Source: https://sp.lyellcollection.org/node/16325.full.print
Timestamp: 2019-04-26 16:11:33+00:00

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In this paper we review workflows used for visualizing and analysing properties that control cross-fault fluid flow; these range from traditional fault plane maps to more complex property distribution calculations and the back-analysis of flow simulation data to inform on fault sealing and baffling.
During the initial exploration stage rapid assessments of fault juxtapositions (Allan 1989; Knipe 1997) are useful in assessing the likelihood of developing a faulted hydrocarbon trap. Fault plane maps, also known as Allan maps (Fig. 1), can highlight the key juxtapositions present (Allan 1989; Marchal et al. 2003). Often the creation of these maps is a time consuming process, and they can be complex and hence difficult to interpret in all except the simplest structural–stratigraphic configurations (compare Fig. 1b, c). We present different visualization styles and show techniques that enable a more rapid generation and interrogation of the data thus enhance the understanding of this data.
(a) A 3D view of a simple fault-bound prospect with three internal zones. (b) The fault plane map along the main prospect-bounding fault (the solid lines show the upthrown markers and the dashed lines show the downthrown side). Note that at the edges of the data it is straightforward to track the juxtapositions of zones across the fault, but in the centre it becomes complicated even with this simple structural–stratigraphic scenario. (c) The fault plane map showing the detailed layer-cake stratigraphy for the model shown in (b). Determining juxtapositions from this data is challenging and likely to lead to inaccuracies. For numerous laterally discontinuous layers the task of determining juxtapositions becomes even more difficult.
An improved understanding of the subtleties in fault sealing has coincided with the increased complexity of stratigraphic layering in geological models and the increased computing capabilities available to model these scenarios. Previously, stratigraphic layers have been modelled as relatively thick, laterally continuous zones populated with relatively uniform properties (e.g. Fig. 1b). Recently, a larger number of layers, typically tens to hundreds of grid layers, have been used to attempt to model rapidly varying host rock properties within each of the laterally and potentially discontinuous layers (e.g. Fig. 1c). These improvements in modelling resolution (continuous fault property variations, laterally varying stratigraphy and high numbers of modelled layers) have lessened the applicability of simple Allan line maps for predicting the sealing and/or cross-fault fluid flow properties. More refined tools are now required for calculating, visualizing and incorporating fault rock flow properties into geological or reservoir models. It is important to note that although these stratigraphic models are being generated at an ever high resolution, a significant uncertainty in that stratigraphic population combined with model geometric and property uncertainties will lead to a compounded uncertainty in the resulting fault seal prediction.
In production situations faults have historically been modelled as either sealing or open to cross-fault fluid flow in reservoir simulations by using transmissibility multipliers (TMs) of 0 or 1, respectively (see Manzocchi et al. 1999). The transmissibility multiplier is the ratio of the faulted to unfaulted cross-fault transmissibilities between adjacent grid blocks across the fault (Knai & Knipe 1998). The direct measurement of fault rocks and inferred fault rock permeabilities from high-resolution well tests around faults indicate that faults are better modelled as membrane baffles rather than either fully open or fully retarding to cross-fault fluid flow (e.g. Fisher & Knipe 1998; Sperrevik et al. 2002; Jolley et al. 2007; Manzocchi et al. 2008). The incorporation of these baffles into flow simulation models is usually achieved via the use of TMs (e.g. Manzocchi et al. 1999, 2002; Al-Busafi et al. 2005; Jolley et al. 2007; Zijlstra et al. 2007). These TM values are applied as modifiers to the transmissibility connection data in the reservoir simulation grid such that the permeability of the fault rock and its thickness are taken into account when computing fluid flow across the faults, and they are central to the inclusion of fault rock properties into flow simulators (e.g. Knai & Knipe 1998; Manzocchi et al. 1999, 2002). As such, the TM is the fault property most often visualized within reservoir modelling packages, but, as will be shown later, the visualization of this parameter is generally of little use for understanding and interpreting the behaviour of faults within the simulator. The TM is an abstracted mathematical tool used to conveniently modify the behaviour of the simulator; it is not a physical property, but is defined in terms of physical variables in a form that has no predictive benefits. For example, a low TM value will generally be found at locations of thicker fault rock (large displacement) and a fault permeability that is lower than the permeability of the adjacent grid cells. A high TM value will generally correspond to a thinner fault rock and a fault permeability that is comparable to the permeability of the adjacent grid cells. The dependency of the TM value on the host grid cell size, which will vary significantly through the model, further complicates these relationships. Both the high and low TM values can thus correspond to areas of high or low cross-fault fluid flow. Therefore, it is clearly more useful to visualize and interpret true physical properties that inform on fluid flow.
Several steps are typically required to predict fault transmissibility variations that occur along fault zones from the host stratigraphic model (the reservoir model must contain spatial predictions of the host clay, permeability and porosity distributions) and the fault geometry. Under the assumption that the correct geometric model has been produced, one such workflow would be: (a) prediction of the host clay distribution through the stratigraphy against the fault (from well logs, e.g. gamma ray); (b) prediction of the clay distribution in the fault zone from the combined influences of the fault displacement and the lithological stacking sequence (e.g. using shale gouge ratio (SGR) and/or clay smears); (c) determination of the relationship between fault rock clay and fault rock permeability; (d) interpolation of the fault rock permeability across the fault; (e) prediction of the fault rock thickness (typically from the fault displacement); and (f) computation of the fault transmissibility multiplier (see Manzocchi et al. 1999). Even for this example single workflow there is currently no consensus on how to determine many of the individual relationships (e.g. Manzocchi et al. 1999, 2008; Sperrevik et al. 2002; Jolley et al. 2007). The majority of published datasets suggest a significant scatter in the nature of the critical parameters (Hull 1988; Childs et al. 1997; Knipe et al. 1997, 1998; Fisher & Knipe 2002; Sperrevik et al. 2002; Jolley et al. 2007; Freeman et al. 2008; Manzocchi et al. 2008). The data also indicate that a range of other factors can influence those relationships; these include burial history (e.g. Fisher & Knipe 2002; Sperrevik et al. 2002; Freeman et al. 2008) and the timing of hydrocarbon migration. With this array of valid but potentially different relationships, it is important to be able to differentiate the most appropriate ones to apply in a given situation, or alternatively to assess what effect selecting different relationships will have on the predicted flow behaviour. Developing a set of visualization tools to rapidly evaluate the effects of the different relationships chosen should lead to more robust interpretations (and simulation results). The relative applicability of the different fault property prediction techniques (e.g. the fault clay prediction, the fault clay to permeability relationship and the prediction of the fault rock thickness variation) is dependent on the reservoir stratigraphy and deformation history (as described in the above publications for each of these techniques). The effective screening of these datasets through visualization and analysis should help to target critical scenarios for simulation and thus more efficiently define the likely range in cross-fault fluid flow. In this contribution we present a series of different techniques that aid in the computation, visualization and interpretation of fault property and cross-fault fluid flow data.
