Sensitivity of fluid flow to fault core architecture and petrophysical properties of fault rocks in siliciclastic reservoirs: a synthetic fault model study Niclas Fredman1,2, Jan Tveranger1, Siv Semshaug1, Alvar Braathen1,3 and Einar Sverdrup4 1
Centre for Integrated Petroleum Research, University of Bergen, Allégaten 41, N-5007 Bergen, Norway Present address: StatoilHydro, Exploration and Production, Strandveien 4, N-7501 Stjørdal, Norway (e-mail:
[email protected]) 3 Present address: University Centre in Svalbard, 9171 Longyearbyen, Norway 4 Ener Petroleum ASA, PO Box 128, N-1325 Lysaker, Norway
2
ABSTRACT: Fluid flow simulation models of faulted reservoirs normally include
faults as grid offset in combination with 2D transmissibility multipliers. This approach tends to oversimplify the way effects caused by the actual 3D architecture of fault zones are handled. By representing faults as 3D rock volumes in reservoir models, presently overlooked structural features may be included and potentially yield a more realistic description of structural heterogeneities. This paper investigates how a volumetric fault zone description, will affect fluid flow in simulation models. An experimental 3D model grid including a single normal fault, defined as a volumetric grid, was constructed. Subsequently, the fault grid was populated with two conceptual fault deformation products – sand lenses and fault rock – using an object-based stochastic facies modelling technique. In order to evaluate the effect of varying petrophysical properties, fault rock permeability and sand lens permeability were varied deterministically between 0.01 mD and 1 mD and 50 mD and 500 mD, respectively. The impact of fault core architecture was investigated by deterministically varying sand lens fraction and sand lens connectivity. This yielded 24 model configurations, executed in 20 stochastic realizations each. Fluid flow simulation was performed on 480 model realizations. Simulation results show that the most important parameters influencing fluid flow across the fault were fault rock matrix permeability, and whether or not the sand lenses were connected to the undeformed host rock. Sand lens permeability and sand lens fraction turned out to be less important for fluid flow than fault rock matrix permeability and sand lens connectivity. KEYWORDS: fault modelling, fluid flow, fault facies, stochastic modelling
INTRODUCTION Reservoir uncertainty characterization and fluid flow simulation are important tools when trying to understand and predict reservoir performance. To date, most efforts have focused on establishing uncertainties attached to sedimentological parameters. Studies focusing on structural parameters, in contrast, have been fewer, and a reservoir model is often restricted to a single or a few deterministic, structural models (Lescoffit & Townsend 2002; Ottesen et al. 2005). Considering the impact faults have on reservoir fluid flow (Bouvier et al. 1989; Knipe 1992; Gibson 1998; Manzocchi et al. 1999; Fisher et al. 2001; Fisher & Knipe 2001; Shipton et al. 2002), further studies aimed at quantifying how structural factors affect reservoir performance are clearly needed. Furthermore, these studies should not be limited by restrictions imposed by the currently available modelling tools, but use field observations as their starting point. Several workers have investigated the impact of subseismic faulting, fault geometry, fault density, fault displacePetroleum Geoscience, Vol. 13 2007, pp. 305–320
ment and sealing properties (e.g. Damsleth et al. 1998; England & Townsend 1998; Hollund et al. 2002; Lescoffit & Townsend 2002; Holden et al. 2003; Ottesen et al. 2005). Previous studies have also used discrete grid cells to investigate single- (López & Smith 1996; Caine & Forster 1999; Flodin et al. 2001) and multi-phase flow properties of fault rocks (e.g. Manzocchi et al. 1998, 2002; Rivenæs & Dart 2002; Al Busafi et al. 2005). Volumetric fault modelling of fault core architecture, in contrast, is still novel work and few publications are available. Berg & Øian (2007) used a hierarchical, deterministic model to investigate the effect of fault zone architecture on multiphase flow. Nøttveit (2005) also used a hierarchical approach for volumetric modelling of fault zone architecture and fractures. Field studies suggest that fault zones can be divided into two main elements (Fig. 1); the central fault core and the outer, surrounding damage zone (e.g. Caine et al. 1996). In poorly lithified sediments this subdivision can be extended by adding a 1354-0793/07/$15.00 2007 EAGE/Geological Society of London
306
N. Fredman et al.
Fig. 1. Based on extensive fieldwork (e.g. Wallace & Morris 1986), a conceptual fault model for siliciclastic rocks has been widely accepted. The Fault Facies project takes this model one step further, by implementing a more detailed fault zone architecture into the model.
Fig. 2. (a) Fault core element model (model A), which has its fault implemented as discrete grid cells. (b) Conventional 2D fault model (model B), implemented with transmissibility multipliers (Manzocchi et al. 1999).
mixed zone between the fault core and the damage zones (Heynekamp et al. 1999). This 3D subdivision acknowledges that tectonic deformation has a volumetric impact on reservoir rocks. Thus, investigation of fluid flow in faults, represented as 3D volumes, is important and aims to improve our understanding of fluid flow in deformed rock volumes. This may be facilitated by object-based stochastic modelling of fault zone architecture and petrophysical properties along similar lines as currently used for modelling sedimentary facies. Stochastic modelling of fault zone architecture may be a good starting point for further development of more sophisticated fault modelling techniques than the ones available today. The present work is part of the Fault Facies project (Tveranger et al. 2005), which aims to model and quantify faults as strained 3D volumes, and to predict and model petrophysical properties and spatial variability of sedimentary rock volumes affected by fault zones. The purpose of this study is to investigate the effect that including a conceptual 3D fault core architecture, composed of two simplified fault facies, has on fluid flow simulation, and also to establish how volumetric fault
modelling differs from the conventional fault modelling method (transmissibility multipliers). To ensure firm control on input–output relations, this study utilizes a synthetic approach with highly simplified parameters and modelling properties. The scope of the sensitivity tests is limited to fault core architecture; therefore, the damage zone is not included. A conceptual, synthetic 3D fault model (Fig. 2a), with a single normal fault, was constructed by defining a fine grid symmetrically positioned around a conventional fault plane (Model A). Thus, the fault core is represented as a 3D volume, allowing its petrophysical properties to be represented as discrete grid cells instead of conventional fault transmissibility multipliers. Discrete grid cells also allow multi-phase flow properties (e.g. relative permeability and capillary pressure) to be included in the fault model as individual grid cell properties. The fault core grid was populated using a stochastic model, including two conceptual fault deformation products – (1) sand lenses and (2) fault rock – referred to as fault core elements. A corresponding conventional fault model (Fig. 2b) with identical host rock lithology and host rock petrophysical properties was
Fault core architecture and fluid flow also constructed using 2D fault transmissibility multipliers (model B). Fluid flow simulation was executed and set up as a two-phase flow using oil and water.
