Changes

The paper is structured in four parts. First, we present a simple conceptual framework to describe clinoform geometries and distributions, which allows them to be incorporated into reservoir volumes deposited in different shallow-marine environments. The framework is used to develop an algorithm-based method to represent clinoform surfaces, which is sufficiently flexible to match clinoform geometries and distributions observed in rich outcrop data sets and also to honor sparse subsurface data. The second part of the paper validates the clinoform-modeling algorithm via construction of a 3-D reservoir model of a single fluvial–deltaic parasequence using high-resolution outcrop data from fluvial-dominated delta-lobe deposits in the Cretaceous Ferron Sandstone Member of east-central Utah. The model is constructed using a framework of surfaces, including flooding surfaces between parasequences, surfaces that represent clinoforms, and surfaces that represent boundaries between facies associations. The third part of the paper demonstrates an application of the clinoform-modeling algorithm to generate a reservoir model using a sparse subsurface data set from the deltaic Jurassic Sognefjord Formation, in a fault-bounded sector of the Troll Field, offshore Norway. The clinoform-modeling algorithm allows flexibility in building a range of surface-based reservoir models that incorporate uncertainty in heterogeneities associated with clinoforms. The resulting 3-D surface-based reservoir models are suitable for flow simulation without upscaling. Finally, in the fourth part of the paper, we demonstrate that the algorithm produces models suitable for flow simulation using the Ferron Sandstone Member outcrop analog and subsurface Sognefjord Formation examples. This latter step is missing in many papers that report new reservoir modeling algorithms. The simulation models are used to assess the potential impact of flow barriers associated with clinoforms on drainage patterns and hydrocarbon recovery.

The paper is structured in four parts. First, we present a simple conceptual framework to describe clinoform geometries and distributions, which allows them to be incorporated into reservoir volumes deposited in different shallow-marine environments. The framework is used to develop an algorithm-based method to represent clinoform surfaces, which is sufficiently flexible to match clinoform geometries and distributions observed in rich outcrop data sets and also to honor sparse subsurface data. The second part of the paper validates the clinoform-modeling algorithm via construction of a 3-D reservoir model of a single fluvial–deltaic parasequence using high-resolution outcrop data from fluvial-dominated delta-lobe deposits in the Cretaceous Ferron Sandstone Member of east-central Utah. The model is constructed using a framework of surfaces, including flooding surfaces between parasequences, surfaces that represent clinoforms, and surfaces that represent boundaries between facies associations. The third part of the paper demonstrates an application of the clinoform-modeling algorithm to generate a reservoir model using a sparse subsurface data set from the deltaic Jurassic Sognefjord Formation, in a fault-bounded sector of the Troll Field, offshore Norway. The clinoform-modeling algorithm allows flexibility in building a range of surface-based reservoir models that incorporate uncertainty in heterogeneities associated with clinoforms. The resulting 3-D surface-based reservoir models are suitable for flow simulation without upscaling. Finally, in the fourth part of the paper, we demonstrate that the algorithm produces models suitable for flow simulation using the Ferron Sandstone Member outcrop analog and subsurface Sognefjord Formation examples. This latter step is missing in many papers that report new reservoir modeling algorithms. The simulation models are used to assess the potential impact of flow barriers associated with clinoforms on drainage patterns and hydrocarbon recovery.

The impact of clinoforms on flow and hydrocarbon recovery in the context of other uncertain reservoir geologic parameters and reservoir engineering decisions remains poorly understood. In a companion article, Graham et al. (2015, this volume) apply the clinoform-modeling algorithm to build a reservoir-scale model of the Ferron Sandstone Member that incorporates multiple, stacked parasequences and provides a case study for fluvial-dominated deltaic reservoirs. The impact of clinoforms on fluid flow in the context of other uncertain reservoir geologic parameters, such as the presence of distributary and fluvial channels, the magnitude of permeability contrasts between facies associations, and the impact of bed-scale heterogeneity on vertical permeability, as well as reservoir engineering decisions including production rates are investigated.

