of theincision (Fig. 10A). The trend of the incision is NW–SE; themaximum depth is approximately 15 m and width on the orderof 600 m. The next step in modelling incision IS1 was the

quantification of the internal facies and reservoir propertypartitioning. As such, the goal was to model the partitioning ofmapped attributes within discrete zones conditioned by digitalrecorded outcrop observations. In order to achieve this, theindividually RTK GPS recorded x, y, z spatial coordinateswere simultaneously tagged with additional geological infor-mation, in the field. For IS1, attribute information wasrecorded on the facies type of the underlying rocks, i.e. if theincision cut into the level of float/rudstones or to the deeperlevel cross-bedded grainstones, and if the overlying fill con-tained dolostone. Thematic maps were created from thedensely spaced RTK GPS recorded data, by creatingvariogram-based facies maps of abundance, geometry andspatial distribution of the recorded facies attributes (Figs 9Band C). Although stochastically populated, the constructionmirrors a deterministic approach.

Figure 10 illustrates the final geocellular outcrop model. Itis clear that the incision cuts into float/rudstones of SequenceI-6 at the flanks and into cross-bedded grainstones of SequenceI-5 in the centre of the incision, as mapped in the field.Furthermore, the dolomitized wackestone or dolostone faciesthat is found within the incision indicates diagenesis played arole in defining the reservoir properties of the incision. Torepresent this, a diagenetic geobody is superimposed on theincision geobody in the model. Grelaud et al. (2006) inter-preted dolomitization to be an early diagenetic event; however,mapping of the orientation of the dolostone facies shows thatit is oriented perpendicular to the main axis of the incision andparallel to large-scale, NE–SW-trending fracture corridors (seealso De Keijzer et al. 2007). This would suggest that thedolomitization might be a later diagenetic event, associatedwith structuration.

CLINOFORM QUANTIFICATION ANDMODELLING

Geological organization

In Madmar 3 Canyon, Sequence I-5 or E3 member has beenmeticulously described and analysed (Homewood et al. 2008).This study focuses upon the SW of Madmar 3 Canyon(Fig. 4). The study window is recognized by two parallel gullies

Fig. 8. Outcrop photograph illustrating mapped stratigraphic surfaces in Madmar 3 Canyon from surface IS1 to surface Base D. The photographis taken approximately at the centre of incision IS1 in the vicinity of Figure 7A. The brownish colour between IS1 and Top Fill indicates dolostones.

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Fig. 9. A series of illustrations that summarize the digital outcrop modelling workflow. (A) Outcrop photograph of the Natih E member in Madmar3 Canyon illustrating excellent quality of outcrop. (B) LiDAR imagery of the same area as the photograph shown in A; the arrows point at the samefeature. Image is displayed without vertical exaggeration. (C) Photograph illustrating RTK GPS recording of bed contacts. In this case, a physicalmeasurement is recorded where the person is standing. Dots indicate other recorded data points. (D) LiDAR data allows extraction of additionaldata points from inaccessible parts of an outcrop, i.e. at steep cliff faces. (E) Digital outcrop model (DOM) integrating the large-amount of digitallycollected field data including DEM and draped satellite imagery. In this example data is only shown for a single horizon, here the base of IncisionIS1. The grey surface representing IS1 was constructed by convergent interpolation. Image is displayed without vertical exaggeration.

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providing a pseudo 3D exposure (Fig. 11A). Stratigraphically,the upper part of Sequence I-5 has been interpreted as ahighstand systems tract (Fig. 6), deposited on a high-energyshallow platform as rudist-rich biostromes and skeletal shoalcomplexes (van Buchem et al. 1996, 2002b; Homewood et al.2008). In outcrop, these shoal complexes contain up to 10 mhigh, hectometre-scale (100 m long), medium-angle to high-angle (1–5() inclined stratal surfaces termed clinoforms (Fig.11B). The clinoforms have a sigmoidal geometry, can bedivided in topset, foreset and bottomset parts, and are charac-terized by texture-based facies partitioning whereby coarse-grained textures grade into fine-grained textures (Fig. 12).Top-set beds are recognized by rudist-rich rudstones (Fig.12A) and cross-bedded coarse-grained bioclastic grainstones(Fig. 12B). Bottom-set beds consist of fine-grained grainstoneswith silicified layers (Fig. 12C). Figure 13 illustrates thereconstruction of key clinoform surfaces; Figure 14 shows fourmeasured stratigraphic sections and correlations. It demon-strates the progradational character of the clinoforms resultingin upward-coarsening trends and lateral gradational inter-fingering of facies transitions. These clinoform geometries, aswell as the nature of vertical and lateral facies transitions, havebeen captured by digital outcrop models.

Stratigraphic modelling

To capture the shoal complex, key surfaces including clinoformswere mapped with RTK GPS by walking out the contacts (Fig.13). All surfaces are located between two surfaces labelled‘Base Grst’ and ‘Base Flst’ (see Figs 6, 7 and 13A). Figure 13Billustrates a schematic sketch of the relationships and namesof the digitally recorded surfaces. Figure 13C illustrates RTKGPS data points and surfaces representing stratigraphic sur-faces and visualizes the reconstructed clinoforms. The curva-ture of the clinoforms is sigmoidal; dips up to 7( wereobserved, and the relief of the clinoforms was up to 10 m.

Attribute modelling

The next step in modelling the shoal complex was the quanti-fication of the internal texture-based facies transitions. Figure14 illustrates four stratigraphic sections that were measuredand digitally recorded in detail to capture facies and texturalvariability. These measured sections were loaded as pseudo-wells into the model. To capture the lateral textural partition-ing, the x, y, z spatial coordinates that were recorded forcapturing the geometry of surfaces were tagged with geologicalinformation. For several surfaces (Surface 1 and 2, Clinoform

Fig. 10. Digital outcrop model of NatihSequences I-3 to I-7 in Madmar 3Canyon, Jabal Madmar, Oman includingIncision IS1. (A) Isopach map of incisionIS1. The trend of the incision is NW–SE;the maximum depth is approximately15 m and width on the order of 600 m.The WNW–ESE trending reverse fault isillustrated by the black line. (B)Thematic map of the facies type of theunderlying rocks, i.e. if the incision cutinto the level of float/rudstones or to thedeeper level consisting of cross-beddedgrainstones. (C) Thematic map of thelower part of the fill containingwackestone and dolostone (i.e.dolomitized wackestone). (D) Modelillustrating stratigraphic levels up to theincision IS1 basal surface. (E) 3D viewof the full model illustrating allstratigraphic levels of the Natih EMember.

