Changes

Key factors influencing fluid flow and reservoir behavior include facies architecture and heterogeneity distribution conditioned to stratal surfaces. Within shallow-marine reservoirs, clinoforms are one such type of stratal surface. Clinoforms are dipping surfaces having geometry that preserves the depositional morphology of the delta-front or shoreface slope; and their distribution reflects the progradation history of the shoreline (Barrell, 1912; Rich, 1951; Gani and Bhattacharya, 2005; Sech et al., 2009) (Figure 1). Clinoforms control aspects of detailed facies architecture within parasequences and can also act as low-permeability barriers or baffles to flow (Wehr and Brasher, 1996; Ainsworth et al., 1999; Dutton et al., 2000; Howell et al., 2008a, b; Jackson et al., 2009; Enge and Howell, 2010). Therefore, it is important to include clinoforms in models of shallow-marine reservoirs to properly characterize facies architecture and volumes of hydrocarbons in place (Sech et al., 2009). Under certain displacement conditions and if the clinoforms are associated with significant barriers to flow, clinoforms must be included in dynamic simulations to accurately predict likely drainage patterns and ultimate recovery of hydrocarbons (Jackson et al., 2009).

Key factors influencing fluid flow and reservoir behavior include facies architecture and heterogeneity distribution conditioned to stratal surfaces. Within shallow-marine reservoirs, clinoforms are one such type of stratal surface. Clinoforms are dipping surfaces having geometry that preserves the depositional morphology of the delta-front or shoreface slope; and their distribution reflects the progradation history of the shoreline (Barrell, 1912; Rich, 1951; Gani and Bhattacharya, 2005; Sech et al., 2009) (Figure 1). Clinoforms control aspects of detailed facies architecture within parasequences and can also act as low-permeability barriers or baffles to flow (Wehr and Brasher, 1996; Ainsworth et al., 1999; Dutton et al., 2000; Howell et al., 2008a, b; Jackson et al., 2009; Enge and Howell, 2010). Therefore, it is important to include clinoforms in models of shallow-marine reservoirs to properly characterize facies architecture and volumes of hydrocarbons in place (Sech et al., 2009). Under certain displacement conditions and if the clinoforms are associated with significant barriers to flow, clinoforms must be included in dynamic simulations to accurately predict likely drainage patterns and ultimate recovery of hydrocarbons (Jackson et al., 2009).

Figure 1 (A) Outcrop view of delta-front clinoforms in the Ferron Sandstone Member at the Ivie Creek amphitheater, north of I-70, east-central Utah (corresponding to parasequences 1.5 and 1.6 of Deveugle et al., 2011). Note the dipping nature of the delta-front sandstones and shales and the erosional contact with an overlying distributary channel sandstone. (B) Corresponding outcrop interpretation showing clinoforms within the delta-front deposits. (C) Corresponding line drawing highlighting approximately 25 clinoforms, shown as black lines on a white background. CP = coastal plain heteroliths; DC = distributary channel sandstone; PD = prodelta shales. Photographs and line drawings have no vertical exaggeration.

[[File:BLTN13190fig1.jpg|thumb|300px|{{figure number|1}}(A) Outcrop view of delta-front clinoforms in the Ferron Sandstone Member at the Ivie Creek amphitheater, north of I-70, east-central Utah (corresponding to parasequences 1.5 and 1.6 of Deveugle et al., 2011). Note the dipping nature of the delta-front sandstones and shales and the erosional contact with an overlying distributary channel sandstone. (B) Corresponding outcrop interpretation showing clinoforms within the delta-front deposits. (C) Corresponding line drawing highlighting approximately 25 clinoforms, shown as black lines on a white background. CP = coastal plain heteroliths; DC = distributary channel sandstone; PD = prodelta shales. Photographs and line drawings have no vertical exaggeration.]]

Standard modeling techniques are not well suited to capturing clinoforms, particularly if they are numerous, below seismic resolution, and/or difficult to correlate between wells. Few studies have attempted to identify and correlate clinoforms in the subsurface (Livera and Caline, 1990; Jennette and Riley, 1996; Løseth and Ryseth, 2003; Matthews et al., 2005; Hampson et al., 2008) or have built two-dimensional (2-D) (Wehr and Brasher, 1996; Forster et al., 2004) or three-dimensional (3-D) (Howell et al., 2008a, b; Jackson et al., 2009; Sech et al., 2009; Enge and Howell, 2010) flow simulation models that incorporate clinoforms. Previous studies of the Ferron Sandstone Member have incorporated simple clinoform geometries into reservoir models by using either object-based (Howell et al., 2008b) or deterministic (Howell et al., 2008a) approaches. Enge and Howell (2010) used data collected by light detection and ranging (LIDAR) equipment to precisely recreate 3-D clinoform geometries from part of the Ferron Sandstone Member outcrops; the resulting flow-simulation model contained deterministically modeled clinoforms but in a volume smaller than most reservoirs (500 × 500 × 25 m [1640 × 1640 × 82 ft]). Sech et al. (2009) used a surface-based modeling approach to produce a deterministic, 3-D model of a wave-dominated shoreface–shelf parasequence from a rich, high-resolution outcrop data set (Cretaceous Kenilworth Member, Utah), and Jackson et al. (2009) used this model to investigate the impact of clinoforms on fluid flow. Jackson et al. (2009) and Enge and Howell (2010) both showed that capturing numerous clinoforms in fluid-flow simulations is feasible. Process-based forward numerical models are capable of generating geologically realistic, 3-D stratigraphic architectures containing clinoforms in shallow-marine strata (e.g., Edmonds and Slingerland, 2010; Geleynse et al., 2011), but it can be difficult to replicate geometries observed in outcrop data, or condition models to subsurface data (e.g., Charvin et al., 2009); consequently, process-based approaches have yet to be developed for routine use in reservoir modeling.

Standard modeling techniques are not well suited to capturing clinoforms, particularly if they are numerous, below seismic resolution, and/or difficult to correlate between wells. Few studies have attempted to identify and correlate clinoforms in the subsurface (Livera and Caline, 1990; Jennette and Riley, 1996; Løseth and Ryseth, 2003; Matthews et al., 2005; Hampson et al., 2008) or have built two-dimensional (2-D) (Wehr and Brasher, 1996; Forster et al., 2004) or three-dimensional (3-D) (Howell et al., 2008a, b; Jackson et al., 2009; Sech et al., 2009; Enge and Howell, 2010) flow simulation models that incorporate clinoforms. Previous studies of the Ferron Sandstone Member have incorporated simple clinoform geometries into reservoir models by using either object-based (Howell et al., 2008b) or deterministic (Howell et al., 2008a) approaches. Enge and Howell (2010) used data collected by light detection and ranging (LIDAR) equipment to precisely recreate 3-D clinoform geometries from part of the Ferron Sandstone Member outcrops; the resulting flow-simulation model contained deterministically modeled clinoforms but in a volume smaller than most reservoirs (500 × 500 × 25 m [1640 × 1640 × 82 ft]). Sech et al. (2009) used a surface-based modeling approach to produce a deterministic, 3-D model of a wave-dominated shoreface–shelf parasequence from a rich, high-resolution outcrop data set (Cretaceous Kenilworth Member, Utah), and Jackson et al. (2009) used this model to investigate the impact of clinoforms on fluid flow. Jackson et al. (2009) and Enge and Howell (2010) both showed that capturing numerous clinoforms in fluid-flow simulations is feasible. Process-based forward numerical models are capable of generating geologically realistic, 3-D stratigraphic architectures containing clinoforms in shallow-marine strata (e.g., Edmonds and Slingerland, 2010; Geleynse et al., 2011), but it can be difficult to replicate geometries observed in outcrop data, or condition models to subsurface data (e.g., Charvin et al., 2009); consequently, process-based approaches have yet to be developed for routine use in reservoir modeling.

:<math>EQUATIONS/BLTN13190eqd1</math>

:<math>EQUATIONS/BLTN13190eqd1</math>

Figure 2 Examples of clinoforms produced by the clinoform-modeling algorithm conditioned to different bounding surfaces and clinoform geometries. (A) Bounding surfaces represent postdepositional compaction and folding of the original (depositional) geometries of the clinoform and the top and base bounding surfaces. (B) Bounding surfaces represent a clinoform within a volume truncated at its top, for example, by a channel (Figure 1). (C) Bounding surfaces represent a clinoform downlapping onto irregular sea-floor topography. (D) Height function, BLTN13190eq2 (equation 1; see Table 1 for nomenclature). (E) Shape function, BLTN13190eq3 (equation 7; Table 1), demonstrating that increasing the exponent, BLTN13190eq4, increases the dip angle of clinoforms.

[[File:BLTN13190fig2.jpg|thumb|300px|{{figure number|2}}Examples of clinoforms produced by the clinoform-modeling algorithm conditioned to different bounding surfaces and clinoform geometries. (A) Bounding surfaces represent postdepositional compaction and folding of the original (depositional) geometries of the clinoform and the top and base bounding surfaces. (B) Bounding surfaces represent a clinoform within a volume truncated at its top, for example, by a channel (Figure 1). (C) Bounding surfaces represent a clinoform downlapping onto irregular sea-floor topography. (D) Height function, BLTN13190eq2 (equation 1; see Table 1 for nomenclature). (E) Shape function, BLTN13190eq3 (equation 7; Table 1), demonstrating that increasing the exponent, BLTN13190eq4, increases the dip angle of clinoforms.]]

This allows the clinoforms to adapt to the morphology of the bounding surfaces (Figure 2A). For cases in which an overlying erosional bounding surface is interpreted to truncate clinoforms (Figure 2B) and/or clinoforms are interpreted to downlap onto a bounding surface that reflects irregular sea-floor topography (Figure 2C), a planar and horizontal dummy surface is used either above the erosional bounding surface or below the bounding surface, reflecting irregular sea-floor topography. The height function BLTN13190eq30 (equation 1), is applied to the planar dummy surfaces to insert clinoforms; and, in a final step, the bounding surface geometries are used to remove the upper and/or lower portions of the clinoforms, where appropriate, to match interpreted truncation (Figure 2B) and/or downlap (Figure 2C).

