quantitative fault seal prediction

ABSTRACT

Fault seal can arise from reservoir/nonreservoirjuxtaposition or by development of fault rock hav-ing high entry pressure. The methodology for eval-uating these possibilities uses detailed seismic map-ping and well analysis.

A first-order seal analysis involves identifying reser-voir juxtaposition areas over the fault surface byusing the mapped horizons and a refined reservoirstratigraphy defined by isochores at the fault surface.

The second-order phase of the analysis assesseswhether the sand/sand contacts are likely to support apressure difference. We define two types of lithology-dependent attributes: gouge ratio and smear factor.Gouge ratio is an estimate of the proportion of fine-grained material entrained into the fault gouge fromthe wall rocks. Smear factor methods (including claysmear potential and shale smear factor) estimate theprofile thickness of a shale drawn along the faultzone during faulting. All of these parameters varyover the fault surface, implying that faults cannotsimply be designated sealing or nonsealing.

An important step in using these parameters is tocalibrate them in areas where across-fault pressuredifferences are explicitly known from wells on bothsides of a fault. Our calibration for a number of datasets shows remarkably consistent results, despitetheir diverse settings (e.g., Brent province, NigerDelta, Columbus basin). For example, a shale gougeratio of about 20% (volume of shale in the slippedinterval) is a typical threshold between minimalacross-fault pressure difference and significant seal.

INTRODUCTION

Faults play an important role in creating hydrocar-bon traps. For a better appreciation of the risks asso-

ciated with fault-controlled prospects and of the pro-duction from faulted fields, it is important to under-stand the processes that contribute to fault seals.Given certain information about a fault cutting areservoir sequence, it is desirable to predict the like-ly sealing behavior of each part of the fault system.

Seals can be considered as membrane seals orhydraulic seals, depending on their likely failuremode (Watts, 1987). The dominant control on fail-ure of membrane seals is the capillary entry pres-sure of the seal rock; that is, the pressure requiredfor hydrocarbons to enter the largest interconnect-ed pore throat of the seal. When the entry pressurehas to exceed the strength of the rock in order tobreach the seal, the seal is considered a hydraulicseal. A number of mechanisms have been recog-nized whereby fault planes can act as a seal (Watts,1987; Knipe, 1992).

(1) Juxtaposition, in which reservoir sands arejuxtaposed against a low-permeability unit (e.g.,shale) with a high entry pressure.

(2) Clay smear (i.e., entrainment of clay or shale)into the fault plane, thereby giving the fault itself ahigh entry pressure.

(3) Cataclasis, which is the crushing of sandgrains to produce a fault gouge of finer grainedmaterial, again giving the fault a high capillaryentry pressure.

(4) Diagenesis, when preferential cementationalong an originally permeable fault plane may par-tially or completely remove porosity, ultimately cre-ating a hydraulic seal.

Juxtaposition seals can be recognized explicitlyby mapping the juxtaposition of units across thefault plane. Although juxtaposition against tightlithologies (such as shales) will give the greatest sealeffect, juxtaposition of two sands with different cap-illary properties will also give rise to a measurablepressure difference across the fault. This differenceis not due to any fault-zone material; nevertheless, itmay correspond to up to 15 m difference in oil col-umn height between the two sands (Berg, 1975).

To identify or predict sealing by clay smear, cata-clasis, or diagenesis requires an ability to relate thesemechanisms to measurable properties of the subsur-face (such as lithology and fault displacement). Afurther desirable feature of any predictive method

897AAPG Bulletin, V. 81, No. 6 (June 1997), P. 897917.

Copyright 1997. The American Association of Petroleum Geologists. Allrights reserved.

1Manuscript received March 7, 1996; revised manuscript received August12, 1996; final acceptance November 21, 1996.

2Badley Earth Sciences Ltd., North Beck House, North Beck Lane,Hundleby, Spilsby, Lincolnshire PE23 5NB, United Kingdom.

We are grateful to Torbjrn Fristad, Dave Phelps, and Jon Arne verlandfor detailed discussions of some of the data sets described in this article, andto Robert Berg and Grant Skerlec for constructive reviews of the initialmanuscript.