The impact of clinoforms on flow and hydrocarbon recovery in the context of other uncertain reservoir geologic parameters and reservoir engineering decisions remains poorly understood. In a companion article, Graham et al.<ref name=Grhm2015>Graham, G. H., M. D. Jackson, and G. J. Hampson, 2015, [http://archives.datapages.com/data/bulletns/2015/06jun/BLTN13191/BLTN13191.html Three-dimensional modeling of clinoforms within shallow-marine reservoirs: Part 2. Impact on fluid flow and hydrocarbon recovery in fluvial-deltaic reservoirs]: AAPG Bulletin, v. 99, p. 1049–1080, doi: 10.1306/01191513191.</ref> apply the clinoform-modeling algorithm to build a reservoir-scale model of the Ferron Sandstone Member that incorporates multiple, stacked parasequences and provides a case study for fluvial-dominated deltaic reservoirs. The impact of clinoforms on fluid flow in the context of other uncertain reservoir geologic parameters, such as the presence of distributary and fluvial channels, the magnitude of permeability contrasts between facies associations, and the impact of bed-scale heterogeneity on vertical permeability, as well as reservoir engineering decisions including production rates are investigated.

==Conceptual framework for clinoform modeling==

==Conceptual framework for clinoform modeling==

The top and base flooding surfaces of parasequence 1.6 were extracted from the model of Deveugle et al.<ref name=Dvgl2011 /> and served as the bounding surfaces used in the clinoform algorithm ([[:File:BLTN13190fig2.jpg|Figure 2]]). The surfaces were cropped to cover a model area of 750 × 3000 m (2461 × 9843 ft) in the Ivie Creek amphitheater ([[:File:BLTN13190fig5.jpg|Figure 5D]]). Additional surfaces representing the boundaries between facies associations from the model of Deveugle et al.<ref name=Dvgl2011 /> were also extracted and similarly cropped; these define the distribution of facies associations present in each rock volume bounded by two clinoforms (i.e., clinothem) (cf. table 1 in Deveugle et al.<ref name=Dvgl2011 />). From distal to proximal, the modeled facies associations are prodelta mudstone (PD), distal delta-front heteroliths (dDF), proximal delta-front sandstones (pDF), and stream-mouth-bar sandstones (SMB) ([[:File:BLTN13190fig5.jpg|Figure 5D]]). Where facies associations pinch out, the facies association boundary surfaces were adjusted to coincide throughout the remainder of the model volume with either the top or base parasequence bounding surface. This ensures that the surface is defined across the entire model volume and is suitable for gridding.<ref name=Jckson2009 /> There are no faults within the model volume of 750 × 3000 × 6 m (2461 × 9843 × 20 ft). In a final step, isochore maps were generated between the top and base flooding surfaces and between facies association boundary surfaces and the base flooding surface. The base bounding surface was flattened, to mimic clinoform progradation over a flat, horizontal sea floor, and isochore maps were used to modify the geometries of the top bounding surface and facies association boundary surfaces above this horizontal base surface. As a result of flattening on the base bounding surface, the bounding surfaces from the existing model of Deveugle et al.<ref name=Dvgl2011 /> have been modified.

The top and base flooding surfaces of parasequence 1.6 were extracted from the model of Deveugle et al.<ref name=Dvgl2011 /> and served as the bounding surfaces used in the clinoform algorithm ([[:File:BLTN13190fig2.jpg|Figure 2]]). The surfaces were cropped to cover a model area of 750 × 3000 m (2461 × 9843 ft) in the Ivie Creek amphitheater ([[:File:BLTN13190fig5.jpg|Figure 5D]]). Additional surfaces representing the boundaries between facies associations from the model of Deveugle et al.<ref name=Dvgl2011 /> were also extracted and similarly cropped; these define the distribution of facies associations present in each rock volume bounded by two clinoforms (i.e., clinothem) (cf. table 1 in Deveugle et al.<ref name=Dvgl2011 />). From distal to proximal, the modeled facies associations are prodelta mudstone (PD), distal delta-front heteroliths (dDF), proximal delta-front sandstones (pDF), and stream-mouth-bar sandstones (SMB) ([[:File:BLTN13190fig5.jpg|Figure 5D]]). Where facies associations pinch out, the facies association boundary surfaces were adjusted to coincide throughout the remainder of the model volume with either the top or base parasequence bounding surface. This ensures that the surface is defined across the entire model volume and is suitable for gridding.<ref name=Jckson2009 /> There are no faults within the model volume of 750 × 3000 × 6 m (2461 × 9843 × 20 ft). In a final step, isochore maps were generated between the top and base flooding surfaces and between facies association boundary surfaces and the base flooding surface. The base bounding surface was flattened, to mimic clinoform progradation over a flat, horizontal sea floor, and isochore maps were used to modify the geometries of the top bounding surface and facies association boundary surfaces above this horizontal base surface. As a result of flattening on the base bounding surface, the bounding surfaces from the existing model of Deveugle et al.<ref name=Dvgl2011 /> have been modified.