This allows the clinoforms to adapt to the morphology of the bounding surfaces (Figure 2A). For cases in which an overlying erosional bounding surface is interpreted to truncate clinoforms (Figure 2B) and/or clinoforms are interpreted to downlap onto a bounding surface that reflects irregular sea-floor topography (Figure 2C), a planar and horizontal dummy surface is used either above the erosional bounding surface or below the bounding surface, reflecting irregular sea-floor topography. The height function BLTN13190eq30 (equation 1), is applied to the planar dummy surfaces to insert clinoforms; and, in a final step, the bounding surface geometries are used to remove the upper and/or lower portions of the clinoforms, where appropriate, to match interpreted truncation (Figure 2B) and/or downlap (Figure 2C).

The depositional processes acting at the shoreline control the plan-view shape and abundance of clinoforms and their associated heterogeneity (Howell et al., 2008a). Maps, satellite images, and aerial photographs of modern systems are used to make a first-order approximation of the distinct plan-view shape of clinoforms in different depositional environments (Figure 3), as described in the subsequent text, because there is a paucity of reliable data of this type from subsurface reservoirs and ancient analogs. This approximation assumes that the modern-day shape of a shoreline represents a snap-shot in time that mimics the geometry of clinoforms and associated depositional elements preserved in the stratigraphic record (Howell et al., 2008a). Mattson and Chan (2004) assumed a simple radial geometry in plan view for fluvial-dominated deltaic clinoforms in the Ferron Sandstone Member outcrop analog, but this geometry is not universally applicable even as a first-order approximation. For example, wave-dominated strandplains are nearly linear in plan view (Figure 3A), wave-dominated deltas have broad arcuate forms (Figure 3B), and fluvial-dominated deltaic shorelines form distinct, lobate protuberances (Figure 3C) (e.g., Galloway, 1975).

The depositional processes acting at the shoreline control the plan-view shape and abundance of clinoforms and their associated heterogeneity (Howell et al., 2008a). Maps, satellite images, and aerial photographs of modern systems are used to make a first-order approximation of the distinct plan-view shape of clinoforms in different depositional environments (Figure 3), as described in the subsequent text, because there is a paucity of reliable data of this type from subsurface reservoirs and ancient analogs. This approximation assumes that the modern-day shape of a shoreline represents a snap-shot in time that mimics the geometry of clinoforms and associated depositional elements preserved in the stratigraphic record (Howell et al., 2008a). Mattson and Chan (2004) assumed a simple radial geometry in plan view for fluvial-dominated deltaic clinoforms in the Ferron Sandstone Member outcrop analog, but this geometry is not universally applicable even as a first-order approximation. For example, wave-dominated strandplains are nearly linear in plan view (Figure 3A), wave-dominated deltas have broad arcuate forms (Figure 3B), and fluvial-dominated deltaic shorelines form distinct, lobate protuberances (Figure 3C) (e.g., Galloway, 1975).

Figure 3 Generalized, first-order approximations of the plan-view geometry of clinoforms in different depositional environments: (A) Nayarit Coast, Mexico, representative of a wave-dominated strandplain (image modified after Google Earth and DigitalGlobe, 2013); (B) Nile Delta, Egypt, representative of a wave-dominated delta (image modified after Google Earth, 2013); and (C) Wax Lake Delta, Louisiana, representative of a fluvial-dominated delta (image modified after Google Earth and TerraMetrics, 2013). Solid white lines represent a first-order approximation of the shoreline at the clinoform top, whereas the dashed white lines represent first-order approximations of the likely maximum extent of the clinoform surface and its downlap termination on the underlying sea floor.

[[File:BLTN13190fig3.jpg|thumb|300px|{{figure number|3}}Generalized, first-order approximations of the plan-view geometry of clinoforms in different depositional environments: (A) Nayarit Coast, Mexico, representative of a wave-dominated strandplain (image modified after Google Earth and DigitalGlobe, 2013); (B) Nile Delta, Egypt, representative of a wave-dominated delta (image modified after Google Earth, 2013); and (C) Wax Lake Delta, Louisiana, representative of a fluvial-dominated delta (image modified after Google Earth and TerraMetrics, 2013). Solid white lines represent a first-order approximation of the shoreline at the clinoform top, whereas the dashed white lines represent first-order approximations of the likely maximum extent of the clinoform surface and its downlap termination on the underlying sea floor.]]

 

As the algorithm is generic, the user can specify the shape of an ellipse that approximates the plan-view geometry of clinoforms (Figure 4A). Using an ellipse, rather than a radial geometry, allows the user to specify a wide range of plan-view clinoform geometries using a simple function, depending on the interpreted environment of deposition and scale of shoreline curvature. Two ellipses are used: the top ellipse represents the shoreline at the clinoform top, and the base ellipse represents the maximum extent of the clinoform at its downlap termination on the underlying sea floor. The user defines the length of the top and base ellipses in depositional dip and strike directions (BLTN13190eq31, BLTN13190eq32, BLTN13190eq33, BLTN13190eq34; Figure 4B, Table 1) relative to the origin of the clinoform. The difference between the user-defined maximum extents of the top and base ellipses yields the clinoform length BLTN13190eq35 (Figure 4D). The maximum extent of the top and base ellipses can then be defined as  

As the algorithm is generic, the user can specify the shape of an ellipse that approximates the plan-view geometry of clinoforms (Figure 4A). Using an ellipse, rather than a radial geometry, allows the user to specify a wide range of plan-view clinoform geometries using a simple function, depending on the interpreted environment of deposition and scale of shoreline curvature. Two ellipses are used: the top ellipse represents the shoreline at the clinoform top, and the base ellipse represents the maximum extent of the clinoform at its downlap termination on the underlying sea floor. The user defines the length of the top and base ellipses in depositional dip and strike directions (BLTN13190eq31, BLTN13190eq32, BLTN13190eq33, BLTN13190eq34; Figure 4B, Table 1) relative to the origin of the clinoform. The difference between the user-defined maximum extents of the top and base ellipses yields the clinoform length BLTN13190eq35 (Figure 4D). The maximum extent of the top and base ellipses can then be defined as  

:<math>EQUATIONS/BLTN13190eqd2</math>

:<math>EQUATIONS/BLTN13190eqd2</math>

:<math>EQUATIONS/BLTN13190eqd3</math>

:<math>EQUATIONS/BLTN13190eqd3</math>

Figure 4 (A) A user specifies the length of the top (solid line) and base (dashed line) ellipses in depositional dip and strike directions (BLTN13190eq36, BLTN13190eq37, BLTN13190eq38, BLTN13190eq39; Table 1) relative to the clinoform origin. The surface representing the clinoform is created in the volume between the top and base ellipses. (B) At a point on the clinoform, the radius relative to the clinoform origin (black arrow, BLTN13190eq40, the radius of the base ellipse (black arrow, BLTN13190eq41 and the radius of the top ellipse (black arrow, BLTN13190eq42 are calculated. (C) Plan view of four adjacent clinoforms. The user specifies the overall progradation direction of the clinoforms relative to north, as well as the coordinates of the initial insertion point BLTN13190eq43. (D) Conceptual depositional-dip-oriented cross-section view of clinoforms. Clinoform spacing, BLTN13190eq44, is defined as the distance between the top truncation points of two adjacent clinoforms. Clinoform length, L, is defined as the distance between the top and base truncations by the user-specified bounding surfaces along a single clinoform.

[[File:BLTN13190fig4.jpg|thumb|300px|{{figure number|4}}(A) A user specifies the length of the top (solid line) and base (dashed line) ellipses in depositional dip and strike directions (BLTN13190eq36, BLTN13190eq37, BLTN13190eq38, BLTN13190eq39; Table 1) relative to the clinoform origin. The surface representing the clinoform is created in the volume between the top and base ellipses. (B) At a point on the clinoform, the radius relative to the clinoform origin (black arrow, BLTN13190eq40, the radius of the base ellipse (black arrow, BLTN13190eq41 and the radius of the top ellipse (black arrow, BLTN13190eq42 are calculated. (C) Plan view of four adjacent clinoforms. The user specifies the overall progradation direction of the clinoforms relative to north, as well as the coordinates of the initial insertion point BLTN13190eq43. (D) Conceptual depositional-dip-oriented cross-section view of clinoforms. Clinoform spacing, BLTN13190eq44, is defined as the distance between the top truncation points of two adjacent clinoforms. Clinoform length, L, is defined as the distance between the top and base truncations by the user-specified bounding surfaces along a single clinoform.]]