In this section we present a series of visualization tools that begin with relatively simple juxtaposition problems; these tools are more appropriate to primary prospect analysis and/or initial risking situations. More complex compound property visualization tools are then considered, before a final interrogation of flow simulation data within and around fault zones is used to provide a series of new and enhanced visualizations of cross-fault fluid flow. As the degree of complexity increases through the various stages, so does the amount of data required to be able to perform those visualization tasks. Initially, only a seismic marker and a simple concept of the stratigraphy is required. For the later cases an understanding of certain specific fault rock properties is needed, and ultimately an understanding of the fluid types, pressure variations and production strategies is required. The development of these new techniques has been driven by a need to better understand the role and impact of faults within exploration prospects and production simulation models.
Often, during the initial stages of exploration, only an interpretation of the top reservoir seismic marker exists. The stratigraphy at the target structure or fault is often only poorly constrained by distant wells. At this early stage a rapid means of screening faulted traps is critical. Given the large uncertainty in the stratigraphic architecture, the required technique must allow for rapid evaluations of the impact of varying stratigraphies. Historically, the construction of a fault plane diagram from such datasets was time consuming and only a limited number of visualizations of the different potential scenarios were generated. Figure 2 shows an example of an alternative ‘quick-look’ fault juxtaposition mapping method. The 3D geometry of the top reservoir surface in Figure 2a is used to develop the associated fault juxtaposition map in Figure 2b. In order to generate the fault map at this early stage of interpretation, several assumptions must be made. The primary assumption is that the interpreter can define a polyline on the seismic marker surface that provides an indication of the fault–horizon intersection lines. However, these interpreter polylines do not often coincide with the ‘real’ fault-horizon intersection lines, since seismic data deteriorates in proximity to the fault. Thus interpreter polylines are a proxy for hanging wall and footwall cut-off lines, which are then combined with a user-defined stratigraphy to generate the fault plane map.
(a) Top reservoir seismic marker and (b) the corresponding ‘quick-look’ juxtaposition map (green zone/dotted lines for downthrown side; brown zone/solid lines for upthrown side). Diagrams (c), (d) and (e) show the low, mid and high cases, respectively, for sand thicknesses for the target reservoir modified ‘on-the-fly’ within the software. Note that the ability to dynamically modify the stratigraphy in (c)–(e) allows the range in geological scenarios to be rapidly evaluated. Note also that in each diagram (b)–(e) the layers have been shown as semi-transparent, so that sand on sand windows can be easily identified.
This tool can utilize multiple seismic markers and incorporate independent layer thickness variations in the hanging wall and footwall stratigraphies. The throw can also be manipulated to account for a degree of seismic, structural and stratigraphic uncertainty. This tool therefore facilitates a rapid assessment of the distribution of different stratigraphic juxtaposition styles along the fault of interest (cf. Fig. 2c–e). This helps in early structural screening to determine the importance of faults within the prospect. However, if the fault juxtapositions are deemed to be critical then the analysis should become more sophisticated, using the greater modelling accuracy available within a 3D model. An alternative approach is to use stochastic stratigraphic population and fault offsets (see James et al. 2004).
Traditionally, juxtapositions are visualized in commercial 3D geological modelling packages by projecting multiple seismic markers onto modelled fault surfaces to define horizon–fault intersection lines (i.e. fault-cut line or juxtaposition maps; e.g. Walsh & Watterson 1987; Allan 1989; Childs et al. 1997). When only a few stratigraphic layers are present and those layers are formed from relatively uniform properties, this is a powerful technique for identifying overlapping reservoir areas or non-overlapping areas on the fault surface (see Figs 1b, 2b & 3a). These types of projection become increasingly complex to interpret and more unreliable where there are complex displacement variations and where an increasing number of layers and/or property variations occur. This effect is compounded where uncertainties surround either throw or property distributions. In order to produce a more interpretable fault plane map the areas of the fault face can be filled with unique colours that relate to the specific juxtaposition type developed. An example is shown in Figure 3b below the unfilled version (Fig. 3a), and in this colour scheme ‘hotter’ colours have generally been applied to areas of higher fault throw (i.e. Zone 1 juxtaposed against Ness 1). This style of simple juxtaposition map can be extended for use in more complex geological situations. The isolation of specific zones (e.g. Fig. 3c) across the fault further enhances the ability to interrogate the data, and applying such filters can rapidly delineate critical juxtapositions and limit the potential for mis-identification.
(a) The horizon–fault intersection lines along a fault (solid lines for upthrown block; dotted lines for downthrown block; two other crossing faults are also partially shown). (b) A colour-filled 3D fault juxtaposition map showing the different reservoir zone overlaps. Note the increased ease of understanding for the colour-filled image (b) v. the traditional line drawing (a). This allows the key areas to be identified more rapidly and with greater confidence. (c) A filtered fault map showing the juxtaposition of the Ness 2 in the footwall against the Tarbert 3 in the hanging wall.
Although this approach can allow gross unit juxtapositions to be interpreted rapidly, there are a number of limitations. The main one is the difficulty of back-relating the juxtaposition areas to the specific stratigraphy that is juxtaposed. A specific zone may be juxtaposed against another specific zone, but unless the stratigraphy within those zones is of a particular type (e.g. high-permeability sands) the knowledge of zonal juxtapositions may be of little relevance. The generalized fault plane windows may contain a wide range of juxtaposition types (e.g. sands against shales, or sands against sands), so that a large number of these very generalized zonal area juxtapositions are not important (particularly for highly heterogeneous stratigraphy). In such circumstances it is only necessary to visualize a few key stratigraphic juxtapositions, and one approach to address this problem is to filter the fault plane map by stratigraphic type. The resulting fault plane map will then be far more useful (see Fig. 4). Figure 4a, b show examples of the stratigraphy against the fault for the upthrown and downthrown blocks, respectively (yellow is sand, green is impure sand and brown is shale). The purple areas on the fault face in Figure 4c represent the key sand on sand juxtapositions along the fault. The ability to view specific property overlaps is a powerful additional tool for isolating critical connections. Figure 4d shows the same sand on sand windows as in Figure 4c, but this time by isolating the cells in the upthrown and downthrown side of the fault that have led to those juxtapositions.
Stratigraphy of (a) the upthrown block and (b) the downthrown block against the fault (sand in yellow; impure sand in green; shale in brown) (this and the other fault views are shown with the horizon–fault intersection lines). (c) The sand on sand windows shown on the fault face. (d) A 3D view of the sand on sand windows shown in the cells adjacent to the fault (also shown is one of the horizons in the grid for perspective purposes). Displaying the cells adjacent to the fault provides both a clearer understanding of those units that led to the development of the fault window and a useful quality check that geologically valid architectures have generated the windows present.
Both of the plot styles in Figure 4c, d are useful, but the visualization of the juxtaposition sand on sand windows via the cells in the grid adjacent to the faults enables those windows to be more easily related back to the stratigraphic layers that created that juxtaposition. In addition, the view in Figure 4d allows the form of the stratigraphy in the immediate area surrounding the juxtaposition window to also be evaluated for its geological validity. In such a view, the juxtaposition windows developed by surfaces that have been poorly interpolated into faults become apparent, and such inaccuracies can then be corrected and are therefore less likely to be carried forward into either the final geological model, subsequent fault seal assessment or the simulation model.
