sequence stratigraphic-based analysis of reservoir connectivity - influence of depositional...

Sequence stratigraphic-based analysis of reservoir connectivity: influenceof depositional architecture – a case study from a marginal marine

depositional setting

R. Bruce AinsworthWoodside Energy Ltd, Woodside Plaza, 240 St George’s Terrace, Perth, WA 6000, Australia and STRAT Group,

Department of Earth Sciences, University of Liverpool, 4 Brownlow Street, Liverpool L69 3GP, UK(Present address: Shell Todd Oil Services Ltd, 167 Devon Street West, Private Bag 2035, New Plymouth, New Zealand;

e-mail: [email protected])

ABSTRACT: This case study of the Sunrise and Troubadour fields (offshorenorthwest Australia) concentrates on the impact of primary depositional architectureon reservoir connectivity via a sequence stratigraphic-based, 3D reservoir modellingapproach. The marginal marine reservoir is composed of fluvial-dominated andwave-dominated depositional environments. The succession is divided into sixsequences and 12 systems tracts. Each systems tract is subdivided into parasequenceswhich form the basic building blocks of the 3D model. The connectivity ofsandbodies within each parasequence, systems tract and sequence were calculatedwhen the models were palinspastically restored to a depositional datum.

The findings indicate that depositional connectivity trends within a sequencestratigraphic framework are predictable. Connectivity trends can be related directlyto depositional and stratigraphic trends and to position in the sequence stratigraphichierarchy. Therefore, with a good understanding of depositional settings and highresolution sequence stratigraphic subdivision of strata, predictions of depositionalconnectivity trends at all stratigraphic hierarchical levels can be made. All connec-tivity trends at sequence and systems tract stratigraphic hierarchical levels remainedthe same when the area of the model was reduced by a factor of four and the volumeof the model was reduced by truncation below the gas–water contact. Hence, therelationships between the stratigraphic trends and the connectivity trends for thethicker stratigraphic units can be said to be scale invariant.

Three-dimensional reservoir models currently provide the best means of quanti-tatively assessing and predicting reservoir connectivity. However, in low fault densitysettings, initial screening of reservoir connectivity can be made using connectivityindicators calculated from simple parameters derived from well data such as‘Thickness divided by Sand/Shale ratio’.

KEYWORDS: 3D reservoir modelling, sequence stratigraphy, reservoir connectivity

INTRODUCTION

Reservoir connectivity is one of the major subsurfaceuncertainties in the evaluation and development of many oiland gas fields. It is a complex uncertainty which is a function ofthe interaction of a number of variables, namely sand/shaleratios, sandbody geometries, sandbody distributions, syn- andpost-depositional faulting and fault transmissibility characteris-tics. These parameters may be subdivided into two groups. Thefirst group comprises those parameters concerned with primaryconnectivity determined by sand/shale ratios and depositionalarchitecture (geometries and distributions of the sandbodies).The result of the combination of these parameters is termedhere ‘depositional connectivity’. The second group comprisesthose parameters that determine secondary connectivity such assyn- and post-depositional faulting and fault transmissibility

characteristics. The result of the combination of these par-ameters is here termed ‘structural connectivity’. As a tertiaryconcern, the ability of a fluid to flow within a sandbody is afunction of the permeability of the reservoir. Permeability isoften related to porosity. Both these properties can be relateddirectly to primary depositional processes (e.g. grain size andangularity, sorting and resultant pore throat geometry) but mayalso be modified by post-depositional diagenetic processes.

The impact of all the above must be considered to analysereservoir connectivity fully (Knipe et al. 1998; Walsh et al. 1998).However, depositional connectivity must initially be under-stood before embarking on further structural and diageneticanalyses. Pulham (2001) concluded that ‘primary depositionalfabrics are fundamentally important in explaining reservoirbehaviour despite the complexities of post-depositional histo-ries’. This study, therefore, concentrates on the impact of

Petroleum Geoscience, Vol. 11 2005, pp. 257–276 1354-0793/05/$15.00 � 2005 EAGE/Geological Society of London

primary depositional architecture on reservoir connectivity.The secondary and tertiary effects of structure and diagenesisare not analysed in this paper. However, the depositionalconnectivity data have been used to analyse the subsequentimpact of structural deformation on the connectivity of thestrata in other publications (Ainsworth et al. 2000; Ainsworth2005).

A reservoir succession must be subdivided stratigraphicallyto assess depositional connectivity sensibly. Sequence stratigra-phy (Van Wagoner et al. 1988, 1990) provides a suitable tool forsuch a subdivision (Bryant & Flint 1993).

Since the connectivity issue is a complex, 3D problem, 3Dreservoir modelling techniques (Dubrule 1989; Weber & VanGeuns 1990; Haldorsen & Damsleth 1990; Flint & Bryant1993) offer the optimal route to assess any potential relation-ships between parameters and hence allow one to forwardmodel or predict reservoir connectivity. This paper andAinsworth (2003) address these connectivity uncertainties via asequence stratigraphic-based, 3D reservoir modelling approach.Other workers (e.g. Walsh et al. 1998; Bailey et al. 2002; Keogh2002) have analysed the combined impact of depositional andstructural architecture on reservoir connectivity. However,none of these studies has analysed the strata and presentedthe depositional connectivity results in a rigorous sequencestratigraphic manner.

Due to the generic nature of the parameters controllingsedimentary architectures, the study of a high-quality dataset,such as the one used in this case study, can lead to insights intoconnectivity prediction that may be applicable to other, lesswell-constrained areas.

Study area

The Sunrise and Troubadour gas-condensate fields form thebasis of this study (Seggie et al. 2000, 2003; Ainsworth 2003;Ainsworth et al. 2005). They are located 450 km to thenorthwest of Darwin, Australia, on the edge of the AustralianContinental Shelf and 50 km from the adjacent, 2500 m deepTimor Trough (Fig. 1a). The in-place gas volumes for the fieldsat a 50% probability level are 13.4�1012 SCF (Seggie et al.2003).

Six wells define the fields (Figs 1b, 2, 3). A total of 285 m ofcore was acquired from three of the wells (Sunset-1, SunsetWest-1 and Sunrise-2; Figs 1b, 2, 3). A full suite of conventionallogs was acquired in each well. The field area is covered by a50�75 km 2D seismic grid (line spacing of 1�2–3 km) and a3D seismic survey (3200 km2; Fig. 1b). However, due to dataquality issues attributed to shallow, overlying, Miocene carbon-ate reef complexes, the seismic data have proved to be oflimited use in terms of stratigraphic analysis (Ainsworth 2003).

REGIONAL SETTING

Tectonic history

The study area is located on the Sunrise High, which forms partof the Sahul Platform, a regional high in the northern portionof the Bonaparte Basin. The basin was initiated in the LateDevonian (Gunn 1988a, b) and has persisted through to thepresent day. It covers a triangular area of approximately270 000 km2 (Mory 1988) and has an onshore expression to thesouth. However, the vast majority (over 90%) of the basin liesoffshore.

In the area of the Sunrise and Troubadour fields, a relativelythin (only 20 m penetrated in Troubadour-1) Palaeozoic suc-cession is overlain by a substantial thickness of Mesozoic toCenozoic deposits. Up to 10 km of sediment accumulated in

some of the northern grabens and up to 4 km over some of theregional highs (Mory 1988). The structural grain is domi-nated by northeasterly-striking extensional faults. Thesenortheasterly-striking faults were postulated by Mory (1988) tobe related to middle to late Jurassic extension resulting from thebreak-up of Argo Land (the separation of India from Australia).This extensional event occurred following the deposition of thestudied succession. Shuster et al. (1998) also identified a lateCarboniferous to early Permian phase of rifting with the sameorientation. At the time of deposition of the study interval(Bathonian to Callovian), the Sunrise area was, therefore, in apre-rift, relatively stable tectonic setting with a predominantunderlying northeasterly-striking structural fabric.

Stratigraphy

The Plover Formation (Figs 2, 3; Hettangian to Callovian)represents the reservoir succession in the Sunrise andTroubadour fields and is the subject of this study. It comprisesan overall transgressive succession of fluvio-deltaic sandstone,siltstone, claystone and coal, grading vertically and laterally (tothe north and east) into marine sandstone, siltstone andclaystone. Coarse clastic deposition was terminated by a majormarine transgression, which progressively flooded much ofAustralia in the Aptian to early Albian. The reservoir atthe Sunrise and Troubadour field locations is Bathonian to

Fig. 1. (a) Study area location map. The Sunrise and Troubadourfields lie approximately 450 km offshore from Darwin, Australia and200 km across the Timor Trench from Timor Leste. Contours arewater depth in metres. (b) Data availability map. Field outlines showthe Sunrise Field to the north and the Troubadour Field to the south.Two-dimensional seismic lines are shown in grey. Only 10 m of corewere recovered from Troubadour-1. Bard-1 failed to reach topreservoir due to drilling difficulties. Note the large areal extent of thefields. Outlines of a typical North Sea block (10�20 km) and theBrent Field (North Sea) are superimposed for comparison. Also notethat the Brent Field could fit between the closest two wells, SunsetWest-1 and Sunset-1, which are approximately 9 km apart. Modifiedfrom Ainsworth (2003).