FAULTS IN RESERVOIR MODELLING Faults in flow simulation It is generally accepted that faults can act as both barriers and conduits to fluid flow (Antonellini & Aydin 1994; Caine et al. 1996; Fulljames et al. 1997; Fisher & Knipe 1998, 2001). There are two processes that can form a sealing fault: (1) juxtaposing a unit with zero permeability against a reservoir unit; or (2) sealing fault rock development, i.e. membrane seal (e.g. Fisher & Knipe 1998; Sperrevik et al. 2002; Yielding 2002). Cementation seals may also develop (Fisher et al. 2000), but these are not considered here. A number of software packages allow users to build sophisticated 3D geological models that serve as input to fluid flow simulators (e.g. IRAP-RMSTM, PetrelTM, GocadTM). The conventional way of including faults in these models is to implement cross-fault flow properties as fault transmissibility multipliers (Manzocchi et al. 1999). What these software solutions have in common is that they describe fault permeability and transmissibility as 2D properties attached to a fault plane. Consequently, 3D fault core architecture is largely ignored and features, such as intra-fault vertical fluid flow, can be handled only ad hoc, using deterministic methods. A common approach to calculate fault transmissibility multipliers is to use the Shale Gouge Ratio (SGR) algorithm (1), defined by Yielding et al. (1997) and the fault zone permeability algorithm (2), defined by Manzocchi et al. (1999). SGR =
兺shale_bed_thickness fault_throw
1 log 共kfz兲 = ⳮ 4SGR ⳮ log 共D兲共1 ⳮ SGR兲5 4
(1) (2)
In equation (1), shale_bed_thickness is the total amount of impure material that has slipped past a particular point on the fault, and fault_throw is the total fault throw. In equation (2), SGR is the Shale Gouge Ratio, D is the fault displacement (m) and kfz is the fault zone permeability (mD). Fault zone displacement/thickness relationships (Hull 1988; Evans 1990; Knott et al. 1996; Foxford et al. 1998; Walsh et al. 1998; Sperrevik et al. 2002), Clay Smear Potential (Bouvier et al. 1989) and Shale Smear Factor (Lindsay et al. 1993) are also common constituents in fault transmissibility multiplier calculations. Stand-alone SGR curves can also be used, allowing users to define their own SGR/fault zone permeability relationship. Typically, the shale content is described by a Vshale parameter in a standard reservoir model. It is also possible to include effects of brittle deformation, cementation and effects related to maximum burial depth and depth at time of deformation. Another important – and expedient – technique in fault seal analysis is to use juxtaposition and fault seal diagrams (Childs et al. 1997; Knipe 1997) to evaluate the sealing capacity. For mature fields, or at least for those fields for which some production data are available, fault transmissibility multipliers can also be derived deterministically from history matching. This may result in transmissibility multipliers with limited relation to the actual structural geology. Deterministic history matching based on homogeneous transmissibility multipliers is able to match present-day history, but may have poor predictive value (Ottesen et al. 2005).
307
Fault core architecture and permeability structure The fault core represents the high-strain central part of the fault zone accommodating most of the displacement and deformation. Common constituents of the fault core in siliciclastic rocks of extensional regimes include: fault gouge, slip surfaces, clay smear, breccia, cataclasites and sand lenses (Wallace & Morris 1986; Peacock & Sanderson 1992; Childs et al. 1996; Clausen et al. 2003; Berg 2004; Kristensen et al. 2005). The fault core is regarded commonly as the key to predicting the sealing potential of fault zones, but conventional modelling techniques do not allow fault core architecture to be included in reservoir models, although the presence of such architectures is observed readily in the field. Two examples may serve to illustrate this. The Hartley Steps fault in Figure 3a exhibits a 1 m thick, heterogeneous fault core consisting of a discrete, unconnected sand lens. The sand lens shows downwards-increasing rotation, intermixed with shale smear, fault gouge and coal. Similarly, the Baba fault (Fig. 3b) displays one larger sand lens (connected to host rock) and four smaller sand lenses further down the core. Although these are only examples of a wide range of possible fault core architectures, they illustrate the point that fault cores may consist of 3D elements with often highly contrasting petrophysical properties. The important question to answer is to what extent this observed fault core architecture influences fluid flow across and parallel to faults. The model used in the present study is conceptual, thus, only two simplified fault core elements are included: (1) sand lenses and (2) fault rock. Sand lenses are defined as elongated, ellipsoid, low-strain bodies, derived from undeformed sandstone host rock and typically aligned with their longest axis parallel or subparallel to fault dip (Clausen et al. 2003; Lindanger 2003). Sand lenses can vary from almost undeformed to intensely deformed, fractured, penetrated by deformation bands and exposed for secondary mineralization. According to Lindanger (2003), sand lenses show a robust internal relative geometrical relationship (x-: y-: z-axis of 1: 10: 9), regardless of size, lithology and stress conditions, where x is normal to the fault (lens thickness), y is fault parallel (lens width) and z is lens height (compare to the lens in Fig. 3 for orientation). Lensoid bodies of the fault core appear on different scales and they can be stacked on top of each other (Gibbs 1984; Gabrielsen & Clausen 2001) or separated and spread out across the fault plane. In any case, they are normally bound by fault rocks or slip surfaces. Preliminary results on field studies related to sand lenses indicate that when represented in the fault core, they typically occur near to the undeformed host rock sand layer (exemplified in Fig. 3b), rather than randomly spread out in the fault core. In the present work, however, no lithologydependent conditioning function was applied for the facies modelling, meaning that the sand lenses in this model are randomly spread out in the fault core. For modelling purposes, in this study a fault rock is defined as a deformation product of the host rock (mudstone, sandstone or both), with modified flow properties and, as such, could represent clay smear, shale gouge, breccia, cataclasite or a combination of these. Low permeability fault rocks are the result of porosity collapse due to grain crushing (e.g. Antonellini & Aydin 1994; Crawford et al. 2002), grain-size reduction, mixing of phyllosilicates with framework grains, increased mineralization and cementation (Sperrevik et al. 2002) and contact quartz dissolution (Sverdrup & Bjørlykke 1996). If the sandstone is non-lithified at the time of deformation, grain re-orientation occurs rather than grain crushing. Generally, there are two end-members of fault rocks: fault gouge and clay smear (Crawford et al. 2002). Fault gouge developed from
308
N. Fredman et al.
Fig. 3. (a) Heterogeneous normal fault from Hartley Steps, UK and (b) the Baba fault, Sinai. Together, the sand lenses are expected to enhance fluid flow across and vertically along the fault. The concept of bringing fault zone architecture into fault modelling is illustrated by (c), where sand lenses are incorporated into the geomodel.
sandstones containing 15–40% shale or mud is referred to as phyllosilicate framework fault rock (Fisher & Knipe 1998) and the permeability in these rocks is controlled by pore characteristics and phyllosilicate content (e.g. Knipe 1992; Fisher & Knipe 1998). Fault rock developed from sequences containing more than 40% clay is referred to here as clay smear and usually forms from clay-rich horizons being dragged and smeared into the fault zone (Lehner & Pilaar 1997).
