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

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1 Improving reservoir models of Cretaceous carbonates with digital outcrop modelling (Jabal Madmar, Oman): static modelling and simulating clinoforms Erwin W. Adams 1,3,*, Carine Grélaud 2, Mayur Pal 1, Anita É. Csoma 1,5, Omar S. Al Ja aidi 3 and Rashid Al Hinai 4 1 Shell International Exploration and Production B.V., Kessler Park 1, 2288 GS Rijswijk, The Netherlands 2 EGID Institute, Bordeaux 3 University, Allée Daguin 1, Pessac Cedex, France 3 Sarawak Shell Berhad, Locked Bag No. 1, Lutong, Sarawak, Malaysia 4 Petroleum Development Oman, P.O. Box 81, Muscat, PC 113, Sultanate of Oman 5 Current Affiliation: ConocoPhillips, 600N Dairy Ashford, PR3064, Houston TX, 77079, USA * Corresponding author ( erwin.adams@shell.com) ABSTRACT: In Jabal Madmar in the Sultanate of Oman, Cretaceous epeiric carbonate platform architectures were characterized by employing a digital outcrop modelling workflow. A framework model for Natih Sequence I (Natih E member) was established, which embeds a meticulously studied platform-top incision and shoal complex. Outcrop-scale clinoforms are recognized in these shoals by hectometre-scale (100 m long) medium to high-angle (1 5() inclined stratal surfaces comprising texture-based facies transitions. These clinoforms are usually beneath the resolution of seismic data and as such are not easily recognized and correlated between wells. Geologically realistic clinoform models were built using a well-defined stratigraphic model that incorporated inclined surfaces in the model grid and if available, data on lateral facies transitions. Waterflood simulations demonstrated improved sweep efficiency in these models. In contrast, simple models without clinoform heterogeneities resulted in less efficient piston-like patterns of sweep. The study presented in this paper demonstrates an outcome contrary to previous studies, as in this study, barriers to flow are absent. Complex clinoform models must be considered in reservoir modelling workflows to correctly derive static and dynamic rock properties. This is because outcrop-scale clinoforms have a potential impact on reservoir behaviour under secondary and tertiary recovery mechanisms. KEYWORDS: carbonate reservoir heterogeneity, static and dynamic reservoir modelling, clinoform, outcrop, Oman, Natih Formation INTRODUCTION Forecasting responses to secondary and tertiary recovery mechanisms are critically dependent on the accurate prediction and robust incorporation of geobodies into reservoir models. A geobody is considered as a volume of rock material bounded by envelopes that are genetically related to a set of specific geological processes (Borgomano et al. 2008). Some of the most reliable information on geobodies and their associated internal heterogeneities comes from outcrop analogues because they allow the examination and sampling of geological variability at all scales up to that of the outcrops themselves (Dreyer et al. 1993; Geehan 1993; White & Barton 1999; Li & White 2003; Falivene et al. 2006; Labourdette et al. 2008). By employing a digital outcrop modelling workflow, geological elements observed in outcrop can be efficiently and accurately quantified (Hodgetts et al. 2004; Bellian et al. 2005; McCaffrey et al. 2005; Verwer et al. 2007). Specifically, outcrop-based geological features can be positioned and recorded spatially with digital mapping and surveying techniques in 3D on scales ranging from centimetre (core plug) to kilometre (reservoir scale). Such large amounts of collected data can be assimilated and visualized by creating a digital outcrop model (DOM) (Bellian et al. 2005). Because a DOM entails a geospatial and numerical framework it not only improves the collection and visualization of outcrop geology (and commonly for this reason indirectly the understanding) but also aids the building of pseudo static reservoir models that can be used for a range of 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 platforms are stratigraphically correlated in a layer cake fashion implying that the platform interior comrised largely flat, extensive and undifferentiated shallow-water environments creating wide, laterally continuous facies belts (Droste 2007). Petroleum Geoscience, Vol , pp DOI / /11/$ EAGE/Geological Society of London

2 310 E. Adams et al. Fig. 1. Geological cross-section and stratigraphic column illustrating the Cretaceous carbonate succession of the Sultanate of Oman (figure from Droste & Van Steenwinkel 2004, reprinted by permission of the AAPG whose permission is required for further use). The line in the inset satellite image locates the cross section; the box outlines the image of Figure 2. The Cretaceous Natih Formation (Late Albian Early Turonian) represents a carbonate succession that was deposited in a shelf interior setting comprising carbonate platforms and intrashelf basins. Jabal Madmar, part of the Adam Foothills, is located in the central part of the Cretaceous carbonate platform. Nevertheless, high-resolution three-dimensional (3D) seismic data from the subsurface of Oman and United Arab Emirates (UAE) revealed complicated stratigraphic architectures including the presence of platform-top incisions and prograding clinoforms delineating platform margins (Droste & Van Steenwinkel 2004; Grélaud et al. 2006; Yose et al. 2006). Although high-resolution 3D seismic data provides information on the peripheral dimensions and geometries of these Cretaceous platform interior geobodies, the internal make up remains largely unknown. In this study, digital outcrop modelling was used in the Cretaceous Natih Formation outcrops in Jabal Madmar, Sultanate of Oman, in order to depict an incision geobody and grainstone shoal geobodies and carefully characterize their related internal heterogeneities. The effectiveness of the digital outcrop modelling workflow is demonstrated by using the incision characterization as an example. Outcrop-scale clinoforms were recognized in shoal geobodies by hectometre-scale (100 m long) medium-angle to high-angle (1 5() inclined sigmoidal stratal surfaces comprising texture-based facies transitions. The clinoform geometries as well as the nature of vertical and lateral facies transitions have been meticulously studied and mapped using a digital outcrop modelling workflow. This study compares simple with more advanced facies modelling methods and accordingly outlines a workflow whereby geologically realistic clinoforms can be incorporated into static models. Finally, waterflood simulations demonstrate the impact on dynamic behaviour, including better sweep in models embedding clinoforms. These outcrop-based static models can be used to produce virtual reality (VR) training datasets by using the DOM as a template. GEOLOGICAL BACKGROUND AND PREVIOUS WORK Geological setting of the Natih Formation During the Permian and Mesozoic, after the break-up of Gondwana, several extensive carbonate platforms developed on the Arabian plate (Murris 1980). By and large, these platforms were bordered to the SW by the Arabian Shield and to the NE by the Tethys Ocean. Stable platform conditions ended in the Late Cretaceous (Turonian) with an Alpine episode of compression and obduction of oceanic crust associated with the collision between Eurasia and the Arabian plate (Warburton et al. 1990). From the Late Cretaceous to Miocene, carbonate sedimentation and stable conditions returned, 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 eastern margin of the Arabian plate where a 1200 m thick and up to 1000 km wide carbonate platform developed (Fig. 1) (Murris 1980; Droste & Van Steenwinkel 2004). This carbonate platform unconformably overlies Jurassic and older strata (see Base Cretaceous Unconformity in Fig. 1). From the Berriasian to Hauterivian, a phase of progradation created a carbonate platform and adjacent slope and basin deposits; a main phase of carbonate deposition in a shelf interior setting occurred during a phase of aggradation from the Barremian to Turonian (Pratt & Smewing 1993; van Buchem et al. 2002a; Droste & Van Steenwinkel 2004). During this period, several phases of subaerial exposure and clastic input suspended carbonate