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Callovian in age and is the most marine-influenced section ofthe Plover Formation encountered in the region (Longley et al.2002). In the Sunrise area, the Plover Formation is overlain bythe transgressive marine sandy to silty claystones of theLaminaria Formation and the condensed marine silty claystonesof the Frigate, Flamingo and Echuca Shoals Formations(Figs 2, 3). These formations are collectively only 15–20 mthick, although they represent a period of approximately 35 Ma(Ainsworth 2003). The peak of the Cretaceous transgression isrepresented by the Darwin Formation Radiolarite (Aptian toEarly Albian), which is a condensed radiolarian claystone/calcilutite (Figs 2, 3).

SEDIMENTOLOGY AND SEQUENCESTRATIGRAPHY

The sedimentology and sequence stratigraphy of the 285 m ofcore in the Sunrise wells (Figs 2, 3) has been described in detailby Ainsworth (2003) and Ainsworth et al. (2005). This sectionsummarizes this work.

Core facies descriptions and interpretations

In 1998/99 detailed sedimentological and ichnological coredescriptions were made by F. J. Burns and J. Thompson on the

Sunset-1, Sunset West-1 and Sunrise-2 wells (Fig. 2; seeAinsworth (2003) and Ainsworth et al. (2005) for more detailedgraphical descriptions). The image log data available in all of thewells were also interpreted by these workers to derive palaeo-current information. These descriptions were used in conjunc-tion with the petrological data (S. E. Phillips, unpublishedWoodside internal report) to develop a facies associationscheme (Table 1). The wireline log characteristics of the faciesassociations identified in core were then used to interpret faciesassociations in the uncored wells (Sunrise-1, Loxton Shoals-1and Troubadour-1; Figs 1b, 2). As can be seen from Figure 2and Table 1, the studied interval was deposited in a tide-influenced, wave/fluvial-dominated marginal marine setting(Ainsworth 2003; Ainsworth et al. 2005).

Key depositional trends

Key depositional trends of note are: (i) a switch from predomi-nantly fluvial-dominated/tide-influenced deposits at the baseof the succession to predominantly wave-dominated/tide-influenced shoreface deposits towards the top of the succession(Fig. 2); and (ii) the parasequence-scale vertical alternationbetween predominantly fluvial-dominated/tide-influenced

Fig. 2. Facies associations and correlatable surfaces summary well cross-section (see Fig. 1b). Note that facies and surfaces identified in core(grey bars) have been extrapolated to uncored wells using core to wireline-log facies calibration criteria. Also note the down depositional dipfacies changes from upper to middle/lower shoreface in HST3 and LST4. Absolute geometries and lateral extents of individual bodies areschematic. Dinoflagellate zones are indicated on the figure together with lithostratigraphic formation names and sequence stratigraphic surfaceterminology. See Figure 3 for the sequence stratigraphic interpretation. Regional seismic stratigraphic markers shown towards the top of thesuccession are: JC, Intra Callovian event; JO, Intra Oxfordian event; JT, Base Tithonian event; KV, Intra Valanginian event; NA, Base Aptianevent; NKA, Near-top Aptian event. Modified from Ainsworth (2003).

Reservoir connectivity and depositional architecture 259

coastal deposits to predominantly wave-dominated/tide-influenced coastal deposits which can be seen to occur twice,across SB-2 and SB-3 (Fig. 2).

High resolution sequence stratigraphy

Core descriptions, biostratigraphic, petrographic, well-log dataand the depositional model were integrated into a high resolu-tion sequence stratigraphic model. The core data were initiallyinterpreted in terms of the vertical and lateral distributions ofdepositional environments and the key stratigraphic surfaces:sequence boundaries (SB), maximum flooding surfaces (MFS),transgressive surfaces of erosion (TSE), regressive surfaces oferosion (RSE) and parasequence flooding surfaces (PFS). Theseinterpretations were then calibrated to the wireline-log data andextrapolated to the three uncored wells (Sunrise-1, LoxtonShoals-1 and Troubadour-1; Figs 1b, 3). The PFS are correlat-able field-wide with a maximum correlation distance of 25 kmdown the depositional dip and 38 km along the depositionalstrike. They form the basic building blocks of the 3D model.There is limited information in the literature on along-strikecorrelation distances for parasequences, but Van Wagoner et al.(1990) detailed outcrop examples from the Book Cliffs of theUSA where parasequence boundaries can be traced for at least19 km in a downdip direction. Reynolds (1999) detailed themean downdip extents of highstand systems tract (HST)

Fig. 3. High resolution sequence stratigraphy summary well cross-section (see Fig. 1b). Note that surfaces and systems tracts identified in core(grey bars) have been extrapolated to uncored wells. Also note that dinoflagellate zones are indicated on the figure together with lithostratigraphicformation names and sequence stratigraphic surface and unit terminology. See Figure 2 for facies association interpretations. The dinoflagellatepercentage plot adjacent to Sunset-1 illustrates the high percentages associated with maximum flooding surfaces (MFS) and the overall upwardincrease in marine influence through the succession. Modified from Ainsworth (2003).

Table 1. Facies Associations (FA) used in 3D reservoir modelling and correlatibility of

FA between adjacent wells.

FA Description Reservoir

FA 1 Wave-Dominated/Tide-Influenced Upper Shoreface YesSandbodies correlatable between adjacent wells

FA 2 Wave-Dominated/Tide-Influenced Middle to Lower Shoreface YesSandbodies correlatable between adjacent wells

FA 3 Fluvial-Dominated/Tide-Influenced Mouthbar YesSandbodies NOT correlatable between adjacent wells

FA 4 Fluvial-Dominated/Tide-Influenced Channel YesSandbodies NOT correlatable between adjacent wells

FA 5 Stacked Fluvial/Tidal Multi-storey Channel Complex YesSandbodies NOT correlatable between adjacent wells

FA 6 Transgressive Lag YesSandbodies correlatable between adjacent wells

FA 7 Brackish Heteroliths NoCorrelatable between adjacent wells

FA 8 Marine Heteroliths NoCorrelatable between adjacent wells

FA 9 Coastal Plain Mudstones NoCorrelatable between adjacent wells

FA were taken from Ainsworth (2003) and Ainsworth et al. (2005). The thirdcolumn shows facies defined as reservoir in the 3D model. Reservoir sandfacies were determined using a gamma-ray log cut-off.

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shoreface parasequences to be 16.4 km, with a maximum extentof 43 km. Bryant (1996) also stressed the large lateral extents ofPFS and their importance for correlation.

In this study the four-systems-tract model is used, whereinsystems deposited during falling sea-level are incorporated intothe falling-stage systems tract (FST; Ainsworth 1991, 1994;Plint & Nummedal 2000). The master sequence boundary isplaced at the base of the incised valley systems in the landwardsector and is coincident with the first evidence of a regressivesurface of marine erosion in the marine sector (cf. Posamentier& Morris 2000, fig. 22, p. 40). The transition from FST to LSTin strata deposited in low accommodation regimes (such as inthis study area) is subtle and difficult to distinguish even inoutcrop examples because an onlap surface must be recognizedin order to separate the two systems tracts. This makes such asubdivision even more problematical in the subsurface and thereader is referred to Posamentier & Allen (1999, p. 34) for adiscussion of the relative merits of ‘falling-stage’ and ‘lowstand’terminology. Consequently, the FST and LST are combined inthis study where it is impossible to identify a surface to separatethe two systems tracts.Sequence stratigraphic model This description of the depositionaland sequence stratigraphic evolution of the studied intervalshould be read in conjunction with Figures 2 and 3. Fluvialdeposits dominate the lower part of the succession (Sequence-1). A marine transgressive event (TSE-1; Figs 2, 3) marks asignificant change in the character of the deposystems.Above this transgressive surface the dominant character of thedeposystems varies from fluvial-dominated to wave-dominated,but there is a consistent tidal signature in all deposits. Fluvial-dominated/tide-influenced mouthbar deposits were initiallydeposited above the flooding surface in an open marine bayenvironment, which gradually became more restricted (Fig. 2).The bay experienced periodic freshwater flushing events. Thesedeposits were superseded by more laterally continuous, sharp-based, wave-dominated/tide-influenced shoreface sedimentsand fluvial valley incision associated with a relative sea-levelfall and the formation of SB-2. A further flooding eventinitiated the fluvial, tide-influenced filling of these valley sys-tems by multi-storey channel complexes (LST-2/TST-2). Thesedeposits are overlain by highstand (HST-2) fluvial-dominated/tide-influenced sediments, which were deposited within amarine bay environment that was prone to freshwater influxevents. More laterally continuous, open coastline, wave-dominated/tide-influenced, sharp-based shoreface sedimentsoverlie these deposits and are associated with the formation ofSB-3. Highstand system tract-3 is also represented by twowave-dominated/tide-influenced parasequences. This wave-dominated/tide-influenced depositional style persisted throughthe three other sequences interpreted above Sequence-3, untilcoarse clastic deposition in the area was terminated by aregional flooding event (MFS-6; Figs 2, 3).

Sequences 1, 2 and 3 form a highstand sequence set (HSS-1;Fig. 3). The time significance of the relatively thin Sequence 4(3.5 Ma; Ainsworth 2003) indicates that it represents a falling-stage/lowstand sequence set (F/LSS-2). Sequences 5 and 6represent a transgressive sequence set (TSS-2; Ainsworth 2003;Ainsworth et al. 2005).