Outcrop modelling and simulating carbonate clinoforms 317

1 and 2; see Fig. 13) attribute information was recorded as thegrain size of either the underlying or overlying rocks or both,i.e. if the grain size was fine, medium, or coarse grained (Fig.15). No lateral changes were observed along Clinoform 3 andtherefore this surface was not input to the attribute model.Subsequently, variogram-based attribute maps were con-structed offering information on abundance and spatial distri-bution of texture-based facies types (Fig. 16).

Model construction

The final step that was taken in this workflow was the con-struction of geocellular outcrop models. For this, two distinctframework grids were built:

1. The first grid incorporated 5 subhorizontal stratigraphichorizons and excluded clinoform surfaces. Vertical layeringwas proportional between surfaces and the number of layersadjusted to obtain mean cell heights of 0.20 m; the mini-mum cell height being 0.1 m. Horizontally, cells widths were0.50 m. The result was a relatively homogeneous subhori-zontal grid (Fig. 17A) consisting of 4 zones of which 1 zone(in this case Zone 2) represents the clinoform complex(between Surface 1 and Surface 2; see Fig. 13B).

2. The second grid incorporated 5 subhorizontal stratigraphichorizons as well as two clinoform surfaces (Clinoform 1 and2; Clinoform 3 was not used; see Fig. 13). As for the firstmodel, horizontal cells had widths of 0.50 m. Proportionallayering represented sigmoidal clinoforms most realisticallybecause internal layers thin in a platform- and basinwarddirection (see Fig. 13B). Vertical layering of the zoneswithin the 3D grid was adjusted to obtain mean cell heightsof 0.20 m. In order to avoid small cell heights because ofmerging layers a minimum cell thickness of 0.1 m was used.Figure 17B illustrates the resulting grid which consisted of 6zones, of which 3 zones represented the clinoform complex(Zone 2 between Surface 1 and Clinoform 2, Zone 3between Clinoform 2 and Clinoform 1, and Zone 4 betweenClinoform 1 and Surface 2; see Figs 13B and 17B).

Texture-based facies were populated within the modelgrids. Truncated Gaussian Simulation (TGS) was used todistribute facies properties in the grids and allow a stochasticdistribution of the property using input variograms, whileassuming an ordered transition through a sequence of facies(MacDonald & Aasen 1994). The amount of data used as inputfor populating the grid ranged from low data densities to highdata densities:

Fig. 11. Outcrop photographs of a high-energy shallow carbonate shoal complex of Natih Sequence I-5. (A) Outcrop photograph illustrating twoparallel gullies providing pseudo 3D exposure. Width of view is approximately 100 m. (B) Outcrop photograph illustrating decametre-scaleclinoform geometries.

E. Adams et al.318

1. Pseudo-well data and the point data collected along clino-forms. As a result, input data for facies modelling com-prised about 400 cells mostly arranged vertically (Fig. 18A).

2. Pseudo-well data and a point attribute set constructed fromthe variogram-based facies maps. This set of input datafor facies modelling comprised approximately 71 000 cellsarranged mostly laterally (Fig. 18B).

EVALUATATION OF THE STATIC MODEL

The purpose of using a digital outcrop modelling approachin this study was to construct high-resolution pseudo-static

reservoir models that captured shoal complexes comprisingclinoforms and associated heterogeneities. As described above,texture-based facies were distributed in two different grids,built using variations in clinoform geometry and with differentamounts of input data. The four resulting model realizationsare illustrated on Figure 19. Based on these model realizations,several general observations can be made:

+ The model comprising a subhorizontal grid uses one zone torepresent the clinoform complex (see Fig. 17A) whereas themodels incorporating inclined clinoform geometries consistof 3 zones (see Fig. 17B). Subhorizontal grids producedrepeating lateral facies transitions from coarse- to medium-grained grainstones without transitioning to fine-grained(Fig. 19A and B). An inclined grid with 3 zones producesclear and complete facies transitions from coarse- to fine-grained facies (Fig. 19C and D). In other words, incorpo-rating inclined stratigraphic surfaces enabled modellingof downslope interfingering between coarse-grained andmedium- to fine-grained facies.

+ There is a major difference in reproduced patterns betweenrealizations carried out with different amounts of inputdata. Obviously, isolated and sharper facies bodies arepredicted more accurately by using more input data (com-pare Fig. 19A with B and C with D). Models with inclinedgrids produced coarse-grained bodies with progradationalstacking patterns surrounded by fine-grained facies (Fig.19C and D). When pseudo-wells plus attribute maps areused as input these progradational geometries are somewhatbetter developed and better match outcrop observations(Fig. 19D).

In summary, the modelling method that has used inclinedgrids (i.e. using clinoform surfaces in the gridding procedure)and texture-based facies maps as input for TGS facies model-ling produced the most realistic and geologically realistic faciespartitioning, comparable to those observed in outcrop (Fig.19D). Models that used a subhorizontal grid with pseudo-welldata resulted in the poorest match when compared to outcropobservations (Fig. 19A).

DYNAMIC MODELLING OF CLINOFORMS

The objectives of this study were to compare simple with moreadvanced facies modelling methods for shoal complexes, com-prising clinoform geometries and associated heterogeneities,by using high-resolution digital outcrop models. Accordinglyone goal was to develop recommendations and guidelines onoptimal incorporation of geologically realistic clinoform geo-bodies and associated heterogeneities into static and dynamicreservoir models. In order to constrain variability between thevarious model realizations, sweep efficiency differences wereassessed.

Assigning dynamic properties

For dynamic reservoir modelling purposes, facies models needto be translated to porosity, permeability, and saturationmodels. For this a well-established and published rock-typingworkflow was used which was developed on analogous subsur-face data from a giant oilfield in North Oman (Creusen et al.2007; Hollis et al. 2010). Following this rock-typing study, acomparison was made with the texture-based facies typesdescribed in this study (Table 1). A simple relationship fromgood-to-bad reservoir properties follows the clinoform depo-sitional profile. In this profile, the best properties are associ-ated with rudist-rich rudstones (rock type 4; Table 1) whilst

Fig. 12. Outcrop photographs illustrating Natih Sequence I-5 faciestypes observed in Madmar 3 Canyon. (A) Rudist-rich rudstone typi-cally found on top of clinoform beds, i.e. topset beds. (B) Photographshowing medium-to-coarse grained cross-bedded grainstone commonlyfound at the upper part of clinoforms. (C) Photograph of fine-grainedgrainstones with silicified layers marking the boundaries of clinoformbundles. These textures are found at the base of clinoforms.