MODEL SET-UP AND EXECUTION Fault modelling Two simple synthetic fault models were constructed: one 3D fault core model (model A) and one 2D fault transmissibility model (model B) (Fig. 2). Host rock lithology, model dimensions and petrophysical properties were identical in the two models (Table 1). The modelling grid was populated with a
Fault core architecture and fluid flow
309
Table 1. Model dimensions and petrophysical properties Model Model dimension x y z (m) Mudstone Thickness (m) Horizontal permeability (mD) Vertical permeability (mD) Porosity Sandstone Thickness (m) Horizontal permeability (mD) Vertical permeability (mD) Porosity Fault throw (m) Fault dip ()
A
B
450400300
450400300
16 0.1 0.01 0.2
16 0.1 0.01 0.2
80 500 50 0.25 100 80
80 500 50 0.25 100 80
Fig. 4. Model A, grid construction. (a) Outer model dimension and grid resolution are defined manually to correspond with model B, and then (b) a fault core grid is defined and refined manually as a grid segment at the location of the fault. (c) Lithology is defined manually (mud, green; sand, yellow; undefined, red) and, finally, (d) undefined parts of the grid are filtered out.
layer-cake stratigraphy, consisting of four mudstone layers interbedded with three sandstone layers. All modelling operations, including the dynamic fluid flow simulation, were executed in RMS using a corner point grid (Ding & Lemonnier 1995). Model A This includes the fault as a 6 m wide, volumetrically defined fault core grid (Fig. 4); the fault core architecture was implemented by populating this grid with sand lenses and fault rock. In order to generate smooth, realistic geometries of the simulated sand lens objects, the fault core element facies modelling was executed in a separate, finer-scale fault core grid (1080100 grid cells). The fine-scale fault core was then discretely upscaled (Fig. 5) to the coarser simulation grid (52025 grid cells). The effect this upscaling has on fluid flow simulation and sand lens connectivity is not addressed in this study. The modelling workflow sequence for models A and B is shown in Figure 6, while grid details are displayed in Table 2.
Fig. 5. (a) Facies modelling grid (1080100 grid cells) discretely upscaled to (b) simulation resolution (520 5 grid cells).
Fig. 6. Modelling workflow. The left branch corresponds to model A, the right branch corresponds to model B.
The stochastic facies modelling was executed using a standard object-based (Haldorsen & Damsleth 1990; Wietzerbin & Mallet 1994) facies modelling tool, the Facies:Composite module in RMS, originally developed for a wide range of sedimentary facies modelling purposes. In this study, however, the module was used in a non-standard fashion, to model fault core architecture. For modelling purposes, the sand lenses served as the simulated object facies and the fault rock served as the passive ‘background’ facies (Fig. 7). The sand lenses were modelled as ellipsoids, and their body geometry defined by a number of Gaussian parameters (Table 3). A continuous 3D grid parameter (Fig. 8) was used as a conditioning factor for the sand lens object modelling; it describes the spatial probability distribution of lenses as a linear function with a maximum value at the centre of the fault core and decreasing outwards, but is kept constant along fault strike. In order to investigate the sensitivity of fluid flow to petrophysical properties, sand lens fraction and sand lens
310
N. Fredman et al.
Table 2. Grid details for models A, B and the facies modelling grid (where the object modelling was executed)
Total number of active grid cells Host rock grid cell size (m) x-direction y-direction z-direction Host rock, number of grid cells x-direction y-direction z-direction Fault core grid cell size (m) x-direction y-direction z-direction Fault core, number of grid cells x-direction y-direction z-direction Width of fault core (m)
Model A
Model B
Facies modelling grid (fault core only)
6300 (3800+2500)
3800
80 000
40 20 16
40 20 16
n/a n/a n/a
10 20 19
10 20 19
n/a n/a n/a
1.3 20 16
n/a n/a n/a
0.65 20 4
5 20 25 6
n/a n/a n/a n/a
10 80 100 6
Fig. 7. One example of a stochastic fault core element realization (grid slice in the y–z plane, fault parallel, from the ten cells wide facies modelling grid). The modelled object facies (sand lenses) (1), and the passive background facies (2) in dark blue, fault rock. The sand lenses are visualized as a body facies parameter. To left, view from the side, showing the width of the fault core, 6 m wide, ten grid cells in the facies modelling grid.
Fig. 8. A conceptual model of the 3D grid parameter used as a conditioning factor for the lens object modelling in this study.
Table 3. Fault core element modelling parameters used in the object modelling Body parameter
Value
Tolerance
Stochastic shape Length (m) Width (m) Thickness (m) Azimuth () Dip () Sand lens volume fraction (%) Maximum repulsion distance (m) Repulsion intensity Centreplane Rugosity
Ellipsoid 60 60 6 0 90 25/50 50 85 21 21
n/a 6 6 1 0.1 1 10 n/a n/a n/a n/a
Gaussian azimuth and dip parameters define the spatial directivity of the sand lens objects, in this case parallel/subparallel to the fault. Sand lens volume fraction refers to the percentage of sand lenses present in the fault core. Maximum repulsion distance and repulsion intensity describe the object/ object interaction. Centreplane and rugosity attributes are used to control undulation and smoothness of the modelled objects.
connectivity to undeformed host rock, a sensitivity matrix (Fig. 9) was implemented. Sand lenses were modelled as ‘undeformed’ (permeability 500 mD), and ‘slightly deformed’
(permeability 50 mD). Both types of sand lenses (50/500 mD) were given the same porosity as the undeformed sandstone host rock, 0.25. Fault rock permeability was set to 0.01 mD, 0.1 mD and 1 mD in three different model configurations, where 0.01 mD corresponds to a fully developed fault rock that has undergone grain crushing and phyllosilicate intermixing, while 1 mD corresponds to a less intensely deformed fault rock without phyllosilicate intermixing. These permeability values are based on previously published data, where Fisher & Knipe (1998, 2001), Gibson (1998) and Torabi & Skar (2004) all suggested a possible fault rock permeability within this range. The porosity of the fault rock was set to 0.05. In addition to these six permeability combinations, two values for sand lens volume fraction in the core were modelled – 25% and 50% – and sand lens connectivity/non-connectivity to undeformed host rock, yielding a total of 24 model combinations (Fig. 9). In the fault core simulation grid, the sand lens volume fraction, in model set-up with 25% sand lenses, varies from approximately 20% in the outer rim of the fault core grid, to 33% in the centre of the grid (Fig. 10a). The corresponding distribution of sand lens volume fraction for the 50% sand lens volume fraction case is 37% in the outer rim, to approximately 57% in the centre of the grid (Fig. 10b). The lens probability
Fault core architecture and fluid flow
311
Fig. 9. Model A. Fluid flow sensitivity analyses on sand lens volume fraction, sand lens connectivity, and permeability were set up according to this sensitivity matrix. Each of the bottom combinations (1–24) was executed in 20 stochastic realizations, giving 480 fault core element realizations. The 24 combinations are text coded according to the boxes in the matrix and are referred to as ‘flow models’. For example, flow model 1 (25%_YES_500_0.01) refers to a model with 25% sand lens volume fraction, connected 500 mD sand lenses, and 0.01 mD fault rock matrix permeability.