THREE-DIMENSIONAL DEPOSITIONALMODELLING

Model generation

All modelling was performed using the Shell in-house propri-etary modelling system GEOCAP (Budding et al. 1992; Taylor

1996; Abbots & van Kuyk 1997; Mijnssen 1997; Ainsworthet al. 1999). Key 3D model dimensional data are given inTable 2. An overview of the modelling workflow is shown inFigures 4 and 5. Initial well correlations representing thereservoir sequence stratigraphic framework were made in thefront-end correlation package (Figs 2–4). Facies associationswere then assigned to each of the bodies penetrated by thewells (Fig. 6a; Table 1). These facies association bodies werethen either left uncorrelated (well bodies; Figure 6b) or werecorrelated with adjacent wells (correlatable bodies; Figure 6b).The decision of whether or not to correlate was made using‘soft knowledge’, such as considerations of the processesinvolved in the deposition of the bodies, and semi-quantitativedata on both the mean body extent and orientation of the faciesassociations (Ainsworth 2003; Ainsworth et al. 2005) and thedistances to the closest well(s).

Once the correlation framework was established, thecorrelated wells were used to build the 3D reservoir model.Palinspastic restoration of the reservoir was achieved by usingthe MFS-4 marker (Figs 2, 3) as a datum. Initially 3D faciesassociation bodies were built from the correlatable stratigraphicunits. The correlatable units (Fig. 6b) were made up of six ofthe nine identified facies associations (wave-dominated/tide-influenced upper shoreface, wave-dominated/tide-influencedmiddle to lower shoreface, transgressive lag, brackish hetero-liths, marine heteroliths, and coastal plain mudstones; Table 1).All of these bodies were considered to extend over the entiremodelling area of interest. Top and base surfaces of thesebodies were kriged between wells. Depositional strike orienta-tions (NE to SW) were derived from borehole image datatogether with regional palaeogeographic information.

Table 2. Key 3D model dimensional data

Area of interest 74�52 km=3848 km2

Total thickness (m) 225Grid type Regular orthogonalGrid block length (m) 250Grid block width (m) 250Grid block thickness (m) 1No. of grid blocks 13.85�106

Rotation about origin (�) 349

Fig. 4. Three-dimensional depositional modelling and analysisworkflow. (a) Workflow for building the 3D depositional model.(b) Workflow for analysing the 3D depositional models. Modifiedfrom Ainsworth (2003).


Fig. 5. Three-dimensional reservoir modelling workflow. (a) Whole model area volume (74 km long � 52 km wide � 225 m thick) flattenedon top reservoir. Note that the vertical dimension of the depositional facies model is highly exaggerated. The locations of the six wells used tobuild the model are also shown. Refer to Figure 1b for well names. (b) Sunrise Field area volume flattened on top reservoir. (c) Sunrise Fieldarea volume structured according to the top reservoir depth map. (d) Sunrise Field area volume structured according to the top reservoir depthmap and truncated below the gas–water contact (GWC). (e) Sunrise Field area volume above the GWC flattened on top reservoir. Full modelclustering for each stratigraphic unit was performed on models (a), (b) and (e). Well clustering was also performed on model (e) for comparisonwith the full model clustering results. See cross-sections through models (a), (b) and (e) in Figure 7. Modified from Ainsworth (2003).

Fig. 6. Explanation of 3D modellingterminology. (a) Schematic cross-sectionbetween two wells in a 3D depositionalmodel showing facies andparasequences. (b) Bodies that arecorrelatable between the two wells(Correlatable bodies) and thosepenetrated only by one well (Wellbodies). (c) The correlatable and wellbodies seen in (b) plus stochasticallygenerated bodies in the inter-well areas(Infill bodies). (d) Example of fullmodel clustering on a parasequencescale. Parasequence ‘a’ has two clusters,parasequence ‘b’ has three clusters andparasequences ‘c’ and ‘d’ both have onecluster. The fewer the number ofclusters, the better the connectivity.(e) A well ‘drilled’ through the model.Parasequences ‘c’ and ‘d’ are 100%connected to the well whilstparasequence ‘a’ has 80% of its volumeconnected and parasequence ‘b’ hasonly 70% connected. Compare thesewell connectivities with the number ofclusters for the same parasequence in(d) and note the inverse relationshipbetween the number of clusters andreservoir connectivity. Refer to Table 1for a fuller description of faciesassociations (FA) shown in (a).Modified from Ainsworth (2003).

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The well bodies (Fig. 6b) were modelled in 3D using therange of dimensions and orientation data taken from theproprietary Shell world-wide depositional body geometry data-base. The geometries are summarized in Ainsworth (2003) andthe dimensions and ranges are similar to those published byReynolds (1999). The well body units were made up of threeof the nine identified facies associations (fluvial-dominatedl/tide-influenced mouthbar, fluvial-dominatedl/tide-influencedchannel, and stacked fluvial/tidal multi-storey channel complex;Table 1). Infill of the 3D reservoir volume between the existingwell and correlatable bodies was achieved using Booleanstochastic methods (Dubrule 1998), which sample from therange of dimensions and orientation data summarized inAinsworth (2003). The generated bodies are termed ‘infillbodies’ (Fig. 6c). The infill bodies were made up of thesame three facies associations that were identified as wellbodies (fluvial-dominated/tide-influenced mouthbar, fluvial-dominated/tide-influenced channel, and stacked fluvial/tidalmulti-storey channel complex; Table 1). The number of gener-ated infill bodies was defined by the average sand/shale ratios(S/Sh) of the basic stratigraphic unit, the parasequence orparasequence equivalent; i.e. for each basic stratigraphic unit,the modelling system was programmed to generate the numberof infill bodies required to match the average S/Sh ratios ofthe six wells used in this study. No seismic conditioningtechniques were applied to guide the stochastic body infill dueto the relatively poor quality of the seismic data. There is,therefore, no preferential control on the geographical distri-bution of stochastic infill bodies within each stratigraphic unit.Cross-sections through the 3D reservoir model are shown inFigure 7.

STATIC CONNECTIVITY ANALYSIS

Static connectivity has been discussed and defined by a numberof authors (e.g. Stiles 1976; Alabert & Modot 1992; Buddinget al. 1992; Cook et al. 1994; Jones et al. 1995; Hird & Dubrule1995; Bailey et al. 2002). Stiles (1976) simply defined ‘con-tinuous pay’ as being ‘the volume of rock that is connectedbetween any two wells’. Subsequent authors added furthercomplexity to this definition by incorporating reservoirproperties such as directional and relative permeabilities intoconnectivity definitions. Cook et al. (1994) built in proposedhydrocarbon recovery strategies (depletion versus fluid dis-placement) to their definitions of connectivity. Alabert &Modot (1992) summarized static connectivity in the followingway ‘. . . the connected pore volume around producer wellscontrols the volume of oil (gas) that can be drained, whilst theconnected pore volume between injector and producer wellscontrols the sweep efficiency’.

Static connectivity in this study follows the simpler premisesof Stiles (1976) and does not take into account variation inreservoir properties or recovery strategy. All connectivity analy-ses in this study are undertaken using a palinspastically restoredmodel and are thus termed ‘depositional connectivity analyses’.The aim of this work is, therefore, to test for reservoirconnectivity trends and patterns related to depositionalarchitecture prior to any structural deformation.

Cluster connectivity analyses

Once the 3D model was constructed, it was interrogated toassess the juxtaposition or static connectivity of sandbodies ona parasequence, systems tract and sequence basis. Connectivitywas analysed by using two clustering methods: full modelclustering and well clustering. Both methods are described

below. A cluster is defined as ‘a discrete group of connectedreservoir facies grid blocks’ (Figs 6d, e). Reservoir facies areidentified in Table 1. There is an inverse relationship betweenthe number of clusters generated and the connectivity of thereservoir; i.e. the more clusters that are generated, the less wellconnected is the reservoir (Fig. 6d). A minimum number of gridblocks was defined to constitute a cluster. This minimumnumber was set to overcome the problem of partial bodiesoccurring at the model boundaries. This number was arbitrarilyset at nine grid blocks (5.625�105 m3). In other words, anycluster of less than nine grid blocks was not counted in thecluster analyses.

Full model clustering connectivity With full model clustering thenumber and the average volume of clusters over the entirereservoir model or a defined area of interest were analysed ona parasequence, systems tract and sequence basis.

Well clustering connectivity With well clustering the number andvolume of clusters connected to the reference case develop-ment wells (Figs 6e, 8) on a parasequence, systems tract andsequence basis were analysed. Note that only one developmentwell-layout scenario was tested. The wells have an approximatespacing of 4 km (Fig. 8). This well spacing was defined asoptimal using dynamic reservoir simulations (Stephenson et al.

2000). Only the model above the gas–water contact (GWC) wasassessed. No limitations were placed on the radius of investi-gation for the well connectivity analyses. The impact ofincreasing or decreasing well numbers and spacings was notpart of this study. The 21 well development layout was ‘drilled’in the flattened Sunrise Field area model that had beentruncated below the GWC (Fig. 5e). Comparisons of wellconnectivity and full model clustering trends were made at eachstratigraphic level to investigate whether the stratigraphic con-nectivity trends from the full model clustering were reflected inthe well connectivity results.