Outcrop modelling and simulating carbonate clinoforms 319

good and average properties are modelled for coarse- andmedium-grained grainstones, respectively. However only onerock type has been assigned (rock type 6; see Table 1). Thediscrimination is arbitrary set by using two-thirds of the lowestporosity values for medium-grained grainstones and two-thirds of the highest values for coarse textures; both texture-based facies types use the same porosity-to-permeabilitytransform (rock type 6; see Table 1). Poorest properties wereassigned to fine-grained grainstones (rock type 7; Table 1).Although the rock typing study defines this rock type as beinga calcite cemented fabric associated with parasequence tops(Hollis et al. 2010), it is assumed to be also representative forfine-grained grainstones comprising silicified layers that arefound at the base of clinoforms. In short, this study evaluatesthe impact of good-reservoir rock types (rock types 4 and 6)interfingering with non-reservoir rocks (rock type 7) on reser-voir behaviour under waterflood.

Other important dynamic properties for multiphase flowsimulation that dictates flow through a reservoir are capillarypressure and relative permeability (Aziz & Settari 1979).Waterflood simulations, where water injection is used todisplace oil, have been considered, therefore, using two-phasewater-oil capillary pressure and relative permeability curvesfor each rock type derived from Hollis et al. (2010).

Initialization, well configuration, and operating conditions

For initialization, well configuration, and operating con-ditions, subsurface parameters that mimic a giant oilfield inNorth Oman were used. The top of each model was set at areference depth of –2000 m and initial reference pressure of3500 PSI. A water–oil contact was set at –2010 m and eachsimulation model was initialized using a hydrostatic initializa-tion model. For each simulation, two well configurations were

Fig. 13. Cross sections across the modelled shoal complex comprising clinoform geometries in Natih Sequence I-5. (A) Outcrop photograph withinterpreted horizons. (B) Schematic sketch illustrating relationship and names of recorded surfaces. (C) Recorded RTK GPS data points,colour-coded according to the respective interpreted horizon. Interpolation between data points was achieved by fitting surfaces. Clinoformgeometries are clearly reconstructed.

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chosen. The first well configuration included insertion of ahorizontal water injector within the topset of the clinoformsand a horizontal producer within the bottomset. The secondconfiguration inserted wells at similar locations, except that theinjector and producer were swapped. For both well configura-tions, the injector and producer were oriented perpendicular tothe clinoform trend. The last configuration was more realisticbecause a configuration of an injector at the top and producerat the bottom is not commonly used in actual waterflooddevelopments. However, it served to compare contrastingflood directions. The injection well operated at a bottom-holepressure of 5000 PSI and at a water injection rate of 8000bbl/day. The production well operated at a bottom-hole press-ure of 1000 PSI. All models were simulated for a total periodof 5 years.

Waterflood simulations in clinoforms

All simulations showed that the dominant control on hydro-carbon sweep under waterflood displacement was zones ofhigher permeability, which were represented by the upper partof the clinoforms (Zone 1, see Fig. 17). In these regions similarpatterns of sweep were depicted all with roughly similar oilrecoveries (Figs 20–24). However, there were differencesbetween grid configurations. Overall, sweep of the modelsusing horizontal grids was less efficient. The models that usedlow data densities as input showed a piston-like sweep, withbest recoveries in the upper part of the model and a sharpboundary to lower recoveries in the underlying strata (Run A;Figs 20–21 and Run A_b; Figs 22, 23). The model usingpseudo-wells plus attribute maps swept more efficiently and

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Fig. 14. Measured stratigraphic sections and correlations across the studied shoal complex illustrating facies, textural, and diagenetic (i.e.silicification) variability. The correlation surfaces physically followed on outcrop correspond to the traced RTK GPS surfaces (see Fig. 13). Thesemeasured sections were loaded as pseudo wells in Petrel.

Fig. 15. DOM with additional geological information tagged to each RTK GPS recorded data point. In this example grain size was recorded. Thedots represent individual RTK GPS data points whereas yellow-to-orange colours correspond to different grain sizes. The orange transparent surfacerepresents horizon Clinoform 1. The shaded surface is a DEM with a horizontal grid spacing of 1 m.

Outcrop modelling and simulating carbonate clinoforms 321

gave higher recoveries (Run B and Run B_b; Figs 21 and 23).This is because areas of good-quality reservoir were morecontinuous. All models that used inclined grids had higherrecoveries (Runs C–E and Runs C_b–E_b; Figs 21 and 23)because good-quality reservoir rocks were connected acrossthe entire model (Fig. 19). The model using low data densitiesas input exhibited the best recovery (Run C and Run C_b; Figs21 and 23). Here, a clinoform composed of medium-grainedgrainstone was continuous across the whole model. The geo-logically most robust simulation used pseudo-wells and attrib-ute maps from outcrop data. In this simulation, the lowerclinoform in the centre of the model interfingered with poorquality rocks and had lower recoveries than the clinoforms inthe upper part of the model that were continuous across theentire model and connected to the injector and producer (RunD and Run D_b; Figs 20–23). An extra model run was carriedout to test the effect of horizontal versus inclined grids if allother parameters were equal. For this, the properties of Run Dand Run D_b were transferred to the horizontal grid, i.e.modelling actual property partitioning with a simplified layer-cake grid (Fig. 19E). Similar patterns of sweep were observed(compare Run D with E and Run D_b with E_b; Figs 20–23).Nevertheless, oil production rates remained high after a pla-teau was reached for the horizontal grid (Fig. 24). Higherproduction rates were most likely related to the effectivevertical-to-horizontal permeability ratio (Kv/Kh) with a largerratio predicting better production (Geehan 1993). In the

horizontal grid, clinoforms were represented by a blockierpattern and hence, good-reservoir rocks were not always foundadjacent to but also on top of each other resulting, in a higherKv/Kh.