Quantitative Fault Seal Prediction1

G. Yielding, B. Freeman, and D. T. Needham2

is that it should use data that are routinely available.At present, significant success has been achieved indeveloping algorithms for predicting seal capacityby clay smear, and this is the main focus of ourpaper. Seal by cataclasis may be similarly under-stood in the near future, whereas seal by diagenesiswill probably be much less amenable to predictionby simple algorithms [e.g., see Knipe (1993) for adiscussion of likely factors].

OBSERVATIONS OF CLAY SMEAR

Weber et al. (1978) presented a series of obser-vations from experiments and outcrop studies.

They noted that faulting of sand-shale sequencescan form a continuous, multilayered, clay gougealong the median slip plane. Clay gouge producedexperimentally proved to be an effective seal towater flow. The structure of the shear zone is typi-cally asymmetric with apices of sand beds pointingin the direction of motion (Figure 1), although thesheared sand does not coalesce into a continuousgouge as does the clay. Observations in open-castmines in an unconsolidated Tertiary deltaicsequence show that the clay gouge zone graduallybecomes thinner away from the clay source bed.The thickest clay gouges observed by Weber et al.(1978) were about 0.5 m, derived from thicker (5m) source beds.

Lindsay et al. (1993) described outcrop studiesof shale smears in a Carboniferous fluvio-deltaicsequence. In contrast to the sequence described byWeber et al. (1978), these rocks were lithified atthe time of faulting (burial depth about 2 km).Lindsay et al. (1993) recognized three types ofshale smear: shear, abrasion, and injection.

(1) Shear smears are analogous to those describedby Weber et al. (1978; Figure 1). The thicknesses ofthe smears generally decrease with distance fromthe source bed, reaching a minimum in the regionmidway between the hanging-wall and footwallbed terminations.

(2) Abrasion smears, which are the commonesttype in this lithified sequence, comprise a wafer-thin veneer that is abraded by a sandstone wall-rock as it slips past a shale bed. These smears tendto be thickest when derived from thicker sourcelayers and when the fault throw is small. Largerthrows tend to erode the shale veneer.

(3) Injection smears are a local response to vol-ume changes during faulting. Injection smear thick-ness is not readily predictable.

Where fault offset is greater than the typical bedthicknesses, the fault zone commonly contains asheared melange of the various wall rocks. Bergand Avery (1995) reported that the capillary entrypressure of such fault rocks can be two orders ofmagnitude greater than adjacent reservoir sands.Alignment of clays in a shear zone introduces astrong directional permeability contrast (see, forexample, Arch and Maltman, 1990).

ALGORITHMS FOR PREDICTING CLAY SMEARPREDICTION

The following factors control the likelihood ofclay/shale smearing: (1) thicker source beds canproduce thicker clay smears, (2) shear-type smearsdecrease in thickness with distance from thesource layer, (3) abrasion-type smears decrease in

898 Predicting Fault Seals

Figure 1Field example of clay smears separating sand-stones from Frechen lignite mines, Germany (Modifiedfrom Weber et al., 1978). Note tapering of clay (black)away from the source bed and the compound nature of theclay smear in the fault zone. (No scale on original figure.)

thickness with increasing throw, and (4) multiplesource beds can give a combined continuoussmear. These relationships imply that a quantitativeapproach to clay smear prediction might be possi-ble. This section outlines different algorithms thathave been proposed by other authors and suggestsfurther possibilities.

Bouvier et al. (1989) presented a study of theNun River field in the Niger Delta. They described aclay smear potential (CSP) as a means of estimatingthe likelihood of clay smearing in areas ofsand/sand juxtaposition on faults. The clay smearpotential is stated to represent the relative amountof clay that has been smeared from individual shalesource beds at a certain point along a fault plane.CSP is stated to (1) increase with shale source bedthickness, (2) increase with the number of sourcebeds displaced past a particular point along a faultplane, and (3) decrease with increased fault throw.