3 Outcrop modelling and simulating carbonate clinoforms 311 Fig. 2. A satellite image of North Oman locating Al Jabal Al Akhdar and the Adam Foothills (Fig. 1 shows location). The Adam Foothills comprise, from east to west, Jabal Madar, Jabal Madmar, Jabal Salakh, Jabal Nadah, and Jabal Qusaybah. A cross-section, which has been reconstructed using the Adam Foothills outcrops as well as a seismic survey (indicated by bracket), is indicated by the black line and shown in Figure 3. sedimentation with tectonic movements of the Arabian Shield and/or eustatic sea level variations being the expected causes (Sharief et al. 1989). One of the largest falls in relative sea level occurred during the late Aptian and created a regional unconformity in the Middle East (Harris et al. 1984). In Oman, this event was associated with karstification and erosion and was followed by clastic input of the Nahr Umr Formation (see Base Nahr Umr Unconformity in Fig. 1). The Natih Formation (Late Albian to Early Turonian) conformably overlies the Nahr Umr Formation and was deposited on a very wide and extensive (more than 800 km) carbonate platform (Alsharhan & Nairn 1988, 1993; van Buchem et al. 1996). In Oman, the Natih Formation is characterized by platform-interior sedimentary cycles ranging from several tens to some 150 m thick mixed carbonate shale sediments at the base, grading into carbonate deposits (van Buchem et al. 1996, 2002b). Platform development terminated in the Turonian due to a regional phase of uplift causing emersion, as indicated by karstification and local incisions (van Buchem et al. 2002b). This uplift is thought to be related to the formation of a peripheral bulge to the south of a foreland basin that developed due to obduction of oceanic crust in North Oman during the first Alpine phase (Warburton et al. 1990; Terken 1999). The emersion lasted a few million years and was followed by deepening in the Coniacian Santonian and shale deposition of the Aruma Group in the developing foreland basin (see Base Aruma Unconformity 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 lithostratigraphic members coded A to G from top to base (Hughes Clarke 1988; Droste & Van Steenwinkel 2004). Highresolution sequence stratigraphic models have been built and three 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; Homewood et 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 sequence shows a similar depositional evolution, with a mixed carbonateclay ramp system at the base, followed by a carbonatedominated platform system in the upper part (van Buchem et al. 1996, 2002b). Organic-rich intra-shelf basins developed in the transgressive parts of Sequence I (Lower Natih E) and Sequence III (Upper Natih B). Similar deposition did not occur in Sequence II, probably because of a greater influx of clay and deposition under shallower water conditions (van Buchem et al. 2002b). The top of each third order sequence (i.e. top Natih E, top Natih C and top Natih A) corresponds to a phase of platform emersion, sometimes associated with the development of incisions (Al-Ja aidi et al. 2002; Grélaud et al. 2006). Architectural elements of the Natih Formation The high-resolution sequence stratigraphic models described above have been built from integrated studies of outcrops in North Oman (Oman Mountains and Adam Foothills; see Figs 2 and 3) and subsurface data including high-resolution 3D seismic 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 Oman demonstrated the presence of a highly differentiated internal topography and complex architectural elements (Droste & Van Steenwinkel 2004; Grélaud et al. 2006). Figure 3 shows an east west stratigraphic transect of Natih Formation Sequence I (i.e. Natih G, F, and E). It illustrates a large-scale (tensof-kilometres) progradational carbonate platform that is bordered by an intra-shelf basin and truncated by incisions that developed during times of relative sea-level falls and platform emergence. Abundant progradational inclined stratal geometries with variable dips, so-called clinoforms, as well as incision geometries have been recorded for the Natih Formation in Oman (Droste & Van Steenwinkel 2004; Grélaud et al. 2006). Clinoforms observed in Sequence I (Natih E member) demonstrate different directions of carbonate platform progradation (Droste & Van Steenwinkel 2004). These clinoform depositional profiles, although of low angles, demonstrate a complicated internal stratigraphic architecture and the short distances over which facies can change. Generally, clinoforms contain facies partitioning following the clinoform depositional profile with coarse-grained textures found at the top of clinoforms that grade into fine-grained facies types down slope. Early diagenetic overprint, resulting in either dissolution or cementation, can occur at topset beds during exposure, whilst hardgrounds can develop along the entire depositional profile or only downslope, at condensed intervals. The scales and dip of clinoforms merit some attention (Fig. 3). Two subdivisions can be made and range from: 1. Low-angle ( () up to 10 km long seismic -scale clinoforms have been extensively documented and exert a significant 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 on the example of the Fahud Field, Oman). Generally, at this scale, the clinoforms are modelled in a subhorizontal fashion. Notwithstanding, interwell stratigraphic interpretations (including inclined correlations) have to be considered because of their potential impact on internal facies partitioning and gradual facies transitions. As mentioned above, seismic-scale clinoforms have been investigated in great detail 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