Clustering methodology The clustering analyses were split into fourdiscrete phases (Figs 4, 5). Full model clustering was usedinitially to analyse the connectivity of the whole model area(3848 km2) volume on a parasequence, systems tract andsequence basis (Figs 5a, 7a). To test for any potential arealscale-dependency relationships, the same connectivity analyseswere then also applied to the model volume within the areacorresponding to the outline of the Sunrise Field (920 km2;Figs 5b, 7b). As can be seen from Figures 5a and b, only thearea of the model was reduced for this analysis. The purpose ofthe third phase was to allow a direct comparison with the wellconnectivity analyses (phase 4); a full model clustering analysison a parasequence, systems tract and sequence basis was runfor the volume above the GWC in the Sunrise Field Area. Thisinvolved structuring the model according to the base case topreservoir depth map (Fig. 5c) and then truncating the modelbelow the GWC (Fig. 5d). The model was then palinspasticallyrestored and the full model clustering analysis was run (Figs 5e& 7c). When the model was truncated below the GWC, notonly the area but also the volume of the units below TST-2were reduced (Figs 5e, 7c). This analysis therefore tests theimpact of reducing volumes on the lower stratigraphic units(below TST-2). Phase four involved the reference case fielddevelopment well layout of 21 producing wells being ‘drilled’ inthe model (Fig. 8). The number and volume of grid blocksconnected to the wells on a parasequence, systems tract andsequence basis were calculated. The well connectivity was thencompared to the previously calculated full model connectivity(see phase 3 above).


Fig. 7. West to east cross-sections through the 3D reservoir model showing sequence stratigraphic units. (a) Whole model area volume;(b) Sunrise Field area volume; (c) Sunrise Field area volume above the GWC. Note that (c) has been structured and then truncated below theGWC before being re-flattened using the top of the model as a datum (Figs 5c–e). Also note that most of the stratigraphy below HST-2psa hasbeen truncated and is discontinuous. Compare with stratigraphy in Figure 2. Modified from Ainsworth (2003).

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KEY MODELLING ASSUMPTIONS,SIMPLIFICATIONS, LIMITATIONS AND ISSUES

A list of key assumptions and simplifications made during thisstudy is given as follows. These assumptions and simplificationsresult in consequential limitations of the model, which are alsodiscussed below. Figure 9 shows the assumptions and simplifi-cations displayed in a Boston Matrix-type plot with the ‘prob-ability of the assumption being incorrect’ plotted against its‘potential impact on connectivity’. The numbers in the figurerelate to the numbers listed below. Figure 9 provides a guide to

the perceived importance or ranking of each assumption,simplification and limitation with regard to its potential impacton reservoir connectivity. Making the sealing parasequenceboundaries transmissible (point 2 below; Fig. 9) and dippingshales in the mouthbars or shorefaces flow barriers (point 6below; Fig. 9) is perceived as having a high potential impact onconnectivity but a low probability of being the correct assump-tion. Modelling channels as being straight (point 3 below) isregarded as having a high probability of being incorrect but willprobably only have a moderate impact on connectivity calcula-tions. All the other issues are considered to have a potentiallylow impact on connectivity results.

1. Only one 3D depositional model was generated and ana-lysed. This model, therefore, represents only one subsurfacerealization (conditional simulation) of many that are possiblefrom the available data. However, the potential for differentstochastic realizations within a stratigraphic unit resulting insignificantly different connectivity results is low (Fig. 9; King1990). This assumption will breakdown if sandbodies arelocated preferentially in certain geographical locations, thusleading to large lateral variations in S/Sh ratios withinindividual stratigraphic units.

2. The parasequence forms the basic ‘flow unit’; i.e. there isno vertical connectivity between parasequences. Theparasequence-bounding shales represent barriers to verticalfluid flow. This is a valid assumption in marginal marinesettings, except where incised channel systems cross-cutparasequence flooding surfaces and hence may form verticalconnectivity pathways. This is the case where the incisedvalley systems of Sequence-2 cut down into the brackish/marine heteroliths of HST-1 (Figs 2, 3). For this reason,HST-1 parasequences ‘d’ to ‘i’ are amalgamated into one unitand combined with FST/LST/TST-2 for the purposes ofthe 3D modelling and subsequent analyses (HST-1psd-i,FST/LST/TST-2).

3. All channels in this study are modelled as straight channels.This is a simplification as channels can have quite sinuousplaniform geometries. However, it has been demonstratedthat increasing channel sinuosity for a given S/Sh ratioincreases the effective connectivity of a system (Jones et al.1995). All channels are, therefore, modelled as straightchannels to simplify the connectivity uncertainties due tochannel planiform geometry.

4. No reservoir property (e.g. porosity or permeability) cut-offshave been applied to the connectivity calculations. Anysand-on-sand contact is assumed to allow reservoir fluids tocross-flow and is connected. This is a simplification as, inreality, the permeability of sand units control the flow offluids through these sands. Hence, some sands with poorreservoir properties would not be considered ‘reservoir’ asfluids would not flow through them.

5. The two heterolithic facies (brackish heteroliths and marineheteroliths; Table 1) are, for the purposes of this work,considered to be non-reservoir facies. In reality, some of thethin, very-fine grained sands in these facies may constitutereservoir rock. However, a high proportion of the sandsin these facies are cemented and would, therefore, beimpermeable or non-hydrocarbon bearing.

6. Foreset shale or cement drapes in shorefaces and mouthbars(e.g. Ainsworth et al. 1999) are assumed to be baffles to fluidflow and are considered not to impact overall connectivity.Hence, these geometries are not modelled.

7. The term ‘connectivity’ in this paper refers to static connec-tivity, i.e. no dynamic calculations are made of fluid flowthrough the model.

Fig. 8. Development scenario for the Sunrise Field. Note that all 21wells have c. 1 km long, sub-horizontal, reservoir intersections. Wellspacing is approximately 4 km. This structured 3D depositionalfacies model has been truncated below GWC. All 21 wells have been‘drilled’ through the model. Once the model has been re-flattened,these wells are used to assess the well connectivity of eachstratigraphic unit. See Figure 7 for facies legend. Modified fromAinsworth (2003).

Fig. 9. Perceived importance or ranking of each assumption,simplification and limitation with regard to its potential impact onreservoir connectivity. Note that numbers 1 to 7 relate to assump-tions, simplifications and limitations listed in the text; 1, number ofrealizations; 2, parasequence-bounding shales are sealing; 3, straightchannels; 4, no reservoir property cut-offs applied; 5, heterolithicfacies considered non-reservoir; 6, foreset shales and cements arebaffles and not barriers to fluid flow; 7, dynamic connectivity notconsidered. Modified from Ainsworth (2003).


Modelling issues

Along with the inherent issues generated by limited datacoverage from wells and seismic surveys, 3D reservoir modelsare also affected by hardware and temporal considerations. Inan ideal world, all the complexity of depositional systems fromthe pore-throat micro-scale up to the sandbody architecturemacro-scale would be captured in the models. However,computers are still not powerful enough to contain all of thisdetail and allow the modeller to build and manipulate detailedmodels in acceptable time frames. Hence, simplifications, whichcan lead to limitations, have to be made to models to allow theirgeneration and manipulation in realistic periods of time. Thetwo issues discussed below relate to the size of the modelgenerated. The model is almost 14 million grid blocks in size(Table 2), which is relatively large for today’s computers.Minimum sandbody thickness The minimum thickness of anysandbody is constrained by the thickness of one grid block.To keep the number of grid blocks at a reasonable level, thegrid blocks in this model are 1 m thick (Table 2). This meansthat any sandbody less than 1 m thick cannot be modelledaccurately. This is an issue as a number of sands from the coresand logs are less than 1 m thick. To overcome this problem,sands that are between 0.5 m and 1 m thick (Ainsworth 2003;Ainsworth et al. 2005) were scaled-up to 1 m, while sandsbetween 0.05 m and 0.5 m thick were modelled as shales.Minimum sandbody width From the sandbody geometry database,the minimum width of the fluvial-dominated/tide-influencedchannels is 20 m. However, the minimum width of the fluvial-dominated/tide-influenced channels in the model is 500 m.This is because the minimum width of any body is constrainedby the grid block dimensions. If adjacent grid blocks of a bodyare to be connected, they have to be at least two grid blockswide. If the body was only one grid block wide and orientatedoblique to the orthogonal grid block orientation, the grid blockswould only touch it at corner points. The modelling softwarewould not consider this to be a connection. In an ideal world,where computing power is not a restriction on model size, thegrid blocks in this model would be 10 m wide � 10 m long tohonour the minimum width of the fluvial-dominated/tide-influenced channel facies. However, if this were the case, themodel would be over 866 million grid blocks in size (assumingthe grid blocks remained at 1 m in thickness), which is totallyunfeasible. Because the grid block dimensions of the model are250 m � 250 m � 1 m thick (Table 2), the minimum widthfor any connected body is 500 m. The implication is thatnarrow channels will be undersampled in the 3D model. Interms of connectivity, this may provide artificially high connec-tivity values for the fluvial-dominated units as channels will bewider in the model than they are in reality.

To test the sensitivities of models to various grid block sizes,smaller ‘sector’ models are often built with finer-scale grids (e.g.10�10 m). These models allow the smaller architectural ele-ments to be modelled at their real scale. Connectivity calcula-tions would then be run at this scale and compared with thosefrom the model with the larger grid block size. However, thisapproach is beyond the scope of this study.

DEPOSITIONAL CONNECTIVITY RESULTS

This section deals with the comparison of the full model andwell clustering analysis trends per stratigraphic unit across thedifferent areal models (whole model area volume, Sunrise Fieldarea volume, Sunrise Field area volume above the GWC; Figs 4,5, 7); that is, comparisons are made on a parasequence, systemstract and sequence basis.