The clinoform is generated in the volume between the top and base ellipses (Figure 4A, B). In this volume, the radius of each point on the clinoform, BLTN13190eq45 (Table 1), is calculated relative to the clinoform origin (BLTN13190eq46), using  

The clinoform is generated in the volume between the top and base ellipses (Figure 4A, B). In this volume, the radius of each point on the clinoform, BLTN13190eq45 (Table 1), is calculated relative to the clinoform origin (BLTN13190eq46), using  

The Ferron Sandstone Member of the Mancos Shale is located in east-central Utah. The unit was deposited during the Late Cretaceous (Turonian–Coniacian) on the western margin of the Western Interior Seaway and, in the study area, records the progradation of the Last Chance delta system from southwest (paleolandward) to northeast (paleoseaward) (Cotter, 1976) (Figure 5A). These deltaic deposits form a basinward-thinning wedge that passes eastward into the offshore deposits of the Mancos Shale. The wedge contains either seven (Ryer, 1991; Gardner, 1993; Barton et al., 2004) or eight sandstone tongues (Anderson and Ryer, 2004; Garrison and Van den Bergh, 2004), such that one tongue is equivalent to a parasequence set of Deveugle et al. (2011) (Figure 5B). A single delta-lobe deposit within the lowermost sandstone tongue is the focus of the study (bedset Kf-1-Iv[a] of Anderson et al., 2004; parasequence 1h of Garrison and Van den Bergh, 2004; parasequence 1.6 of Deveugle et al., 2011) (Figure 5C, D). The delta-lobe deposit is fluvial dominated with low-to-moderate wave influence (Gardner, 1993; Garrison and Van den Bergh, 2004; Ryer and Anderson, 2004) and contains numerous, well-documented clinoforms in the exposures of the Ivie Creek amphitheater (Anderson et al., 2002, 2003, 2004; Forster et al., 2004; Enge and Howell, 2010) (Figure 5D). Clinoform-related bedding geometries and facies distributions imply that clinoforms mapped by previous workers, and used as input data for the models presented below (Figure 6A, after Forster et al., 2004), bound clinothems equivalent to mouth bars (sensu Bhattacharya, 2006). Subtle, apparently cyclic variations in clinoform spacing and dip angle probably define mouth-bar assemblages (sensu Bhattacharya, 2006; “bedsets” sensu Enge et al., 2010). Smaller-scale lithologic variation at the scale of individual beds occurs between the mapped clinoforms and records incremental growth of a mouth bar because of varying water and sediment discharge through the feeder distributary channel. Deveugle et al. (2011) used a high-resolution outcrop data set to build a reservoir-scale (7200 × 3800 × 50 m [23622 × 12467 × 164 ft]), surface-based model of the lower two tongues (parasequence sets) of the Ferron Sandstone Member. Clinoforms were not represented in the delta-lobe deposits (cf. parasequences) of the Deveugle et al. (2011) model, and their surface-based model is used here as the context in which the clinoform-modeling algorithm should be applied.

The Ferron Sandstone Member of the Mancos Shale is located in east-central Utah. The unit was deposited during the Late Cretaceous (Turonian–Coniacian) on the western margin of the Western Interior Seaway and, in the study area, records the progradation of the Last Chance delta system from southwest (paleolandward) to northeast (paleoseaward) (Cotter, 1976) (Figure 5A). These deltaic deposits form a basinward-thinning wedge that passes eastward into the offshore deposits of the Mancos Shale. The wedge contains either seven (Ryer, 1991; Gardner, 1993; Barton et al., 2004) or eight sandstone tongues (Anderson and Ryer, 2004; Garrison and Van den Bergh, 2004), such that one tongue is equivalent to a parasequence set of Deveugle et al. (2011) (Figure 5B). A single delta-lobe deposit within the lowermost sandstone tongue is the focus of the study (bedset Kf-1-Iv[a] of Anderson et al., 2004; parasequence 1h of Garrison and Van den Bergh, 2004; parasequence 1.6 of Deveugle et al., 2011) (Figure 5C, D). The delta-lobe deposit is fluvial dominated with low-to-moderate wave influence (Gardner, 1993; Garrison and Van den Bergh, 2004; Ryer and Anderson, 2004) and contains numerous, well-documented clinoforms in the exposures of the Ivie Creek amphitheater (Anderson et al., 2002, 2003, 2004; Forster et al., 2004; Enge and Howell, 2010) (Figure 5D). Clinoform-related bedding geometries and facies distributions imply that clinoforms mapped by previous workers, and used as input data for the models presented below (Figure 6A, after Forster et al., 2004), bound clinothems equivalent to mouth bars (sensu Bhattacharya, 2006). Subtle, apparently cyclic variations in clinoform spacing and dip angle probably define mouth-bar assemblages (sensu Bhattacharya, 2006; “bedsets” sensu Enge et al., 2010). Smaller-scale lithologic variation at the scale of individual beds occurs between the mapped clinoforms and records incremental growth of a mouth bar because of varying water and sediment discharge through the feeder distributary channel. Deveugle et al. (2011) used a high-resolution outcrop data set to build a reservoir-scale (7200 × 3800 × 50 m [23622 × 12467 × 164 ft]), surface-based model of the lower two tongues (parasequence sets) of the Ferron Sandstone Member. Clinoforms were not represented in the delta-lobe deposits (cf. parasequences) of the Deveugle et al. (2011) model, and their surface-based model is used here as the context in which the clinoform-modeling algorithm should be applied.

Figure 5 (A) Paleogeographic reconstruction of the Late Cretaceous Last Chance and Vernal delta systems of the Ferron Sandstone Member of the Mancos Shale in present-day Utah (after Cotter, 1976; used with permission of Brigham Young University). The location of the Deveugle et al. (2011) model (Figure 5D) and a regional cross section (Figure 5B) are highlighted. (B) Schematic regional cross section through the Last Chance delta system of the Ferron Sandstone Member and its eight-component shallow-marine tongues (termed “pararasequence sets,” using the nomenclature of Deveugle et al., 2011, and numbered PSS1 to PSS8), from southwest (paleolandward) to northeast (paleoseaward) (after Anderson and Ryer, 2004; used with permission of AAPG). (C) Detailed cross section through the lowermost shallow-marine tongues (termed “parasequences,” using the nomenclature of Deveugle et al., 2011, and forming PSS1 in Figure 5B) and associated coastal-plain strata (after Garrison and Van den Bergh, 2004; used with permission of AAPG). The tongue is subdivided into constituent parasequences (after Deveugle et al., 2011). Parasequence 1.6 is modeled in this study. (D) Distribution of facies-association belts at the top of parasequence 1.6, in the Deveugle et al. (2011) model area in the Ivie Creek amphitheater. The area of the model constructed in this study (Figures 7–10) lies within the dashed lines.

[[File:BLTN13190fig5.jpg|thumb|300px|{{figure number|5}}(A) Paleogeographic reconstruction of the Late Cretaceous Last Chance and Vernal delta systems of the Ferron Sandstone Member of the Mancos Shale in present-day Utah (after Cotter, 1976; used with permission of Brigham Young University). The location of the Deveugle et al. (2011) model (Figure 5D) and a regional cross section (Figure 5B) are highlighted. (B) Schematic regional cross section through the Last Chance delta system of the Ferron Sandstone Member and its eight-component shallow-marine tongues (termed “pararasequence sets,” using the nomenclature of Deveugle et al., 2011, and numbered PSS1 to PSS8), from southwest (paleolandward) to northeast (paleoseaward) (after Anderson and Ryer, 2004; used with permission of AAPG). (C) Detailed cross section through the lowermost shallow-marine tongues (termed “parasequences,” using the nomenclature of Deveugle et al., 2011, and forming PSS1 in Figure 5B) and associated coastal-plain strata (after Garrison and Van den Bergh, 2004; used with permission of AAPG). The tongue is subdivided into constituent parasequences (after Deveugle et al., 2011). Parasequence 1.6 is modeled in this study. (D) Distribution of facies-association belts at the top of parasequence 1.6, in the Deveugle et al. (2011) model area in the Ivie Creek amphitheater. The area of the model constructed in this study (Figures 7–10) lies within the dashed lines.]]

Figure 6 (A) Interpreted line drawing of clinoforms in parasequence 1.6 at the Junction Point section of Ivie Creek amphitheater (Figure 5D) (modified after Forster et al., 2004). Each clinoform bounds a mouth bar and equivalent delta-front deposits. Data from 104 clinoforms were collected to condition the clinoform-modeling algorithm. Frequency distributions of values measured from outcrop data for (B) clinoform length (Figure 4D), and (C) clinoform spacing (Figure 4D), which are used as input parameters in the clinoform-modeling algorithm (Table 2).

[[File:BLTN13190fig6.jpg|thumb|300px|{{figure number|6}}(A) Interpreted line drawing of clinoforms in parasequence 1.6 at the Junction Point section of Ivie Creek amphitheater (Figure 5D) (modified after Forster et al., 2004). Each clinoform bounds a mouth bar and equivalent delta-front deposits. Data from 104 clinoforms were collected to condition the clinoform-modeling algorithm. Frequency distributions of values measured from outcrop data for (B) clinoform length (Figure 4D), and (C) clinoform spacing (Figure 4D), which are used as input parameters in the clinoform-modeling algorithm (Table 2).]]

===Model Construction===

===Model Construction===

The parameters used to insert clinoforms into the model volume are summarized in Table 2. The delta lobe in parasequence 1.6 is approximately 8.1 km (5.03 mi) wide and 12.2 km (7.58 mi) long, giving a plan-view aspect ratio of 0.7 (Deveugle et al., 2011), comparable to values for lobes of the Pleistocene Lagniappe delta (after data in Kolla et al., 2000; Roberts et al., 2004) and the modern Wax Lake Delta lobe (after data in Wellner et al., 2005) (Figure 3C). These dimensions were likely smaller during the growth of the delta lobe, and it is assumed here that the lobe initiated with dimensions (BLTN13190eq73, BLTN13190eq74) that were a third of those of the final preserved delta lobe, consistent in areal proportions to a single mouth-bar assemblage or jet-plume complex in the modern Wax Lake Delta lobe (after data in Wellner et al., 2005). The length, BLTN13190eq75, and spacing, BLTN13190eq76, of clinoforms in depositional dip cross section were extracted from the bedding-diagram interpretations of Forster et al. (2004) (Figure 6A), clinoform length and dip statistics of Enge et al. (2010), and the LIDAR data used to create the model of Enge and Howell (2010). A database of clinoform lengths, dips, and spacings was compiled from these data sources, yielding frequency distributions from which the geometry or spatial arrangement of clinoforms that bound mouth-bar clinothems (sensu Bhattacharya, 2006), or a trend in these parameters, can be extracted (Figure 6B, C). The clinoform-modeling algorithm was used to build 31 clinoforms in the modeled volume of parasequence 1.6 (Figure 7). For simplicity, clinoform spacing is fixed at 25 m (82 ft), which is the average value observed at outcrop (Figure 6C). Heterogeneity at bed scale is recognized to be present but is not explicitly captured by surfaces in the model; rather, the effective petrophysical properties assigned to the facies associations (particularly the ratio of vertical-to-horizontal permeability) are modified to account for these (e.g., Jackson et al., 2009; Deveugle et al., 2011; Graham et al., 2015, this volume). A constant value of 2 was assigned to the clinoform shape-function exponent, P (Figure 2E), to ensure that the clinoform dip angle is always in the range extracted from the data of Enge et al. (2010). The initial clinoform insertion point, BLTN13190eq77 (Figure 4C), was qualitatively matched with a plan-view map of facies association belts at the top of parasequence 1.6 (Figure 5D). The overall progradation direction for the clinoforms BLTN13190eq78 was assigned an azimuth of 274° relative to north, which corresponds to the interpreted progradation direction of the delta lobe in parasequence 1.6 (Deveugle et al., 2011). In a subsequent step, the facies association boundary surfaces extracted from the model of Deveugle et al. (2011) were used to create facies association zones within each clinothem. Application of the clinoform-modeling algorithm yields a surface-based model measuring 750 × 3000 × 6 m (2461 × 9843 × 20 ft), which contains 95 surfaces: the top- and base-parasequence bounding surfaces, 31 clinoforms, and 62 facies-association boundary surfaces (Figure 8).