4 312 E. Adams et al. Fig. 3. East west stratigraphic cross section of Natih Formation Sequence I (i.e. Natih G, F, and E members) integrating outcrop and subsurface data (modified figure from Grélaud et al. 2006; reprinted by permission of SEPM (Society for Sedimentary Geology) whose permission is required for further use). It illustrates (1) the presence of a large-scale (tens-of-kilometres) carbonate platform which prograded into an intra-shelf basin to the west and (2) subaerial exposure surfaces highlighted by incisions (IS1 and IS2) that developed during times of relative sea-level falls. The location of the cross-section is shown in Figure 2. The dotted box illustrates the setting and location of the Jabal Madmar study area. Madmar, Oman, and are the focus of this study. This scale of clinoform is usually beneath the resolution of seismic data and as such is not easily recognized and correlated between wells. Nevertheless, there is a key potential impact to interwell-scale modelling and reservoir behaviour if secondary and tertiary recovery mechanisms are adopted. A detailed geological description and the quantification and modelling of these outcrop-scale clinoforms is presented in subsequent sections of this paper. High-angle foresets, with angles up to 30 35(, are commonly observed in outcrop and frequently interpreted from logs (dipmeter and micro image). These are interpreted as the products of depositional processes that demonstrate the migration of bedforms and are not considered as part of this study. GEOLOGICAL SETTING OF THE NATIH FORMATION JABAL MADMAR OUTCROPS In Jabal Madmar, the complete Natih Formation is exposed (Sequence I to III or A to G members) and has been described by Philip et al. (1995) and Homewood et al. (2008). Jabal Madmar is part of the Adam Foothills in North Oman (Figs 1 3). The origin of the Adam Foothills is interpreted as being either related to diapiric structures (the case for Jabal Madar) or to basement-involved compressional structures (Mount et al. 1998). Jabal Madmar is related to the second interpretation, i.e. an anticline developed in response to a small amount of slip over a reverse fault, which extends into the basement. A published field guide provides detailed information on outcrops exposing the Natih Formation in northern Oman including Jabal Madmar (Homewood et al. 2008). The structural setting of Jabal Madmar, including characterization of faults and style plus scale of fractures, have been extensively studied (De Keijzer et al. 2007). The reader is referred to these studies for detailed geological background on Jabal Madmar. The outcrop exposures of the Natih Formation in Jabal Madmar are regarded as analogues to oilfields in the subsurface vicinity (Homewood et al. 2008; Hollis et al. 2010). For example, the Fahud Field is located geographically about 150 km west from Jabal Madmar (Fig. 2) and is considered to be part of the same regional structural domain where faults and fractures have similar orientations (De Keijzer et al. 2007). In terms of the palaeogeographic setting, Jabal Madmar is located in a slightly more proximal part of the carbonate platform compared to the Fahud Field (Homewood et al. 2008). DIGITAL OUTCROP MODELLING Historically, high quality, detailed and integrated outcrop studies have been used to provide constructive analogue

5 Outcrop modelling and simulating carbonate clinoforms 313 Fig. 5. (A) Digital elevation model (DEM) of the area around Madmar 3 Canyon (for location see Fig. 4). The DEM was used as a reference surface or base map for digital outcrop modelling. For Jabal Madmar a Quickbird satellite image with pixel resolution of about 0.7 m was draped over a DEM with a resolution of approximately 90 m. The image is displayed with 2 times vertical exaggeration. (B) Photograph of Madmar 3 Canyon showing approximately the same area as the DEM in A. White arrows point at Madmar 3 Canyon. Fig. 4. Quickbird satellite images of Jabal Madmar. A Quickbird satellite image has a pixel resolution of about 0.7 m. (A) Image showing Jabal Madmar anticline. (B) Enlargement of image showing the area around Madmar 3 Canyon. A reverse fault is represented by the black dashed line. information for subsurface studies. In the last ten years, digital surveying technologies and digital outcrop modelling have been increasingly invoked. These provide valuable, quantitative and accurate assessments of carbonate depositional systems, carbonate geobodies, and partitioning of carbonate facies and lithology within a deterministic framework (Adams et al. 2005, 2009; Bellian et al. 2007; Janson et al. 2007; Phelps et al. 2008; Verwer et al. 2009; Adams & Hasler 2010). It is likely that in the coming decades this trend will develop to provide field geologists with a reliable and efficient technique for assembling numerical geological outcrop data. Methods and workflow In order to quantify geobodies and associated heterogeneities observed in Jabal Madmar, a digital outcrop modelling workflow was employed, which comprises four steps. These steps are described below and result in a DOM that defines the framework of Natih Sequence I (Natih E member). It also describes the assimilation of all collected data with which more detailed geobody modelling studies were carried out (for a more comprehensive description and background of a similar workflow the reader can refer to Verwer et al. 2007). Finally, the characterization of a platform-top incision is used as example for demonstrating the workflow. Step 1: Outcrop selection Jabal Madmar provides a reservoir-scale outcrop (Figs 4 and 5). The outcrop is nearly 90% exposed; scree with limited vegetation covers recessive units, but uncovered exposures can be found. Weathering varnish is present but does not obscure fabric and textural observations. The Jabal Madmar anticline is oriented ENE WSW and is roughly 15 km long, 5 km wide, and 500 m high (Fig. 4). Several deep canyons cut perpendicular into the anticline to provide pseudo 3D exposure. The research area is primarily focused in and around Madmar 3 Canyon (Figs 4 and 5) where Sequence I is exceptionally exposed (Fig. 3). A WNW ESEtrending reverse fault, with a maximum throw of about 5 m and for which the north block is the hanging wall, is the only relevant structural feature present in the study area. It does not hamper the reconstruction of geobodies (see Fig. 4B). Figure 6 illustrates a composite stratigraphic section measured at Madmar 3 Canyon. It shows the further subdivision of Sequence I (I-3 to I-7; Natih E member E4 to E1) including a sequence stratigraphic interpretation by Homewood et al. (2008). Figures 7 and 8 show the outcrop expression of Sequence I and important stratigraphic surfaces. An area of approximately 1000 m by 1000 m was selected for construction of a DOM that constrained the framework of Sequence I and embedded the incision geobody (Fig. 4B). A high-energy shallow carbonate shoal complex comprising outcrop -scale clinoforms of the Natih E3 member was studied in detail in an area 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 with real-time kinematic global positioning systems (RTK GPS) and LiDAR (light detection and ranging) that was subsequently integrated with a digital elevation model (DEM) and high-resolution satellite imagery. A Quickbird satellite image