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,<ref name=Dvgl2011 /> 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) ([[:File:BLTN13190fig3.jpg|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 (''t_D'', ''t_s'') 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, ''L'', and spacing, ''S'', of clinoforms in depositional dip cross section were extracted from the bedding-diagram interpretations of Forster et al. <ref name=Frstr2004 /> ([[:File:BLTN13190fig6.jpg|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.<ref name=EH2010 /> 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 ([[:File:BLTN13190fig6.jpg|Figure 6B, C]]). The clinoform-modeling algorithm was used to build 31 clinoforms in the modeled volume of parasequence 1.6 ([[:File:BLTN13190fig7.jpg|Figure 7]]). For simplicity, clinoform spacing is fixed at 25 m (82 ft), which is the average value observed at outcrop ([[:File:BLTN13190fig6.jpg|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.<ref name=Jckson2009 /><ref name=Dvgl2011 /> Graham et al., 2015, this volume). A constant value of 2 was assigned to the clinoform shape-function exponent, ''P'' ([[:File:BLTN13190fig2.jpg|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, ''P_o'' ([[:File:BLTN13190fig4.jpg|Figure 4C]]), was qualitatively matched with a plan-view map of facies association belts at the top of parasequence 1.6 ([[:File:BLTN13190fig5.jpg|Figure 5D]]). The overall progradation direction for the clinoforms (''θ'') was assigned an azimuth of 274° relative to north, which corresponds to the interpreted progradation direction of the delta lobe in parasequence 1.6.<ref name=Dvgl2011 /> In a subsequent step, the facies association boundary surfaces extracted from the model of Deveugle et al.<ref name=Dvgl2011 /> 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 ([[:File:BLTN13190fig8.jpg|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,<ref name=Dvgl2011 /> 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) ([[:File:BLTN13190fig3.jpg|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 (''t_D'', ''t_s'') 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, ''L'', and spacing, ''S'', of clinoforms in depositional dip cross section were extracted from the bedding-diagram interpretations of Forster et al. <ref name=Frstr2004 /> ([[:File:BLTN13190fig6.jpg|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.<ref name=EH2010 /> 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 ([[:File:BLTN13190fig6.jpg|Figure 6B, C]]). The clinoform-modeling algorithm was used to build 31 clinoforms in the modeled volume of parasequence 1.6 ([[:File:BLTN13190fig7.jpg|Figure 7]]). For simplicity, clinoform spacing is fixed at 25 m (82 ft), which is the average value observed at outcrop ([[:File:BLTN13190fig6.jpg|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.<ref name=Jckson2009 /><ref name=Dvgl2011 /><ref name=Grhm2015 /> A constant value of 2 was assigned to the clinoform shape-function exponent, ''P'' ([[:File:BLTN13190fig2.jpg|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, ''P_o'' ([[:File:BLTN13190fig4.jpg|Figure 4C]]), was qualitatively matched with a plan-view map of facies association belts at the top of parasequence 1.6 ([[:File:BLTN13190fig5.jpg|Figure 5D]]). The overall progradation direction for the clinoforms (''θ'') was assigned an azimuth of 274° relative to north, which corresponds to the interpreted progradation direction of the delta lobe in parasequence 1.6.<ref name=Dvgl2011 /> In a subsequent step, the facies association boundary surfaces extracted from the model of Deveugle et al.<ref name=Dvgl2011 /> 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 ([[:File:BLTN13190fig8.jpg|Figure 8]]).

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; <ref name=Sch09 />). 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.;<ref name=Sch09 /> it ensures that the grid layering conforms to the architecture of the clinoform surfaces, preserving their dip and geometry, and captures facies association boundaries ([[:File:BLTN13190fig9.jpg|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; <ref name=Sch09 />). 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.;<ref name=Sch09 /> it ensures that the grid layering conforms to the architecture of the clinoform surfaces, preserving their dip and geometry, and captures facies association boundaries ([[:File:BLTN13190fig9.jpg|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.