The parameters used to insert clinoforms into the model volume are summarized in Table 2. The delta lobe in parasequence 1.6 is approximately 8.1 km (5.03 mi) wide and 12.2 km (7.58 mi) long, giving a plan-view aspect ratio of 0.7 (Deveugle et al., 2011), comparable to values for lobes of the Pleistocene Lagniappe delta (after data in Kolla et al., 2000; Roberts et al., 2004) and the modern Wax Lake Delta lobe (after data in Wellner et al., 2005) (Figure 3C). These dimensions were likely smaller during the growth of the delta lobe, and it is assumed here that the lobe initiated with dimensions (BLTN13190eq73, BLTN13190eq74) that were a third of those of the final preserved delta lobe, consistent in areal proportions to a single mouth-bar assemblage or jet-plume complex in the modern Wax Lake Delta lobe (after data in Wellner et al., 2005). The length, BLTN13190eq75, and spacing, BLTN13190eq76, of clinoforms in depositional dip cross section were extracted from the bedding-diagram interpretations of Forster et al. (2004) (Figure 6A), clinoform length and dip statistics of Enge et al. (2010), and the LIDAR data used to create the model of Enge and Howell (2010). A database of clinoform lengths, dips, and spacings was compiled from these data sources, yielding frequency distributions from which the geometry or spatial arrangement of clinoforms that bound mouth-bar clinothems (sensu Bhattacharya, 2006), or a trend in these parameters, can be extracted (Figure 6B, C). The clinoform-modeling algorithm was used to build 31 clinoforms in the modeled volume of parasequence 1.6 (Figure 7). For simplicity, clinoform spacing is fixed at 25 m (82 ft), which is the average value observed at outcrop (Figure 6C). Heterogeneity at bed scale is recognized to be present but is not explicitly captured by surfaces in the model; rather, the effective petrophysical properties assigned to the facies associations (particularly the ratio of vertical-to-horizontal permeability) are modified to account for these (e.g., Jackson et al., 2009; Deveugle et al., 2011; Graham et al., 2015, this volume). A constant value of 2 was assigned to the clinoform shape-function exponent, P (Figure 2E), to ensure that the clinoform dip angle is always in the range extracted from the data of Enge et al. (2010). The initial clinoform insertion point, BLTN13190eq77 (Figure 4C), was qualitatively matched with a plan-view map of facies association belts at the top of parasequence 1.6 (Figure 5D). The overall progradation direction for the clinoforms BLTN13190eq78 was assigned an azimuth of 274° relative to north, which corresponds to the interpreted progradation direction of the delta lobe in parasequence 1.6 (Deveugle et al., 2011). In a subsequent step, the facies association boundary surfaces extracted from the model of Deveugle et al. (2011) were used to create facies association zones within each clinothem. Application of the clinoform-modeling algorithm yields a surface-based model measuring 750 × 3000 × 6 m (2461 × 9843 × 20 ft), which contains 95 surfaces: the top- and base-parasequence bounding surfaces, 31 clinoforms, and 62 facies-association boundary surfaces (Figure 8).

Figure 7 Surfaces generated by the clinoform-modeling algorithm for the model of part of parasequence 1.6 of the Ferron Sandstone Member (Figure 5C, D). (A) Single three-dimensional (3-D) surface representing a clinoform generated by the clinoform modeling algorithm. (B) 3-D dip cross section showing the concave-upward geometry of the clinoforms. (C) 3-D strike section of the model showing surfaces that exhibit bidirectional dips. Not all surfaces used in the model of part of the Ferron Sandstone Member (Figure 8) are shown.

[[File:BLTN13190fig7.jpg|thumb|300px|{{figure number|7}}Surfaces generated by the clinoform-modeling algorithm for the model of part of parasequence 1.6 of the Ferron Sandstone Member (Figure 5C, D). (A) Single three-dimensional (3-D) surface representing a clinoform generated by the clinoform modeling algorithm. (B) 3-D dip cross section showing the concave-upward geometry of the clinoforms. (C) 3-D strike section of the model showing surfaces that exhibit bidirectional dips. Not all surfaces used in the model of part of the Ferron Sandstone Member (Figure 8) are shown.]]

Figure 8 Surface-based model of part of parasequence PS1.6 of the Ferron Sandstone Member (Figure 5C, D), a fluvial-dominated delta lobe. (A) Three-dimensional view of the surface-based model, generated using bounding surfaces that were modified from the outcrop model of Deveugle et al. (2011), superimposed on a digital elevation map of the present day study area, with no vertical exaggeration and orientations of regional depositional dip and strike shown. (B) Plan-view section of model showing curved clinoforms, consistent with the geometry of fluvial-dominated delta lobes. (C) A two-dimensional (2-D) dip section and (D) a 2-D strike section through the model, showing details of the complex internal architecture. Red lines indicate facies boundaries, and blue lines indicate parasequence-bounding flooding surfaces. Black lines represent clinoforms. SMB = stream-mouth-bar; pDF = proximal delta-front; dDF = distal delta-front.

[[File:BLTN13190fig8.jpg|thumb|300px|{{figure number|8}}Surface-based model of part of parasequence PS1.6 of the Ferron Sandstone Member (Figure 5C, D), a fluvial-dominated delta lobe. (A) Three-dimensional view of the surface-based model, generated using bounding surfaces that were modified from the outcrop model of Deveugle et al. (2011), superimposed on a digital elevation map of the present day study area, with no vertical exaggeration and orientations of regional depositional dip and strike shown. (B) Plan-view section of model showing curved clinoforms, consistent with the geometry of fluvial-dominated delta lobes. (C) A two-dimensional (2-D) dip section and (D) a 2-D strike section through the model, showing details of the complex internal architecture. Red lines indicate facies boundaries, and blue lines indicate parasequence-bounding flooding surfaces. Black lines represent clinoforms. SMB = stream-mouth-bar; pDF = proximal delta-front; dDF = distal delta-front.]]

A cornerpoint gridding scheme in which variations in facies architecture are represented by variations in grid architecture was used (White and Barton, 1999; Jackson et al., 2005; Sech et al., 2009). The grid has vertical pillars with a constant spacing of 20 m (66 ft) in x and y (horizontal) directions. Grid layering in the z (vertical) direction within each facies-association zone conforms to the underlying clinoform surface, so layers are parallel to, and build up from, the underlying clinoform. Grid layers have a constant thickness of 0.2 m (0.66 ft); however, each facies-association zone is gridded separately, and the grid layers pinch out against facies-association boundaries and parasequence-bounding flooding surfaces. This gridding approach was used by Sech et al. (2009); it ensures that the grid layering conforms to the architecture of the clinoform surfaces, preserving their dip and geometry, and captures facies association boundaries (Figure 9). Where a grid layer pinches out, the grid cells have zero thickness and are set to be inactive in flow simulations. These zero-thickness cells are bridged using nonneighbor connections so that they do not act as barriers to flow. The chosen cell size of 20 × 20 × 0.2 m (66 × 66 × 0.66 ft) yields a total of approximately 5 million cells, of which 140,000 (2.6%) are active. Because the number of active grid cells is small, fluid-flow simulations can be performed on the grid without upscaling.

A cornerpoint gridding scheme in which variations in facies architecture are represented by variations in grid architecture was used (White and Barton, 1999; Jackson et al., 2005; Sech et al., 2009). The grid has vertical pillars with a constant spacing of 20 m (66 ft) in x and y (horizontal) directions. Grid layering in the z (vertical) direction within each facies-association zone conforms to the underlying clinoform surface, so layers are parallel to, and build up from, the underlying clinoform. Grid layers have a constant thickness of 0.2 m (0.66 ft); however, each facies-association zone is gridded separately, and the grid layers pinch out against facies-association boundaries and parasequence-bounding flooding surfaces. This gridding approach was used by Sech et al. (2009); it ensures that the grid layering conforms to the architecture of the clinoform surfaces, preserving their dip and geometry, and captures facies association boundaries (Figure 9). Where a grid layer pinches out, the grid cells have zero thickness and are set to be inactive in flow simulations. These zero-thickness cells are bridged using nonneighbor connections so that they do not act as barriers to flow. The chosen cell size of 20 × 20 × 0.2 m (66 × 66 × 0.66 ft) yields a total of approximately 5 million cells, of which 140,000 (2.6%) are active. Because the number of active grid cells is small, fluid-flow simulations can be performed on the grid without upscaling.

Figure 9 View of Figure 8C showing grid layering that conforms to the architecture of the clinoforms, facies association boundaries, and parasequence-bounding flooding surfaces. The same facies association scheme as in Figure 8 applies.

[[File:BLTN13190fig9.jpg|thumb|300px|{{figure number|9}}View of Figure 8C showing grid layering that conforms to the architecture of the clinoforms, facies association boundaries, and parasequence-bounding flooding surfaces. The same facies association scheme as in Figure 8 applies.]]