Fig. 10. The fault core simulation grid (52025 grid cells) seen from the side (x–z plane), which conceptually illustrates the ellipsoid shape and distribution of the sand lens bodies, and the concept of connected/unconnected sand lenses. (a) 25% sand lens volume fraction with connected lenses; (b) 50% sand lens volume fraction with connected lenses. (c) 25% sand lens volume fraction with unconnected lenses; (d) 50% sand lens volume fraction with unconnected sand lenses.
factor (Fig. 8) and the ellipsoid object body shape contribute to the outwards decreasing body density trend. Consequently, in the two cases, 25% and 50% sand lens volume fraction, approximately 20% and 37% respectively of the sand lens facies grid cells connect to the undeformed host rock (sandstone or
mudstone). Sand lens connectivity to host rock is defined as having at least one sand lens facies grid cell in physical contact with one host rock facies grid cell. Non-connectivity between host rock and sand lenses was implemented by deterministically setting the outer grid cells (1/5, 5/5) to background fault rock
312
N. Fredman et al.
Fig. 11. Sensitivity matrix for model B. Five fault zone thicknesses, Wfz (0.5–6 m), and three fault zone permeabilities, Kfz (1 mD, 0.1 mD and 0.01 mD) are investigated, and compared to model A.
facies (Figs 10c, 10d). A direct consequence of this modification is a reduction of the number of sand lens facies grid cells that are present in the fault core. The ‘25% lens fraction’ case with unconnected cells now actually contains approximately 19%, and the ‘50% lens fraction’ case approximately 32% sand lens facies cells in the fault core. Model B The fault in model B was implemented with fault transmissibility multipliers (Manzocchi et al. 1999). The fault transmissibility multipliers were calculated with equation (3) (Roxar 2005), where Tmult is the fault transmissibility multiplier, Wfz is the fault zone thickness (m), kfw is the footwall grid block permeability (mD), khw is the hanging-wall grid block permeability (mD), L is the grid cell dimension (m) and kfz is the fault zone permeability (mD):
Tmult =
关共
关k1 + k1 兴 fw
hw
共 兲兲 共 共 兲兲
Wfz 1ⳮ L kfw
Wfz 1ⳮ L + khw
共2Wfz兲 L + kfz
兴
(3)
The fault zone permeability, kfz, was calculated using equation (2). In the study, however, the effect of including sand lenses in model A with respect to simulation response is being investigated, so the fault zone permeability, kfz, in model B, must correspond to the fault rock matrix permeability in model A. In that way it is possible to evaluate sand lens impact on fluid flow. The matching of kfz to the fault rock permeability requires three values for kfz, 0.01 mD, 0.1 mD and 1 mD, which were achieved by the use of a Vshale parameter, in combination with a multiplier called ‘brittle factor’ (Roxar 2005). Vshale is the mud fraction in every grid cell, and the brittle factor is a deterministic multiplier between 1 and 100, which operates uniformly on the entire fault plane and directly on the fault zone permeability, kfz. The idea behind the brittle factor is that brittle deformation by cataclasis and/or brecciation may initiate cracks and microfractures, and actually increase the pore space, thus increasing the effective permeability (Roxar 2005). For the present lithology, equation (2) calculates the fault zone permeability, kfz, to approximately 0.14 mD (SGR=0.16–0.18 , D=100 m). Subsequently, Vshale and brittle factor were used together to adjust 0.14 to the matching values of 0.01 mD, 0.1 mD and 1 mD. Finally, to investigate the fluid flow sensitivity to varying fault zone permeability (kfz), and fault zone thickness (Wfz), a simple sensitivity scheme was set up for model B (Fig. 11). Fluid flow simulation RMSflowsimTM was used for the dynamic fluid flow simulation. It is a multi-phase, three-dimensional, black oil finite difference commercial software package within the IRAP-RMS suite. Both models use the same fluid flow simulation set-up, implemented as a two-phase flow using oil and water. Two
Table 4. Rock, fluid and production parameters used for all fluid flow simulations Initial reservoir pressure (bars) Datum depth, top of model (m) Oil formation factor Water formation factor Oil density (kg m3) Water density(kg m3) Oil viscosity (cp) Water viscosity (cp) Production rate Water injection rate
Initial water saturation Relative permeability Simulation time (years) Rock/water/oil compressibility Oil–water contact (m) Well diameter (m) .
70 at datum depth 550 1.19 1.03 919 1033 1.8 0.38 Liquid rate 1500 Sm3/day, min BHP 30 bars 100% voidage replacement, max BHP 150 bars, max injection rate 1800 Sm3/day 0.25 Figure 12 11 105/bar 1000 0.30
Fig. 12. Relative permeability curve used for all flow simulations, where the same curve was used for all lithologies. Sw is water saturation, kr is relative permeability, krw is water relative permeability, kro is oil relative permeability.
wells were used, one injector (left side of fault) and one producer (right side of fault), and both wells were perforated throughout the model. Details of rock, fluid and production parameters are presented in Table 4. As the fault is implemented with discrete grid cells, the technical framework for including multi-phase flow properties is available, which means that for future studies it is straightforward to include capillary pressure effects and different relative permeability curves for fault rocks. Some 480 fault core element realizations and 15 2D fault transmissibility models were flow simulated. In addition, for
Fault core architecture and fluid flow
313
Fig. 13. Oil recovery and days to water breakthrough plotted as spreading outcome bars for each of the 24 flow models, which means that every bar contains the outcome from 20 stochastic fluid flow realizations. It is then possible to observe the influence that the stochastic variation of sand lens distribution has on fluid flow. (a) Oil recovery after 4018 days simulation time, broken lines represent model A with uniform 1 mD, 0.1 mD and 0.01 mD fault core permeability (0.28, 0.15 and 0.04). (b) Days to water breakthrough (group IV has no water breakthrough). Broken lines represent model A with uniform 1 mD and 0.1 mD fault core permeability (1765 and 366 days) (n is number of realizations).
reasons of comparison, three additional fault core element models with uniform fault core permeability (1 mD, 0.1 mD and 0.01 mD) were flow simulated, giving a total of 498 flow simulated models.
FLUID FLOW SIMULATION RESULTS All fluid flow simulations were run for 4018 days. Monitored parameters include oil recovery, mean injection well bottom
314
N. Fredman et al.
Fig. 14. Oil recovery plotted against simulation time, total run time 4018 days. (a) Model A plotted as envelopes of outcome for all 480 realizations, and an additional three broken lines/curves for uniform fault core permeability (no sand lenses). (b)–(d) Model B with 1 mD, 0.1 mD and 0.01 mD fault zone permeability (kfz), respectively, and the additional broken lines/curves show model A with corresponding uniform fault core permeability. Fault core characteristics for each group are given in the table and every flow model that is included in the group is stated (n is number of realizations).
hole pressure (BHP), days to water breakthrough, and water cut. Simulation response for five fault core parameters was investigated: (p1) sand lens volume fraction; (p2) connectivity between sand lenses and undeformed host rock; (p3) sand lens permeability; (p4) stochastic distribution of sand lenses; (p5) fault rock matrix permeability.