6 314 E. Adams et al. Fig. 6. Schematic stratigraphic section logged in Madmar 3 Canyon. Nine stratigraphic surfaces were constructed using either GPS (g) or LiDAR (l) data (l); one stratigraphic surface was modelled (m); red lines indicate incision surfaces (IS1 and IS2). with 0.7 m pixel resolution and Shuttle Radar Topography Mission (SRTM) DEM with 90 m grid resolution were used (Figs 4 and 5). SRTM is an international research effort that possesses digital elevation models on a near-global scale and can be downloaded freely over the internet. Indian Remote Sensing (IRS) satellite imagery with 5.8 m pixel resolution was also available. LiDAR images were taken in Madmar 3 Canyon (Fig. 9A and B). A LiDAR dataset contains thousands of individual x, y, z points including backscatter intensity (Fig. 9B) (Bellian et al. 2005). RTK GPS was used to collect ground control points in order to georeference the LiDAR dataset (Fig. 9C). The cumulative error in spatial positioning is in the order of 15 cm and results from merging the RTK GPS and LiDAR datasets into one single coordinate system. LiDAR allows extraction of data points from inaccessible parts of an outcrop. As such, polylines can be digitized representing fractures or stratigraphic horizons (Fig. 9D). Spatial point data of stratigraphic horizons were recorded with RTK GPS by walking along stratal contacts and, for characterizing internal heterogeneity, simultaneously tagged with additional geological information (for example, grain size or facies type observed above or below the bed contact; Fig. 9C). Stratigraphic sections were georeferenced and incorporated into the dataset as pseudo-wells. Step 3: Data integration and visualization The combination of the Quickbird satellite image and DEM was used as a base map. Spatial point data collected with RTK GPS, digitized data from LiDAR, and the digital base map were loaded and visualized in Petrel. Locally, the base map was refined by using the collected spatial point data (both from RTK GPS and LiDAR). For example, for the Natih E3 member clinoform study area, a DEM with a horizontal grid spacing of 1 m was acquired manually across the outcrop surface topography using RTK GPS. Fig. 7. Outcrop photographs illustrating mapped stratigraphic surfaces in Madmar 3 Canyon from surface Base E to surface IS1. (A) Photograph taken at a location in the centre of incision IS1. The tree in the lower right above label Base E is roughly 3 m high. (B) Photograph taken outside incision IS1. A person is standing in the upper left corner next to label B. Note the difference in exposed stratigraphy with Figure A having IS1 being incised into Sequence I-5 grainstones and B into Sequence I-6 floatstones only (see also Fig. 6).

7 Outcrop modelling and simulating carbonate clinoforms 315 Fig. 8. Outcrop photograph illustrating mapped stratigraphic surfaces in Madmar 3 Canyon from surface IS1 to surface Base D. The photograph is taken approximately at the centre of incision IS1 in the vicinity of Figure 7A. The brownish colour between IS1 and Top Fill indicates dolostones. Step 4: Construction of geospatial context Interrelating and connecting recorded data points was the final step in the digital outcrop modelling workflow. To confine a framework for geobodies, first, a DOM was constructed for the complete Natih E member. For this purpose, the focus was mainly on constructing surfaces representing stratigraphic horizons (Fig. 9E). Figure 4B indicates the size of the area for which a DOM was built. Figures 6, 7 and 8 show the stratigraphic surfaces that were mapped for establishing the framework of the model. Several surfaces were directly recorded with RTK GPS in the field (Fig. 9C); other surfaces were obtained by digitizing polylines on LiDAR data (Fig. 9D); some surfaces were modelled by assuming constant thicknesses of some of the stratigraphic units in the area of investigation (Fig. 6). The resulting DOM (Figs 9E and 10) confined the framework of Natih Sequence I or E member and assimilated all collected data with which more detailed geobody analysis was carried out. Digital outcrop modelling of incision IS1 In Madmar 3 Canyon, incision IS1 can be observed, mapped and modelled in detail (see Figs 3, 6, 9 and 10). The observed geometries 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-bedded grainstones of Sequence I-5 (Fig. 6). The complex fill (i.e. Sequence I-7) is recognized by a basal packstone or rudstone lag deposit, followed by low-energy confined wackestone in the lower 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 incision surface itself was mapped with RTK GPS by physically walking-out the contact (Fig. 9C). At steep cliff faces, no RTK GPS measurements were taken and the LiDAR imagery was used to digitize additional datapoints (Fig. 9D). Next, the surface representing the top of the fill was recorded by physically walking the contact with RTK GPS. An isopach map was computed from the incision surface and top fill horizon, resulting in a thickness model of the incision (Fig. 10A). The trend of the incision is NW SE; the maximum depth is approximately 15 m and width on the order of 600 m. The next step in modelling incision IS1 was the quantification of the internal facies and reservoir property partitioning. As such, the goal was to model the partitioning of mapped attributes within discrete zones conditioned by digital recorded outcrop observations. In order to achieve this, the individually RTK GPS recorded x, y, z spatial coordinates were simultaneously tagged with additional geological information, in the field. For IS1, attribute information was recorded on the facies type of the underlying rocks, i.e. if the incision cut into the level of float/rudstones or to the deeper level cross-bedded grainstones, and if the overlying fill contained dolostone. Thematic maps were created from the densely spaced RTK GPS recorded data, by creating variogram-based facies maps of abundance, geometry and spatial distribution of the recorded facies attributes (Figs 9B and C). Although stochastically populated, the construction mirrors a deterministic approach. Figure 10 illustrates the final geocellular outcrop model. It is clear that the incision cuts into float/rudstones of Sequence I-6 at the flanks and into cross-bedded grainstones of Sequence I-5 in the centre of the incision, as mapped in the field. Furthermore, the dolomitized wackestone or dolostone facies that is found within the incision indicates diagenesis played a role in defining the reservoir properties of the incision. To represent this, a diagenetic geobody is superimposed on the incision geobody in the model. Grelaud et al. (2006) interpreted dolomitization to be an early diagenetic event; however, mapping of the orientation of the dolostone facies shows that it is oriented perpendicular to the main axis of the incision and parallel to large-scale, NE SW-trending fracture corridors (see also De Keijzer et al. 2007). This would suggest that the dolomitization might be a later diagenetic event, associated with structuration. CLINOFORM QUANTIFICATION AND MODELLING Geological organization In Madmar 3 Canyon, Sequence I-5 or E3 member has been meticulously 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