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.<ref name=Dvgl2011 />). 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.<ref name=Dvgl2011 />). 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.<ref name=Grhm2015 />

===Geologic Model Results===

===Geologic Model Results===

===Production Strategy===

===Production Strategy===

Waterflooding was simulated using conventional black oil simulation software, using a line drive of four vertical injector wells and six vertical producer wells ([[:File:BLTN13190fig10.jpg|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 175 S m³/day (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; [[:File:BLTN13190fig10.jpg|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 ([[:File:BLTN13190fig10.jpg|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 175 S m³/day (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; [[:File:BLTN13190fig10.jpg|Figure 10B]]). All other parameters remain fixed between the simulations. In a companion article, Graham et al.<ref name=Grhm2015 /> 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.

===Simulation Results===

===Simulation Results===

Several previous studies of the Ferron Sandstone Member have incorporated clinoforms in flow simulation models using a combination of object-based methods to place barriers along clinoforms<ref name=Hwll2008b /> or deterministic methods to map clinoforms.<ref name=Hwll2008a /><ref name=EH2010 /> Although these studies have demonstrated that, under certain displacement conditions, it is important to include clinoforms in models of shallow-marine reservoirs, it is not clear how these models could be applied in the subsurface or at the full-field scale. Other studies have indicated that surfaces should be used to incorporate clinoforms into reservoir models, as surfaces are much less computationally expensive to generate and manipulate than large 3-D geocellular grids.<ref name=Jckson2009 /><ref name=Sch09 /><ref name=EH2010 /> Jackson et al., 2014). These deterministic approaches are appropriate for modeling clinoforms that are tightly constrained by outcrop data but do not allow flexibility in conditioning clinoform geometry and distribution to sparser data sets with a large degree of uncertainty.

Several previous studies of the Ferron Sandstone Member have incorporated clinoforms in flow simulation models using a combination of object-based methods to place barriers along clinoforms<ref name=Hwll2008b /> or deterministic methods to map clinoforms.<ref name=Hwll2008a /><ref name=EH2010 /> Although these studies have demonstrated that, under certain displacement conditions, it is important to include clinoforms in models of shallow-marine reservoirs, it is not clear how these models could be applied in the subsurface or at the full-field scale. Other studies have indicated that surfaces should be used to incorporate clinoforms into reservoir models, as surfaces are much less computationally expensive to generate and manipulate than large 3-D geocellular grids.<ref name=Jckson2009 /><ref name=Sch09 /><ref name=EH2010 /> Jackson et al., 2014). These deterministic approaches are appropriate for modeling clinoforms that are tightly constrained by outcrop data but do not allow flexibility in conditioning clinoform geometry and distribution to sparser data sets with a large degree of uncertainty.

Our results support previous work in demonstrating that it is important to include clinoforms in models of shallow-marine reservoirs to accurately predict fluid-flow patterns and hydrocarbon recovery. However, the work presented here differs from previous modeling investigations in providing a generic method of incorporating clinoforms with geologically realistic geometries and spacing into models of shallow-marine reservoirs. The algorithm can be also be applied at a variety of lengthscales, as demonstrated in Graham et al. (2015, this volume), in which a reservoir scale model that comprises multiple stacked parasequences is used to investigate the impact of clinoforms under geologic uncertainty and reservoir engineering decisions.

Our results support previous work in demonstrating that it is important to include clinoforms in models of shallow-marine reservoirs to accurately predict fluid-flow patterns and hydrocarbon recovery. However, the work presented here differs from previous modeling investigations in providing a generic method of incorporating clinoforms with geologically realistic geometries and spacing into models of shallow-marine reservoirs. The algorithm can be also be applied at a variety of lengthscales, as demonstrated in Graham et al.,<ref name=Grhm2015 /> in which a reservoir scale model that comprises multiple stacked parasequences is used to investigate the impact of clinoforms under geologic uncertainty and reservoir engineering decisions.