DISCUSSION

This study has used digital outcrop modelling to quantitativelydescribe outcrop-scale clinoform geobodies, incorporating par-titioning of texture-based facies within a high-energy shallowcarbonate shoal complex. The geobodies observed in outcropare of similar age and are considered depositional analoguesof the Natih reservoirs of Oman including the Fahud, Natihand Burhaan fields. Age-equivalent formations are the WasiaGroup of Saudi Arabia, the Mauddud and Mishrif andFormations of Qatar and the United Arab Emirates, SarvakFormation of Iran, and Mauddud Formation of Iraq(Alsharhan & Nairn 1988; Sadooni & Alsharhan 2003). Inaddition, clinoforms have been depicted in several other car-bonate reservoirs around the world (Masaferro et al. 2004;Yose et al. 2006).

Sweep efficiency in clinoforms

This study demonstrated that model realizations using awell-defined stratigraphic model that incorporated inclined

Fig. 16. Texture-based facies maps for 6 different stratigraphic time slices constructed using variography. Note that only 4 stratigraphic surfaces arepresented (surfaces 1, 2, and clinoforms 1, 2).

E. Adams et al.322

surfaces representing clinoforms produced geologically realis-tic facies transitions from coarse- to fine-grained facies withinprogradational inclined geobodies. It is these models thatsweep most efficiently and have higher recoveries (Figs 21C, Dand 23C, D). Currently, inclined grids are not often usedfor dynamic modelling, because they require re-gridding toavoid grid-orientation effects, for example, caused by aK-orthogonal grid (Aarnes et al. 2007). Nevertheless, it isimperative to use a well-defined stratigraphic model incorpo-rating inclined surfaces representing clinoforms for creating aninclined model grid before interwell population commences.Unswept sections are predicted along the clinoform if inclinedgrids are used because progradation of higher reservoir qualityfacies, and interfingering with poorer reservoir quality facies,are replicated (Figs 20C, D and 22C, D). However, transfer-ring properties from an inclined grid to a horizontal grid doesproduce similar patterns of sweep (for example, compare Fig.21D and E) with higher Kv/Kh ratios and hence productionrates (Fig. 24).

In this study, the cell size used for facies modelling was1 m � 1 m with an average height of about 30 cm. Simulationwas carried out on grids of 4 m � 4 m horizontally withoutaltering vertical layering, resulting in a total of about 325 000–40 000 cells. Upscaling such high-resolution models toreservoir-scale models will not be trivial. But as demonstratedabove, inclined geometries are of importance on hydrocarbondisplacement, and should be considered and incorporatedduring upscaling. In conclusion, using high-resolution modelsfor correctly deriving static and dynamic rock properties for

input into a full-field model must be considered in reservoirmodelling workflows (see also Creusen et al. 2007).

A study for the Permian Slaughter San Andres field, WestTexas, USA, similarly looked at the effect of inclined geo-metries, i.e. clinoforms, on waterflood simulations (Jennings2001). One model used inclined geometries that were combinedwith lateral and vertical trends, whereas another model had asimplified vertical trend only. The simulations showed thatwaterflood displacement was dominated by the overall verticaltrend, with both models predicting similar oil recovery. Asimilar finding was produced by the study presented here. Forthe Permian models, cumulative injection versus time indicatedthat the models with a simplified vertical trend overestimatedthe injectivity by about 20% because the flow was not forced tocross the lower permeabilities in the bottom of each cyclepresent in the inclined model (Jennings 2001). Another study,focused on interwell scale clinoform heterogeneities affectingrecovery efficiency in the Upper Thamama, Lower Cretaceouscarbonate outcrops of the United Arab Emirates (Vaughanet al. 2004; Strohmenger et al. 2006). In this study, models wereconstructed with inclined fine-scale grids as well as horizon-tally layered coarse-scale models, where the fine model was notas homogeneous causing fluids to encounter more baffles andtravel more slowly through the dipping layers (Vaughan et al.2004). Similar studies on shallow-marine clastic reservoirs,including both modern and outcrop studies, have been carriedout to provide accurate geometric information on the effectson fluid flow of deltaic clinoforms including cemented andshale-covered barriers (Howell et al. 2008a, b; Jackson et al.

Fig. 17. Mapped horizons were used to define zones. Vertical layering of the 3D grid was adjusted to obtain average cell heights of approximately0.30 m; minimum cell thickness is 0.1 m. The cell width was set at 0.5 m. (A) Subhorizontal grid including 5 horizons with 4 zones. Zone 2 representsthe clinoform complex. (B) Inclined grid including 7 horizons of which two represent inclined surfaces (i.e. clinoforms) resulting in 6 zones. Zones2, 3 and 4 represent the clinoform complex.

Outcrop modelling and simulating carbonate clinoforms 323

2009; Sech et al. 2009; Enge & Howell 2010). For modellingshoreface and delta-front deposits, the facies modelling toolsincluded a Truncated Gaussian Simulation. The studies havereported that the gridding strategy can account for up to 30%of the difference in simulated production when modellingclinoform systems. It was concluded that a horizontal gridappeared to produce significantly better than an inclined one,because it fails to capture barriers and the geometry ofclinoforms (Howell et al. 2008a).

The study presented in this paper demonstrates a contraryoutcome to these previous studies primarily since, in this study,barriers to flow are absent. The models containing clinoformsin this study sweep efficiently; however, clinoforms that are notconnected between wells show lower recoveries. Similarly,Jackson et al. (2009) demonstrated this preferential flow ofinjected water through higher reservoir quality rocks situatedin the upper part of clinoforms when compared to the lowersweep efficiency in clinoforms interfingering with relatively

poorer reservoir quality rocks. The main explanation for themore efficient sweep in the models embedding clinoformspresented in this paper compared to the more traditionalmodels is that the highly connected geobodies are embedded inthe static models through the use of inclined grids. In contrast,traditional modelling methods that use horizontal grids pro-duce isolated sharp bodies at best but homogenously patchyfacies patterns at worst.

What both these published studies and this study clearlyshow is that a more correct distribution of heterogeneitiesassociated with clinoforms has implications for the effectivevertical-to-horizontal permeability ratio (Kv/Kh) and there-fore also for the sweep efficiency in a reservoir. For example,horizontal wells can be considered for field developmentbecause of potential increases in productivity but these wouldonly be more effective if Kv/Kh was sufficiently large (Geehan1993). Mature Middle East fields commonly have manywells by which to constrain facies, porosity, permeability, and

Fig. 18. Images illustrating the amount of data used as input for populating the grid. (A) Cells represent logged sections and the point data collectedalong clinoforms, i.e. exampling vertical and horizontal pseudo wells. (B) Filled cells were obtained from logged sections and variogram-based faciesmaps (Fig. 16).