8 316 E. Adams et al. Fig. 9. A series of illustrations that summarize the digital outcrop modelling workflow. (A) Outcrop photograph of the Natih E member in Madmar 3 Canyon illustrating excellent quality of outcrop. (B) LiDAR imagery of the same area as the photograph shown in A; the arrows point at the same feature. Image is displayed without vertical exaggeration. (C) Photograph illustrating RTK GPS recording of bed contacts. In this case, a physical measurement is recorded where the person is standing. Dots indicate other recorded data points. (D) LiDAR data allows extraction of additional data points from inaccessible parts of an outcrop, i.e. at steep cliff faces. (E) Digital outcrop model (DOM) integrating the large-amount of digitally collected field data including DEM and draped satellite imagery. In this example data is only shown for a single horizon, here the base of Incision IS1. The grey surface representing IS1 was constructed by convergent interpolation. Image is displayed without vertical exaggeration.

9 Outcrop modelling and simulating carbonate clinoforms 317 Fig. 10. Digital outcrop model of Natih Sequences I-3 to I-7 in Madmar 3 Canyon, Jabal Madmar, Oman including Incision IS1. (A) Isopach map of incision IS1. The trend of the incision is NW SE; the maximum depth is approximately 15 m and width on the order of 600 m. The WNW ESE trending reverse fault is illustrated by the black line. (B) Thematic map of the facies type of the underlying rocks, i.e. if the incision cut into the level of float/rudstones or to the deeper level consisting of cross-bedded grainstones. (C) Thematic map of the lower part of the fill containing wackestone and dolostone (i.e. dolomitized wackestone). (D) Model illustrating stratigraphic levels up to the incision IS1 basal surface. (E) 3D view of the full model illustrating all stratigraphic levels of the Natih E Member. providing a pseudo 3D exposure (Fig. 11A). Stratigraphically, the upper part of Sequence I-5 has been interpreted as a highstand systems tract (Fig. 6), deposited on a high-energy shallow platform as rudist-rich biostromes and skeletal shoal complexes (van Buchem et al. 1996, 2002b; Homewood et al. 2008). In outcrop, these shoal complexes contain up to 10 m high, hectometre-scale (100 m long), medium-angle to highangle (1 5() inclined stratal surfaces termed clinoforms (Fig. 11B). The clinoforms have a sigmoidal geometry, can be divided in topset, foreset and bottomset parts, and are characterized by texture-based facies partitioning whereby coarsegrained 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 grainstones with silicified layers (Fig. 12C). Figure 13 illustrates the reconstruction of key clinoform surfaces; Figure 14 shows four measured stratigraphic sections and correlations. It demonstrates the progradational character of the clinoforms resulting in upward-coarsening trends and lateral gradational interfingering of facies transitions. These clinoform geometries, as well as the nature of vertical and lateral facies transitions, have been captured by digital outcrop models. Stratigraphic modelling To capture the shoal complex, key surfaces including clinoforms were 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 13B illustrates a schematic sketch of the relationships and names of the digitally recorded surfaces. Figure 13C illustrates RTK GPS data points and surfaces representing stratigraphic surfaces and visualizes the reconstructed clinoforms. The curvature of the clinoforms is sigmoidal; dips up to 7( were observed, and the relief of the clinoforms was up to 10 m. Attribute modelling The next step in modelling the shoal complex was the quantification of the internal texture-based facies transitions. Figure 14 illustrates four stratigraphic sections that were measured and digitally recorded in detail to capture facies and textural variability. These measured sections were loaded as pseudowells into the model. To capture the lateral textural partitioning, the x, y, z spatial coordinates that were recorded for capturing the geometry of surfaces were tagged with geological information. For several surfaces (Surface 1 and 2, Clinoform

10 318 E. Adams et al. Fig. 11. Outcrop photographs of a high-energy shallow carbonate shoal complex of Natih Sequence I-5. (A) Outcrop photograph illustrating two parallel gullies providing pseudo 3D exposure. Width of view is approximately 100 m. (B) Outcrop photograph illustrating decametre-scale clinoform geometries. 1 and 2; see Fig. 13) attribute information was recorded as the grain 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 and therefore this surface was not input to the attribute model. Subsequently, variogram-based attribute maps were constructed offering information on abundance and spatial distribution of texture-based facies types (Fig. 16). Model construction The final step that was taken in this workflow was the construction of geocellular outcrop models. For this, two distinct framework grids were built: 1. The first grid incorporated 5 subhorizontal stratigraphic horizons and excluded clinoform surfaces. Vertical layering was proportional between surfaces and the number of layers adjusted to obtain mean cell heights of 0.20 m; the minimum cell height being 0.1 m. Horizontally, cells widths were 0.50 m. The result was a relatively homogeneous subhorizontal 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 stratigraphic horizons as well as two clinoform surfaces (Clinoform 1 and 2; Clinoform 3 was not used; see Fig. 13). As for the first model, horizontal cells had widths of 0.50 m. Proportional layering represented sigmoidal clinoforms most realistically because internal layers thin in a platform- and basinward direction (see Fig. 13B). Vertical layering of the zones within the 3D grid was adjusted to obtain mean cell heights of 0.20 m. In order to avoid small cell heights because of merging layers a minimum cell thickness of 0.1 m was used. Figure 17B illustrates the resulting grid which consisted of 6 zones, of which 3 zones represented the clinoform complex (Zone 2 between Surface 1 and Clinoform 2, Zone 3 between Clinoform 2 and Clinoform 1, and Zone 4 between Clinoform 1 and Surface 2; see Figs 13B and 17B). Texture-based facies were populated within the model grids. Truncated Gaussian Simulation (TGS) was used to distribute facies properties in the grids and allow a stochastic distribution of the property using input variograms, while assuming an ordered transition through a sequence of facies (MacDonald & Aasen 1994). The amount of data used as input for populating the grid ranged from low data densities to high data densities:

11 Outcrop modelling and simulating carbonate clinoforms 319 reservoir models that captured shoal complexes comprising clinoforms and associated heterogeneities. As described above, texture-based facies were distributed in two different grids, built using variations in clinoform geometry and with different amounts of input data. The four resulting model realizations are illustrated on Figure 19. Based on these model realizations, several general observations can be made: + The model comprising a subhorizontal grid uses one zone to represent the clinoform complex (see Fig. 17A) whereas the models incorporating inclined clinoform geometries consist of 3 zones (see Fig. 17B). Subhorizontal grids produced repeating lateral facies transitions from coarse- to mediumgrained grainstones without transitioning to fine-grained (Fig. 19A and B). An inclined grid with 3 zones produces clear and complete facies transitions from coarse- to finegrained facies (Fig. 19C and D). In other words, incorporating inclined stratigraphic surfaces enabled modelling of downslope interfingering between coarse-grained and medium- to fine-grained facies. + There is a major difference in reproduced patterns between realizations carried out with different amounts of input data. Obviously, isolated and sharper facies bodies are predicted more accurately by using more input data (compare Fig. 19A with B and C with D). Models with inclined grids produced coarse-grained bodies with progradational stacking patterns surrounded by fine-grained facies (Fig. 19C and D). When pseudo-wells plus attribute maps are used as input these progradational geometries are somewhat better developed and better match outcrop observations (Fig. 19D). In summary, the modelling method that has used inclined grids (i.e. using clinoform surfaces in the gridding procedure) and texture-based facies maps as input for TGS facies modelling produced the most realistic and geologically realistic facies partitioning, comparable to those observed in outcrop (Fig. 19D). Models that used a subhorizontal grid with pseudo-well data resulted in the poorest match when compared to outcrop observations (Fig. 19A). DYNAMIC MODELLING OF CLINOFORMS Fig. 12. Outcrop photographs illustrating Natih Sequence I-5 facies types observed in Madmar 3 Canyon. (A) Rudist-rich rudstone typically found on top of clinoform beds, i.e. topset beds. (B) Photograph showing medium-to-coarse grained cross-bedded grainstone commonly found at the upper part of clinoforms. (C) Photograph of fine-grained grainstones with silicified layers marking the boundaries of clinoform bundles. These textures are found at the base of clinoforms. 1. Pseudo-well data and the point data collected along clinoforms. As a result, input data for facies modelling comprised about 400 cells mostly arranged vertically (Fig. 18A). 2. Pseudo-well data and a point attribute set constructed from the variogram-based facies maps. This set of input data for facies modelling comprised approximately cells arranged mostly laterally (Fig. 18B). EVALUATATION OF THE STATIC MODEL The purpose of using a digital outcrop modelling approach in this study was to construct high-resolution pseudo-static The objectives of this study were to compare simple with more advanced facies modelling methods for shoal complexes, comprising clinoform geometries and associated heterogeneities, by using high-resolution digital outcrop models. Accordingly one goal was to develop recommendations and guidelines on optimal incorporation of geologically realistic clinoform geobodies and associated heterogeneities into static and dynamic reservoir models. In order to constrain variability between the various model realizations, sweep efficiency differences were assessed. Assigning dynamic properties For dynamic reservoir modelling purposes, facies models need to be translated to porosity, permeability, and saturation models. For this a well-established and published rock-typing workflow was used which was developed on analogous subsurface data from a giant oilfield in North Oman (Creusen et al. 2007; Hollis et al. 2010). Following this rock-typing study, a comparison was made with the texture-based facies types described in this study (Table 1). A simple relationship from good-to-bad reservoir properties follows the clinoform depositional profile. In this profile, the best properties are associated with rudist-rich rudstones (rock type 4; Table 1) whilst

12 320 E. Adams et al. Fig. 13. Cross sections across the modelled shoal complex comprising clinoform geometries in Natih Sequence I-5. (A) Outcrop photograph with interpreted 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. Clinoform geometries are clearly reconstructed. good and average properties are modelled for coarse- and medium-grained grainstones, respectively. However only one rock type has been assigned (rock type 6; see Table 1). The discrimination is arbitrary set by using two-thirds of the lowest porosity values for medium-grained grainstones and twothirds of the highest values for coarse textures; both texturebased facies types use the same porosity-to-permeability transform (rock type 6; see Table 1). Poorest properties were assigned to fine-grained grainstones (rock type 7; Table 1). Although the rock typing study defines this rock type as being a calcite cemented fabric associated with parasequence tops (Hollis et al. 2010), it is assumed to be also representative for fine-grained grainstones comprising silicified layers that are found at the base of clinoforms. In short, this study evaluates the impact of good-reservoir rock types (rock types 4 and 6) interfingering with non-reservoir rocks (rock type 7) on reservoir behaviour under waterflood. Other important dynamic properties for multiphase flow simulation that dictates flow through a reservoir are capillary pressure and relative permeability (Aziz & Settari 1979). Waterflood simulations, where water injection is used to displace oil, have been considered, therefore, using two-phase water-oil capillary pressure and relative permeability curves for each rock type derived from Hollis et al. (2010). Initialization, well configuration, and operating conditions For initialization, well configuration, and operating conditions, subsurface parameters that mimic a giant oilfield in North Oman were used. The top of each model was set at a reference depth of 2000 m and initial reference pressure of 3500 PSI. A water oil contact was set at 2010 m and each simulation model was initialized using a hydrostatic initialization model. For each simulation, two well configurations were