Figure 13a shows oil recovery at the end of simulation after 4018 days, while Figure 13b shows days to water breakthrough. Results from Figures 14 and 15 facilitate a subdivision of the 24 flow models based on oil recovery and days to water breakthrough into six groups – I, II, III, IV, V and VI – each highlighted with a different colour. The first four groups (I–IV)
Fault core architecture and fluid flow
315
Fig. 15. Water cut plotted against simulation time, total run time 4018 days. (a) Model A plotted as envelopes of outcome for all 400 realizations that have water breakthrough (not group IV), and the additional two broken curves with uniform fault core permeability. (b)–(d) Model B with 1 mD, 0.1 mD and 0.01 mD fault zone permeability (kfz), respectively, and the additional broken curves show model A with corresponding uniform fault core permeability. Fault core characteristics for each group are given in table and every flow model that is included in the group is stated (n is number of realizations).
are distinguished based on oil recovery and the last two groups (V–VI) on the basis of days to water breakthrough. The subdivision of the groups is summarized in Table 5; they can also be identified in Figure 13. Figures 14 and 15 show oil recovery and water cut over time for models A and B. The spread of simulation responses for the
six groups, shown as coloured sectors (outcome envelopes), indicates the total spread of all model realizations over time for each group. Three separate simulations with uniform fault core permeability (no sand lenses) for model A are shown in Figures 13, 14 and 15 as ‘uniform fault core permeability’. Figures 14b–d, 15b–d show oil recovery and water cut for model B
316
N. Fredman et al.
Table 5. Compilation of results from the fluid flow simulations Group
Flow models
Characterized by
Common elements for group
I
1, 2, 3, 9, 13, 14, 15, 21
Highest oil recovery of the six groups. Large spreading between realizations.
II
4, 5, 6, 12, 16, 17, 18, 24
Second highest oil recovery of the six groups.
III IV
8, 11, 20, 23 7, 10, 19, 22
V
9, 12, 13, 14, 15, 16, 17, 18, 21, 24 1, 2, 3, 4, 5, 6
Intermediate oil recovery. Late water breakthrough. Lowest oil recovery and no water breakthrough. Narrow spread of realizations. Narrow spread in days to water breakthrough.
500 mD sand lenses. Lenses are connected to host rock OR have 1 mD fault rock permeability in combination with no host rock connection. 50 mD sand lenses. Lenses are connected to host rock OR have 1 mD fault rock permeability in combination with no host rock connection. 0.1 mD fault rock permeability. No sand lens connection. 0.01 mD fault rock permeability. No sand lens connection.
VI
Significantly higher spreading in days to water breakthrough than group V.
Fig. 16. Mean oil recovery plotted together with 1 standard deviation for every parameter investigated in the sensitivity matrix (Fig. 9). Mean injection BHP (87–115 bar) is plotted above oil recovery. 25/50% sand lens volume fraction, YES/NO for sand lens connection to host rock, 50/500 mD sand lens permeability and 1/0.1/0.01 mD fault rock matrix permeability.
using fault zone permeability (kfz) values of 1 mD, 0.1 mD, and 0.01 mD and a fault zone thickness ranging from 0.5 m to 6 m. From Figures 14b–d, 15b–c it is seen that, for the same fault zone thickness (6 m), model A – with uniform fault core permeability – corresponds well to model B. Finally, simulation statistics are summed up in Figure 16, where the investigated parameters are plotted separately, together with the standard deviation. From Figure 16 it can be seen that fault rock matrix permeability and sand lens connection are the most sensitive for parameter change. The large change in oil recovery and standard deviation should be noted when going from connected lenses to unconnected lenses, as compared to the relatively small change in oil recovery and standard deviation when going from 50 mD sand lenses to 500 mD sand lenses INTERPRETATION AND DISCUSSION Fault core architecture and fluid flow Flow simulation results suggest that there is a threshold value at 1 mD (0.002 fault rock/sandstone host rock permeability ratio) for the fault rock permeability, at which the fault rock matrix permeability is the controlling factor for oil recovery, and thus
50% sand lenses in fault core+flow models 9 and 12. Not distinguishable based on oil recovery. 25% sand lenses in fault core (connected). Not distinguishable based on oil recovery.
the fluid flow across the fault. When the fault rock matrix permeability is high (1 mD), the rest of the parameters (p1–p4) are of secondary importance for fluid flow. This is an interesting result and suggests that once the fault rock matrix permeability is high enough, fault core architecture is less important for oil recovery. Interestingly enough, a similar pattern is also observed for model B, with kfz =1 mD (Figs 14b, 15b), where all five thicknesses (Wfz) plot almost as a single line/curve. Apparently, with 1 mD fault zone permeability, the fault zone thickness (Wfz) for this particular study becomes secondary in importance, thus, two fundamentally different fault modelling techniques suggest the same permeability threshold value when fault zone architecture/thickness becomes less important. It must be stressed however, that this is a synthetic, experimental model, which makes it complicated to conclude absolute numbers, but the relative differences in simulation response should still be a valid conclusion. These results are also highly dependent upon the petrophysical input to the model, model geometry, boundary conditions and fluid flow simulation parameters. In order to investigate fault rock matrix permeability threshold further, more quantitative data are needed to support a wider range of fault core deformation. Permeability measurements from outcrop and subsurface data, as well as experimental studies, should be utilized to obtain these data. Also, more realistic geometries should be modelled, and more advanced conditioning functions that honour in-field observations of clustering of lenses near its own host rock. Sand lenses act as substitutes for high fault rock matrix permeability and constitute an important flow path across the fault, regardless of fault rock matrix permeability. However, sand lenses as permeability ‘highways’ generate a larger outcome spread when compared to high fault rock matrix permeability. A uniform, high matrix permeability fault is thus favourable when trying to predict fluid flow across faults. Fieldwork (Childs et al. 1997; Davatzes et al. 2003, 2005) and production data (Ottesen et al. 2005), however, suggest that homogeneous faults are rarely the case, and a heterogeneous flow pattern across faults is more likely. Sand lens permeability has less effect on oil recovery than do sand lens connection and fault rock matrix permeability. However, 500 mD sand lenses show 13% higher oil recovery than 50 mD sand lenses (see groups I and II in Fig. 14a and Fig. 16 for comparison). An order of magnitude increase in sand lens permeability thus increased oil recovery only by 13%, which indicates that sand lenses with reduced effective permeability may also provide communication across faults. The injection BHP difference (Fig. 16) between 50 mD and
Fault core architecture and fluid flow
317
Fig. 17. (a) Oil production rates presented as one p50 probability curve for each of the 24 flow models. (b) Water injection rates for the 24 flow models, also presented as p50 probability curves. Both oil recovery and injection rates cluster according to the group subdivision.