Parasequence comparison

The comparison of parasequence connectivity trends acrossthe three different models is shown in Figure 10. The textbelow highlights the trends and key differences between themodels. Parasequences are plotted on the vertical axis instratigraphic order (younging upwards). A number of trendscan be seen in these data. An overall increase in connectivityoccurs upwards through the stratigraphic column. An increasein connectivity is observed upwards from TST-1psb to HST-1psa and an upward increase in connectivity can also be seenfrom HST-1psa to HST-1psc. If HST-1psd-i are combinedwith FST/LST/TST-2 then a decrease in connectivityupwards from HST-1psc is observed. This would simulate anincised valley system cutting into a background of mouthbarand fluvial bay-fill deposits. However, since all the wells inthis stratigraphic interval penetrate either relatively thickshoreface or relatively thick incised valley deposits (Figs 2, 3),the actual background facies into which the systems erode isuncertain. A sensitivity scenario, wherein the incised valleysand sharp-based shoreface erode into a shale (i.e. HST-1psd-iis treated as a shale), is also modelled (FST/LST/TST-2). Inthis case, an increase in connectivity vertically upwards fromHST-1psc to FST/LST/TST-2 is observed. An increase inconnectivity upwards from the combined HST-1psd-i, FST/LST-2/TST-2 to HST-2psa can be seen compared with adecrease in connectivity upwards from the combinedFST/LST/TST-2 to HST-2psa. An increase in connectivityupwards from HST-2psa through HST-2psb and HST-2pscalso occurs. The connectivity remains constant at 100% fromHST-2psc through to LST-4.

As can be seen from Figure 10, the stratigraphic trendsobserved in the whole model area volume clustering runs arerepeated in the other three models. The minor exceptions tothe trends occur towards the base of the succession (particularlyin HST-1) when the volumes are truncated below the GWC(Figs 5e, 7c).

Systems tract comparison

The comparison of systems tract connectivity trends acrossthe four different models is shown in Figure 11. The textbelow highlights the trends and the key differences betweenthe models. A number of trends can be seen in these data.An overall increase in connectivity is observed upwardsthrough the stratigraphic column. However, a decrease inconnectivity occurs upwards from TST-1 to HST-1. A de-crease in connectivity also occurs upwards from TST-1 to thecombined HST-1, FST/LST/TST-2. This is the scenario inwhich the incised valleys have cut into mouthbar and channeldeposits and are hence connected to HST-1 deposits. Anincrease in connectivity vertically upwards from HST-1 to thecombined FST/LST/TST-2 can also be seen. This is thescenario in which the incised valleys have cut into shaledeposits and are hence unconnected to the HST-1 sanddeposits (see section on parasequences above). An increase inconnectivity vertically upwards from the combined HST-1,FST/LST/TST-2 to HST-2 is observed, whereas a decreasein connectivity upwards from the combined FST/LST/TST-2to HST-2 occurs. An increase in connectivity vertically up-wards from HST-2 to the combined FST/LST/TST-3 canalso be seen. A decrease in connectivity occurs upwards fromthe combined FST/LST/TST-3 to HST-3 and an increase inconnectivity upwards from HST-3 to LST-4 can also beobserved.

As can be seen from Figure 11, the stratigraphic trendsobserved in the whole model area volume clustering runs are

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repeated in the other three models. The only exception occursin the well clustering model towards the base of the successionin TST-1. This is where the volumes are truncated below theGWC (Fig. 7c).

Sequence comparison

The comparison of sequence connectivity trends across thefour different models is shown in Figure 12. A number oftrends can be seen in these data. An overall increase in

Fig. 10. Scale invariance ofparasequence connectivity trends.Parasequence stratigraphy vs. full modelclustering connectivity analyses for (a)the whole model area volume; (b) theSunrise Field area volume; and (c) theSunrise Field area volume above theGWC. Note the identical connectivitytrends shown in (a) and (b), despite thefactor 4 reduction in model area. Thedifferences between (b) and (c) occurbecause parasequences in systems tractsTST-1 and HST-1 are truncated belowthe GWC (Fig. 7c). This suggests thatthe connectivity trends forparasequences are not volumetricallyscale independent. (d) Well connectivitytrends for the Sunrise Field areavolume above the GWC, which can becompared directly with (c). Connectivitytrends above the parasequences thathave been truncated below the GWCreplicate those seen in the full modelclustering analyses of (c). Also note that(a), (b) and (c) are plotted onlogarithmic scales. Modified fromAinsworth (2003).

Fig. 11. Scale invariance of systemstract connectivity trends. Note thattrends are identical between (a) thewhole model area volume, (b) theSunrise Field area volume and (c) theSunrise Field area volume above theGWC. The trends are also identical for(d) the well connectivity model aboveHST-1, FST/LST/TST-2. Trends belowFST/LST/TST-2 in (c) and (d) havebeen affected by the truncation of stratabelow the GWC. Note that (a), (b) and(c) are plotted on logarithmic scales.Modified from Ainsworth (2003).


connectivity occurs upwards through the stratigraphic column.An increase in connectivity is observed upwards from S-2 toS-3 and an increase in connectivity also occurs upwards fromS-3 to S-4.

As can be seen from Figure 12, the stratigraphic trendsobserved in the whole model area volume clustering runs arerepeated in the other three models.

RELATIONSHIPS BETWEEN DEPOSITIONALAND CONNECTIVITY TRENDS

Figure 2 illustrates the interpreted evolution of the depositionalsystems in the Sunrise and Troubadour areas. Two keydepositional and related connectivity trends are seen. Thefirst is a switch from predominantly fluvial-dominated/tide-influenced deposits at the base of the succession to predomi-nantly wave-dominated/tide-influenced shoreface depositstowards the top of the succession. All connectivity plots(Figs 13b, d, f) show a concomitant increase in connectivity ina vertical stratigraphic sense; i.e. there is a direct relationshipbetween depositional style and connectivity. The wave-dominated systems show better connectivity than the fluvial-dominated systems (Fig. 14b). A second trend is thedeposystem-scale vertical alternation between predominantlyfluvial-dominated/tide-influenced deposits to predominantlywave-dominated/tide-influenced deposits which can be seento occur twice (compare Figs 2, 3, 14b). In both cases,the wave-dominated systems show improved connectivitycompared with the fluvial-dominated systems.

RELATIONSHIPS BETWEEN SEQUENCESTRATIGRAPHIC AND CONNECTIVITY TRENDS

Parasequences

Parasequence thickness trends Trends in average parasequencethickness are also observed. In Figures 3 and 13a the thicknessof parasequences in HST-1 can be seen to consistently decreaseupwards with the exception of HST-1 psd. This trend can alsobe noted in some wells for HST-2, e.g. Sunset-1 and Sunrise-2.However, the overall trend in this systems tract is that para-sequence ‘b’ is thicker than parasequence ‘a’. The uppermostparasequence ‘c’ is the thinnest of the three parasequences in

this systems tract. HST-3 also shows an upward decrease inparasequence thickness. This upward decrease in marginalmarine parasequence thickness observed through the HSTs hasbeen postulated theoretically by Posamentier & Allen (1999).They suggested that a decreasing rate of relative sea-level risefrom early to late highstand will result in a decreasing rate ofaccommodation addition. If a constant or increased sedimentflux is assumed, this will result in an upward-decreasing rate ofsediment aggradation and increased rate of progradation, lead-ing to thicker parasequences towards the base and thinnerparasequences towards the top of the HST. Similar effects arepostulated in low gradient alluvial slope environments, resultingin increased amalgamation of fluvial channel systems from earlyto late highstand (Geehan 1993; Shanley & McCabe 1994;Posamentier & Allen 1999).

The thickness of parasequences in TST-1 can be seen toincrease upwards in three wells (Sunset-1, Loxton Shoals-1 andSunrise-2) and decrease upwards in three wells (Troubadour-1,Sunset West-1 and Sunset-1). The average thickness of thesetwo parasequences in all wells is almost identical (less than 0.8m difference). However, the trend is for TST-1psa to be slightlythicker than TST-1psb. Following the same reasoning as for theHSTs, the thickness of consecutive TST parasequences wouldbe expected to increase upwards as the rate of relative sea-levelrise should theoretically increase towards the maximum flood-ing surface. However, in the only pair of TST parasequences inthe succession (TST-1; Fig. 13a), the average thickness can beseen to decrease upwards.

As the FST/LSTs only consist of one parasequence it isimpossible to analyse thickness trends within individual FST/LSTs.Whole model parasequence connectivity The whole model depositionalconnectivity parasequence trends can be compared directly withthe stratigraphic thickness trends in Figures 13a and b. Notethat due to the 3D reservoir modelling constraints describedabove, only the first three parasequences in HST-1 weremodelled discretely. Also note that in a connectivity sense,FST/LST-2 and TST-2, together with FST/LST-3 and TST-3,cannot be separated as they are sandy units in direct contactwith each other (Figs 2, 7, 13b).

The key stratigraphic trends identified in average para-sequence thickness can also be related to parasequence

Fig. 12. Scale invariance of sequenceconnectivity trends. Note that trendsare identical between (a) the wholemodel area volume, (b) the SunriseField area volume, (c) the Sunrise Fieldarea volume above the GWC for fullclustering and (d) well connectivity.Note that Sequence 5 was notdeposited in the Sunrise Field area.Also note that (a), (b) and (c) areplotted on logarithmic scales. Modifiedfrom Ainsworth (2003).