13 Outcrop modelling and simulating carbonate clinoforms 321 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). These measured sections were loaded as pseudo wells in Petrel. chosen. The first well configuration included insertion of a horizontal water injector within the topset of the clinoforms and a horizontal producer within the bottomset. The second configuration inserted wells at similar locations, except that the injector and producer were swapped. For both well configurations, the injector and producer were oriented perpendicular to the clinoform trend. The last configuration was more realistic because a configuration of an injector at the top and producer at the bottom is not commonly used in actual waterflood developments. However, it served to compare contrasting flood directions. The injection well operated at a bottom-hole pressure of 5000 PSI and at a water injection rate of 8000 bbl/day. The production well operated at a bottom-hole pressure of 1000 PSI. All models were simulated for a total period of 5 years. Waterflood simulations in clinoforms All simulations showed that the dominant control on hydrocarbon sweep under waterflood displacement was zones of higher permeability, which were represented by the upper part of the clinoforms (Zone 1, see Fig. 17). In these regions similar patterns of sweep were depicted all with roughly similar oil recoveries (Figs 20 24). However, there were differences between grid configurations. Overall, sweep of the models using horizontal grids was less efficient. The models that used low data densities as input showed a piston-like sweep, with best recoveries in the upper part of the model and a sharp boundary to lower recoveries in the underlying strata (Run A; Figs and Run A_b; Figs 22, 23). The model using pseudo-wells plus attribute maps swept more efficiently and Fig. 15. DOM with additional geological information tagged to each RTK GPS recorded data point. In this example grain size was recorded. The dots represent individual RTK GPS data points whereas yellow-to-orange colours correspond to different grain sizes. The orange transparent surface represents horizon Clinoform 1. The shaded surface is a DEM with a horizontal grid spacing of 1 m.

14 322 E. Adams et al. Fig. 16. Texture-based facies maps for 6 different stratigraphic time slices constructed using variography. Note that only 4 stratigraphic surfaces are presented (surfaces 1, 2, and clinoforms 1, 2). gave higher recoveries (Run B and Run B_b; Figs 21 and 23). This is because areas of good-quality reservoir were more continuous. All models that used inclined grids had higher recoveries (Runs C E and Runs C_b E_b; Figs 21 and 23) because good-quality reservoir rocks were connected across the entire model (Fig. 19). The model using low data densities as input exhibited the best recovery (Run C and Run C_b; Figs 21 and 23). Here, a clinoform composed of medium-grained grainstone was continuous across the whole model. The geologically most robust simulation used pseudo-wells and attribute maps from outcrop data. In this simulation, the lower clinoform in the centre of the model interfingered with poor quality rocks and had lower recoveries than the clinoforms in the upper part of the model that were continuous across the entire model and connected to the injector and producer (Run D and Run D_b; Figs 20 23). An extra model run was carried out to test the effect of horizontal versus inclined grids if all other parameters were equal. For this, the properties of Run D and Run D_b were transferred to the horizontal grid, i.e. modelling actual property partitioning with a simplified layercake 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 plateau was reached for the horizontal grid (Fig. 24). Higher production rates were most likely related to the effective vertical-to-horizontal permeability ratio (Kv/Kh) with a larger ratio predicting better production (Geehan 1993). In the horizontal grid, clinoforms were represented by a blockier pattern and hence, good-reservoir rocks were not always found adjacent to but also on top of each other resulting, in a higher Kv/Kh. DISCUSSION This study has used digital outcrop modelling to quantitatively describe outcrop-scale clinoform geobodies, incorporating partitioning of texture-based facies within a high-energy shallow carbonate shoal complex. The geobodies observed in outcrop are of similar age and are considered depositional analogues of the Natih reservoirs of Oman including the Fahud, Natih and Burhaan fields. Age-equivalent formations are the Wasia Group of Saudi Arabia, the Mauddud and Mishrif and Formations of Qatar and the United Arab Emirates, Sarvak Formation of Iran, and Mauddud Formation of Iraq (Alsharhan & Nairn 1988; Sadooni & Alsharhan 2003). In addition, clinoforms have been depicted in several other carbonate reservoirs around the world (Masaferro et al. 2004; Yose et al. 2006). Sweep efficiency in clinoforms This study demonstrated that model realizations using a well-defined stratigraphic model that incorporated inclined