500 mD sand lenses is also minor, which further supports this conclusion. It is complicated to control injection/production rates when comparing realizations with highly contrasting fluid flow properties. Voidage replacement was chosen for well control, which means that the water injection rate is set to maintain a certain mean field pressure, meaning that the injector injects the same amount of liquid that is being produced. When communication across the fault is poor, for example group IV, the pressure builds up on the injector side of the fault. This means that even though the injector injects with a relatively high pressure, the actual water injection rate will still be low because there is no actual communication with the producer. This is why the low permeability models (especially group IV) have high injection pressures and low water injection rates (and low production rates). The injection rates actually have a linear relationship with the production rates and they are both a direct consequence of the permeability and architecture of the fault. The injection/production rates are presented in Figure 17; these rates are thus ultimately controlled by variations in the five parameters (p1–p5). Different injection rates also mean different injection BHP, which also reflect the level of communication across the fault. Mean injection well BHP (Fig. 16) for models with unconnected sand lenses (NO) show elevated pressure, which then also reflects poor communication across the fault. It is possible that groups containing unconnected sand lenses will show even poorer communication, if subjected to open boundary conditions, or implemented in a larger simulation model. Fault core architecture vs. uniform fault core permeability It is important to acknowledge that this study is not trying to compare the results that transmissibility multipliers and volumetric fault modelling would yield when applied to the same lithology, but it is comparing simulation response to different input. Model A, without sand lenses, gives a corresponding flow simulation result (Figs 14b–d, 15b–c) to model B, which implies that any deviation between the two models is due to the
sand lenses and not because two different modelling techniques are used. Figure 14a shows that model A with uniform fault core permeability shows substantially lower oil recovery than model A with sand lenses included. For group IV, the oil recovery varies between 0.03 and 0.06, which is 50–200% higher than the uniform case with 0.01 mD fault rock permeability, which produces 0.02. The oil recovery is, however, insignificant with a fault rock permeability as low as 0.01 mD and no sand lenses connected to host rock, but it still shows a significant relative difference between the two cases. For group III, the difference is also considerable at 0.15 for 0.1 mD uniform permeability, and 0.19–0.27 when sand lenses are included. Group III contains only sand lenses unconnected to the host rock (and 0.1 mD fault rock permeability) and it still produces 25–80% more oil than model A with uniform 0.1 mD permeability. Hence, a continuous sandstone contact through the fault core is unnecessary to increase communication across the fault. For water breakthrough, a different pattern is observed, and it seems that sand lens volume fraction is more important for water breakthrough than it is for oil recovery. Comparing group V to group VI, one notices the big difference in data spreading (Fig. 13b). Water breakthrough is apparently more sensitive to sand lens volume fraction than is oil recovery. As previously implied, sand lenses in the fault core will increase fluid communication across the fault, but it also means that sand lenses may cause early water breakthrough by providing a direct flow path to the producing well. For example, flow model 5 (Fig. 13b) shows water breakthrough after 151 days for the most ‘unfavourable’ sand lens connectivity configuration, whereas the ‘best’ sand lens connectivity configuration in flow model 5 shows water breakthrough after 516 days, a year later. Thus, it is suggested that for producing wells in the vicinity of faults, a random, unfavourable sand lens connectivity configuration in the fault core can actually cause unexpected early water breakthrough, and even killing of the well. Volumetric fault modelling An example of vertical communication into apparently sealed off compartments is illustrated in Figure 18. Model A is able to
318
N. Fredman et al.
Fig. 18. Oil saturation for three time steps (injector to the left, producer to the right). Corresponding slice in x–z plane for models A and B, at time 0, 365 and 3550 days (y=11). (a) Single fault core element realization (model A) with 25% connected sand lenses in fault core and (b) model B with corresponding kfz and Wfz.
capture and model vertical fluid movement within the fault core, based on available data in the simulation model. Model B, however, will handle this model as compartmentalized, because it lacks the capability to implement vertical fluid flow. In model A (Fig. 18a), notice the vertical communication and the fluid saturated fault core, highlighted by circles. Also, notice the highly heterogeneous waterfront in model A after 365 days, caused by the heterogeneous fault core permeability. Model B (Fig. 18b) can only implement intra-fault vertical movements ad hoc, using, for instance, non-neighbour-connections (NNC) in a non-standard fashion (Fig. 19), described in more detail by Pettersen (pers. comm.) and Foley et al. (1998). In particular, compartmentalized reservoirs will probably benefit if introduced to volumetric fault modelling. CONCLUSIONS This study is a first effort towards including fault core architecture in fluid flow simulation. A simple synthetic test case like this can be used to test fundamental issues and may provide clues as to where to focus further work. A number of conclusions may be drawn from the study.
1. The most sensitive parameters for across-fault flow are fault rock matrix permeability, and whether or not the sand lenses are actually connected to the host rock. Sand lenses in physical connection with undeformed host rock may be a substitute for high fault rock matrix permeability and can possibly constitute an important flow path across faults. Sand lens permeability will, however, generate a larger outcome spreading and is thus more uncertain than high fault rock matrix permeability. 2. Flow simulation results suggest that there is a threshold value for the fault rock permeability (1 mD), at which the fault rock matrix permeability will be the controlling factor for flow across the fault, and sand lens architecture becomes less important. In this particular study, this threshold value is 1 mD (fault rock/sandstone host rock permeability ratio 0.002). 3. Unfavourable, intra-fault sand lens connectivity configurations may act as permeability ‘highways’ through faults and cause early water breakthrough in near-fault wells. 4. Sand lens permeability and sand lens volume fraction are of secondary importance for oil recovery, when compared to (1). The implications are two-fold: (i) sand lenses that have
Fault core architecture and fluid flow
Fig. 19. NNC can be used to explicitly assign grid connection between any two grid cells, e.g. from compartment 2 to compartment 4, from the lower white grid cell to the upper white grid cell. This is a deterministic way of assigning grid cell communication, so some sort of pre-knowledge is required, for instance, observed pressure communication or water flow patterns between the assumed compartments.
undergone deformation and, thus, have lower effective permeability, may still provide a fluid flow pathway across faults; (ii) a doubling of the volumetric proportion of sand lenses in the fault core (from 25% to 50%) increased oil recovery by only 13%, suggesting that fluid flow can be relatively insensitive to the actual volume fraction of sand present in the core. 5. Volumetric fault modelling is able to capture and model properties that standard 2D fault transmissibility multipliers cannot handle, such as stochastic modelling of fault core architecture, vertical communication between compartments (non-deterministic) and multi-phase flow properties. Intrareservoir compartment connectivity can be modelled in a non-deterministic, geology driven way, based on stochastic modelling of fault core architecture and petrophysical properties of the fault rocks. This work was carried out as part of the Fault Facies project at the Centre for Integrated Petroleum Research (CIPR). Statoil, ConocoPhillips and NFR are thanked for supporting the project, NFR through the PETROMAKS Programme. The authors would also like to thank Øystein Pettersen, Silje Berg and Arne Skorstad for valuable comments on the manuscript. Roxar is thanked for providing software. Anonymous reviewers are also thanked for thorough and constructive comments, which improved the quality of the manuscript. IRAP-RMS and RMSflowsim are trademarks of Roxar Software Solutions; Petrel is a trademark of Schlumberger; Gocad is a trademark of Earth Decision.