When dealing with stratigraphies that contain significant internal facies variations (e.g. fluvio–deltaic systems, turbidites or mass transport complexes), the applicability of fault-cut line maps is limited. The approach described above of defining specific single property juxtapositions is useful and can be readily extended to allow the simultaneous filtering of numerous parameters, potentially providing greater insight into cross-fault connectivity. Figure 5 shows the juxtaposition of only a specific facies in one zone against a specific facies in another zone across the fault. In general, the window geometry will be defined by certain sets of parameter criteria and the colour fill of the windows then displays a different property. In this case, the property mapped onto these windows is the harmonic average host permeability of the rocks juxtaposed across the fault, an example of one parameter that controls the fluid flow across the fault zone. This type of multiple parameter property visualization and filtering can be extremely useful for determining the critical parameters that influence cross-fault fluid flow.
Fault plane property map showing the result of multiple filtered geometry criteria, and displaying a separate property on the resulting fault windows. The fault plane map shows the juxtaposition of the silt and sand facies of the Ness against the silt and sand facies of the Tarbert. The property shown on the fault windows is the harmonic average of the juxtaposed host permeability.
By combining the visualization of stratigraphic juxtapositions via stratigraphic fault-cut lines, fault connection areas and grid cells adjacent to the fault, we are now able to achieve a much clearer definition of the critical windows and the relationship of those windows to the general stratigraphic architecture, and also to understand the validity of the windows. This provides a robust and efficient interpretation process, in which fault plane maps or 3D juxtaposition zones are used to understand the likely cross-fault flow behaviour of a prospect or field. For these techniques to add the maximum value they need to be rapid to apply, and easy to understand and interpret.
It is becoming more common to both compute and display a variety of different predicted fault rock properties – principally in order to estimate the flow properties of faults for flow simulation. Most geological modelling packages now permit the computation of the shale gouge ratio (SGR; Yielding et al. 1997), and this is used either as a direct proxy for sealing (e.g. Yielding 2002) or more commonly as a step toward defining the likely cross-fault permeability, and ultimately the fault TMs for input to flow simulation (e.g. Manzocchi et al. 1999, 2008; Sperrevik et al. 2002; Jolley et al. 2007; Freeman et al. 2008). Fault permeabilities and TMs along the fault surface can then be viewed as values for each cross-fault grid connection (e.g. Dee et al. 2005). Figure 6 shows an example of the clay content distribution predicted using the SGR algorithm and the corresponding TM data calculated for the cross-fault grid connections, displayed on the fault surfaces.
(a) The fault clay distribution map defined by the shale gouge ratio (SGR) and (b) the fault transmissibility multiplier (TM) data, shown on the fault faces. Also shown are the horizon–fault intersection lines. This data forms some of the primary input (fault clay content) and output (fault transmissibility multiplier) from fault seal analysis.
At present, most ‘general’ geological reservoir modelling packages only allow the two fault property parameters of SGR and the related TM to be computed and imaged (although some specialist packages allow more complex algorithm choices, and the export of other fault seal parameters to flow simulation models. There are two major flaws in this approach. Firstly, the implementation of only a small number of specific algorithms is likely to fail to capture the probable range in fault rock properties. The literature documents a number of other widely accepted techniques (e.g. Lindsay et al. 1993; Lehner & Pilaar 1997) and the need to select data and algorithms that are suited to the specific geology of a specific reservoir and the fluid phases that it contains (e.g. Fisher & Jolley 2007). Secondly, and more importantly for visualization and interpretation purposes, the SGR and TM data in isolation are not directly related to the likely cross-fault fluid flow. Both limitations will be addressed in more detail in the following section and potential solutions to these problems will be suggested.
The SGR algorithm is a linear mixing model that estimates the fault zone clay distribution that arises as a result of the mixing of clays from the host stratigraphy within the fault zone (Yielding et al. 1997; Yielding 2002). The originally defined prediction of the SGR value at any point on a fault is by a uniform (weighted by unit thickness) average of the clay contents in the stratigraphies/lithologies that have moved past that point. At any point P on the fault where the displacement is dP, the SGR can therefore be defined as where the summation is over all units i with thicknesses Δzi and clay content Vi that have slipped past that point P (note that here the fault displacement and the thicknesses of the units are measured in a direction down-dip along the fault surface). Two major shortcomings are present when applying the SGR algorithm. The first issue is the assumption that perfect mixing occurs both across and vertically within the fault zone to produce the fault rock. This assumption is not consistent with outcrop observations (e.g. Shipton et al. 2002; Fredman et al. 2007).
The effective shale gouge ratio algorithm (ESGR) applies a further weighting function to the (thickness-weighted) averaging and assumes that a greater contribution of the clays in the fault rock is derived from the units closer to the point of interest (see Fig. 7a for a comparison of the SGR and ESGR clay mixing algorithms). Extending the definition of the SGR above, at any point P on the fault we define the ESGR as where wi is the weighting factor applied to each unit i and the denominator becomes the displacement at P if each weight is set to 1. The weighting factor wi at a particular location through the slipped interval is defined from a weighting function that depends on the relative distance through that slipped interval from P to the most distal unit. The general form of the weighting function must be specified and the sensitivity of its precise form to the fault seal prediction must be investigated, but the function should generally provide an increased weighting for the most proximal units. For example, the weighting function may have a Gaussian form with maximum value at the point of interest P and that approaches the tail at the most distal point. ESGR overcomes the assumption of perfect vertical and across-fault mixing in the fault zone. A comparison of the difference in clay content prediction between the SGR and ESGR algorithms is shown in Figure 8a, b, respectively, and the stratigraphic and structure model used to generate these plots is that shown in Figure 4. The difference in potential clay content prediction is also shown in Figure 8c. A c. 10% difference in the predicted fault clay content typically implies a predicted fault permeability variation of approximately an order of magnitude (see Jolley et al. 2007). Note that two similar clay prediction algorithms can, in certain stratigraphic and structural situations, imply a considerably different fault clay distribution and hence fault permeability or sealing capacity.
(a) Conceptual diagrams of the SGR and ESGR clay mixing models. The SGR algorithm estimates the fault zone clay distribution by assuming a uniform mixing of clays from the host stratigraphy into the fault zone. The ESGR algorithm utilizes a weighting function in the clay averaging and assumes that a greater contribution of the clays in the fault zone is derived from the units closer to the point of interest. (b) Conceptual diagrams of the various clay smearing models, namely the shale smear factor (SSF), the clay smear potential (CSP) and the probabilistic shale smear factor (PSSF).
Fault clay mixing predictions using (a) the SGR algorithm and (b) the averaged ESGR algorithm, and (c) a comparison between the fault clay prediction derived from the SGR and ESGR algorithms (SGR minus averaged ESGR). Note that the general observed relationships are that higher fault clay contents relate to lower fault permeabilities and higher static sealing capacities.
The second issue with the SGR algorithm is that it is often computed from the clay content from the hanging wall stratigraphy alone (according to the original SGR definition). Thus, in situations with growth faulting and syn-tectonic sedimentation, the calculation of SGR in this way will not provide a true indication of the average clay content of the host stratigraphy that has moved past that point on the fault because the algorithm's fundamental assumptions are negated. For the implementation of SGR or ESGR in syn-tectonic sedimentation systems the clay mixing algorithm must be computed independently for both the hanging wall and footwall. To avoid the assumption of perfect lateral mixing through the fault zone these results can then either be retained separately or combined via different functions (i.e. averaging or minimum/maximum values).