15 Outcrop modelling and simulating carbonate clinoforms 323 Fig. 17. Mapped horizons were used to define zones. Vertical layering of the 3D grid was adjusted to obtain average cell heights of approximately 0.30 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 represents the clinoform complex. (B) Inclined grid including 7 horizons of which two represent inclined surfaces (i.e. clinoforms) resulting in 6 zones. Zones 2, 3 and 4 represent the clinoform complex. surfaces representing clinoforms produced geologically realistic facies transitions from coarse- to fine-grained facies within progradational inclined geobodies. It is these models that sweep most efficiently and have higher recoveries (Figs 21C, D and 23C, D). Currently, inclined grids are not often used for dynamic modelling, because they require re-gridding to avoid grid-orientation effects, for example, caused by a K-orthogonal grid (Aarnes et al. 2007). Nevertheless, it is imperative to use a well-defined stratigraphic model incorporating inclined surfaces representing clinoforms for creating an inclined model grid before interwell population commences. Unswept sections are predicted along the clinoform if inclined grids are used because progradation of higher reservoir quality facies, and interfingering with poorer reservoir quality facies, are replicated (Figs 20C, D and 22C, D). However, transferring properties from an inclined grid to a horizontal grid does produce similar patterns of sweep (for example, compare Fig. 21D and E) with higher Kv/Kh ratios and hence production rates (Fig. 24). In this study, the cell size used for facies modelling was 1m 1 m with an average height of about 30 cm. Simulation was carried out on grids of 4 m 4 m horizontally without altering vertical layering, resulting in a total of about cells. Upscaling such high-resolution models to reservoir-scale models will not be trivial. But as demonstrated above, inclined geometries are of importance on hydrocarbon displacement, and should be considered and incorporated during upscaling. In conclusion, using high-resolution models for correctly deriving static and dynamic rock properties for input into a full-field model must be considered in reservoir modelling workflows (see also Creusen et al. 2007). A study for the Permian Slaughter San Andres field, West Texas, USA, similarly looked at the effect of inclined geometries, i.e. clinoforms, on waterflood simulations (Jennings 2001). One model used inclined geometries that were combined with lateral and vertical trends, whereas another model had a simplified vertical trend only. The simulations showed that waterflood displacement was dominated by the overall vertical trend, with both models predicting similar oil recovery. A similar finding was produced by the study presented here. For the Permian models, cumulative injection versus time indicated that the models with a simplified vertical trend overestimated the injectivity by about 20% because the flow was not forced to cross the lower permeabilities in the bottom of each cycle present in the inclined model (Jennings 2001). Another study, focused on interwell scale clinoform heterogeneities affecting recovery efficiency in the Upper Thamama, Lower Cretaceous carbonate outcrops of the United Arab Emirates (Vaughan et al. 2004; Strohmenger et al. 2006). In this study, models were constructed with inclined fine-scale grids as well as horizontally layered coarse-scale models, where the fine model was not as homogeneous causing fluids to encounter more baffles and travel 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 carried out to provide accurate geometric information on the effects on fluid flow of deltaic clinoforms including cemented and shale-covered barriers (Howell et al. 2008a, b; Jackson et al.

16 324 E. Adams et al. Fig. 18. Images illustrating the amount of data used as input for populating the grid. (A) Cells represent logged sections and the point data collected along clinoforms, i.e. exampling vertical and horizontal pseudo wells. (B) Filled cells were obtained from logged sections and variogram-based facies maps (Fig. 16). 2009; Sech et al. 2009; Enge & Howell 2010). For modelling shoreface and delta-front deposits, the facies modelling tools included a Truncated Gaussian Simulation. The studies have reported that the gridding strategy can account for up to 30% of the difference in simulated production when modelling clinoform systems. It was concluded that a horizontal grid appeared to produce significantly better than an inclined one, because it fails to capture barriers and the geometry of clinoforms (Howell et al. 2008a). The study presented in this paper demonstrates a contrary outcome to these previous studies primarily since, in this study, barriers to flow are absent. The models containing clinoforms in this study sweep efficiently; however, clinoforms that are not connected between wells show lower recoveries. Similarly, Jackson et al. (2009) demonstrated this preferential flow of injected water through higher reservoir quality rocks situated in the upper part of clinoforms when compared to the lower sweep efficiency in clinoforms interfingering with relatively poorer reservoir quality rocks. The main explanation for the more efficient sweep in the models embedding clinoforms presented in this paper compared to the more traditional models is that the highly connected geobodies are embedded in the static models through the use of inclined grids. In contrast, traditional modelling methods that use horizontal grids produce isolated sharp bodies at best but homogenously patchy facies patterns at worst. What both these published studies and this study clearly show is that a more correct distribution of heterogeneities associated with clinoforms has implications for the effective vertical-to-horizontal permeability ratio (Kv/Kh) and therefore also for the sweep efficiency in a reservoir. For example, horizontal wells can be considered for field development because of potential increases in productivity but these would only be more effective if Kv/Kh was sufficiently large (Geehan 1993). Mature Middle East fields commonly have many wells by which to constrain facies, porosity, permeability, and Table 1. Summary of facies types, facies associations, rock types and rock property data Facies Lithofacies association Rock type Porosity Porosity to permeability transform Mean, range, stdev Rudist-rich rudstone LA4 Rudist shoal RT4 32.3, , 9.9 (17.5 Phi) k = e Coarse-grained bioclastic LA6 Foraminiferal shoal and LA7 Marginal/ RT6 31.9, , 4.2 (17.6 Phi) k = e grainstone inter-foraminiferal shoal Medium-grained bioclastic LA6 Foraminiferal shoal and LA7 Marginal/ RT6 24.2, , 5.1 (17.6 Phi) k = e grainstone Fine-grained grainstone comprising silica layers inter-foraminiferal shoal LA6 Foraminiferal shoal and LA7 Marginal/ inter-foraminiferal shoal RT7 16.7, , 6.4 k = e (20.6 Phi) Lithofacies associations, rock types and rock property data summarized and permeability transforms using the same dataset computed from Hollis et al. (2010)

17 Outcrop modelling and simulating carbonate clinoforms 325 Fig. 19. Realizations of texture-based facies models originating from two different grids and two sets of input data. All realizations use Truncated Gaussian Simulation (TGS) for facies modelling. (A) Horizontal grid (see Fig. 17A) using pseudo wells (see Fig. 18A). (B) Horizontal grid using logged sections plus attribute maps (see Fig. 18B). (C) Inclined grid (see Fig. 17B) using pseudo wells. (D) Inclined grid using logged sections plus attribute maps. (E) Horizontal grid as in A and B with properties transferred from model run D.

18 326 E. Adams et al. 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 grid perpendicular to the figures (see also part A).

19 Outcrop modelling and simulating carbonate clinoforms 327 Fig. 21. Flow simulations as shown in Figure 20. Colour bar shows recovery factor after 4 years of simulating a waterflood.

20 328 E. Adams et al. Fig. 22. Flow simulations using the different model realizations shown in Figure 19A E. Colour bar shows oil saturation after 4 years of simulating a waterflood. A horizontal water injector is inserted at the northern lower-right cells and a horizontal producer at the southern top-left cells of the grid perpendicular to the figures (see also part A).

21 Outcrop modelling and simulating carbonate clinoforms 329 Fig. 23. Flow simulations as shown in Figure 22. Colour bar shows recovery factor after 4 years of simulating a waterflood.