REFERENCES Al Busafi, B., Fisher, Q.J. & Harris, S.D. 2005. The importance of incorporating the multi-phase flow properties of fault rocks into production simulation models. Marine and Petroleum Geology, 22, 365–374. Antonellini, M. & Aydin, A. 1994. Effect of faulting on fluid flow on porous sandstones: Petrophysical Properties. American Association of Petroleum Geologists Bulletin, 78, 355–377. Berg, S.S. 2004. The architecture of normal fault zones in sedimentary rocks: analyses of fault core composition, damage zone asymmetry, and multi-phase flow properties. PhD thesis. University of Bergen, Norway.
319
Berg, S.S. & Øian, E. 2007. Hierarchical approach for simulating fluid flow in normal fault zones. Petroleum Geoscience, 13, 25–35. Bouvier, J.D., Sijpesteijn, K., Kluesner, D.F., Onyejekwe, C.C. & van der Pal, R.C. 1989. Three-dimensional seismic interpretation and fault sealing investigations, Nun River field, Nigeria. American Association of Petroleum Geologists Bulletin, 73, 1397–1414. Caine, J.S. & Forster, C.B. 1999. Fault zone architecture and fluid flow; insights from field data and numerical modeling: Faults and subsurface fluid flow in the shallow crust. Geophysical Monograph, 113, 101–127. Caine, J.S., Evans, J.P. & Forster, C.B. 1996. Fault zone architecture and permeability structure. Geology, 24, 1025–1028. Childs, C., Watterson, J. & Walsh, J.J. 1996. A model for the structure and development of fault zones. Journal of the Geological Society, London, 153, 337–340. Childs, C., Walsh, J.J. & Watterson, J. 1997. Complexity in fault zone structure and implications for fault seal prediction. In: Møller-Pedersen, P. & Koestler, A.G. (eds) Hydrocarbon Seals: Importance for Exploration and Production. Norwegian Petroleum Society (NPF) Special Publication, 7, 61–72. Clausen, J.A., Gabrielsen, R.H., Johnsen, E. & Korstgård, J.A. 2003. Fault architecture and clay smear distribution. Examples from field studies and drained ring-shear experiments. Norwegian Journal of Geology, 83, 131–146. Crawford, B.R., Myers, R.D., Woronow, A., Faulkner, D.R. & Rutter, E.H. 2002. Porosity–permeability relationships in clay-bearing fault gouge. Paper SPE/ISRM 78214. Damsleth, E., Sangolt, V. & Aamodt, G. 1998. Sub-seismic Faults Can Seriously Affect Fluid Flow in the Njord Field off Western Norway – A Stochastic Fault Modeling Case study. Paper SPE49024. Davatzes, N.C., Aydin, A. & Eichhubl, P. 2003. Overprinting faulting mechanisms during the development of multiple fault sets in sandstone, Chimney Rock fault array, Utah, USA. Tectonophysics, 363, 1–18. Davatzes, N.C., Eichhubl, P. & Aydin, A. 2005. Structural evolution of fault zones in sandstone by multiple deformation mechanisms: Moab Fault, southeast Utah. Geological Society of America Bulletin, 117, 135–148. Ding, Y. & Lemonnier, P. 1995.. England, W.A. & Townsend, C. 1998. The effects of faulting on production from a shallow marine reservoir – A study of the relative importance of fault parameters. Paper SPE49023. Evans, J.P. 1990. Thickness–displacement relationships for fault zones. Journal of Structural Geology, 12, 1061–1065. Fisher, Q.J. & Knipe, R.J. 1998. Fault sealing processes in siliclastic sediments. In: Jones, G., Fisher, Q.J. & Knipe, R.J. (eds) Faulting, Fault Sealing and Fluid Flow in Hydrocarbon Reservoirs. Geological Society, London, Special Publications, 147, 117–134. Fisher, Q.J. & Knipe, R.J. 2001. The permeability of faults within siliciclastic petroleum reservoirs of the North Sea and Norwegian Continental Shelf. Marine and Petroleum Geology, 18, 1063–1081. Fisher, Q.J., Knipe, R.J. & Worden, R.H. 2000. Microstructures of deformed and non-deformed sandstones from the North Sea: implications for the origins of quartz cement in sandstones. In: Worden, R.H. & Morad, S. (eds) Quartz cementation in Sandstone. Special Publication of the International Association of Sedimentologists, 29, 129–146. Fisher, Q.J., Harris, S.D., McAllister, E., Knipe, R.J. & Bolton, A.J. 2001. Hydrocarbon flow across faults by capillary leakage revisited. Marine and Petroleum Geology, 18, 251–257. Flodin, E.A., Aydin, A., DurlofskiL, J. & Yeten, B. 2001. Representation of fault zone permeability in reservoir flow models. Paper SPE71617. Foley, L., Daltaban, T.S. & Wang, J.T. 1998. Numerical simulation of fluid flow in complex faulted regions. In: Coward, L., Daltaban, T.S. & Johnson, H. (eds) Structural Geology in Reservoir Characterization. Geological Society, London, Special Publications, 127, 121–132. Foxford, K.A., Walsh, J.J., Watterson, J., Garden, I.R., Guscott, S.C. & Burley, S.D. 1998. Structure and content of the Moab Fault Zone, Utah, USA, and its implications for fault seal prediction. In: Jones, G., Fisher, Q.J. & Knipe, R.J. (eds) Faulting, Fault Sealing, and Fluid Flow in Hydrocarbon Reservoirs. Geological Society, London, Special Publications, 147, 87–103. Fulljames, J.R., Zijerveld, L.J.J. & Franssen, R.C.M.W. 1997. Fault seal processes: systematic analysis of fault seals over geological and production time scales. In: Møller-Pedersen, P. & Koestler, A.G. (eds) Hydrocarbon Seals – Importance for Exploration and Production. Norwegian Petroleum Society (NPF) Special Publication, 7, 51–59. Gabrielsen, R.H. & Clausen, J.A. 2001. Horses and duplexes in extensional regimes: A scale-modeling contribution. In: Koyi, H.A. & Mancktelow, N.S. (eds) Tectonic modelling: A Volume in Honor of Hans Ramberg. Geological Society of America Memoir, 193, 207–220. Gibbs, A. 1984. Structural evolution of extensional basin margins. Journal of the Geological Society, London, 141, 609–620. Gibson, R.G. 1998. Physical character and fluid-flow properties of sandstonederived fault zones. In: Coward, M.P., Daltaban, T.S. & Johnson, H. (eds) Structural Geology in Reservoir Characterization. Geological Society, London, Special Publications, 127, 83–97.