The differences between the fault clay prediction algorithms are significant in the example shown in Figure 8. This is due to variations in the host clay content in the stratigraphy. The differences between the fault clay predictions tend to be focused close to stratigraphic interfaces, so this has particular impact where the reservoir units are relatively thin in comparison to any surrounding or inter-collated shales. The range in fault clay prediction between the different algorithms along the reservoir sands is up to c. 20%. Using published fault clay to fault permeability relationships (e.g. Jolley et al. 2007), this increase or decrease in fault clay content equates to a permeability change of in excess of one order of magnitude within the fault rock. For a fault rock of 30 cm thickness (equivalent to a fault displacement of 30 m with a 1:100 fault thickness to displacement ratio) and a host permeability of 1000 mD, a fault permeability reduction from 0.01 to 0.001 mD is equivalent to a transmissibility reduction of around an order of magnitude across a 1000 m section of reservoir (assuming layer-bound flow). The difference between the SGR and ESGR methods for estimating the fault clay content can therefore produce an order of magnitude decrease in the predicted or modelled cross-fault fluid flux, for a constant phase pressure differential. With such large potential implications, it is clearly important to implement a variety of techniques in order to quantify the likely range in cross-fault fluid flux and the resulting impact on the reservoir flow simulation and ultimately the hydrocarbon resource model.
Both the SGR and ESGR fault clay prediction algorithms are clay mixing models. A second set of processes are generally agreed to occur in fault zones. These are ductile smears in which shale (clay) beds and sands are sheared and smeared along fault planes (e.g. Aydin & Eyal 2002; Kristensen et al. 2008). Algorithms that describe the redistribution of clay within fault zones due to these processes include the shale smear factor (SSF; Lindsay et al. 1993), clay smear potential (CSP; Lehner & Pilaar 1997) and probabilistic shale smear factor (PSSF; Childs et al. 2007). These different clay smearing algorithms are compared in Figure 7b and defined as follows. The shale smear factor defines the critical offset Δz that can be achieved before the shale (thickness t) becomes discontinuous. The clay smear potential at a point is defined as follows: and represents the sum of the contributions for each clay unit i of thickness ti that has moved a distance di past the point of interest. The majority of clay smearing algorithms initiate the clay smear at the clay bed against the fault and shear that clay unit along the fault plane. In contrast, the PSSF algorithm stochastically populates smear patches along the fault surface between the footwall and hanging wall cut-offs. The central assumption is that the smears are continuous for SSF≤SSFc (SSFc=critical shale smear factor) and the smears are placed at a random location between the source shale layers for SSF>SSFc. The model is based on observations from Lindsay et al. (1993) of a critical shale smear factor related to the displacement to bed thickness ratio that the beds can endure prior to the smear becoming discontinuous. When SSFc is exceeded for a single clay smear, the probability that a gap occurs in that smear at some point between the source layer cut-offs is where d is the fault displacement and t is the apparent source layer thickness along the fault (Childs et al. 2007). The PSSF value at P then defines the probability of having no smear and is obtained from applying the previous equation for all clay layers that have moved past P. If SSF≤SSFc for any of these layers then PSSF=0.
Continuous shale beds (and sands) provide clay contents in the fault zone that are controlled by the original clay content that was present in the undeformed or unsheared stratigraphy. The impact of including clay smears in the calculation of the fault clay content distribution can be considerable, and in extreme cases it can switch the faults from being modelled as being open to being closed to flow. Figure 9a shows an example of the clay smear distribution generated from an inter-collated sand, impure sand and shale sequence (see Fig. 4). Also shown in Figure 9b is the prediction of the fault clay content distribution resulting from including clay smears in addition to the averaged ESGR clay mixing algorithm (compare this with Fig. 8b). The local variations of 50% in fault clay content in the most extreme areas of the fault equates to a permeability reduction of two orders of magnitude using a typical clay to permeability transform function (e.g. Jolley et al. 2007).
(a) The fault clay smear distribution (smear factor=3). (b) The fault clay distribution computed by combining the fault clay smearing algorithm and a fault clay mixing algorithm (averaged ESGR). The fault property data is shown only across the reservoir juxtaposition overlap areas. Combined clay mixing and smearing algorithms often produce local low-clay windows (high permeability) in high-clay (low permeability) zones.
A significant natural variability in fault properties is known to occur, and to account for this a series of algorithms have been developed that aim to capture the different processes that arise (e.g. Lindsay et al. 1993; Yielding et al. 1997; Childs et al. 2007). Currently only a limited suite of these algorithms are available in most commercial geological modelling packages, but the varying impact that they can have on the resulting flow simulation is significant (e.g. Freeman et al. 2008). Furthermore, it is not sufficient to only implement the algorithms, as the associated visualization capabilities are also required if the geoscientist or reservoir engineer is to be able to understand or make informed choices on the influence of modifying the spatial variability of these properties. Without the ability to view and analyse the data the system is a ‘black box’ of unknown validity. Fault clay prediction methods are a proxy tool used to infer fault permeabilities, transmissibilities or sealing capacities; the clay content distribution alone provides only a limited indication of the likely impact on fluid flow. In the following section more direct indicators of flow and/or sealing are discussed.
Fluid flow in a porous medium (e.g. a rock mass) is proportional to the transmissibility of the medium, and inversely proportional to the viscosity of the fluid, under an applied pressure gradient (Darcy's law; Darcy 1856). The transmissibility between two points in the medium is defined as the product of the harmonic average medium permeability and the area through which the flow occurs, divided by the distance between the two points. In order to integrate fault rock permeability data into flow simulation models, faults are usually incorporated as modifier values to the inter-block transmissibility applied between the grid cells on either side of the faults; this modifier is known as the transmissibility multiplier (TM) (Knai & Knipe 1998; Manzocchi et al. 1999). To be able to compute this value the host and fault rock permeabilities need to be combined with an estimate of the fault rock thickness and host grid cell sizes of the geocellular reservoir model (e.g. Manzocchi et al. 1999). Fault rock thickness values are typically estimated from the fault displacement (e.g. Hull 1988; Blenkinsop 1989; Evans 1990; Knott 1994; Antonellini & Aydin 1995; Knott et al. 1996; Childs et al. 1997; Manzocchi et al. 1999), but significant uncertainties are inherent in this prediction (Childs et al. 2009). The fault rock thickness estimate can usually be visualized on the fault plane but its simple relationship with displacement provides little additional geological understanding. The fault TM data can also be viewed on the fault surfaces within reservoir modelling packages. This data provides the fundamental control on the cross-fault fluid flow modelled by the reservoir flow simulator and can therefore be useful to interrogate. A TM value of zero indicates that the fault is modelled as a complete seal, whereas a TM value of one indicates that the fault has no impact on fluid flow (e.g. Manzocchi et al. 1999). The TM values typically vary between these ranges, although a TM value of greater than one is possible if the fault rock is more permeable than the host rock at that location.