Table 1. Summary of facies types, facies associations, rock types and rock property data

Facies Lithofacies association Rock type Porosity Porosity to permeabilitytransform

Mean, range, stdev

Rudist-rich rudstone LA4 Rudist shoal RT4 32.3, 7.8–45.1, 9.9 k = 0.360 e (17.5 � Phi)

Coarse-grained bioclasticgrainstone

LA6 Foraminiferal shoal and LA7 Marginal/inter-foraminiferal shoal

RT6 31.9, 24.6–43.9, 4.2 k = 0.052 e (17.6 � Phi)

Medium-grained bioclasticgrainstone

LA6 Foraminiferal shoal and LA7 Marginal/inter-foraminiferal shoal

RT6 24.2, 10.3–31.7, 5.1 k = 0.052 e (17.6 � Phi)

Fine-grained grainstonecomprising silica layers

LA6 Foraminiferal shoal and LA7 Marginal/inter-foraminiferal shoal

RT7 16.7, 1.8–34.1, 6.4 k = 0.026 e (20.6 � Phi)

Lithofacies associations, rock types and rock property data summarized and permeability transforms using the same dataset computed from Holliset al. (2010)

E. Adams et al.324

Fig. 19. Realizations of texture-based facies models originating from two different grids and two sets of input data. All realizations use TruncatedGaussian Simulation (TGS) for facies modelling. (A) Horizontal grid (see Fig. 17A) using pseudo wells (see Fig. 18A). (B) Horizontal grid usinglogged sections plus attribute maps (see Fig. 18B). (C) Inclined grid (see Fig. 17B) using pseudo wells. (D) Inclined grid using logged sections plusattribute maps. (E) Horizontal grid as in A and B with properties transferred from model run D.

Outcrop modelling and simulating carbonate clinoforms 325

Fig. 20. Flow simulations of the different model realizations shown in Figure 19A–E. Colour bar shows oil saturation after 4 years of water injection.A horizontal water injector is inserted in the cells in the top south of the grid and a horizontal producer in the cells in the bottom south of the gridperpendicular to the figures (see also part A).

E. Adams et al.326

Fig. 21. Flow simulations as shown in Figure 20. Colour bar shows recovery factor after 4 years of simulating a waterflood.

Outcrop modelling and simulating carbonate clinoforms 327

Fig. 22. Flow simulations using the different model realizations shown in Figure 19A–E. Colour bar shows oil saturation after 4 years of simulatinga waterflood. A horizontal water injector is inserted at the northern lower-right cells and a horizontal producer at the southern top-left cells of thegrid perpendicular to the figures (see also part A).

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Fig. 23. Flow simulations as shown in Figure 22. Colour bar shows recovery factor after 4 years of simulating a waterflood.

Outcrop modelling and simulating carbonate clinoforms 329

production data. In terms of rock properties, the verticalvariability may be picked up from log and core data; howeverfacies changes in clinoforms in dip direction and along strikeare difficult to validate and subsequently difficult to propagatein interwell space. In this paper, recommendations were madeas to how to embed clinoform geobodies and heterogeneitiesinto static models. A potential step towards model improve-ment would be to incorporate input data to define lateral faciestransitions. These types of data might be obtained fromhorizontal wells. If sufficient well data is not available, deter-ministically constructed facies maps defining lateral faciestransitions could be tested and used as input data. Modelrealizations using inclined grids in this study produced clearand complete facies transitions from coarse- to fine-grainedfacies because incorporated stratigraphic surfaces definedzones by which coarse-grained facies could be distributeddown-slope to interfinger with medium- and fine-grainedfacies. As such, it is recommended that inclined grids are used.Essentially, because of the impact of clinoforms on reservoirbehaviour demonstrated by this study, geologically robustclinoform models should be used to predict sweep efficiencyduring field development planning for secondary and tertiaryrecovery.

Delineating and embedding clinoforms

In this paper, waterflood simulations demonstrated that clino-form sections swept more efficiently in geologically correctmodels. Well spacing relative to clinoform length will definewhether high permeability streaks or low permeable baffles orbarriers will be important (Figs 21D and 23D). Nevertheless,when designing a well test configuration for a waterflood,directionality and heterogeneity of clinoforms both horizon-tally and vertically can have an impact on preferred floodingdirections. On the basis of scales and dips of clinoforms, twosubdivisions can be made. As demonstrated by the waterfloodsimulations, ‘outcrop’-scale clinoforms have an impact onsweep efficiency. It can be debated if seismic’-scale clinoforms,associated with prograding carbonate platforms, have a majorimpact, but on this scale, correlatability of units and internalfacies partitioning and gradual facies transitions are present.These two scales of clinoforms do not have to be similarlyoriented. Regionally, progradation of the Natih Formationcarbonate platform was towards the west in the Jabal Madmararea (Homewood et al. 2008); in Madmar 3 Canyon, shoalprogradation was observed in a northward direction (Fig. 19).This means that platform progradation and shoal prograda-tion in this example are obliquely oriented. Such trends are acommon observation in tidally influenced carbonate shoalsystems with main shoal facies belts trending parallel to

platform margin, whereas individual shoal bars may developperpendicular to the platform margin (Ball 1967; Rankey et al.2006; Harris 2010). High-resolution 3D seismic data allows anassessment of the direction of platform progradation but is ofinsufficient resolution to image the clinoforms and thereforethe progradation direction of individual shoals. High-angleforesets, with angles up to 30–35(, are recorded in dipmeterlogs and observed in core but are of a much smaller scale.Hence, traditional sedimentological interpretations are neededto assess the environment of deposition and judge the directionof progradation of individual shoals (Ball 1967). Nevertheless,because of the potential impact on reservoir flow simulations,the presence and recognition of clinoforms in subsurfacereservoirs, on both production and field scales, should signifi-cantly impact the way units are correlated and grids areconstructed. This will allow for a more realistic definition offacies architecture and therefore a more robust distribution ofproperties.

CONCLUSIONS

This paper evaluated and presented the impact of clinoformson static subsurface modelling and dynamic simulations withspecial emphasis on waterflooding. The following conclusionsare made regarding the interwell-scale modelling of the MiddleCretaceous grainstone shoals of Jabal Madmar, Oman:

1. Digital outcrop modelling is an important aid for predict-ing, modelling, and upscaling geobodies and associatedinternal heterogeneities that are usually beneath the resolu-tion of seismic data, and as such not easily recognized andcorrelated between wells. For example, facies transitionsand associated property partitioning in clinoforms in a dipdirection and along strike are difficult to validate andsubsequently difficult to propagate in interwell space.