These relationships have recently been expressedmore explicitly by Fulljames et al. (1996) as

(1)

for distance less than fault offset (this algorithm isillustrated in Figure 2a). For a point lying within theoffset between an upthrown shale bed and the cor-responding downthrown bed, the distances fromthat bed and the bed thickness are measured. Thepoint will typically be nearer to either theupthrown termination or the downthrown termi-nation of the bed; the distance is measured fromthe nearest termination of the bed because thesmear profile is assumed to be symmetric. If thepoint lies within the offset of more than one shalebed, smear distances and bed thicknesses are mea-sured for all relevant beds and summed accordingto equation 1. The CSP algorithm models thebehavior of shear-type smears as previouslydescribed; i.e., distance-tapering and an additiveeffect for compound smears.

Bouvier et al. (1989) calibrated their CSP calcula-tions against known sealing and nonsealing faults,and divided the observed range into high, medium,and low CSP. Low CSP represents little chance forthe presence of continuous clay smear seals thatcan trap hydrocarbons. Jev et al. (1993) used thesame technique on the Akaso field (in the NigerDelta) and quoted a CSP of less than 15 as nonseal-ing and a CSP of more than 30 as sealing for faultsbounding undrilled prospects. Bentley and Barry(1991) used CSP values to constrain a reservoir sim-ulation of Cormorant Block IV (Brent province,northern North Sea) and found that on a produc-tion time scale a CSP of 5 marked the generalthreshold for fault sealing.

In the CSP algorithm (equation 1), the shale bedthickness is raised to the power of 2. This has beenjustified by fluid dynamics arguments by Lehnerand Pilaar (1996), who stated that the outflux fromthe shale or clay layer should be proportional to thesquare of its thickness if the material undergoesNewtonian flow. CSP as defined by equation 1 thenhas dimensions of distance. We suggest that CSPcan be considered as one example of a generalizedsmear factor given by (see Figure 2b)

The exponents m and n can be regarded as addi-tional variables whose values may be justified byexperimental or observational studies. When m =n, the result is dimensionless and is therefore inde-pendent of the units of measurement. As n increas-es above 1 (as in the CSP equation), thicker sourcebeds contribute more to the calculation than dothin beds; i.e., they are weighted by a greater pro-portion than their increase in thickness.

Lindsay et al. (1993) proposed a shale smear fac-tor to constrain the likelihood of shale smear conti-nuity. Based on their observations of abrasionsmears in a lithified sequence, they define the shalesmear factor (SSF) as (see Figure 2c)

(3)

The shale smear factor remains constant betweenthe offset terminations because it does not dependon smear distance (although lateral variations infault throw would have a corresponding effect onthe calculated SSF). SSF thus models the profile ofabrasion-type smears. From a study of 80 faults(excluding composite smears), Lindsay et al. (1993)concluded that shale smears may become incom-plete for an SSF greater than 7. Smaller values of SSFare more likely to correspond to continuous smearsand therefore to a sealing layer on the fault surface.The values of SSF are not additive for compoundsmears because thin shales give higher SSF anddominate the sum. In such cases, a simple applica-tion of SSF values would take the minimum value(most sealing) from the relevant shale beds at thatpoint on the fault.

The CSP, smear factor, and SSF algorithmsdescribed depend upon a consideration of thethickness and offset of individual shale beds.However, such an approach may be difficult toapply directly in thick heterogeneous sequencesbecause it is often not feasible to map every shale

Yielding et al. 899

SSFFault throw

Shale layer thickness=

bed and consider its effect at the fault surface. Insuch cases, we suggest the simpler approach ofconsidering only the bulk properties of the

sequence at the scale of the reservoir mapping. Wedefine an attribute called the shale gouge ratio(SGR), which is simply the percentage of shale or


Figure 2Smear factor algorithms for estimating likelihood of clay smear on a fault plane. (a) Clay smear potential(CSP) (Bouvier et al., 1989; Fulljames et al., 1996) given by the square of source-bed thickness divided by smear dis-tance; (b) generalized smear factor, given by source-bed thickness divided by smear distance, with variable exponents;(c) shale smear factor (SSF) (Lindsay et al., 1993) given by fault throw divided by source-bed thickness. Methods (a)and (b) model the distance-tapering of shear-type smears, whereas method (c) models the form of abrasion smears.