In the final step before fluid-flow simulation, the grid cells were populated with petrophysical properties from a mature subsurface reservoir analog (table 1 of Deveugle et al., 2011). Petrophysical properties were assigned to each facies association, which typically have permeabilities that differ by approximately one order of magnitude from their overlying or underlying neighbor. In a separate step, transmissibility multipliers are assigned along the base of the grid cells in the layer directly above each clinoform surface to represent baffles and barriers to fluid flow along clinoforms in a geometrically accurate and efficient way. The transmissibility multipliers were assigned using a stochastic technique that decreases the probability of barriers being present along the upper part of the clinoform. This aspect of modeling is discussed in greater detail in a companion article (Graham et al., 2015, this volume).

In the final step before fluid-flow simulation, the grid cells were populated with petrophysical properties from a mature subsurface reservoir analog (table 1 of Deveugle et al., 2011). Petrophysical properties were assigned to each facies association, which typically have permeabilities that differ by approximately one order of magnitude from their overlying or underlying neighbor. In a separate step, transmissibility multipliers are assigned along the base of the grid cells in the layer directly above each clinoform surface to represent baffles and barriers to fluid flow along clinoforms in a geometrically accurate and efficient way. The transmissibility multipliers were assigned using a stochastic technique that decreases the probability of barriers being present along the upper part of the clinoform. This aspect of modeling is discussed in greater detail in a companion article (Graham et al., 2015, this volume).

Waterflooding was simulated using conventional black oil simulation software, using a line drive of four vertical injector wells and six vertical producer wells (Figure 10A). The producer and injector wells were spaced 750 m (2461 ft) apart, with water being injected down the local depositional dip, from east to west. Oil production and water injection were set to maintain a group target production rate over 20 yr of BLTN13190eq118 (1100 bbl/day), a minimum bottom hole pressure constraint of 50 bars (725 psi) for each production well, and a maximum bottom hole pressure constraint of 150 bars (2175 psi) for each injection well. Further information on reservoir properties is summarized in Table 3. Heterogeneity along clinoforms is specified in terms of the percentage of each clinoform surface that acts as a barrier to flow. The volume of the barriers along clinoforms is negligible, so they have little impact on the volume of oil in place. Two simulations were completed in which (1) clinoforms are not associated with barriers to flow (0% barrier coverage along clinoforms) and (2) clinoforms are associated with significant barriers to flow (90% barrier coverage along clinoforms; Figure 10B). All other parameters remain fixed between the simulations. In a companion article, Graham et al. (2015, this volume) apply the clinoform-modeling algorithm to build a range of models to investigate the impact of a broader range of uncertainties in clinoform parameters, such as clinoform spacing and barrier coverage, on hydrocarbon recovery in the context of uncertain geologic parameters and engineering decisions.

Waterflooding was simulated using conventional black oil simulation software, using a line drive of four vertical injector wells and six vertical producer wells (Figure 10A). The producer and injector wells were spaced 750 m (2461 ft) apart, with water being injected down the local depositional dip, from east to west. Oil production and water injection were set to maintain a group target production rate over 20 yr of BLTN13190eq118 (1100 bbl/day), a minimum bottom hole pressure constraint of 50 bars (725 psi) for each production well, and a maximum bottom hole pressure constraint of 150 bars (2175 psi) for each injection well. Further information on reservoir properties is summarized in Table 3. Heterogeneity along clinoforms is specified in terms of the percentage of each clinoform surface that acts as a barrier to flow. The volume of the barriers along clinoforms is negligible, so they have little impact on the volume of oil in place. Two simulations were completed in which (1) clinoforms are not associated with barriers to flow (0% barrier coverage along clinoforms) and (2) clinoforms are associated with significant barriers to flow (90% barrier coverage along clinoforms; Figure 10B). All other parameters remain fixed between the simulations. In a companion article, Graham et al. (2015, this volume) apply the clinoform-modeling algorithm to build a range of models to investigate the impact of a broader range of uncertainties in clinoform parameters, such as clinoform spacing and barrier coverage, on hydrocarbon recovery in the context of uncertain geologic parameters and engineering decisions.

Figure 10 (A) Plan-view facies association map of the Ferron Sandstone Member extracted from our reservoir model (Figure 5D), showing location of injector and producer wells, and the cross section illustrated in Figure 10B–D. The color scheme is the same as in Figures 5D and 8A. (B) Depositional-dip-oriented cross section showing the internal facies architecture of the modeled parasequence with the location of flow barriers along the clinoforms shown as black lines. Flow barriers are placed stochastically along clinoforms according to a trend that decreases the probability of barriers being present along the upper part of a clinoform. (C, D) The same depositional-dip-oriented cross section showing water saturation after 5 yr of production where water has been injected down the depositional dip, from east to west, for models with (C) 0% barrier coverage along clinoforms and (D) 90% barrier coverage along clinoforms. Oil is bypassed below the clinoforms in (D). SMB = stream-mouth-bar; pDF = proximal delta-front; dDF = distal delta-front.

[[File:BLTN13190fig10.jpg|thumb|300px|{{figure number|10}}(A) Plan-view facies association map of the Ferron Sandstone Member extracted from our reservoir model (Figure 5D), showing location of injector and producer wells, and the cross section illustrated in Figure 10B–D. The color scheme is the same as in Figures 5D and 8A. (B) Depositional-dip-oriented cross section showing the internal facies architecture of the modeled parasequence with the location of flow barriers along the clinoforms shown as black lines. Flow barriers are placed stochastically along clinoforms according to a trend that decreases the probability of barriers being present along the upper part of a clinoform. (C, D) The same depositional-dip-oriented cross section showing water saturation after 5 yr of production where water has been injected down the depositional dip, from east to west, for models with (C) 0% barrier coverage along clinoforms and (D) 90% barrier coverage along clinoforms. Oil is bypassed below the clinoforms in (D). SMB = stream-mouth-bar; pDF = proximal delta-front; dDF = distal delta-front.]]

===Simulation Results===

===Simulation Results===

When clinoforms are not associated with barriers to flow, they have little impact on production (Figure 10C); however, if barriers occupy 90% of the clinoform surfaces, then their impact on recovery is significant. Models that omit barriers to flow along clinoforms can overestimate recovery by up to 36% (cf. Figures 10C, D; 11A), consistent with previous simulation studies of the Ferron Sandstone Member that found barrier-lined clinoforms reduced hydrocarbon recovery by several tens of percent (Howell et al., 2008b; Enge and Howell 2010). Reduced recovery is caused by decreased sweep efficiency as each clinothem becomes hydraulically separated from its neighbors. Consequently, significant oil is bypassed in the reservoir, particularly beneath barriers along clinoforms and at the toe of each clinothem (Figure 10D). Increased reservoir compartmentalization also means that the target oil production rate cannot be met; and, as a result, models that include barriers along clinoforms produce significantly lower volumes of oil per day (Figure 11B). Enge and Howell (2010) also found that including barriers along clinoforms in reservoir models of the Ferron Sandstone Member increased reservoir compartmentalization.

When clinoforms are not associated with barriers to flow, they have little impact on production (Figure 10C); however, if barriers occupy 90% of the clinoform surfaces, then their impact on recovery is significant. Models that omit barriers to flow along clinoforms can overestimate recovery by up to 36% (cf. Figures 10C, D; 11A), consistent with previous simulation studies of the Ferron Sandstone Member that found barrier-lined clinoforms reduced hydrocarbon recovery by several tens of percent (Howell et al., 2008b; Enge and Howell 2010). Reduced recovery is caused by decreased sweep efficiency as each clinothem becomes hydraulically separated from its neighbors. Consequently, significant oil is bypassed in the reservoir, particularly beneath barriers along clinoforms and at the toe of each clinothem (Figure 10D). Increased reservoir compartmentalization also means that the target oil production rate cannot be met; and, as a result, models that include barriers along clinoforms produce significantly lower volumes of oil per day (Figure 11B). Enge and Howell (2010) also found that including barriers along clinoforms in reservoir models of the Ferron Sandstone Member increased reservoir compartmentalization.

Figure 11 (A) Recovery factor and water cut as function of time in the simulation model of part of parasequence 1.6 of the Ferron Sandstone Member. Note the significant decrease in recovery factor for the model with 90% barrier coverage along clinoforms. (B) Oil and water production rate as a function of time. In the models with 90% barrier coverage along clinoforms, the target production rate was not met and water breakthrough occurred earlier than in models where barriers were not present along clinoforms.

[[File:BLTN13190fig11.jpg|thumb|300px|{{figure number|11}}(A) Recovery factor and water cut as function of time in the simulation model of part of parasequence 1.6 of the Ferron Sandstone Member. Note the significant decrease in recovery factor for the model with 90% barrier coverage along clinoforms. (B) Oil and water production rate as a function of time. In the models with 90% barrier coverage along clinoforms, the target production rate was not met and water breakthrough occurred earlier than in models where barriers were not present along clinoforms.]]

Finally, models that include barriers along clinoforms have earlier water breakthrough than models that lack barriers along clinoforms (Figure 11). Including barrier-lined clinoforms increases the tortuosity of flow pathways because the fluids can only move between clinothems by exploiting the gap in the barriers at the top of each clinoform. However, as the number of potential flow pathways is decreased by including barriers to flow along clinoforms, the injected water exploits the pathways between the injectors and producers faster, which leads to earlier water breakthrough. Similar results were obtained in clinoform-bearing models of a wave-dominated shoreface system (Jackson et al., 2009).

Finally, models that include barriers along clinoforms have earlier water breakthrough than models that lack barriers along clinoforms (Figure 11). Including barrier-lined clinoforms increases the tortuosity of flow pathways because the fluids can only move between clinothems by exploiting the gap in the barriers at the top of each clinoform. However, as the number of potential flow pathways is decreased by including barriers to flow along clinoforms, the injected water exploits the pathways between the injectors and producers faster, which leads to earlier water breakthrough. Similar results were obtained in clinoform-bearing models of a wave-dominated shoreface system (Jackson et al., 2009).