2. Truncated Gaussian Simulation should be used when mod-elling systems with naturally occurring facies transitionssuch as those occurring in, for example, clinoform systems.

3. When using only vertical well data, it is difficult to embedcoherent facies transitions and stacking such as prograda-tional patterns. Improvement can be made if input datadefining lateral facies transitions are used. These types ofdata might be available from horizontal wells.

4. The benefit of using an inclined grid instead of subhorizon-tal grid is that natural facies juxtapositions are incorporatedautomatically when using variogram-based populationmethods. Although often difficult to realize, given the lackof data, the way units are correlated and grids are con-structed should be considered if clinoform geobodies are tobe modelled correctly.

5. Inclined grids are not often used for static modellingbecause re-gridding is required to avoid grid-orientationeffects during simulation. However, the inclined, complexgrid in this study demonstrated that incorporation of natu-ral facies juxtapositions and distribution of properties wasmore geologically robust. Hence, although upscaling needsconsiderable attention, complex and high-resolution modelsmust be considered in reservoir modelling workflows inorder to correctly derive static and dynamic rock propertiesfor input into a full field model.

6. In this paper, models comprising clinoforms sweepefficiently because highly connected geobodies were embed-ded in the static models by the use of inclined grids. Modelsthat did not incorporate clinoforms swept poorly becausetraditional modelling methods using horizontal grids pro-duce sharp, isolated bodies at best, and a homogenously

Fig. 24. Graph illustrating oil production rate in bbl/day for the first1.5 years of a simulated waterflood.

E. Adams et al.330

patchy facies pattern at worst. The study presented in thispaper demonstrates a contrary outcome to previous studies,the main reason for this is that, in this study, barriers toflow are absent.

7. Medium-angle to high-angle (1–5() hectometre (100 mlong) or ‘outcrop’-scale clinoforms have a potentially largeimpact on reservoir behaviour if secondary and tertiaryrecovery mechanisms are selected. The reasons for thisrelate to sweep efficiency. Therefore, an appropriate mod-elling approach has to be considered and carefully selectedbefore a well test configuration for a waterflood is designed.

8. The importance of the presence and recognition of clino-forms in subsurface reservoirs does not only apply to‘outcrop’-scale clinoforms observed in the Cretaceous NatihFormation in Oman. Recommendations listed above couldapply to ‘seismic’-scale clinoforms and to clinoforms ofother carbonate and clastic settings, largely because clino-forms can be considered as scale-invariant geometries rang-ing from bed scale to basin scale that commonly containfacies partitioning following the clinoform depositionalprofile.

We acknowledge the Bureau of Economic Geology (BEG) of theUniversity of Texas at Austin and especially Xavier Janson foracquiring the LiDAR imagery; Jerry Bellian is acknowledged for geo-referencing the LiDAR dataset. We thank Kike Beintema for technicalassistance during fieldwork. Sincere thanks go to Henk Droste forintroducing the geology of Jabal Madmar and for technical input andstimulating discussions on the subject of geobodies of the NatihFormation of Oman. Ravi Borkhatatia is acknowledged for reviewingan earlier version of the manuscript. We greatly credit improvingcommentary by reviewers Stefan Lubeseder, an anonymous reviewerand editor Cathy Hollis.

REFERENCES

Aarnes, J.E., Gimse, T. & Lie, K.-A. 2007. An introduction to the numericsof flow in porous media using Matlab. In: Hasle, G. & Lie, K.-A.&Quak, E. (eds) Geometric Modelling, Numerical Simulation, and Opti-mization. Springer, Berlin, 1–42.

Adams, E.W. & Hasler, C.-A. 2010. The intrinsic effect of shape on theretrogradation motif and timing of drowning of carbonate patchreef systems (Lower Frasnian, Bugle Gap, Canning Basin, WesternAustralia). Sedimentology, 57, 956–984.

Adams, E.W., Grotzinger, J.P. et al. 2005. Digital characterization ofthrombolite-stromatolite reef distribution in a carbonate ramp system(terminal Proterozoic, Nama Group, Namibia). American Associationof Petroleum Geologists Bulletin, 89, 1293–1318.

Adams, E.W., Bellian, J.A. & Reyes, R. 2009. Digital outcrop modelsreduce uncertainty and improve reservoir characterization. World Oil,September Issue, 46–49.

Al-Ja’aidi, O.S., Al-Siyabi, H.A. & Al-Saadi, Y. 2002. Natih unconven-tional play: Reviving a dormant play. AAPG International Conferenceand Exhibition Cairo, Egypt, October 27–30, 2002.

Alsharhan, A.S. & Nairn, A.E.M. 1988. A review of the CretaceousFormations in the Arabian Peninsula and Gulf: Part II. Mid Creta-ceous (Wasia Group) stratigraphy and paleogeography. Journal ofPetroleum Geology, 11, 89–112.

Alsharhan, A.S. & Nairn, A.E.M. 1993. Carbonate platform models ofArabian Cretaceous reservoirs. In: Simo, A.J., Scott, R.W. & Masse,J.-P. (eds) Cretaceous Carbonate Platforms. American Association ofPetroleum Geologists, Memoir, 56, 173–184.

Aziz, K. & Settari, A. 1979. Petroleum Reservoir Simulation. AppliedScience Publishers, London.

Ball, M.M. 1967. Carbonate sand bodies of Florida and the Bahamas.Journal of Sedimentary Petrology, 37, 556–591.

Bellian, J.A., Kerans, C. & Jennette, D.C. 2005. Digital outcrop models:Application of terrestrial scanning LiDAR technology in stratigraphicmodelling. Journal of Sedimentary Research, 75, 166–176.

Bellian, J.A., Beck, R. & Kerans, C. 2007. Analysis of hyperspectral andLiDAR data: Remote optical mineralogy and fracture identification.Geosphere, 3, 491–500.

Borgomano, J.R.F., Fournier, F., Viseur, S. & Rijkels, L. 2008. Strati-graphic well correlations for 3-D static modelling of carbonate reservoirs.American Association of Petroleum Geologists Bulletin, 92, 789–824.

Creusen, A., Maamari, K. et al. 2007. Property modelling small scaleheterogeneity of carbonate facies. SPE paper, SPE-111451-PP.