clay in the slipped interval. Figure 3a illustrateshow this would be calculated, at a given point on afault surface, for explicit shale beds

(4)

The shale thicknesses are measured in a win-dow with a height equal to the throw; therefore,this window represents the column of rock thathas slid past this point on the fault. The definitioncan be extended for cases where the stratigraphicbreakdown is by reservoir zone rather than byindividual beds. In these cases, the net contribu-tion of fine-grained material from each reservoirzone can be related to the clay content and thick-ness of the zone. The corresponding equation is(see Figure 3b)

(5)

Equation 5 reduces to equation 4 as the zonationapproaches individual beds (assuming shale/claybeds are 100% clay material). The SGR represents,in a general way, the proportion of shale or claythat might be entrained in the fault zone by a vari-ety of mechanisms. The more shaly the wall rocks,the greater the proportion of shale in the faultzone, and therefore the higher the capillary entrypressure. Although this is undoubtedly an oversim-plification of the detailed processes occurring inthe fault zone, it represents a tractable upscaling ofthe lithological diversity at the fault surface; therequired information is simply fault displacementand shale fraction through the sequence.

The gouge ratio algorithm can be extended toinclude other lithologies in addition to shale/clay.For example, if numerous coal beds are presentthey may contribute to the fine-grained fault gouge,although less efficiently than smeared clay. In thiscase the coal units can be included in the summa-tion and down-weighted with respect to the shale.

There is an alternative computation, widely usedin the petroleum industry, that is also referred to bythe abbreviation SGR, standing for smear gougeratio. This method is based on the ratio of sand toshale that has moved past some critical portion ofthe fault plane (Skerlec, 1996). Although not sim-ply relatable to the shale gouge ratio, the smeargouge ratio varies in an inverse manner; i.e., highshale gouge ratio corresponds to low smear gougeratio and vice versa.

Equations 15 involve only a limited number ofvariables; therefore, the clay smear attributes arenot independent. The SSF of Lindsay et al. (1993)

Yielding et al. 901

Figure 3Gouge ratio algorithms for estimating likeli-hood of clay entrainment in the fault gouge zone. Thegouge ratio reflects the proportion of the sealing litholo-gy in the rock interval that has slipped past a givenpoint on the fault. (a) Calculation for explicit shale/claybeds in an otherwise shale-free sequence; D z is thethickness of each shale bed. (b) Calculation for asequence of reservoir zones; D z is the thickness of eachreservoir zone and Vcl is the clay volume fraction inthe zone.

SGRShale bed thickness

Fault throw=

( )

100%

SGR

Zone thickness Zone clay fraction

Fault throw

=

( ) ( )[ ]100%

was defined for the case of single-shale smears(i.e., fault throw less than shale spacing). In suchcases, SSF is the reciprocal of the proportion ofshale in the rock interval displaced by the fault.For example, a shale smear factor (SSF) of 4implies that the shale bed thickness is 0.25 of thethrow; therefore, the rock interval displaced pastthis point on the fault comprises 25% shale, andthe equivalent SGR is 25%. An SSF of 7, whichLindsay et al. (1995) quote as a threshold for con-tinuous smears, is similarly equivalent to an SGRof about 14%. In cases where composite smearsare present, however, calculation of a minimumSSF (from the thickest shale) would not exactlycorrespond to the reciprocal of the smear gaugeratio (SGR) because the SGR uses all the shalebeds in the throw window, not just the one giv-ing the smallest SSF.

None of the attributes described is, in itself, ameasure of sealing capacity of the fault surface.Instead, these attributes are estimates of the rela-tive likelihood of clay smear being developed atthe fault surface. To use these attributes as esti-mates of seal capacity, they must be calibrated indata sets where sealing behavior is documentedfrom well data. The next section describes exam-ples of such calibration.