Reservoir zones in the Troll West accumulation are defined by alternating layers of fine-grained, micaceous sandstone and coarse-grained sandstone (informally referred to as m sands and c sands, respectively). The coarse-grained sandstones have higher porosity and permeability (hundreds to thousands of millidarcys) than the fine-grained, micaceous sandstones (tens to hundreds of millidarcys) (Gibbons et al., 1993; Dreyer et al., 2005). Each couplet of fine-grained, micaceous sandstone and overlying coarse-grained sandstones corresponds to the lower and upper part of a single delta-front parasequence (Dreyer et al., 2005). The 3-D seismic data image laterally extensive (up to 30 km [19 mi] along depositional strike), near-linear, north-northeast–south-southwest-trending clinoforms that dip west-northwestward at 1.5°–4° (Dreyer et al., 2005; Patruno et al., 2015). The structure of the Troll West reservoir is defined by two rotated fault blocks that formed after reservoir deposition, and the reservoir is further segmented by smaller postdepositional faults that trend west-northwest–east-southeast to north-northwest–south-southeast (Dreyer et al., 2005) (Figure 12B).

Reservoir zones in the Troll West accumulation are defined by alternating layers of fine-grained, micaceous sandstone and coarse-grained sandstone (informally referred to as m sands and c sands, respectively). The coarse-grained sandstones have higher porosity and permeability (hundreds to thousands of millidarcys) than the fine-grained, micaceous sandstones (tens to hundreds of millidarcys) (Gibbons et al., 1993; Dreyer et al., 2005). Each couplet of fine-grained, micaceous sandstone and overlying coarse-grained sandstones corresponds to the lower and upper part of a single delta-front parasequence (Dreyer et al., 2005). The 3-D seismic data image laterally extensive (up to 30 km [19 mi] along depositional strike), near-linear, north-northeast–south-southwest-trending clinoforms that dip west-northwestward at 1.5°–4° (Dreyer et al., 2005; Patruno et al., 2015). The structure of the Troll West reservoir is defined by two rotated fault blocks that formed after reservoir deposition, and the reservoir is further segmented by smaller postdepositional faults that trend west-northwest–east-southeast to north-northwest–south-southeast (Dreyer et al., 2005) (Figure 12B).

Figure 12 (A) Paleogeographic reconstruction of gross depositional environments in the central and northern North Sea during the early-to-mid Kimmeridgian (modified from Fraser et al., 2003), marked by retreat and drowning of the Troll delta system (6-series of the Sognefjord Formation; Dreyer et al., 2005). (B) Simplified outline of the Troll Field, highlighting major blocks bounded by normal faults that post-date deposition of the Sognefjord Formation. The location of the modeled area and a stratigraphic cross section across Troll West (Figure 12C) are shown. (C) Schematic cross section through the Troll delta system of the Sognefjord Formation in Troll West, from west (paleoseaward) to east (paleolandward). Major shallow-marine tongues (labeled 1-series to 6-series, using the nomenclature of Dreyer et al., 2005) and their component parasequences are shown (after Gibbons et al., 1993).

[[File:BLTN13190fig12.jpg|thumb|300px|{{figure number|12}}(A) Paleogeographic reconstruction of gross depositional environments in the central and northern North Sea during the early-to-mid Kimmeridgian (modified from Fraser et al., 2003), marked by retreat and drowning of the Troll delta system (6-series of the Sognefjord Formation; Dreyer et al., 2005). (B) Simplified outline of the Troll Field, highlighting major blocks bounded by normal faults that post-date deposition of the Sognefjord Formation. The location of the modeled area and a stratigraphic cross section across Troll West (Figure 12C) are shown. (C) Schematic cross section through the Troll delta system of the Sognefjord Formation in Troll West, from west (paleoseaward) to east (paleolandward). Major shallow-marine tongues (labeled 1-series to 6-series, using the nomenclature of Dreyer et al., 2005) and their component parasequences are shown (after Gibbons et al., 1993).]]

Troll West contains a thin oil column (11–26 m [36–85 ft]) that is exploited through the use of horizontal wells (Dreyer et al., 2005), the productivity of which is sensitive to the ratio of vertical-to-horizontal permeability (cf. Joshi, 1987). This ratio is predicted to be influenced by the calcite-cemented concretionary beds that are abundant in the Sognefjord Formation (Kantorowicz et al., 1987; Lien et al., 1992; Evensen et al., 1993). These are present within delta-front parasequences, which are seismically imaged as clinoform sets, and along their bounding flooding surfaces (Gibbons et al., 1993; Bakke et al., 1996; Dreyer et al., 2005; Holgate et al., 2014; Patruno et al., 2015). The Jurassic Bridport Sand Formation, a close sedimentologic analog present onshore United Kingdom, contains similarly abundant calcite-cemented concretionary beds. These are observed at the outcrop to be laterally extensive (>80% areal coverage) along bedding planes and in a producing subsurface reservoir; their presence is marked by breaks in pressure and fluid saturation within seismically imaged clinoform sets (Morris et al., 2006; Hampson et al., 2014). Thus it appears probable that permeability barriers and baffles in the form of calcite-cemented concretionary layers occur along clinoforms in the Troll Field reservoir and could influence drainage patterns and recovery from the thin oil zone (Gibbons et al., 1993); this may have been recognized previously and shown to impact on well test interpretations (Lien et al., 1991; Haug, 1992). However, to date, the heterogeneity associated with clinoforms has not been explicitly included in reservoir or flow-simulation models of the Sognefjord Formation in the Troll Field. Dilib et al. (2015) created a sector model of the Sognefjord Formation (dimensions: 3200 × 750 × 150 m [10,499 × 2461 × 492 ft]) to investigate production optimization using intelligent wells for a range of uncertainty in geologic parameters and their model, extracted and refined from the existing full field geological model, was used here as the context in which to apply the clinoform-modeling algorithm.

Troll West contains a thin oil column (11–26 m [36–85 ft]) that is exploited through the use of horizontal wells (Dreyer et al., 2005), the productivity of which is sensitive to the ratio of vertical-to-horizontal permeability (cf. Joshi, 1987). This ratio is predicted to be influenced by the calcite-cemented concretionary beds that are abundant in the Sognefjord Formation (Kantorowicz et al., 1987; Lien et al., 1992; Evensen et al., 1993). These are present within delta-front parasequences, which are seismically imaged as clinoform sets, and along their bounding flooding surfaces (Gibbons et al., 1993; Bakke et al., 1996; Dreyer et al., 2005; Holgate et al., 2014; Patruno et al., 2015). The Jurassic Bridport Sand Formation, a close sedimentologic analog present onshore United Kingdom, contains similarly abundant calcite-cemented concretionary beds. These are observed at the outcrop to be laterally extensive (>80% areal coverage) along bedding planes and in a producing subsurface reservoir; their presence is marked by breaks in pressure and fluid saturation within seismically imaged clinoform sets (Morris et al., 2006; Hampson et al., 2014). Thus it appears probable that permeability barriers and baffles in the form of calcite-cemented concretionary layers occur along clinoforms in the Troll Field reservoir and could influence drainage patterns and recovery from the thin oil zone (Gibbons et al., 1993); this may have been recognized previously and shown to impact on well test interpretations (Lien et al., 1991; Haug, 1992). However, to date, the heterogeneity associated with clinoforms has not been explicitly included in reservoir or flow-simulation models of the Sognefjord Formation in the Troll Field. Dilib et al. (2015) created a sector model of the Sognefjord Formation (dimensions: 3200 × 750 × 150 m [10,499 × 2461 × 492 ft]) to investigate production optimization using intelligent wells for a range of uncertainty in geologic parameters and their model, extracted and refined from the existing full field geological model, was used here as the context in which to apply the clinoform-modeling algorithm.

Table 4 shows the parameters used in the clinoform-modeling algorithm. To honor the nearly linear plan-view geometry of clinoforms observed in seismic data (figures 3, 12 in Dreyer et al., 2005), a width for the top-clinoform ellipse (BLTN13190eq120 that is far greater than the depositional-dip extent of the bounding surfaces in the model area (3200 m [10,499 ft]) was defined; the top-clinoform ellipse length BLTN13190eq121 is half of BLTN13190eq122, to give a plan-view aspect ratio of 2 (cf. wave-dominated shoreface systems in Howell et al., 2008a). Seismically resolved clinoform dip values of 1.5°–4° (Dreyer et al., 2005; Patruno et al., 2015) were used in conjunction with the estimated parasequence thickness to calculate clinoform length (BLTN13190eq123) using simple trigonometry. As there are only a small number of seismically resolved clinoforms in a few paleogeographic locations and within a few stratigraphic levels to extract clinoform length, a normal distribution based on the extracted data was generated (Figure 13A), and values were then drawn at random from this distribution to populate the model volume (Figure 13A). Finally, the premodeling lengths were compared with the seismically resolved clinoforms (Dreyer et al., 2005) to validate that the algorithm-generated lengths are reasonable. Similarly, the horizontal spacing of seismically resolved clinoforms (figures 3, 12 in Dreyer et al., 2005) was used to generate a normal distribution of values for clinoform spacing, S (Figure 13B), and values were drawn at random from this distribution to populate the model volume (Figure 13B). The resulting values of clinoform length and spacing are consistent with those observed at the outcrop for other wave-dominated shorelines (e.g., Hampson, 2000; Sech et al., 2009) (Figure 13). A value of 2 was used for the exponent in the clinoform shape function (defined by BLTN13190eq124 in equation 8), as this gives a good match to the seismically resolved clinoforms; and, furthermore, it was assumed that a similar geometry is shared by clinoforms in all parasequences in all locations throughout the model volume, consistent with observations of seismically resolved clinoforms over similar-size volumes (Patruno et al., 2015). Although, BLTN13190eq125 has the same value as used in the Ferron Sandstone Member example, BLTN13190eq126 values in the Troll Field sector model are larger (Figure 13A, Table 4) such that clinoform dip angles are shallower, consistent with the seismically resolved clinoforms (Dreyer et al., 2005; Patruno et al., 2015). As a first step, the insertion point of the first clinoform (BLTN13190eq127) was arbitrarily selected in the center of the proximal model boundary, and consistent west-northwest progradation of clinoforms (Dreyer et al., 2005; Patruno et al., 2015) was used to define a BLTN13190eq128 of 320°. The facies-association boundary surfaces extracted from the model of Dilib et al. (2015) were then used to create zones of m sands and c sands within each clinothem. The application of the clinoform-modeling algorithm yields a model containing 100 clinoforms. A visual quality control check was then performed to ensure that the clinoforms produced by the algorithm are consistent with the geologic concepts of the model (e.g., clinoform spacing, dip, length) in the absence of postdepositional faults.