De Keijzer, M., Hillgärtner, H. et al. 2007. A surface-subsurface study ofreservoir-scale fracture heterogeneities in Cretaceous carbonates, NorthOman. In: Lonergan, L., Jolly, R.J.H. & Rawnsley, K. (eds) FracturedReservoirs. Geological Society, London, Special Publications, 270,227–244.

Dreyer, T., Fält, L.-M. et al. 1993. Sedimentary architecture of fieldanalogueues for reservoir information (SAFARI); a case study of thefluvial Escanilla Formation, Spanish Pyrenees. In: Flint, S.S. & Bryant,I.D. (eds) The Geological Modelling of Hydrocarbon Reservoirs andOutcrop Analogues. International Association of Sedimentologists,Special Publication, 15, 57–80.

Droste, H.J. 2007. The myth of the flat and monotonous Mesozoic epeiriccarbonate platforms in the Middle East. AAPG International Confer-ence and Exhibition, Athens, Greece, November 18–21, 2007.

Droste, H.H.J. & Van Steenwinkel, M. 2004. Stratal geometries andpatterns of platform carbonates: the Cretaceous of Oman. In: Eberli,G.P., Masaferro, J.L. & Sarg, J.F. (eds) Seismic Imaging of CarbonateReservoirs and Systems. American Association of Petroleum Geolo-gists, Memoir, 81, 185–206.

Enge, H.H. & Howell, J.A. 2010. Impact of deltaic clinothems on reservoirperformance: Dynamic studies of reservoir analogues from the FerronSandstone Member and Panther Tongue, Utah. American Associationof Petroleum Geologists Bulletin, 94, 139–161.

Falivene, O., Arbués, O. et al. 2006. Best practice stochastic faciesmodelling from a channel-fill turbidite sandstone analogue (the Quarryoutcrop, Eocene Ainsa basin, northeast Spain). American Association ofPetroleum Geologists Bulletin, 90, 1003–1029.

Falivene, O., Arbués, O. et al. 2010. Synthetic seismic models fromoutcrop-derived reservoir-scale three-dimensional facies models: TheEocene Ainsa turbidite system (southern Pyrenees). American Associ-ation of Petroleum Geologists Bulletin, 94, 317–343.

Geehan, G. 1993. The use of outcrop data and heterogeneity modellingin development planning. In: Eschard, R. & Doligez, B. (eds) Sub-surface Reservoir Characterization from Outcrop Observations. ÉditionsTechnip, Paris, 53–64.

Grélaud, C., Razin, P. et al. 2006. Development of incisions on aperiodically emergent carbonate platform (Natih Formation, LateCretaceous, Oman). Journal of Sedimentary Research, 76, 647–669.

Harris, P.M. 2010. Delineating and quantifying depositional facies pattersin carbonate reservoirs: Insight from modern analogues. AmericanAssociation of Petroleum Geologists Bulletin, 94, 61–86.

Harris, P.M., Frost, S.H. et al. 1984. Regional unconformities and depo-sitional cycles, Cretaceous of the Arabian Peninsula. In: Schlee, J.S.(ed.) Interregional Unconformities and Hydrocarbon Accumulation.American Association of Petroleum Geologists, Memoir, 36, 67–80.

Hodgetts, D., Drinkwater, N.J. et al. 2004. Three-dimensional geologicalmodels from outcrop data using digital data collection techniques: anexample from the Tanqua Karoo depocentre, South Africa. In: Curtis,A. & Wood, R. (eds) Geological Prior Information: Informing Scienceand Engineering. Geological Society, London, Special Publications, 239,57–75.

Hollis, C., Vahrenkamp, V. et al. 2010. Pore system characterisation inheterogeneous carbonates: An alternative approach to widely-usedrock-typing methodologies. Marine and Petroleum Geology, 27, 772–793.

Homewood, P.W., Razin, P. et al. 2008. Outcrop sedimentology of theNatih Formation, northern Oman: A field guide to selected outcrops inthe Adam Foothills and Al Jabal al Akhdar areas. GeoArabia, 13,39–120.

Howell, J., Vassel, Å. & Aune, T. 2008a. Modelling of dipping clinoformbarriers within deltaic outcrop analogueues from the CretaceousWestern Interior Basin, USA. In: Robinson, A., Griffiths, P., Price, S.,Hegre, J. & Muggeridge, A. (eds) The Future of Geological Modelling inHydrocarbon Development. Geological Society, London, Special Publi-cations, 309, 99–121.

Howell, J.A., Skorstad, A. et al. 2008b. Sedimentological parameterizationof shallow-marine reservoirs. Petroleum Geoscience, 17, 17–34.

Hughes Clarke, M.W. 1988. Stratigraphy and rock unit nomenclature inthe oil-producing area of interior Oman. Journal of Petroleum Geology,11, 5–60.

Jackson, M.D., Hampson, G.J. & Sech, R.P. 2009. Three-dimensionalmodelling of a shoreface-shelf parasequence reservoir analogue: Part 2.

Outcrop modelling and simulating carbonate clinoforms 331

Geological controls on fluid flow and hydrocarbon recovery. AmericanAssociation of Petroleum Geologists Bulletin, 93, 1183–1208.

Janson, X., Kerans, C. et al. 2007. Three-dimensional geological andsynthetic model of Early Permian redeposited basinal carbonatedeposits, Victorio Canyon, West Texas. American Association of Petro-leum Geologists Bulletin, 91, 1–32.

Jennings, J.W. 2001. Permeability trend models and their effects on fluid flow.Unpublished internal report, Carbonate Reservoir CharacterizationResearch Laboratory (RCRL), Bureau of Economic Geology (BEG),the University of Texas at Austin.

Labourdette, R., Crumeyrolle, P. & Remacha, E. 2008. Characterisation ofdynamic flow patterns in turbidite reservoirs using 3D outcrop analo-gueues: Example of the Eocene Morillo turbidite system (south-centralPyrenees, Spain). Marine and Petroleum Geology, 25, 255–270.

Li, H. & White, C.D. 2003. Geostatistical models for shales in distributarychannel point bars (Ferron Sandstone, Utah): From ground-penetrating radar data to three-dimensional flow modelling. AmericanAssociation of Petroleum Geologists Bulletin, 87, 1851–1868.

Loosveld, R.J.H., Bell, A. & Terken, J.M.J. 1996. The tectonic evolution ofinterior Oman. GeoArabia, 1, 28–51.