320
N. Fredman et al.
Haldorsen, H.H. & Damsleth, E. 1990. Stochastic Modeling. Journal of Petroleum Technology, 42, 404–412. Heynekamp, M.R., Goodwin, L.B., Mozley, P.S. & Haneberg, W.C. 1999. Controls on fault-zone architecture in poorly lithified sediments, Rio Grande Rift, New Mexico: Implications for fault-zone permeability and fluid flow. In: Goodwin, L.B., Mozley, P.S., Moore, J.M. & Haneberg, W.C. (eds) Faults and Subsurface Fluid Flow in the Shallow Crust. Geophysical Monograph. American Geophysical Union, 113, 27–49. Holden, L., Mostad, P., Nielsen, B.F., Gjerde, J., Townsend, C. & Ottesen, S. 2003. Stochastic Structural Modeling. Mathematical Geology, 35 (8), 899–914. Hollund, K., Mostad, P., Nielsen, B.F. et al. 2002. Havana – a fault modelling tool. In: Koestler, A.G. & Hunsdale, R. (eds) Hydrocarbon Seal Quantification. Norwegian Petroleum Society (NPF), Special Publication, 11, 157–171. Hull, J. 1988. Thickness–displacement relationships for deformation zones. Journal of Structural Geology, 10, 431–435. Knipe, R.J. 1992. Faulting processes and fault seal. In: Larsen, R.M., Brekke, H., Larsen, B.T. & Talleraas, E. (eds) Structural and Tectonic Modelling and its Application to Petroleum Geology. Norwegian Petroleum Society (NPF) Special Publication, 1, 325–342. Knipe, R.J. 1997. Juxtaposition and seal diagrams to help analyze fault seals in hydrocarbon reservoirs. American Association of Petroleum Geologists Bulletin, 81, 187–195. Knott, S.D., Beach, A., Brockbank, P.J., Brown, J.L., McCallum, J.E. & Welbon, A.I. 1996. Spatial and mechanical controls on normal fault populations. Journal of Structural Geology, 18, 359–372. Kristensen, M.B., Childs, C.J. & Korstgård, J.A. 2005. Fault zone structure and smear distribution within soft sediment faults. In: Kristensen, M.B. (ed.) Soft sediment folding – Investigation of the 3D geometry and fault zone properties. PhD thesis. University of Aarhus, Danmark. Lehner, F.K. & Pilaar, W.F. 1997. The emplacement of clay smears in synsedimentary normal faults: inferences from field observations near Frechen, Germany. In: Møller-Pedersen, P. & Koestler, A.G. (eds) Hydrocarbon Seals: Importance for Exploration and Production. Norwegian Petroleum Society (NPF) Special Publication, 7, 39–50. Lescoffit, G. & Townsend, C. 2002. Quantifying the impact of the fault modelling parameters on production forecasting from clastic reservoirs. AAPG Hedberg Research Conference, ‘Evaluating the Hydrocarbon Sealing Potential of Faults and Caprocks’, December 1–5, Barossa Valley, South Australia, 46–48. Lindanger, M. 2003. A study of rock lenses in extensional faults, focusing on factors controlling shapes and dimensions. Cand.Scient. thesis. University of Bergen, Norway. Lindsay, N.G., Murphy, F.C., Walsh, J.J. & Watterson, J. 1993. Outcrop studies of shale smear on fault surfaces. In: Flint, S. & Bryant, A.D. (eds) The Geological Modelling of Hydrocarbon Reservoirs and Outcrop Analogues. Special Publication of the International Association of Sedimentologists, 15, 113–123. López, D.L. & Smith, L. 1996. Fluid flow in fault zones: Influence of hydraulic anisotropy and heterogeneity on the fluid flow and heat transfer regime. Water Resources Research, 32, 3227–3235. Manzocchi, T., Ringrose, P.S. & Underhill, J.R. 1998. Flow through fault systems in high-porosity sandstones. In: Coward, M.P., Daltaban, T.S. &
Johnson, H. (eds) Structural Geology in Reservoir Characterization. Geological Society, London, Special Publications, 127, 65–82. Manzocchi, T., Walsh, J.J., Nell, P. & Yielding, G. 1999. Fault transmissibility multipliers for flow simulation models. Petroleum Geoscience, 5, 53–63. Manzocchi, T., Heath, A.E., Walsh, J.J. & Childs, C. 2002. The representation of two phase fault-rock properties in flow simulation models. Petroleum Geoscience, 8, 119–132. Nøttveit, H. 2005. Fault zone modelling: A hierarchical approach for numerical modelling of fault structures, upscaling and flow simulation. MSc thesis. University of Bergen, Norway. Ottesen, S., Townsend, C. & Øverland, K.M. 2005. Investigating the effect of varying fault geometry and transmissibility on recovery. Using a new workflow for structural uncertainty modeling in a clastic reservoir. In: Boult, P. & Kaldi, J. (eds) Evaluating fault and cap rock seals. AAPG Hedberg series, 2, 125–136. Peacock, D.C.P. & Sanderson, D.J. 1992. Effects of layering and anisotropy on fault geometry. Journal of the Geological Society, London, 149, 793–802. Rivenæs, J.C. & Dart, C. 2002. Reservoir compartmentalisation by watersaturated faults – Is evaluation possible with today’s tools? In: Koestler, A.G. & Hunsdale, R. (eds) Hydrocarbon Seal Quantification. Norwegian Petroleum Society (NPF), Special Publication, 11, 173–186. Roxar 2005. Irap RMS 7.4 User Guide. Roxar, Stavanger. Shipton, Z.K., Evans, J.P., Robeson, K.R., Forster, C.B. & Snelgrove, S. 2002. Structural heterogeneity and permeability in eolian sandstone: Implications for subsurface modeling of faults. American Association of Petroleum Geologists Bulletin, 86, 863–883. Sperrevik, S., Gillespie, P.A., Fisher, Q.J., Halvorsen, T. & Knipe, R.J. 2002. Empirical estimation of fault rock properties. In: Koestler, A.G. & Hunsdale, R. (eds) Hydrocarbon Seal Quantification. Norwegian Petroleum Society (NPF), Special Publication, 11, 109–125. Sverdrup, E. & Bjørlykke, K. 1996. Fault properties and development of cemented fault zones in sedimentary basins. Field examples and predictive models. In: Møller-Pederson, P. & Koestler, A.G. (eds) Hydrocarbon Seals: Importance for Exploration and Production. Norwegian Petroleum Society (NPF), Special Publication, 7, 91–106. Torabi, A. & Skar, T. 2004. Petrophysical properties of faulted sandstone reservoirs – A compilation study. Report, Centre for Integrated Petroleum Research, University of Bergen, Norway. Tveranger, J., Braathen, A., Skar, T. & Skauge, A. 2005. Centre for Integrated Petroleum Research – Research activities with emphasis on fluid flow in fault zones. Norwegian Journal of Geology, 85, 63–71. Wallace, R.E. & Morris, H.T. 1986. Characteristics of faults and shear zones in deep mines. PAGEOPH, 124, 107–125. Walsh, J.J., Watterson, J., Heath, A. & Childs, C. 1998. Representation and scaling of faults in fluid flow models. Petroleum Geoscience, 4, 241–251. Wietzerbin, L.J. & Mallet, J.L. 1994. Parametrization of complex 3D heterogeneities: A new CAD approach. Paper SPE26423. Yielding, G., Freeman, B. & Needham, D.T. 1997. Quantitative fault seal prediction. American Association of Petroleum Geologists Bulletin, 81, 897–917. Yielding, G. 2002. Shale Gouge Ratio – calibration by geohistory. In: Koestler, A.G. & Hundsdale, R. (eds) Hydrocarbon Seal Quantification. Norwegian Petroleum Society (NPF), Special Publication, 11, 1–17.
Received 3 July 2006; revised typescript accepted 5 June 2007.