We recognize that the algorithm does not explicitly incorporate every clinoform that may be present, but instead provides a mechanism to include clinoforms at a level of stratigraphic detail defined by the user, based on a combination of stratigraphic understanding, available data, and computing resources. Nor does the algorithm represent the detailed geometry of clinoforms, as may be possible using process-based forward numerical models (e.g., Edmonds and Slingerland;<ref name=ES2010 /> Geleynse et al.<ref name=Glnyse />). However, there are uncertainties in the values of input parameters to use in process-based models, and these parameters cannot be easily extracted from outcrop analog or subsurface data sets. There are also no explicit relationships between the input parameters for process-based models and the parameters that describe the geometries of clinoforms produced, such as clinoform length, spacing, or width. Process-based models are also difficult to condition to available data and require large computational times, which make them less feasible for modeling a range of realizations. The clinoform-modeling algorithm presented here provides a flexible and efficient method for incorporating multiple clinoforms with realistic geometries into models of shallow-marine reservoirs.

We recognize that the algorithm does not explicitly incorporate every clinoform that may be present, but instead provides a mechanism to include clinoforms at a level of stratigraphic detail defined by the user, based on a combination of stratigraphic understanding, available data, and computing resources. Nor does the algorithm represent the detailed geometry of clinoforms, as may be possible using process-based forward numerical models (e.g., Edmonds and Slingerland;<ref name=ES2010 /> Geleynse et al.<ref name=Glnyse />). However, there are uncertainties in the values of input parameters to use in process-based models, and these parameters cannot be easily extracted from outcrop analog or subsurface data sets. There are also no explicit relationships between the input parameters for process-based models and the parameters that describe the geometries of clinoforms produced, such as clinoform length, spacing, or width. Process-based models are also difficult to condition to available data and require large computational times, which make them less feasible for modeling a range of realizations. The clinoform-modeling algorithm presented here provides a flexible and efficient method for incorporating multiple clinoforms with realistic geometries into models of shallow-marine reservoirs.

# Google Earth and DigitalGlobe, 2013, accessed January 14, 2013, http://www.google.co.uk/intl/en_uk/earth/index.html.

# Google Earth and DigitalGlobe, 2013, accessed January 14, 2013, http://www.google.co.uk/intl/en_uk/earth/index.html.

# Google Earth and TerraMetrics, 2013, accessed January 14, 2013, http://www.google.co.uk/intl/en_uk/earth/index.html.

# Google Earth and TerraMetrics, 2013, accessed January 14, 2013, http://www.google.co.uk/intl/en_uk/earth/index.html.

# Graham, G. H., M. D. Jackson, and G. J. Hampson, 2015, Three-dimensional modeling of clinoforms within shallow-marine reservoirs: Part 2. Impact on fluid flow and hydrocarbon recovery in fluvial-deltaic reservoirs: AAPG Bulletin, v. 99, p. 1049–1080, doi: 10.1306/01191513191.

#  

# Hampson, G. J., 2000, Discontinuity surfaces, clinoforms and facies architecture in a wave-dominated, shoreface-shelf parasequence: Journal of Sedimentary Research, v. 70, no. 2, p. 325–340, doi: 10.1306/2DC40914-0E47-11D7-8643000102C1865D.

# Hampson, G. J., 2000, Discontinuity surfaces, clinoforms and facies architecture in a wave-dominated, shoreface-shelf parasequence: Journal of Sedimentary Research, v. 70, no. 2, p. 325–340, doi: 10.1306/2DC40914-0E47-11D7-8643000102C1865D.

# Hampson, G. J., J. E. Morris, and H. D. Johnson, 2014, Synthesis of time-stratigraphic relationships and their impact on hydrocarbon reservoir distribution and performance, Bridport Sand Formation, Wessex Basin, UK, inD. G. Smith, R. J. Bailey, P. M. Burgess, and A. J. Fraser, eds., Strata and time: Probing the gaps in our understanding: Geological Society, London, Special Publication 404, first published online on March 19, 2014, doi: 10.1144/SP404.2.

# Hampson, G. J., J. E. Morris, and H. D. Johnson, 2014, Synthesis of time-stratigraphic relationships and their impact on hydrocarbon reservoir distribution and performance, Bridport Sand Formation, Wessex Basin, UK, inD. G. Smith, R. J. Bailey, P. M. Burgess, and A. J. Fraser, eds., Strata and time: Probing the gaps in our understanding: Geological Society, London, Special Publication 404, first published online on March 19, 2014, doi: 10.1144/SP404.2.