CALIBRATION OF CLAY SMEAR ATTRIBUTES

Fault seal is commonly recognized by differencesin hydrocarbon contacts on either side of the fault.However, such observations give only a limitedamount of information about the degree and extentof the fault seal. A much more comprehensiveapproach is to map out the difference in pore pres-sure across the fault. This measurement can showwhich parts of the fault surface are responsible forseparating aquifers as well as hydrocarbons, andgives a minimum estimate of the seal capacity. Thefollowing examples describe calibration of the claysmear attributes in terms of the observed across-fault pressure difference.

Our approach is to model the fault surface as athree-dimensional grid, and use this grid as a frame-work for calculating a variety of attributes, such asfault displacement, clay smear parameters, andpore-pressure distributions. Details of the method-ology are given elsewhere (Fristad et al., 1996;Needham et al., 1996; Freeman et al., in press), andonly a brief overview is given here. The typical pro-cedure is as follows.

(1) Produce closely spaced depth sections acrossthe fault. When a seismic interpretation of a struc-ture is converted from two-way traveltime todepth, the fault traces (sticks) picked on the verti-cal seismic sections are usually discarded, and

depth maps are made of the horizon surfaces only.The faults are represented only as gaps in the hori-zon surfaces between the footwall and hanging-wall cutoffs. (These gaps are variously referred toas fault polygons, heave polygons, or separationpolygons.) Thus, reconstructing the fault surfacefrom the fault gaps on the horizon maps is impor-tant. Fault traces are drawn on depth sectionsacross the fault, passing through the fault gaps ineach of the horizons. In our procedure, sectionsare typically at 25100 m spacing.

(2) Generate gridded fault surfaces. The groupof fault traces that comprises a single fault planeis used to construct a grid that accurately match-es the three-dimensional shape of the fault. Thisgrid is the base on which all the calculations offault attributes are performed. The grid spacing isusually comparable to the spacing of the sectionsconstructed across the fault (i.e., 25100 m).Fault throws (vertical offsets) at each horizon areused to interpolate the throw over the fault grid.

(3) Create a geological layer model. In additionto the seismically mapped horizons, a moredetailed stratigraphic template is interpolatedinto the fault analysis using a zonation and thick-nesses based on well data. Posting of these addi-tional horizons for both the footwall and hanging-wall sides of the fault results in a detailed juxtapo-sition plot.

(4) Gather clay-volume data. Petrophysical analy-sis of well data, where available, is used to definethe clay fraction in each stratigraphic unit.

(5) Calculate clay smear attributes. Having con-structed the fault grid, with detailed juxtapositionsand compositional data for all layers, we then calcu-late the clay smear attributes at all grid nodes, usingequations 15 as required.

(6) Compare with pressure data. Where a wellis present on each side of a fault, repeat forma-tion tester (RFT) and other data are used to con-struct a pore-pressure distribution for both thefootwall and hanging wall of the fault. The differ-ence between these distributions defines theacross-fault pressure difference for every point onthe fault surface.

The gridded fault surface, and the attributescalculated on it, can be visualized in perspectiveview (e.g., Needham et al., 1996; Freeman et al.,in press). For reference to hydrocarbon contactsand pore-pressure data, however, it is usuallymore convenient to use a vertical strike projec-tion (Figure 4). A vertical strike projection can bethought of as an isometric view of the fault sur-face looking horizontally and perpendicular tostrike. A number of the examples that follow areillustrated by strike projections. The examplescome from a variety of locations, such as theNiger Delta, North Sea, and offshore Trinidad.


Nun River Field, Niger Delta

Bouvier et al. (1989) gave detailed strike projec-tions of one fault (fault K) within this field, fromwhich we have reconstructed the fault surface.(This surface is stored as a grid with node spac-ings of 100 m laterally and 50 m downdip.) Figure5 shows three strike projections that reproducethe geometric information in the original figuresof Bouvier et al. (1989). The throw pattern (Figure5c) is typical of an antithetic fault, with throwdiminishing downward at the bottom of the faultsurface. The growth-faulted sequence comprisesstacked shoreface sandstones separated by field-wide shales. Hydrocarbons are proven in a num-ber of the footwall reservoirs (Figure 5a), thusthey will generate a buoyancy pressure wherethey are trapped at the fault surface. Comparisonof the hydrocarbon distribution (Figure 5a) withthe juxtaposition pattern (Figure 5b) shows thatthe hydrocarbons are trapped at sand/sand over-laps, suggesting that clay smear contributes to thefault seal.