Table 4 shows the parameters used in the clinoform-modeling algorithm. To honor the nearly linear plan-view geometry of clinoforms observed in seismic data (figures 3, 12 in Dreyer et al., 2005), a width for the top-clinoform ellipse (BLTN13190eq120 that is far greater than the depositional-dip extent of the bounding surfaces in the model area (3200 m [10,499 ft]) was defined; the top-clinoform ellipse length BLTN13190eq121 is half of BLTN13190eq122, to give a plan-view aspect ratio of 2 (cf. wave-dominated shoreface systems in Howell et al., 2008a). Seismically resolved clinoform dip values of 1.5°–4° (Dreyer et al., 2005; Patruno et al., 2015) were used in conjunction with the estimated parasequence thickness to calculate clinoform length (BLTN13190eq123) using simple trigonometry. As there are only a small number of seismically resolved clinoforms in a few paleogeographic locations and within a few stratigraphic levels to extract clinoform length, a normal distribution based on the extracted data was generated (Figure 13A), and values were then drawn at random from this distribution to populate the model volume (Figure 13A). Finally, the premodeling lengths were compared with the seismically resolved clinoforms (Dreyer et al., 2005) to validate that the algorithm-generated lengths are reasonable. Similarly, the horizontal spacing of seismically resolved clinoforms (figures 3, 12 in Dreyer et al., 2005) was used to generate a normal distribution of values for clinoform spacing, S (Figure 13B), and values were drawn at random from this distribution to populate the model volume (Figure 13B). The resulting values of clinoform length and spacing are consistent with those observed at the outcrop for other wave-dominated shorelines (e.g., Hampson, 2000; Sech et al., 2009) (Figure 13). A value of 2 was used for the exponent in the clinoform shape function (defined by BLTN13190eq124 in equation 8), as this gives a good match to the seismically resolved clinoforms; and, furthermore, it was assumed that a similar geometry is shared by clinoforms in all parasequences in all locations throughout the model volume, consistent with observations of seismically resolved clinoforms over similar-size volumes (Patruno et al., 2015). Although, BLTN13190eq125 has the same value as used in the Ferron Sandstone Member example, BLTN13190eq126 values in the Troll Field sector model are larger (Figure 13A, Table 4) such that clinoform dip angles are shallower, consistent with the seismically resolved clinoforms (Dreyer et al., 2005; Patruno et al., 2015). As a first step, the insertion point of the first clinoform (BLTN13190eq127) was arbitrarily selected in the center of the proximal model boundary, and consistent west-northwest progradation of clinoforms (Dreyer et al., 2005; Patruno et al., 2015) was used to define a BLTN13190eq128 of 320°. The facies-association boundary surfaces extracted from the model of Dilib et al. (2015) were then used to create zones of m sands and c sands within each clinothem. The application of the clinoform-modeling algorithm yields a model containing 100 clinoforms. A visual quality control check was then performed to ensure that the clinoforms produced by the algorithm are consistent with the geologic concepts of the model (e.g., clinoform spacing, dip, length) in the absence of postdepositional faults.

Figure 13 Normal distributions, shown as black lines, for (A) clinoform length (Figure 4D) and (B) clinoform spacing (Figure 4D) generated from published seismic data from the Sognefjord Formation (figures 3, 12 in Dreyer et al., 2005). Gray bars represent the values for clinoform length and spacing drawn at random from the normal distribution and used to populate the Troll sector model.

[[File:BLTN13190fig13.jpg|thumb|300px|{{figure number|13}}Normal distributions, shown as black lines, for (A) clinoform length (Figure 4D) and (B) clinoform spacing (Figure 4D) generated from published seismic data from the Sognefjord Formation (figures 3, 12 in Dreyer et al., 2005). Gray bars represent the values for clinoform length and spacing drawn at random from the normal distribution and used to populate the Troll sector model.]]

After this validation, the clinoform-modeling algorithm was applied with the same parameters (Table 4) but using the faulted parasequence-bounding flooding surfaces and the faulted facies-association boundary surfaces. The resulting surface-based model contains clinoforms with geometries and distributions that reflect present-day reservoir structure, measures approximately 3200 × 750 × 150 m (10,499 × 2461 × 492 ft), and contains 215 surfaces: the 8 top and base parasequence bounding surfaces, 100 clinoform surfaces, and 107 facies-association-boundary surfaces between clinoform pairs. A hybrid gridding method is used, because previous work shows that this approach better captures the movement of gas and water in the vicinity of a horizontal production well located in a thin oil rim (Vinje et al., 2011). The areal grid resolution of the model is fixed (50 × 25 m [164 × 82 ft]), but the vertical resolution varies. In the gas cap and aquifer, the vertical layering is stratigraphic, conforming to the flooding surfaces that bound the parasequences and with a single grid layer representing each facies association zone. In an interval of the reservoir that contains the oil column, from 3 m (10 ft) above the gas–oil contact (GOC) to 3 m (10 ft) below the oil–water contact (OWC), the grid is horizontal and regular, with finer layering (0.25–2 m [0.82–7 ft]) parallel to the initial GOC and OWC (Dilib et al., 2015). Very fine grid resolution is required to capture the geometry of clinoforms in this regular, orthogonal part of the grid. For the model to be suitable for flow simulation, it is not possible to have this level of grid resolution everywhere in the model. Petrophysical properties were assigned by facies association in a similar manner to the model of the Ferron Sandstone Member reservoir analog. Clinoform-related heterogeneity was incorporated in flow-simulation models by using transmissibility multipliers along clinoform surfaces, where a trend was used to enforce greater continuity and extent of heterogeneity in the m sands that lie above the lower part of each clinoform. A different approach was used to model the clinoform-controlled heterogeneity than for the Ferron Sandstone Member model, because part of the grid is horizontal and regular. Transmissibility multipliers representing the heterogeneity along clinoforms are placed in the cells adjacent to the clinoform surface in the orthogonal part of grid around the oil rim. As the orthogonal grid is very fine, this approach honors the geometry of the clinoform surfaces.

After this validation, the clinoform-modeling algorithm was applied with the same parameters (Table 4) but using the faulted parasequence-bounding flooding surfaces and the faulted facies-association boundary surfaces. The resulting surface-based model contains clinoforms with geometries and distributions that reflect present-day reservoir structure, measures approximately 3200 × 750 × 150 m (10,499 × 2461 × 492 ft), and contains 215 surfaces: the 8 top and base parasequence bounding surfaces, 100 clinoform surfaces, and 107 facies-association-boundary surfaces between clinoform pairs. A hybrid gridding method is used, because previous work shows that this approach better captures the movement of gas and water in the vicinity of a horizontal production well located in a thin oil rim (Vinje et al., 2011). The areal grid resolution of the model is fixed (50 × 25 m [164 × 82 ft]), but the vertical resolution varies. In the gas cap and aquifer, the vertical layering is stratigraphic, conforming to the flooding surfaces that bound the parasequences and with a single grid layer representing each facies association zone. In an interval of the reservoir that contains the oil column, from 3 m (10 ft) above the gas–oil contact (GOC) to 3 m (10 ft) below the oil–water contact (OWC), the grid is horizontal and regular, with finer layering (0.25–2 m [0.82–7 ft]) parallel to the initial GOC and OWC (Dilib et al., 2015). Very fine grid resolution is required to capture the geometry of clinoforms in this regular, orthogonal part of the grid. For the model to be suitable for flow simulation, it is not possible to have this level of grid resolution everywhere in the model. Petrophysical properties were assigned by facies association in a similar manner to the model of the Ferron Sandstone Member reservoir analog. Clinoform-related heterogeneity was incorporated in flow-simulation models by using transmissibility multipliers along clinoform surfaces, where a trend was used to enforce greater continuity and extent of heterogeneity in the m sands that lie above the lower part of each clinoform. A different approach was used to model the clinoform-controlled heterogeneity than for the Ferron Sandstone Member model, because part of the grid is horizontal and regular. Transmissibility multipliers representing the heterogeneity along clinoforms are placed in the cells adjacent to the clinoform surface in the orthogonal part of grid around the oil rim. As the orthogonal grid is very fine, this approach honors the geometry of the clinoform surfaces.

The clinoforms incorporated into the Troll sector model show similar geometries and spacing to those that are seismically resolved in the Sognefjord Formation (Dreyer et al., 2005; Patruno et al., 2015). The clinoforms are linear in plan view over the small (750 m [2461 ft]) depositional-strike extent of the model (Figure 14B), consistent with the interpreted plan-view geometries of wave-dominated shoreface systems (Figure 3A) (Howell et al., 2008a), consistently prograde west-northwestward (BLTN13190eq140), as established through 3-D seismic data (Dreyer et al., 2005; Patruno et al., 2015), and have the concave-upward geometry observed in seismic dip sections through the Sognefjord Formation (Dreyer et al., 2005; Patruno et al., 2015) (Figures 14A, 15B). In depositional strike cross section, the algorithm produces near-horizontal clinoform geometries, consistent with seismically resolved clinoforms (Dreyer et al., 2005; Patruno et al., 2015) (Figure 15C). The stochastic component of the clinoform-modeling algorithm distributes clinoforms with cross-sectional geometries and spacings (Figures 14A, 15B) that are consistent with outcrop studies of wave-dominated deltas (Hampson, 2000; Sech et al., 2009) (Figure 13) and honor the sparse subsurface data. In contrast to the Ferron Sandstone Member example, the Troll West sector model does not contain subtle clinoform geometries, such as onlap and downlap of younger clinoforms on to older clinoforms (Figures 14A, 15B). Such features are below the resolution of the seismic data used to extract the parameters that were used in the algorithm. The clinoforms are also faulted in the same way as the parasequence-bounding flooding surfaces (cf. Figures 2A, 15C).