There are difficulties in interpreting TM data in isolation because the TM is intimately associated with the host rock permeability and the grid cell sizes. The result is that both high and low TM values can define zones of high (or low) cross-fault flux. Figure 10 shows an example of the computed SGR and TM data for a single fault situated between an injector and producer pair, from a larger model with many more wells. As this 3D view demonstrates, the relationship between the SGR and TM values and the cross-fault fluid flow predicted by a streamline simulation is complex to interpret. The fluid flow across the fault is observed to be concentrated within the lower-TM zones (Fig. 10b). These areas are associated with the juxtaposition of high-permeability units (e.g. the reservoir sands). The intervening fault rock causes a reduction in the total cross-fault transmissibility and results in the low predicted TM values (c. 0.1–0.2). Within these general low-TM zones, higher-TM sections tend to concentrate the flow; this is due to locally higher transmissibility fault rocks situated between equivalent high-transmissibility host rocks. The very high TM values with no associated streamlines (low or no flow) are zones of impermeable shale against the fault. Here, although the predicted shale-rich fault rock has a low permeability, this is almost equivalent to the host shale permeabilities, and so the incorporation of the fault rock has little effect on the cross-fault transmissibility. Both higher and lower TM values can therefore correspond to increasing fluid flow depending on the host rock properties. It is only in circumstances when there is a constant host permeability in the hanging wall and footwall that the TM can be directly related to the impact on cross-fault fluid flow; this is a highly unusual situation. A similar discrepancy between cross-fault fluid flow and the SGR is present (Fig. 10a). The fault rock permeability distribution does have a correlation with the fault clay content (normally log–linear with higher clay contents having considerably lower permeabilities), but because the fault rock is often very thin in comparison to the total flow path length the low fault permeability may have only a minor impact on the location and amount of cross-fault flow. Flow through the fault rock itself is controlled by the fault transmissibility, which (per unit area) is a combination of the fault permeability and thickness. Viewing only a proxy for the fault permeability (i.e. the fault clay content distribution) thus limits the interpretability of this parameter. The flow in general will be controlled by the interaction of both the host and the fault transmissibility distributions, something that typically cannot be ascertained from visualizing the fault clay content in isolation. It is important to note that additional uncertainties in fault transmissibility predictions will result from the presence of uncertainty in the definition of the grid geometry, and critically this will influence the cross-fault juxtapositions that are developed.
(a) Predicted fault clay content distribution (using the SGR algorithm) shown on the fault face and the streamline simulation results (white lines) showing the high-flow zones between an injector and producer pair. (b) Fault TM values and the same streamline simulation results. Note that neither the fault SGR nor TM values show much correlation with the high-flow zones as indicated by the streamline simulation. In the case of the TM image the high-flow zones occur in the low-TM areas, but low TMs would typically be expected to retard flow. This is a consequence of the varying host permeabilities that are controlling the flow.
To provide a more useful means of assessing the variation in potential cross-fault fluid flow, the visualization of a set of parameters is required that intuitively relate the displayed property value to its effect on fluid flow. The fault hydraulic resistance, is the ratio of the predicted fault rock thickness, tf, to the fault permeability, kf, and is thus the inverse of the local transmissibility across the fault (i.e. the inverse of the transmissibility per unit area). This provides a measure of the local cross-fault fluid flow resistance created by the fault rock (see Fig. 11a). The fault hydraulic resistance values typically range over several orders of magnitude, with a larger hydraulic resistance value corresponding to a larger flow retardation by the fault rock alone (i.e. neglecting the local host rock juxtapositions across the fault). For example, a fault hydraulic resistance of 1 m/mD may arise from either a fault rock with a thickness of 0.1 m (i.e. a 10 m displacement fault with a thickness to displacement ratio of 1:100) and a permeability of 0.1 mD (e.g. a cataclasite), or a fault rock with a thickness of 0.001 m (i.e. a 10 cm displacement fault with a thickness to displacement ratio of 1:100) and a permeability of 0.001 mD (e.g. a clay smear).
Fault plane maps showing examples of (a) the fault hydraulic resistance and (b) the fault transmissibility.
Fault hydraulic resistance aids in understanding the impact of the fault rock on the cross-fault fluid flow because it takes into account both the thickness and the permeability of the fault rock without relating it to its neighbouring host cells. In contrast, the TM is in part controlled by the juxtaposed host permeabilities and the size of the grid cells in the hanging wall and footwall. These latter parameters are grid-specific and are therefore only indirectly related to any physical property of the geological model.
A second useful parameter is the cross-fault transmissibility (see Fig. 11b) where A is the cross-fault grid connection area. The cross-fault transmissibility is therefore a direct measure of the potential fluid flux across the grid connection area of the fault for a given pressure gradient and phase, and hence should provide a good indication of potential cross-fault fluid flow. The cross-fault hydraulic resistance and fault transmissibility values are direct results of the mapped or estimated geological distributions and unrelated to any user-specified spatial grid geometry attributes. As such, they should provide consistent transferable parameters that can be compared across and between faults, and also between different fields or prospects. The cross-fault hydraulic resistance is likely to be more useful for investigations in exploration settings, where small leaky windows may have a significant impact on fluid flow, and hence hydrocarbon accumulations over geological time-scales. In contrast, the cross-fault transmissibility provides more information on fluid flow in a production environment, where the integrated effect of the cross-fault fluxes over all interfaces is the most important fault-related factor.
Figure 12 shows the fault TM value v. the fault transmissibility for a faulted sand–shale sequence. The inter-grid block transmissibility will have a linear relationship to the cross-fault fluid flow for constant pressure and phase conditions. The figure shows that many orders of magnitude variation are present in the inter-block transmissibility for similar TM values. This highlights the potential pitfalls of interpreting TM data to infer cross-fault fluid fluxes.
Comparison between the transmissibility multiplier and fault transmissibility value for cross-fault connections across a geocellular model. Note that, due to the strong influence of the host permeability on the TM, single TM values can relate to widely varying fault transmissibilities.
Apart from the TM, the above parameters (cross-fault hydraulic resistance and cross-fault transmissibility) provide an independent indication of the impact of fault rocks on fluid flow. The true impact on cross-fault flow is, however, a combination of both the pathways to, through and out of the fault zone. One method to assess this is to compute the compound transmissibility of the hanging wall, fault and footwall. A limitation with the usual inter-block transmissibility values is that the user-specified spatial grid geometry, as well as the geology, controls the result. In order to generate a transferable rating scheme, the widths of the hanging wall and footwall host rock used to define the total transmissibility need to be defined and normalized. This new parameter, the normalized cross-fault transmissibility (hereafter called the effective cross-fault transmissibility or ECFT), is computed using the harmonic average of the permeabilities of the undeformed footwall adjacent to the fault, the fault rock and the undeformed hanging wall across the fault for a specific width of host wall rock on each side of the fault, and the fault rock thickness defined by the local displacement: where D is the specific width of host rock on each side of the fault, kh1 and kh2 are the permeabilities of the host on sides 1 and 2, respectively, th1 and th2 are the thicknesses of the host on sides 1 and 2, respectively, kf1 and kf2 are the permeabilities of the fault on sides 1 and 2, respectively, and tf1 and tf2 are the thicknesses of the fault on sides 1 and 2, respectively.
For a constant phase and pressure differential this value should therefore be proportional to the cross-fault fluid flux, without having to separately consider the host rock permeabilities, grid cell geometries or juxtaposition type. In this contribution we have chosen to define the ECFT by combining the predicted fault rock thickness (calculated from the fault displacement) and the associated fault rock permeability with prescribed 50 m thicknesses (a typical reservoir simulator grid block size) of each of the juxtaposed footwall and hanging wall lithologies (minus the fault rock width on each side) and the associated permeabilities of these lithologies.