Figures 6 and 7 show our calculated distribu-tions for the various clay smear attributes definedin the previous section. (The clay smear attributesare calculated on a refined grid of 20 10 m.)Despite the differences between the algorithms,there is a considerable degree of similarity among

the plots (and to the original CSP plot shown byBouvier et al., 1989). The c4 and c5 clays on theupper part of the fault both produce areas of highpotential for clay smear for all attributes. Beneaththis, a relatively clay-poor part of the sequencegives much lower potential for clay smear.Likelihood of clay smear then increases downwardagain as the clays become thicker and more closelyspaced.

The smear factor plots (Figure 6) show the effectof the (1/distance) factor modeling the smear taper.Fringes of higher values occur adjacent to individualclay beds, especially visible for the upper threeclays. Note that for clay smear potential (thickness2)in Figure 6a there is a strong weighting toward thethicker clays (e.g., the uppermost clay, c4, pro-duces values greater than 30 on the left of the dis-play, whereas the third clay, d1, does not give val-ues above 15). The linear smear factor (Figure 6b)has more modest differences between the thickand thin source beds.

Figure 7 compares SSF with SGR. The color cod-ing for SSF in Figure 7a is the inverse of that forSGR (Figure 7b); e.g., SGR = 30% (orangeredboundary) corresponds to SSF = 3.33. This empha-sizes the reciprocal relationship between these twoattributes, particularly for the upper part of thefault where only one clay bed can contribute to thesmear at any point. On the lower part of the fault,the SSF underestimates the smearing potential rela-tive to the SGR because it uses only the thickestclay bed where smears are likely to be compound.Figure 8a shows a crossplot of these two attributesfor all the areas of sand/sand juxtaposition, andconfirms the reciprocal relationship. Note thatadditional curve segments to the right of the mainsegment represent points lower on the fault wheremore than one clay bed contributes to the SGR cal-culation. At these points, the SSF value is an overes-timate (i.e., shale smearing is underestimated bythe SSF value).

A crossplot of CSP against SGR is shown inFigure 8b. Although the detailed distributions onthe fault surface exhibit differences (i.e., there isdistance-tapering with CSP but not with SGR),there is a broad correlation between the twoattributes. The underlying relationship is expectedto be quadratic (rearranging equation 4 in terms ofbed thickness, and substituting in equation 1).Thus, in general terms, the areas of high and lowlikelihood of clay smear are the same for eachattribute, as shown by the overall similarity of colordistribution in Figures 6 and 7. If these attributeswere being used to rank fault-bound traps, similarconclusions would be drawn from each attribute.

Values of likely across-fault pressure differencehave been calculated on the fault grid using thebuoyancy pressure generated by the hydrocarbons.

Yielding et al. 903

Figure 4Diagram illustrating the relationship betweenfault plane and projection plane for a strike projection(as used in Figures 57). Data for the fault plane arestored as a grid, with typical grid spacing of 25100 m.


Figure 5Strike projections of fault K in the Nun River field, Nigeria, constructed from figures in Bouvier et al. (1989).(a) Distribution of footwall lithologies (black = shale, white = sandstone) and proven hydrocarbons in the footwallsands (green = oil, red = gas). (b) Juxtaposition diagram showing footwall shales in black [as in (a)] and hanging-wallshales in gray. White areas are sand/sand overlaps. Blue arrows indicate examples of sand/sand contacts where hydro-carbons are trapped at the fault [compare with arrowed locations in (a)]. (c) Fault throw (vertical offset) in meters.