The clinoforms incorporated into the Troll sector model show similar geometries and spacing to those that are seismically resolved in the Sognefjord Formation (Dreyer et al., 2005; Patruno et al., 2015). The clinoforms are linear in plan view over the small (750 m [2461 ft]) depositional-strike extent of the model (Figure 14B), consistent with the interpreted plan-view geometries of wave-dominated shoreface systems (Figure 3A) (Howell et al., 2008a), consistently prograde west-northwestward (BLTN13190eq140), as established through 3-D seismic data (Dreyer et al., 2005; Patruno et al., 2015), and have the concave-upward geometry observed in seismic dip sections through the Sognefjord Formation (Dreyer et al., 2005; Patruno et al., 2015) (Figures 14A, 15B). In depositional strike cross section, the algorithm produces near-horizontal clinoform geometries, consistent with seismically resolved clinoforms (Dreyer et al., 2005; Patruno et al., 2015) (Figure 15C). The stochastic component of the clinoform-modeling algorithm distributes clinoforms with cross-sectional geometries and spacings (Figures 14A, 15B) that are consistent with outcrop studies of wave-dominated deltas (Hampson, 2000; Sech et al., 2009) (Figure 13) and honor the sparse subsurface data. In contrast to the Ferron Sandstone Member example, the Troll West sector model does not contain subtle clinoform geometries, such as onlap and downlap of younger clinoforms on to older clinoforms (Figures 14A, 15B). Such features are below the resolution of the seismic data used to extract the parameters that were used in the algorithm. The clinoforms are also faulted in the same way as the parasequence-bounding flooding surfaces (cf. Figures 2A, 15C).

Figure 14 Surfaces generated by the clinoform-modeling algorithm for the Troll sector model. (A) Three-dimensional (3-D) dip cross section of clinoforms in the model demonstrating their concave-upward geometry. (B) 3-D view of clinoforms in the model showing close to linear clinoforms in plan view within fault-bounded compartment. Not all surfaces used in the Troll sector model (Figure 15) are shown.

[[File:BLTN13190fig14.jpg|thumb|300px|{{figure number|14}}Surfaces generated by the clinoform-modeling algorithm for the Troll sector model. (A) Three-dimensional (3-D) dip cross section of clinoforms in the model demonstrating their concave-upward geometry. (B) 3-D view of clinoforms in the model showing close to linear clinoforms in plan view within fault-bounded compartment. Not all surfaces used in the Troll sector model (Figure 15) are shown.]]

Figure 15 (A) Plan-view facies-association map through the uppermost parasequence of the Sognefjord Formation in our Troll West sector model, showing the location of compartmentalizing faults and a horizontal well. Cross sections along (B) depositional dip and (C) depositional strike, showing bounding flooding surfaces (blue), surfaces representing facies-association boundaries (red), and clinoforms generated by the modeling algorithm (black) for all parasequences in the model volume.

[[File:BLTN13190fig15.jpg|thumb|300px|{{figure number|15}}(A) Plan-view facies-association map through the uppermost parasequence of the Sognefjord Formation in our Troll West sector model, showing the location of compartmentalizing faults and a horizontal well. Cross sections along (B) depositional dip and (C) depositional strike, showing bounding flooding surfaces (blue), surfaces representing facies-association boundaries (red), and clinoforms generated by the modeling algorithm (black) for all parasequences in the model volume.]]

===Production Strategy===

===Production Strategy===

The clinoform-bearing Troll West sector model was then used to simulate production through gas expansion and aquifer influx using a 2600 m (8530 ft) long horizontal well, placed 2 m (7 ft) above the initial OWC (Figures 15A, 16A). The well is controlled by maximum gas production rate, minimum oil production rate, and minimum bottom-hole-pressure constraints. Reservoir, rock, and fluid properties are summarized in Table 5. Similar to the Ferron Sandstone Member example, the presence of permeability barriers along clinoforms was modeled using transmissibility modifiers and specified in terms of the percentage of each clinoform surface that acts as a barrier to flow. Two simulations of the Troll Field sector model were conducted in which (1) clinoforms are not associated with barriers to flow (0% barrier coverage along clinoforms) and (2) clinoforms are associated with significant barriers to flow (90% barrier coverage along clinoforms, Figure 16B). All other parameters remain fixed between the simulations.

The clinoform-bearing Troll West sector model was then used to simulate production through gas expansion and aquifer influx using a 2600 m (8530 ft) long horizontal well, placed 2 m (7 ft) above the initial OWC (Figures 15A, 16A). The well is controlled by maximum gas production rate, minimum oil production rate, and minimum bottom-hole-pressure constraints. Reservoir, rock, and fluid properties are summarized in Table 5. Similar to the Ferron Sandstone Member example, the presence of permeability barriers along clinoforms was modeled using transmissibility modifiers and specified in terms of the percentage of each clinoform surface that acts as a barrier to flow. Two simulations of the Troll Field sector model were conducted in which (1) clinoforms are not associated with barriers to flow (0% barrier coverage along clinoforms) and (2) clinoforms are associated with significant barriers to flow (90% barrier coverage along clinoforms, Figure 16B). All other parameters remain fixed between the simulations.

Figure 16 (A) Plan-view facies-association map through the uppermost parasequence of the Sognefjord Formation in our Troll West sector model, showing the location of the horizontal well and the cross section shown in Figure 16B–D. (B) Depositional-dip-oriented cross section showing the internal facies architecture of the modeled parasequences with the location of barriers along the clinoforms shown as black lines. (C, D) The same depositional-dip-oriented cross section showing water saturation after 1000 days of production, for models with (C) 0% barrier coverage along clinoforms and (D) 90% barrier coverage along clinoforms.

[[File:BLTN13190fig16.jpg|thumb|300px|{{figure number|16}}(A) Plan-view facies-association map through the uppermost parasequence of the Sognefjord Formation in our Troll West sector model, showing the location of the horizontal well and the cross section shown in Figure 16B–D. (B) Depositional-dip-oriented cross section showing the internal facies architecture of the modeled parasequences with the location of barriers along the clinoforms shown as black lines. (C, D) The same depositional-dip-oriented cross section showing water saturation after 1000 days of production, for models with (C) 0% barrier coverage along clinoforms and (D) 90% barrier coverage along clinoforms.]]

===Simulation Results===

===Simulation Results===

The presence of barriers along 90% of the area of each clinoform surface significantly alters the movement of fluids in the reservoir by increasing the tortuosity of flow pathways. As a result, gas breakthrough is later when calcite-cemented barriers are present along clinoforms (30 vs. 15 days, Figure 17C), and oil production remains at plateau for longer (30 vs. 15 days, Figure 17A). However, after gas breakthrough, the rate of oil production rapidly falls below that for the case lacking calcite-cemented barriers (Figure 17A). Water cut is significantly lower for the model containing calcite-cemented barriers throughout production (Figure 17B). The calcite-cemented barriers along clinoforms prevent lateral movement of oil and the upward movement of water from the aquifer to the well (Figure 16D) but have a less significant effect on the downward movement of more mobile gas. As a result, the gas:oil ratio increases for production in the model containing calcite-cemented barriers along clinoforms. Most importantly, the recovery of oil could be overestimated by up to 14% if calcite cements associated with clinoforms were omitted from the reservoir model (Figure 17D; cf. Figure 16C, D); this is consistent with the results of Jackson et al. (2009), which showed that omitting clinoforms from wave-dominated shoreface systems could lead to overprediction of hydrocarbon recovery.

The presence of barriers along 90% of the area of each clinoform surface significantly alters the movement of fluids in the reservoir by increasing the tortuosity of flow pathways. As a result, gas breakthrough is later when calcite-cemented barriers are present along clinoforms (30 vs. 15 days, Figure 17C), and oil production remains at plateau for longer (30 vs. 15 days, Figure 17A). However, after gas breakthrough, the rate of oil production rapidly falls below that for the case lacking calcite-cemented barriers (Figure 17A). Water cut is significantly lower for the model containing calcite-cemented barriers throughout production (Figure 17B). The calcite-cemented barriers along clinoforms prevent lateral movement of oil and the upward movement of water from the aquifer to the well (Figure 16D) but have a less significant effect on the downward movement of more mobile gas. As a result, the gas:oil ratio increases for production in the model containing calcite-cemented barriers along clinoforms. Most importantly, the recovery of oil could be overestimated by up to 14% if calcite cements associated with clinoforms were omitted from the reservoir model (Figure 17D; cf. Figure 16C, D); this is consistent with the results of Jackson et al. (2009), which showed that omitting clinoforms from wave-dominated shoreface systems could lead to overprediction of hydrocarbon recovery.

Figure 17 (A) Oil, (B) water, (C) gas production rates, and (D) cumulative oil production as a function of time in the simulation model of the Sognefjord Formation in a fault-bounded sector of the Troll Field (Figure 12B) for production from a single horizontal well through gas cap expansion and aquifer influx (Figure 16). In the models with 90% barrier coverage along clinoforms, free gas breakthrough is delayed (Figure 17C) and liquid production is decreased (Figure 17A, B, D) relative to the models lacking barriers along clinoforms.

[[File:BLTN13190fig17.jpg|thumb|300px|{{figure number|17}}(A) Oil, (B) water, (C) gas production rates, and (D) cumulative oil production as a function of time in the simulation model of the Sognefjord Formation in a fault-bounded sector of the Troll Field (Figure 12B) for production from a single horizontal well through gas cap expansion and aquifer influx (Figure 16). In the models with 90% barrier coverage along clinoforms, free gas breakthrough is delayed (Figure 17C) and liquid production is decreased (Figure 17A, B, D) relative to the models lacking barriers along clinoforms.]]

==Discussion==

==Discussion==