McCaffrey, K.J.W., Jones, R.R. et al. 2005. Unlocking the spatial dimen-sion: digital technologies and the future of geoscience fieldwork.Journal of the Geological Society, London, 162, 927–938.

MacDonald, A.C. & Aasen, J O. 1994. A prototype procedure forstochastic modelling of facies tract distribution in shoreface reservoirs.In: Yarus, J.M. & Chambers, R.L. (eds) Stochastic Modelling andGeostatistics; Principles, Methods, and Case Studies. American Associ-ation of Petroleum Geologists Computer Applications in Geology, 3,91–108.

Masaferro, J.L., Bourne, R. & Jauffred, J.-C. 2004. Three-dimensionalseismic volume visualization of carbonate reservoirs and structures. In:Eberli, G.P., Masaferro, J.L. & Sarg, J.F. (eds) Seismic Imaging ofCarbonate Reservoirs and Systems. American Association of PetroleumGeologists, Memoir, 81, 11–41.

Mount, V.S., Crawford, R.I.S. & Bergman, S.C. 1998. Regional structuralstyle of the Central and Southern Oman Mountains: Jebel Akhdar, SaihHatat, and the Northern Ghaba Basin. GeoArabia, 3, 475–490.

Murris, R.J. 1980. Middle East: Stratigraphic evolution and oil habitat.American Association of Petroleum Geologists Bulletin, 64, 597–618.

Phelps, R.M., Kerans, C. et al. 2008. Three-dimensional modelling andsequence stratigraphy of a carbonate ramp-to-shelf transition, PermianUpper San Andres Formation. Sedimentology, 55, 1777–1813.

Philip, J., Borgomano, J. & Al-Maskiry, S. 1995. Cenomanian-EarlyTuronian carbonate platform of Northern Oman: Stratigraphy andpalaeo-environments. Palaeogeography, Palaeoclimatology, Palaeoecol-ogy, 119, 77–92.

Pratt, B.R. & Smewing, J.D. 1993. Early Cretaceous platform-marginconfiguration and evolution in the central Oman Mountains, ArabianPeninsula. American Association of Petroleum Geologists Bulletin, 77,225–244.

Rankey, E.C., Riegl, B.M. & Steffen, K. 2006. Form, function andfeedbacks in a tidally dominated ooid shoal, Bahamas. Sedimentology,72, 1191–1210.

Sadooni, F.N. & Alsharhan, A.S. 2003. Stratigraphy, microfacies, andpetroleum potential of the Mauddud Formation (Albian–Cenomanian)in the Arabian Gulf basin. American Association of Petroleum Geolo-gists Bulletin, 87, 1653–1680.

Schwab, A.M., Homewood, P.W. et al. 2005. Seismic forward model of aNatih Formation outcrop: The Adam Foothills Transect (northernOman). GeoArabia, 10, 17–44.

Sech, R.P., Jackson, M.D. & Hampson, G.J. 2009. Three-dimensionalmodelling of a shoreface-shelf parasequence reservoir analogue: Part 1.Surface-based modelling to capture high-resolution facies architecture.American Association of Petroleum Geologists Bulletin, 93, 1155–1181.

Sharief, F.A., Magara, K. & Abdulla, H.M. 1989. Depositional system andreservoir potential of the Middle Cretaceous Wasia Formation incentral-eastern Arabia. Marine and Petroleum Geology, 6, 303–315.

Strohmenger, C.J., Weber, L.J. et al. 2006. High-resolution sequencestratigraphy and reservoir characterisation of Upper Thamama (LowerCretaceous) reservoirs of a giant Abu Dhabi oil field, United ArabEmirates. In: Harris, P.M. & Weber, L.J. (eds) Giant HydrocarbonReservoirs of the World: From Rocks to Reservoir Characterization andModeling. American Association of Petroleum Geologists, Memoir, 88,139–171.

Terken, J.M.J. 1999. The Natih petroleum system of North Oman.GeoArabia, 4, 157–180.

van Buchem, F.S.P., Razin, P., Homewood, P.W. et al. 1996. Highresolution sequence stratigraphy of the Natih Formation (Cenomanian/Turonian) in northern Oman: distribution of source rocks and reservoirfacies. GeoArabia, 1, 65–91.

van Buchem, F.S.P., Pittet, B. et al. 2002a. High-resolution sequencestratigraphic architecture of Barremian/Aptian carbonate systems inNorthern Oman and the United Arab Emirates (Kharaib and Shu’aibaFormations). GeoArabia, 7, 461–500.

van Buchem, F.S.P., Razin, P. et al. 2002b. Stratigraphic organization ofcarbonate ramps and organic-rich intrashelf basins: Natih Formation(middle Cretaceous) of northern Oman. American Association of Petro-leum Geologists Bulletin, 86, 21–53.

Vaughan, R.L., Khan, S.A., Weber, L.J. et al. 2004. Integrated characteri-zation of UAE outcrops: From rocks to fluid flow simulation, 11th AbuDhabi International Petroleum Exhibition and Conference, AbuDhabi, U.A.E. SPE Paper, SPE-88730-MS.

Verwer, K., Adams, E.W. & Kenter, J.A.M. 2007. Digital outcrop models:technology and applications. First Break, 25, 57–63.

Verwer, K., Della Porta, G. et al. 2009. Controls and predictability ofcarbonate facies architecture in a Lower Jurassic three-dimensionalbarrier-shoal complex (Djebel Bou Dahar, High Atlas, Morocco).Sedimentology, 56, 1801–1831.

Warburton, J., Burnhill, T.J. et al. 1990. The evolution of the OmanMountains Foreland Basin. In: Robertson, A.H.F., Searle, M.P. &Ries, A.C. (eds) The Geology and Tectonics of the Oman region.Geological Society, London, Special Publications, 49, 419–427.

White, C.D. & Barton, M.D. 1999. Translating outcrop data to flowmodels, with applications to the Ferron Sandstone. SPE ReservoirEvaluation and Engineering, 2, 341–350.

Yose, L.A., Ruf, A.S. et al. 2006. Three-dimensional characterization of aheterogeneous carbonate reservoir, Lower Cretaceous, Abu Dhabi(United Arab Emirates). In: Harris, P.M. & Weber, L.J. (eds)Giant Hydrocarbon Reservoirs of the World: From Rocks to ReservoirCharacterization and Modeling. American Association of PetroleumGeologists Memoir, 88, 173–212.

Received 13 July 2010; revised typescript accepted 30 March 2011.

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