Figure 13 shows the relationship between the fault transmissibility (fault rock only) and the ECFT (incorporating fault rock and the juxtaposed host rocks). The graph shows how the ECFT (a better gauge of the likely fluid flow response) changes from being dominated by the host rock permeability to being dominated by the fault rock permeability as the fault rock permeability decreases (in general, the contrast between the host and fault rock transmissibilities controls this dominance). This figure demonstrates that considering the fault transmissibility alone can also be misleading because there is no simple relationship to bulk transmissibility and hence cross-fault fluid flow.
Fault rock transmissibility v. the ECFT (for one square metre of fault area) of the fault zone and host. The ECFT indicates the ease of flow through the host to the fault, through the fault and then out through the host. The ECFT depends on the specific width D of host rock on each side of the fault; in this example, a prescribed 50 m thickness was used. The fault transmissibility indicates the ease of flow through the fault rock only. Note that at places where the data points follow a horizontal straight line the host permeability is the dominant control on the flow (the juxtaposed host permeabilities kh1 and kh2 are indicated for each of these horizontal asymptotes), and where the points follow a 45° inclined straight line the fault rock controls the flow. The transition between the flow being dominated by the host and fault permeability is very narrow; the location of this transition depends on the relative transmissibilities of the juxtaposed host rocks and the fault rock.
Following a similar approach to the ECFT (to remove the influence of the user-specified spatial grid geometry), we define the effective cross-fault permeability (ECFP) to be the length-weighted harmonic average of the host footwall, host hanging wall and fault rock permeabilities: Again, the host rock length is normalized to a specified representative length scale (in this case 50 m minus the half-width of the fault rock applied on each side). The result is a new parameter that has direct relevance to flow simulation results because it measures the ability of fluids to move through the upstream host rock toward the fault, through the fault rock, and then through the juxtaposed host rock on the downstream side of the fault (as with the ECFT). The parameter also has the advantage that it is intuitive for geologists, geophysicists and reservoir engineers to understand and interpret. The ECFP displays a bulk permeability value that is scaled to a value equivalent to the reservoir grid cell scale (e.g. a 50 m or 100 m grid block size). A value of 40 mD for the ECFP in a grid of 50 mD host permeability is likely to have little effect, whereas an ECFP value of 0.1 mD will have a significant impact.
Normalizing the host rock contribution to a specified length scale has the additional advantage that linear changes in the ECFP directly relate to linear changes in flow rate through the fault zone for a given phase, viscosity and pressure differential (as with the ECFT). This relationship is illustrated in Figure 14. In this example the streamline simulation results can be seen to closely follow the locations predicted by the higher ECFT (or ECFP) values. The low ECFT values correspond to the low or no flow zones.
An example of ECFT and the streamline simulation data from Figure 10. Note that the majority of streamlines are focused in the high-ECFT zones (hotter fault property colours). The low-ECFT areas are almost devoid of flow. The streamlines shown are limited to this particular injector–producer pair and are clipped by the time of flight. The high-ECFT areas not showing streamlines are associated with either longer flight time flow paths or flow between different well pairings.
Figure 15 shows a series of possible cross-fault flow predictor properties mapped onto an example fault based on the methods discussed above. These images allow a comparison of the locations of likely cross-fault flow areas on the fault when considering: (a) the fault rock specific properties (i.e. the fault rock clay content, permeability and transmissibility; see Fig. 15a–c); (b) the host rock juxtaposed properties (i.e. the harmonic average host permeability; see Fig. 15d); and (c) the combined fault and host rock properties (e.g. the TM, ECFT or ECFP distributions; see Fig. 15e, f). The diagram shows how interpreting either the fault rock or the host rock permeabilities alone leads to a significantly different interpretation of the likely cross-fault flux in comparison to using the combined fault and host data.
A comparison of different parameters to indicate likely cross-fault flow zones. Fault rock specific properties: (a) fault clay distribution estimated via the SGR algorithm; (b) fault permeability from the SGR using the Manzocchi et al. (1999) fault clay to permeability transform; and (c) fault transmissibility based on the fault permeability in (b). The fault transmissibility provides the most direct indicator of flow potential through the fault rock alone. Host rock specific property: (d) juxtaposed harmonic host permeability. Combined host and fault rock properties: (e) transmissibility multipliers; and (f) ECFP (normalized to 50 m host cells).
The techniques described so far facilitate the visualization of predicted fault rock properties and the visualization of the probable responses of cross-fault fluid flow to those properties. These static fault properties that are used to infer the flow response assume a uniform or buoyancy-driven pressure field. Production techniques may raise the pressure at injectors and/or the drawing down at producers, thereby negating the previous assumption. To improve the prediction or visualization of cross-fault fluid flow the impact of production and injection needs to be taken into account.
The majority of fault seal analysis techniques are only aimed at predicting inputs for flow simulation, and do not analyse in any detail the results of that flow simulation to better understand how the cross-fault fluid flow has contributed to the general hydrocarbon distribution within the model. By back-analysing the resulting cross-fault fluid flux developed during the simulation process a better understanding of the system can be gained and this can provide a more informed quality assessment of the result, as well as input for the calibration and validation of fault seal workflows.
The majority of flow simulation results provide relatively little direct data with which to understand the impact of faults on the fluid flow (apart from at specific places, e.g. where phase boundaries are influenced by faults). The flow simulation results (e.g. phase pressure, saturation and mobility) tend to be single average values per grid cell, but unfortunately related vector (directional) data is often not available for interrogation. This simulation data does, however, have the potential to provide a detailed understanding of how the faults have influenced fluid flow at different times through the simulation. If the resulting data are resolved across the faults at any particular time step then the local cross-fault fluid flow component can be ascertained. With these results, the degree and importance of cross-fault fluid flow can be defined at any particular time during the simulation run. Figure 16 shows the normalized cross-fault fluid flux derived from a streamline simulation model for a single fluid phase using the instantaneous pressure field at a certain time through the simulation.
Fault face colour-coded for the back-computed normalized cross-fault fluid flux (‘hotter’ colours denote higher cross-fault fluid fluxes) derived from the pressure field generated from a streamline simulation. Also shown are the streamlines between one of the injector and producer pairs in the simulation model. Note that, as expected, the streamlines pass through the high cross-fault flux zones. Back-computing the cross-fault fluid flux and visualizing this property on the fault plane allows a direct imaging of the role that the fault plays in controlling flow in the reservoir.
The direct visualization and analysis of the cross-fault fluid flux developed during a flow simulation should help to answer two questions relating to the geological model. Firstly, are the predicted flow and no flow locations along the faults geologically realistic? Secondly, what impact does changing the fault property prediction algorithm have on the observed cross-fault fluid flow? These questions have previously been difficult to answer, but are fundamental in controlling and assessing the resulting flow simulation prediction. A more routine interrogation of the detail involved in the flow simulation in relation to faults is a natural progression of the drive toward increasingly sophisticated simulation models. This workflow can be readily extended to incorporate geometric (e.g. fault throw) uncertainty, but this involves additional visualization challenges due to the change in cross-fault juxtaposition architecture.