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connectivity trends and the relative sea-level curve (Figs 13a, b,14b). In the HSTs the stratigraphic trend related to decreas-ing rates of accommodation development is predominantlytowards decreasing parasequence thickness upwards. In HST-1and HST-2 the trend in parasequence connectivity is for anupwards increase through the HST (Fig. 14b). In terms ofphysical processes, this increase in connectivity can be relatedto the increased sandbody clustering and amalgamation, whichresults from decreased ratios of accommodation/sedimentsupply (Posamentier & Allen 1999).

Following the same reasoning, the thickness of consecutiveTST parasequences would be expected to increase upwards asthe rate of relative sea-level rise increased towards the maxi-mum flooding surface. Conversely, in the only pair of TSTparasequences in the succession (TST-1; Fig. 13a), the averagethickness decreases upwards. However, in three of the six wellsthe expected increase in thickness upwards is observed. Thismay partly explain why, in a connectivity sense, the resultsappear to fit the theory. As can be seen in Figures 13b and 14b,the connectivity decreases upwards through TST-1. Perhapsthis mismatch indicates that the relationship between connec-tivity and parasequence thickness is not simply a one-to-onerelationship. Other complicating factors, such as sand/shaleratios and perhaps areal sandbody distributions, may also comeinto play.

The general statement that the thinner the stratigraphic unit,the better the connectivity, seems to hold true in a gross sense(note the general increase in connectivity upwards associatedwith the general decrease in parasequence thickness; Figs 13a,b). However, the FST/LST parasequences are the exception tothe rule. They are relatively thick compared with the HSTparasequences directly underlying them (Fig. 13a). This transi-tion from HST to FST/LST is also associated with thetransition from fluvial-dominated/tide-influenced to wave-dominated/tide-influenced deposition (Ainsworth 2003;Ainsworth et al. 2005; Figures 2 and 3). The wave-dominated/tide-influenced systems are laterally more extensive and corre-latable and, hence, have a better connectivity. Thus, the increasein thickness in the FST/LST parasequences, which shouldtheoretically lead to decreased connectivity (if the simplethickness to connectivity relationships discussed above areapplied), is compensated by the change in depositional styleto laterally more extensive and, therefore, better connected,wave-dominated/tide-influenced shoreline systems.

The fact that the FST/LST parasequences are markedlythicker than the HST and TST parasequences may indicatethat they cannot be compared on a parasequence scale. TheFST/LST units may well be more representative of systemstracts with their component parasequences being too thin oramalgamated to be identified.

Fig. 13. Whole model area volumeaverage stratigraphic unit thicknessversus full model clustering connectivityanalyses. Note that for the clusteringanalyses for parasequences (a and b)and systems tracts (c and d), somestratigraphic units are combined (seetext for explanation). Incompletestratigraphic units are not shown. Notethat gross trends in thickness arereflected in the trends in connectivity.Sequences (e) and (f) are shaded bysequence set. Note the similarities intrends between the sequence sets andthe parasequences (a and b). Also notethat the cluster results are presented ona logarithmic scale. Modified fromAinsworth (2003).


Parasequence areal and volumetric connectivity dependency Figure 10illustrates the scale invariance of parasequence connectivitytrends across different areas and volumes and across differentconnectivity analysis techniques. When the area is reduced by afactor of four (3848 km2 to 920 km2; Figs 10a, b), all trendsremain the same; the trends can, therefore, be said to be areallyscale independent. Figures 10b and c illustrate the parasequenceconnectivity results for the Sunrise Field area volume and theSunrise Field area volume above the GWC. It can be seen fromthis plot that there has been no impact on the connectivityresults above LST-2 and TST-2. This is to be expected as thearea and volumes of the two models are identical above thisstratigraphic zone (compare Figs 7b, c). However, the unitsbelow LST-2 and TST-2 show deviations in connectivity trendsfrom the Sunrise Field area volume plot. At the base of thesuccession, TST-1psa has been fully truncated below the GWCand does not appear on the plot in Figure 10c. The number ofclusters and the upward increase in connectivity seen in theSunrise Field area volume (Fig. 10b) are not observed in thevolume truncated below the GWC (Fig. 10c). These resultssuggest that when the volumes of the lower stratigraphic unitsare reduced by truncating those parts of the succession belowthe GWC, the depositional connectivity trends observed in thefull volume are not preserved. This suggests that the trendsobserved in the whole model volume and the Sunrise Field areavolume are only valid for relatively continuous systems; i.e. ifdepositional systems terminate abruptly (as simulated by thetruncation of the lower stratigraphic units) then the observedtrends in relatively continuous units breakdown. Indeed, this isalso demonstrated by the absence of LST-5 and LST-6 on theSunrise Field area volume plot. They are not present on thisplot because they were not deposited in the Sunrise Field area;i.e. the stratigraphic trend shown in the whole model area

volume has been broken by discontinuous deposits (LST-5 andLST-6).Parasequence well connectivity Figures 10c and d illustrate theparasequence full clustering connectivity results for the SunriseField area volume above the GWC (Fig. 10c) and the wellconnectivity results for the Sunrise Field area volume above theGWC (Fig. 10d). It can be seen from these two plots that thetrends in the full clustering analyses above the combinedparasequences LST-2 and TST-2 (Fig. 10c) are replicated in thewell connectivity results (Fig. 10d).

The trends below the combined LST-2 and TST-2 para-sequences are different. As discussed above, this is probablydue to the discontinuous nature of the reservoir close to theGWC (Fig. 7c). The increasing isolation of the sandbodies asone moves closer to the GWC is, perhaps, reflected in thedownward decreasing well connectivity trend seen belowFST/LST/TST-2 (Fig. 10d).

Systems tracts

Systems tract thickness trends Trends in average systems tractthickness can also be seen. In Figure 13c, the thickness ofFST/LSTs, TSTs and HSTs can all be seen to decreaseupwards consistently through the succession. The consistentdecrease in thickness upwards through the succession ofindividual FST/LSTs, TSTs and HSTs (Fig. 13c) can beattributed to the overall decrease in the thickness of individualsequences upwards (Fig. 13e).

Comparisons of thickness on a systems tract scale seem tofollow theoretical patterns (Fig. 13c); i.e. FST/LST para-sequences and systems tracts would be predicted to be relativelythin compared with HST parasequences or systems tracts,respectively, as they are deposited during falling relative

Fig. 14. Relationship between relative sea-level, accommodation/sediment supply, depositional style and reservoir connectivity. (a) Relativesea-level curve superimposed on the systems tract connectivity plot, Sunrise field area volume. Note that there is a direct relationship betweenrelative sea-level rise, high ratios of accommodation/sediment supply and relatively low connectivity. (b) Greater detail shown in terms ofparasequence trends. The upward increase in parasequence connectivity in the HSTs can be related to decreasing ratios of accommodation/sediment supply during the late HST. A similar overall trend can be observed for the whole succession. This appears to be related to a lowerorder (second-order?) overall decrease in accommodation/sediment supply ratios which is associated with highstand sequence set-1 (Fig. 3).When convolved with the underlying palaeomorphology, this upward decrease in accommodation/sediment supply may also trigger the switchfrom predominantly fluvial-dominated/tide-influenced deposystems to predominantly wave-dominated/tide-influenced systems (Ainsworth2003; Ainsworth et al. 2005). Modified from Ainsworth (2003).

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sea-levels (FST; rapidly decreasing accommodation) and slowlyrising sea-levels (LST; slowly increasing accommodation). Incontrast, HST parasequences and systems tracts are depositedduring rising relative sea-levels (rapidly to slowly increasingaccommodation).

There is also a consistent pattern of systems tract thicknesswithin a sequence. In Sequences 2 and 3 (the only instanceswhere all three systems tracts are identified; Fig. 13c), the TSTis thinner than the FST/LST, and the HST is thicker than boththe TST and the FST/LST. These two sequences form partof a highstand sequence set (HSS-1; Fig. 3). In a HSS, thehighstand phase of the higher frequency relative sea-level curveis expected to be amplified thereby resulting in thicker HSTaccumulations.Whole model systems tract connectivity The whole model depositionalconnectivity systems tract trends can be compared directly withthe stratigraphic trends in Figures 13c and d. The key strati-graphic trends identified in average systems tract thickness canalso be related directly to systems tract connectivity trends.There is an almost one-to-one correlation between decreasingsystems tract thickness and increasing depositional connectivity(Figs 13c, d). The only exception is that HST-3 is thinner thanthe combined FST/LST-3 and TST-3.

The consistent decrease in thickness upwards through thesuccession of FST/LSTs, TSTs and HSTs is also reflected inthe consistent increase in connectivity of the consecutivesystems tracts upwards through the succession (Figs 13c, d).

The relationship between increasing rates of accommodationdevelopment and decreasing connectivity discussed for theparasequences (Fig. 14b) is also exhibited by the systems tracts(Fig. 14a). High rates of accommodation development (TST &HST) result in relatively poor connectivity, while low rates ofaccommodation development (LST) and accommodationdestruction (FST) result in relatively high connectivity.Systems tract areal and volumetric connectivity dependency Figure 11illustrates the scale invariance of systems tract connectivitytrends across different areas and volumes and across differentconnectivity analysis techniques. When the area is reduced by afactor of four (3848 km2 to 920 km2; Figs 11a, b), all trendsremain the same and can be said to be areally scale independent.It can be seen from this plot that the only impact of reducingthe area of analysis was to reduce the total number of clustersper systems tract and to remove LST-5 and LST-6 from theanalyses (both of which were not deposited in the Sunrise Fieldarea). In contrast to the effects seen with the parasequences(Fig. 10c), the trends in the systems tracts for the modeltruncated below the GWC appear to mimic the trends for theSunrise Field area volume model (Fig. 11c). This is probablydue to the increase in thickness associated with the systemstract versus parasequence analysis. The thicker systems tractswill tend to mask the impact of the discontinuous nature of thethinner parasequences. Since all trends remain the same at asystems tract level, they can be said to be areally and volumetri-cally scale independent. Note that the numbers of clusters inTST-1 and HST-1 are much reduced.Systems tract well connectivity Figures 11c and d illustrate thesystems tract full clustering connectivity results for the SunriseField area volume above the GWC and the well connectivityresults for the Sunrise Field area volume above the GWC. Itcan be seen from these two plots that the trends seen in the fullclustering analyses (Fig. 11c) are replicated in the well connec-tivity results (Fig. 11d). The only exception to this is withsystems tract TST-1 at the base of the succession, which has alower well connectivity than predicted by the full clusteringanalyses. As discussed for the parasequences, the lower part of

the succession is of a more discontinuous nature due to itscloser proximity to the GWC. The increasing isolation of thesandbodies as one moves closer to the GWC (Fig. 7c) is,perhaps, reflected by the lower than predicted connectivity seenin TST-1 (Fig. 11d).

Sequences

Sequence thickness trends Trends in average sequence thickness canalso be observed. In Figure 13e the thickness of sequences canbe seen to decrease upwards consistently through the succes-sion. When sequence thicknesses are viewed in terms of thesequence set to which the sequence belongs (Fig. 13e), the sametrends observed in the parasequences can be observed. Forexample, the upward decrease in HST parasequence thickness(Fig. 13a) is replicated for sequences in the highstand sequenceset (Fig. 13e). A trend of decreasing thickness from highstandsequence set to falling-stage/lowstand sequence set to trans-gressive sequence set is observed. This is the same consistenttrend observed in the systems tract plot (Fig. 13c), whereconsecutive HSTs are thicker than FST/LSTs, which arethicker than subsequent TSTs. This repetition of trends atdifferent hierarchical levels suggests a scale invariance on thecontrols governing the stratigraphic architecture.Whole model sequence connectivity The whole model depositionalconnectivity sequence trends can be compared directly with thestratigraphic trends in Figures 13e and f. There is an almostone-to-one correlation between decreasing sequence thicknessand increasing depositional connectivity. The thickness andconnectivity trends of decreasing thickness, which lead toincreasing connectivity upwards through the highstandsequence set, are the same as seen in the parasequences of theHSTs (Fig. 14b). These identical trends suggest a sequencestratigraphic scale hierarchy invariance to thickness andconnectivity relationships.Sequence areal and volumetric connectivity dependency Figure 12 illus-trates the scale invariance of sequence connectivity trendsacross different areas and volumes and also across differentconnectivity analysis techniques. When the area is reduced by afactor of four (3848 km2 to 920 km2), all trends remain thesame; the trends can, therefore, be said to be areally scaleindependent. It can be seen from this plot (Fig. 12b) that theonly effect of reducing the area of analysis was to reduce thetotal number of clusters per sequence. As with the systemstracts (Fig. 11c), the trends in the sequences for the modeltruncated below the GWC (Fig. 12c) appear to mimic thetrends for the Sunrise Field area volume model (Fig. 12b). Aswith the systems tracts, this is probably due to the increase inthickness associated with the sequence versus the parasequenceanalysis. The thicker sequences tend to mask the impact of thediscontinuous nature of the thinner parasequence analysis. Alltrends remain the same. At a sequence level, therefore, they canbe said to be areally and volumetrically scale independent.Sequence well connectivity Figures 12c and d illustrate the sequencefull clustering connectivity results for the Sunrise Field areavolume above the GWC and the well connectivity results forthe Sunrise Field area volume above the GWC. It can be seenthat the trends in the full clustering analyses (Fig. 12c) arereplicated fully in the well connectivity results (Fig. 12d).

OTHER METHODS OF CONNECTIVITYANALYSIS

Three other methods of connectivity analysis are discussed andcompared and contrasted below.


Average cluster volumes

Another method of analysing the connectivity results is to plotthe average cluster volume against the stratigraphy (Fig. 15b).Theoretically this should provide a better estimate of connec-tivity trends than using the number of clusters, as it is avolume-weighted analysis. The analysis based on the number ofclusters does not take any account of the size of the clusters.The cluster size analyses for parasequences of the Sunrise areavolume above the GWC are compared with the average clustervolume analyses and the well connectivity analyses in Figure 15.In these plots, it can be seen that the trends in all three methodsof analysis are similar, but there are some key differences. Theaverage cluster volume plot (Fig. 15b) predicts relatively betterconnectivity in the combined HST-1psd-i, FST/LST-2 andTST-2 unit than in the underlying HST-1psc. The ‘number ofclusters’ analysis (Fig. 15a) predicts the opposite trend. The wellconnectivity analyses show that this unit is relatively betterconnected than HST-1psc (Fig. 15c), thereby indicating that theaverage cluster volume analysis was the more accurate predic-tor. However, in the younger stratigraphic interval the clustervolume analysis suggests connectivity variation. The clusternumber analysis suggests uniformly good connectivity. The wellconnectivity analyses confirm the excellent connectivity in theupper sections of the succession.

In conclusion, it can be said that both the cluster numberand the average cluster volume analyses provide useful strati-graphic connectivity trend information. The ‘number of clus-ters’ analysis (Fig. 15a) appears to be more robust in predictingconnectivity in more laterally extensive, well-connecteddeposits.

Well connectivity and sand/shale ratios

King (1990) experimentally derived 3D relationships betweennet: gross ratios (NG) (sand/shale [S/Sh] ratios in this study)and reservoir connectivity. He concluded that there is a distinctthreshold in NG below which there is no overall connectivity(c. NG=0.15). Above this threshold the connectivity increasesrapidly. For a NG of 0.35, connectivities are anywhere between60% and 80% (depending on sandbody size). The average S/Shratios for parasequences derived from this study are plottedagainst the well connectivity results in Figure 16c. King’s (1990)

experimental results were derived using 3D models randomlypopulated with sandbodies; i.e. no stratigraphic organizationwas introduced into the experiments.

Figure 16c shows the same trend as King (1990) towardsdecreasing connectivity with decreasing average S/Sh. How-ever, the key difference between the two studies is that thiswork does not include S/Sh ratios below 0.2: the low side ofthe S/Sh spectrum is, therefore, not sampled. In fact, there areonly five S/Sh values below 0.3 in the whole dataset. A trendline cannot, therefore, be drawn through these data for com-parison with King’s (1990) trend lines. The lateral and verticalspread of data points for similar S/Sh and connectivity valuesmay be a product of the differing geometries of sandbodieswith similar S/Sh ratios. This effect is demonstrated by King’s‘L’ factor which represents the ratio of the system (or model)size to the sandbody size.

Non-volume-based methods

All of the connectivity analysis and prediction methods consid-ered previously in this paper have involved 3D reservoirmodelling techniques. However, such methods of analysis arevery complex and time consuming. A simpler, more time-efficient method of predicting connectivity trends in low faultdensity areas would be advantageous. This section reviewssome simple connectivity prediction techniques, using tworeadily available parameters from any well-based analysis: aver-age unit thickness and average S/Sh ratio. These two par-ameters are used to generate stratigraphically referenced plots,which are then compared to the connectivity trends derivedfrom the 3D model (Fig. 17). The different combinations ofparameters that are compared and contrasted with the para-sequence full model clustering of the Sunrise Field areavolume are average unit thickness (Fig. 17b), average S/Sh ratio(Fig. 17c) and average unit thickness divided by average S/Sh(Fig. 17d). All of these combinations of parameters illustratetrends that could be related to increasing connectivityupwards. For example, the average parasequence thicknessgenerally decreases upwards (Fig. 17b); however, the averageS/Sh generally increases upwards (Fig. 17c). As has beenstated in the discussion above, the unit thickness at all strati-graphic unit scales provides a good indicator of stratigraphic

Fig. 15. Number of clusters, average cluster volume and well connectivity. All plots are for the same volume (Sunrise Field area volume aboveGWC; Fig. 7c). Note that the average cluster volume plot (b) mimics the connectivity trends seen in the well connectivity plot (c). However,the ‘number of clusters’ plot (a) appears to be more robust in predicting well connectivity in the more laterally extensive, better-connecteddeposits (FST/LST/TST-3 to LST-4). Modified from Ainsworth (2003).

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connectivity trends. When the thickness trends and S/Sh trendsare compared with the cluster connectivity trends (Figs 17a–c),it appears that the thickness trends resemble the connectivitytrends more closely. The thickness graph certainly shows abetter correspondence with the connectivity trends in theHSTs. The main issues with the thickness plot are that thecombined FST/LSTs and TSTs of sequences 2 and 3 do notmatch the connectivity trends.