One of the current shortcomings in typical geological visualization packages is the inability to visualize both the detailed fault property data and the larger-scale field geometry simultaneously. This is primarily due to the often near-vertical nature of faults and the near-horizontal nature of the horizon topography, combined with the very high densities of property data that typically vary most rapidly in the fault dip direction. One of the most common visualization methods in the broader field of computational fluid dynamics is the use of vector fields for the visual analysis of property data or flow results. Figure 17 shows an example of a vector field representing the cross-fault harmonic average host permeability, which is often one of the principal controls on cross-fault fluid flow. The vectors have been drawn normal to the fault surface (oriented vertically upwards) and their magnitude represents the harmonic average of the juxtaposed host permeabilities. The vectors (e.g. Fig. 17) provide a means of integrating a detailed understanding of the fault in the context of the geocellular model and nearby well control. Similarly, such vector field plots can be used to provide a rapid means of visualizing the cross-fault fluid flow distribution in the model at specific time steps through the flow simulation.
The cross-fault harmonic average host permeability represented by vectors (drawn normal to the fault surface and oriented in the local dip direction of the fault). In this example, the low-throw faults or the tip zones show high juxtaposed harmonic host permeabilities and this is likely to correspond to high-flow zones, whereas higher-throw zones correspond to juxtapositions with lower permeability host units. Also shown is the base reservoir topography. Using vectors to show flow or fault properties allows that data to be more easily visualized and understood in the context of the overall reservoir geometry and production configuration.
In this paper a series of different techniques have been presented that either enhance those currently in use within the industry, or have been recently developed to aid our understanding of fault seal and cross-fault fluid flow issues. Comparative studies are required in order to assess how well these techniques, and the fault permeability and flow property measures introduced, predict the flow behaviour in the subsurface. Comparative parameter datasets have only become available following the development of techniques to back-analyse the flow simulation results with respect to cross-fault fluid flux.
Figure 18 shows various comparisons between four different parameter predictions and the cross-fault fluid flux derived from the reservoir simulation. Limited correlation exists between the resulting cross-fault fluid flow and either the predicted fault rock clay content or the corresponding fault permeability (Fig. 18a, b). A reservoir simulation is itself only a model of the physical processes operating in a reservoir that attempts to match or predict reservoir flow parameters – it is not a truth. Thus, ‘correlations’ derived from a model are subject to the extent to which the ‘model’ is valid. This is not surprising because these parameters, although controlling the flow across the fault rock in isolation, do not capture any of the host rock flow properties. Similarly, Figure 18c demonstrates the poor relationship between the harmonic-averaged juxtaposed host permeabilities and the cross-fault fluid flux. Considered in isolation, the separate fault and host rock permeabilities will show good correlations with the cross-fault fluid flow for certain geometric and property configurations. The accuracy of the correlation will be controlled by the relative contributions of the fault and host transmissibility to the bulk transmissibility of the entire system. This relationship is characterized in Figure 13. The ECFT described above is a parameter that takes into account both the host and the fault rock properties and shows a better correlation with the observed cross-fault fluid flux (Fig. 18d). The scatter in this relationship is primarily a function of the cross-fault pressure differential developed along the faults. A direct correlation between the cross-fault fluid flux and the ECFT should be present for a single-phase fluid subject to a constant pressure differential. However, in the fluid flow simulation used to produce Figure 18, the cross-fault pressure difference varied continuously across the fault, thereby causing the observed scatter. When additionally the local pressure difference is considered, the data in Figure 18d show highly clustered corridors that highlight the good correlation between the observed and predicted cross-fault fluid flow for a certain constant pressure difference.
Predicted static fault property values v. observed normalized cross-fault fluid flux defined via flow simulation. A consistent straight line would indicate a direct correlation between the modelled cross-fault flow and the approximation parameter. Deviations away from the line show the level of accuracy of the prediction. (a) The predicted fault clay content v. the associated observed cross-fault fluid flux. Very little correlation is present, with very low clay contents both relating to very high and very low cross-fault flux values. (b) The predicted fault permeability v. the observed cross-fault fluid flux. Again little correlation exists, with high cross-fault fluxes observed across low, moderate and high-permeability fault rocks. (c) The harmonic host permeability (or transmissibility) v. the observed cross-fault fluid flux. A clear clustering of the data is present, but this only demonstrates that the flow occurs across zones with the juxtaposition of high-permeability sands against high-permeability sands. Within that style of juxtaposition no further correlation is evident. (d) The predicted ECFT v. the observed cross-fault fluid flux. A good correlation is evident. The principal reason for the scatter in the data is due to the variation in pressure across the fault. The ECFT should provide a linear correlation with flux for a constant pressure differential.
The general result is that neither the fault nor the host rock property relationships should be used in isolation to define the likely cross-fault fluid flux. Variations in this relationship are due to lateral and vertical cross-fault pressure changes. In the absence of flow simulation data, the ECFT (or equivalent permeability) should provide an accurate assessment of the probable cross-fault fluid flux throughout the model, assuming that multiphase fluid flow effects can be neglected. An additional and useful step prior to full field simulation is the application of a streamline simulation and then the back-analysis of the observed cross-fault fluid flow. This should provide a good estimate of the pressure field and hence provide a method to compare the geologically-predicted cross-fault fluid flux with the cross-fault fluid flow data provided by a full-field production simulation.
The ECFT and ECFP parameters can therefore provide a useful predictive tool that can help screen the geological reservoir and production simulation models prior to and during initial or full-field simulation. The computation of these properties is fast and can also be applied ‘on-the-fly’ (for the static properties). These parameters can therefore aid in the quality control and optimization of geological scenarios used in flow simulations by reservoir engineers. The application of this technology should therefore help to enhance integrated reservoir modelling practice, and improve the quality of the models being taken forward.
The fault seal analysis process contains a wide variety of uncertainties and potential workflows (Fisher & Jolley 2007). Determining which approach is suitable for determining flow within a given faulted reservoir is often quite complex. Properties such as the ECFT are helpful because they provide a potential proxy for fluid flow that is quick to compute from the static model. Visualizations such as cross-fault vectors can provide a more global context to better understand how these different parameters affect flow; however, visualizations or computations that generate comparable scenarios are required.
Figure 19 shows a simple example of the use of a pie chart plot that allows the comparison of numerous geological sealing scenarios against one another. The bulk cross-fault permeability for each cross-fault connection has been summed for each fault segment, based on the two scenarios of including (red pie segments) or excluding (blue pie segments) the predicted fault properties. The overall size of the pie chart for each fault indicates the total cross-fault permeability. Such plots allow the relative impact of different fault sealing scenarios on potential reservoir flow to be compared and evaluated on a field-wide scale whilst retaining the geological context. Such plots are particularly useful, for example, for analysing variations in cross-fault facies juxtaposition areas for varying facies models. Engineering requirements to make the modelled fault system more or less open to flow in order to honour production history data can hence be more easily constrained by visualizing the geological parameters that should control that system.
Top reservoir structure map with pie charts showing the bulk cross-fault permeability (juxtaposed host rock and fault rock permeabilities) per fault (and located near the centre of each fault). The red segments show the bulk permeability (area-weighted average cross-fault permeability) when fault rock is included in the calculation and the blue segments show the bulk permeability calculated by neglecting the fault permeability. The size of the pie charts indicates the total cross-fault permeability, so that faults with larger pie charts are more likely to have a higher cross-fault fluid flux. Note that the pie charts with small red segments show that incorporating fault rock dramatically reduces the cross-fault permeability across that structure. The plot allows a reservoir scale perspective and a rapid understanding of how different scenarios will impact on the potential cross-fault flow, and highlights where the critical faults are in the system.
Developing tools to analyse and visualize cross-fault fluid flow is a natural progression in the drive toward improved exploration, prospect evaluation and reservoir simulation. As more fault property data becomes available, a wider range in structural processes, physical properties and flow behaviour are being defined (Yielding et al. 1997; Manzocchi et al. 2000, 2002; Fisher & Knipe 2002; Sperrevik et al. 2002; Childs et al. 2007). Thus, it is becoming increasingly clear that using a best-fit single fault property model that can be uniformly applied to all faults in all reservoirs is unrealistic (see Fisher & Jolley 2007 for discussion). Proxy or direct cross-fault fluid flow visualization tools allow informed choices to be made on which fault parameter relationships are appropriate for any given reservoir. In favourable circumstances, observable parameters such as time-lapse seismic and well tracer data may help to constrain the choice of potential scenarios.
The geocellular visualization of fault properties within the grid adjacent to the faults, on the faulted grid connections (i.e. the fault face), or as vectors across the fault, can provide a greater understanding of the potential for cross-fault flow and how that could vary within a modelled reservoir. Using a combination of different techniques a variety of subsurface datasets can be visualized simultaneously with fault zone properties. This can help in developing a better understanding of the inter-relationships between different properties (e.g. cross-fault flow, well distribution and reservoir architecture), and also helps in the validation of the model geometry that underpins compartmentalization studies. These visualization tools are equally applicable to exploration and production environments. In the exploration environment the rapid effective screening (e.g. via fault plane property maps of facies juxtapositions) is equally relevant but often on a different scale to the production situation.
In addition to juxtaposition and traditional fault membrane properties, we have introduced and visualized parameters that can be considered to be proxies for cross-fault fluid flow (e.g. ECFT and ECFP). The direct analysis of cross-fault fluid flux from flow simulation data allows this technology to bridge the gap between geoscientists and reservoir engineers, and vice versa. These forms of data have historically been overlooked during fault seal studies but are the next logical step in improving the accuracy and speed of these analyses.
Thus, in this paper, we have shown that combinations of techniques and datasets, along with the re-computing of data, informs more directly on specific cross-fault fluid flow. This has the potential to improve understanding and therefore improves the prediction of hydrocarbon flow across fault zones or hydrocarbon sealing behind faults.
The hydraulic resistance and the transmissibility of faults provide direct and easily interpretable measures of the fault rock impedance or flux potential for a given pressure differential, phase and viscosity. These parameters are independent of the user-specified spatial grid geometry and so should represent transferable parameters with which to assess the likely fluid flow across different parts of faults, different faults, different fault geometries, different model vintages and between different fields. This is in contrast to the fault TM, which is the fault property commonly used and visualized in the reservoir modelling packages. This property is useful as a mechanism to integrate fault retardation into the simulator, but is often difficult to interpret relative to the likely or observed fluid flow across faults.
The ECFT and associated ECFP are parameters that provide more intuitive, direct proxies to inform on the cross-fault fluid flux of faults. These parameters take into account the host rocks and fault rocks, and are normalized for a specified length scale. This results in a set of parameters that are linearly associated with the fluid flux across the fault zone, for a single phase fluid at a given pressure differential and viscosity, and their values are directly comparable between different faults and grids.
Back-computing and visualizing the results of a flow simulation in comparison to the static fault property predictions is an important stage for the validation of integrated reservoir modelling scenarios. In this contribution we have back-calculated the cross-fault fluid flux developed during a synthetic simulation, and displayed this data back in its geological context (here as fault face values and cross-fault vectors). This method allows the relative contributions of the faults to reservoir compartmentalization to be constrained, and the models updated accordingly, since the critical areas of the model are highlighted and the associated geological parameters can then be revised. This should lead to a more geologically robust and rapid means of attaining reservoir and production simulation models that honour all of the subsurface and production data.
As with all other areas of reservoir modelling, numerous potentially valid scenarios are possible for the fault seal prediction. Techniques are therefore required to effectively screen the impact of the different approaches to the problem. The screening technique needs to occur at the scale of the overall system. By generating fault-wide summations, the impact of different scenarios can be seen at the reservoir scale and the impact of choosing between the different scenarios is easier to discern.
The analysis and visualization techniques outlined in this paper will, if utilized appropriately, allow for a more robust quality control and an improved understanding of the role of fault sealing in geological and flow simulation models that are being used to identify, evaluate and manage the extraction of hydrocarbon reserves.
Developing tools that analyse or visualize the fault zone geology in a way that directly relates to the flow simulation process should facilitate a closer collaboration between geologists, geophysicists and reservoir engineers involved in integrated reservoir modelling.
At the exploration stage, rapid ‘quick-look’ fault plane maps provide an effective means of de-risking prospects. At the evaluation stage, more detailed juxtaposition maps are useful. Traditional line drawings of stratigraphic juxtapositions are complex to interpret, whereas the colour-filled, property-specific filtering of fault plane maps is more informative and intuitive to use.
Highlighting host and fault rock parameter windows across faults, particularly when visualized as cells adjacent to the fault, allows for the rapid identification of key areas of the geological or simulation grid that will impact on the resulting flow simulation model.
Implementing the range in different prediction algorithms and testing the probable impacts of those models is important if the variability in geological properties is to be understood.
Fault transmissibility and hydraulic resistance provide transferable rating schemes to visualize and analyse the likely cross-fault fluid flow predicted through a fault rock.
The effective cross-fault transmissibility (ECFT) and associated permeability (ECFP) provide transferable parameters that can be used to infer likely cross-fault fluid flux from static models.
The back-analysis of flow simulation data to highlight the predicted cross-fault fluid flux provides a method to constrain fault parameter calculations in geological reservoir and production simulation models. This simulation data provides the most direct indication of the likely effects of faults on hydrocarbon distribution and flow.
The ECFT prediction shows a strong correlation with the resulting cross-fault fluid flux, especially when considering the variable pressure differences across the faults. In the examples shown, the ECFT provided the most accurate prediction of the cross-fault fluid flux observed via simulation. This property therefore appears to provide a useful proxy to infer cross-fault fluid flow that can be generated from the static geological model prior to flow simulation.
Cross-fault fluid flux computed from either streamline or full simulation data provides a more intuitive method of visualizing the impact of faults on hydrocarbon fluid distributions. This information can be used in conjunction with knowledge of the uncertainties in the seal analysis to generate new models that better honour both the geology and the dynamic data.
Different viable geological scenarios can be defined that more appropriately capture the likely range in fault seal contribution to reservoir compartmentalization. Some potential techniques are outlined here that might help visualize the impact of these scenarios and the uncertainties associated with them. However, additional techniques are now required that provide data at the scale relevant to the simulation, that is, on a field-wide scale. The goal for the future is to develop techniques that rapidly define the most viable geological models whilst honouring dynamic data. Extending the analysis to incorporate geometric uncertainty is key to developing a holistic understanding.
First of all, we thank the reviewers (Fred Dula and Stephen Dee) and the Lead Editor (Steve Jolley) for their insightful comments that have helped to improve the focus of this paper. We also thank all of the members of RDR Ltd that have been involved in the development process of the fault property tools and workflows presented in this paper, particularly Raoul Treverton, Will Bradbury, Philip Jones, Kevin Wood, Rick Berry, Nikki McCabe and Viki O'Connor.
The visualization techniques presented here have been developed within RDR and are implemented within Petrel™ (www.slb.com).
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