When the S/Sh and unit thickness parameters are combined(average unit thickness divided by average unit S/Sh; Fig. 17d),the overall correspondence with connectivity improves.FST/LST-3 and TST-3 come more into line with the connec-tivity trends. The overall upward connectivity trend in HST-2 isalso improved, as is the decreasing connectivity trend in TST-1.The main issue here is with FST/LST-2 and TST-2 which,although much improved, still do not quite fit the connectivitytrend shown in Figure 17a. The thickness divided by S/Shparameter can also be plotted against well connectivity forthe Sunrise Field area volume above the GWC (Fig. 16d). Aswith the S/Sh versus well connectivity plot (Fig. 16c), theinterpretation is hampered by limited data points and asampling bias, which this time is towards the low thicknessdivided by S/Sh ratios. For comparison, Figure 16 also showssome quantitative results from the 3D model studies. Whenparasequence well connectivity is cross-plotted against thenumber of clusters (Fig. 16a), there appears to be no relation-ship. However, when parasequence well connectivity is cross-plotted against the average cluster volume on a logarithmicscale (Fig. 16b), a relatively good fit, similar to that derivedfrom the thickness divided by S/Sh ratio plot (Fig. 16d), isobserved.

It can be concluded from these analyses that in low faultdensity settings, simple, well-based calculations, such as averageunit thickness and average S/Sh ratios, can be combined toproduce stratigraphic connectivity trend predictions that aresimilar to those derived using complex 3D reservoir modellingtechniques. The average thickness divided by the average S/Shparameter per stratigraphic unit appears to provide the bestapproximation of connectivity trends. This is probably becausethe average thickness parameter provides an additional com-ponent that reflects the average accommodation available inwhich the reservoir sands were deposited. In effect, thethickness parameter mimics the accommodation parameterand the S/Sh ratio parameter the sediment supply parameterin the accommodation/sediment supply (A/S) relationshipwhich, when convolved with palaeomorphology and relativeeffectiveness of wave and fluvial energy, is important indefining coastal depositional style (Ainsworth et al. 2005).Predictions of connectivity on the basis of well-derived par-ameters can also be refined using the depositional interpreta-tions from the wireline-log data (e.g. Table 1). Wave-dominatedsystems with similar thickness divided by S/Sh values asfluvial-dominated systems would thus be expected to yieldbetter connectivity due to their greater lateral extents.

IMPLICATIONS FOR FIELD DEVELOPMENTPLANNING

The connectivity analyses described above re-emphasize thefact that not all stratigraphic or reservoir units should beexpected to behave alike. They will behave differently in termsof reservoir connectivity due to their different stratigraphic

Fig. 16. Quantitative connectivityprediction. Note that well connectivityis for the 21 development wellreference case layout (4 km wellspacing; Fig. 8). (a) Parasequence wellconnectivity plotted against number ofclusters. Note that althoughconnectivity trends can be observedwhen the number of clusters areanalysed in a stratigraphic context (e.g.Figs 15a, c), there is no observedrelationship between well connectivityand the number of clusters once thestratigraphic context of theparasequence is removed. However, ifthe average cluster volume perparasequence is considered (b), a trendappears to be visible (note averagecluster volume is on a logarithmicscale). As would be expected, the largerthe average cluster volume within aparasequence, the better thatparasequence is connected to the 21wells. (c) Well connectivity versusaverage S/Sh for all parasequences. Thegeneral trend of decreasing connectivitywith decreasing S/Sh is observed, butthe low side of the S/Sh spectrum isundersampled and there is only oneS/Sh value below 0.25. The spread ofvalues above 0.25 is too great toprovide a reliable trend line. (d) Thewell-based parameters’ averageparasequence thickness divided byaverage S/Sh are plotted against wellconnectivity. A relatively good trend ofincreasing value with decreasingconnectivity is observed. Modified fromAinsworth (2003).


architectures, which resulted from varying wave and fluvialenergy, palaeomorphological settings, rates of accommodationdevelopment and sediment supply. The results of these bound-ary conditions and processes can be observed from core, welland seismic data and can be used to predict well connectivityand connectivity trends using 3D reservoir modelling tech-niques. Initial screening analyses for reservoir connectivity instructurally simple areas can also be made using connectivityindicators calculated from simple well-derived values, such asS/Sh ratios and stratigraphic unit thicknesses. In such low faultdensity settings, depositional connectivity can be used as aproxy for reservoir connectivity.

Any depositional connectivity analysis relies on the develop-ment of robust depositional models and a detailed (at leastdown to parasequence-scale) sequence stratigraphic framework.These deliverables are prerequisites before reservoirs can becharacterized and field management and well perforationstrategies developed and executed.

Connectivities calculated from full model clustering analysesare indicative of connectivity trends, but no direct relationshipsto absolute well connectivity values were derived (Fig. 16a).Average cluster volume calculations appear to give a betterestimation of absolute well connectivity than ‘number ofclusters’ (Fig. 16b). However, the connectivity trends alone(Fig. 15) are useful for reservoir management planning. Indeed,it can be speculated that an optimal field development welllayout should produce the same stratigraphic trends in wellconnectivity as can be shown in full model connectivity

analyses. If trends are different then perhaps there are too fewwells in the development scenario, or wells are sub-optimallyplaced.

A final point is that none of the connectivity predictionsgenerated in this study have been ‘ground-truthed’ againstactual dynamic production data. The loop from 2D well-basedconnectivity predictions to 3D full clustering and 3D wellclustering needs to be closed by cross-checking the results withdynamic field production data (Fig. 18) so that the 3D modelscan be iteratively updated. However, the Sunrise Field is not yetin production and hence, in this case, the loop cannot beclosed. In structurally more complex settings, initial deposi-tional architectures may be overprinted by faulting (Ainsworth2005), in which case the initial screening of 2D well cross-plotsmay not be representative of well connectivity.

CONCLUSIONS

Five principal conclusions can be drawn from this case study.

1. Connectivity analyses of the 3D reservoir models indicatethat trends in depositional connectivity are related directlyto those observed in stratigraphic architecture. Connectiv-ity trends are therefore related directly to ratios ofaccommodation/sediment supply which, in turn, are con-trolled by changes in relative sea-level and palaeo-morphology. This means that predictions of depositionalconnectivity trends at all stratigraphic hierarchy levels can bemade where there is a good understanding of marginal

Fig. 17. Comparison of well-basedconnectivity prediction methods with3D reservoir modelling connectivityresults for parasequences in the SunriseField area volume (Fig. 7b). Comparethe trends in plots (b) to (d) with the3D reservoir modelling trends shown in(a). Note that although the individualaverage thickness (b) and average S/Sh(c) plots honour some of the trends,the average thickness divided byaverage S/Sh plot (d) provides the besttrend match with (a). Modified fromAinsworth (2003).

R. B. Ainsworth274

marine depositional settings and a high resolution sequencestratigraphic subdivision of strata.

2. With reference to depositional environments, wave-dominated systems demonstrate better connectivity thanfluvial-dominated systems. In the HSTs, the stratigraphictrend related to upward decreasing rates of accommodationdevelopment is a decrease in parasequence thickness. Thistrend correlates with an upward increase in parasequenceconnectivity through the HSTs. In terms of physical pro-cesses, the increase in connectivity can be related to theincreased sandbody amalgamation, which resulted fromdecreased ratios of accommodation/sediment supply.

3. All connectivity trends at all stratigraphic hierarchical levelsremained the same when the area of the model was reducedby a factor of four. The relationships between the strati-graphic trends and the connectivity trends can, therefore, besaid to be areally scale independent. Likewise, all connectiv-ity trends at sequence and systems tract stratigraphichierarchical levels remained the same when the volume ofthe model was reduced by truncation below the GWC.Hence, the relationships between the stratigraphic trendsand the connectivity trends for the thicker stratigraphic units(systems tracts and sequences) can also be said to bevolumetrically scale independent. However, at a para-sequence scale, the lateral continuity of the parasequencesclose to the GWC was disrupted and, consequently, thetrends were not replicated fully.

4. There is a sequence stratigraphic level of hierarchy invari-ance with regard to depositional connectivity; i.e. connectiv-ity trends observed at parasequence and systems tract scalesare replicated at the sequence set scale.

5. Three-dimensional reservoir models currently provide thebest means of quantitatively assessing and predicting reser-voir connectivity. However, in structurally simple, low faultdensity settings, initial screening of reservoir connectivitycan be made using connectivity indicators calculated fromwell-derived parameters. Plots of average stratigraphic unitthickness and average S/Sh versus stratigraphy reasonablyresemble the 3D model depositional connectivity plots.However, when the two parameters are combined as‘Thickness divided by S/Sh’ a much-improved fit to the 3Dmodel connectivity plots is observed.

This work comprises part of a PhD thesis completed by the authorat the University of Liverpool, UK. Steve Flint and John Howell arethanked for their supervision of the thesis work. Their constantsupport, insightful comments and astute guidance proved invaluablein the realization of the study. Thanks are also due to thesisexaminers Trevor Elliott and Mike Mayall for the time they donatedto the critical assessment of the manuscript, their thought-provokingviva questions and their suggestions for improving the document.Colleagues at Woodside Energy provided stimulating discussionsthat significantly aided in sharpening and refining ideas; PeterUnstead, in particular, was a constant source of encouragement.Journal referees Janok Bhattacharya and Gary Hampson are thankedfor their reviews, which improved the focus and clarity of the paper.The joint venture partners in the Sunrise Gas Project, WoodsideEnergy, Shell Development Australia, ConocoPhillips Australia andOsaka Gas Australia are thanked for allowing access to their data andfor consenting to the publication of this paper. The Shell Groupis acknowledged for permission to use proprietary 3D reservoirmodelling software for this study.

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Received 5 August 2004; revised typescript accepted 6 April 2005.

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