According to Bouvier et al. (1989), the sequence isnormally pressured above about 3500 m for bothsides of the fault. The pressure differential at thefault surface is therefore caused by the buoyancypressure of the hydrocarbons trapped in the foot-wall sands. Oil densities average 0.83 g/cm3(Evamy et al., 1978) and an in-situ density of 0.3g/cm3 has been assumed for gas. Resultant buoy-ancy pressures reach about 7 bars (100 psi) at thetops of the gas columns. In Figure 9 these across-fault pressure differences are crossplotted againstthe clay smear attributes (for sand/sand overlapsonly). For each attribute, a bounding line can bedrawn to the distribution of data points; this linerepresents an estimate of the seal capacity of thefault. The large cloud of points lying below thedashed line on each plot indicates that manypoints on the fault do not achieve their full sealcapacity because they lie outside the closuredefined by dip away from the fault. The line isimportant as a calibration in describing the maxi-mum pressure difference supportable by that partof the fault, if other factors are favorable.

The plot for clay smear potential (Figure 9a)shows the onset of seal at a little over 10 m, and ata CSP of 30 there is up to 2 bars (30 psi) across-faultpressure difference. Jev et al. (1993), in their studyof the Akaso field, Nigeria, quoted clay smearpotential of less than 15 as nonsealing and CSPgreater than 30 as sealing. The plot for SGR (Figure9b) has a similar distribution to that for CSP. No

fault-sealed hydrocarbons are observed at values ofless than 20% because the shale content of theslipped interval is too low. Above 20%, the maxi-mum observed pressure difference progressivelyincreases, reaching about 7 bars (about 100 psi) atan SGR of about 60%. This relationship agrees wellwith the statement by Weber (1987, p. 97) that itusually requires some 25 to 30% of shale to rendera fault sealing. The distribution for SSF (Figure 9c)is almost a mirror image of that for SGR. Seal is notobserved at SSF greater than 6, which agrees verywell with the outcrop observations of Lindsay et al.(1993) that shale smears may become incompletefor an SSF greater than 7.

Picking the onset of fault seal from plots such asFigure 9 makes the assumption that all of the pres-sure difference is generated by the fault-zone mate-rial. As has been noted, if the juxtaposed sandshave different capillary properties, some smallpressure difference may arise from this effect.Definition of the onset of fault seal should not beregarded as precise.

The plots in Figure 9 show that the greatestacross-fault pressure differences occur where gasis trapped by fault seal. These differences occurfor two reasons. First, the much greater densitycontrast between gas and water (compared to oiland water) gives a much greater buoyancy pres-sure for the gas caps than the oil rims. However, ifthe dip closure were favorable, we might thenexpect that the same fault seal could hold back a

Yielding et al. 905

Figure 5Continued.

much thicker oil column than gas column, givingthe same resultant buoyancy pressure at the fault.A second, complicating factor is that the sealcapacity is likely to be fluid dependent. The inter-facial tension for the gas-water system is generallyhigher than that for the oil-water system (by a fac-tor of up to 1.5), and therefore a given seal rockwill (generally) form a better seal against gas thanagainst oil (Schowalter, 1979). Note that this is the

opposite of the permeability behavior. We areconcerned here with the entry pressure into theseal, not the flow across it when the seal capacityis exceeded. We would therefore expect that afault seal would usually support a larger across-fault pressure difference for gas than for oil. Thisis not true for all oil compositions and depths (seeWatts, 1987), but is likely to apply in the majorityof cases.


Figure 6Smear factor attributes calculated for fault K, Nun River field. Attributes are displayed on a strike projec-tion, as in Figure 5. Footwall and hanging-wall shales are shown in black. (a) Clay smear potential (CSP) as definedby Fulljames et al. (1996); i.e., square of shale bed thickness divided by smear distance. (b) Linear smear factor; i.e.,shale bed thickness divided by smear distance. Note that CSP gives a greater weighting to thicker shales than doesthe linear smear factor (compare fringes around uppermost three shales, labeled c4c, c5c, d1).

Oseberg Syd, Northern North Sea

Fristad et al. (1996) provided a fault-seal calibra-tion for a group of structures in the Brent provinceof the North Sea. The data set was constructedfrom a depth-converted seismic interpretationusing the described method. The faults cut thedeltaic Brent Group reservoir sequence, which atthe time of faulting was less than 500 m deep. Thisshallow burial depth would suggest that the clayswere unconsolidated during faulting, and evidenceof clay smearing is seen in core.

A number of wells drilled in different fault com-partments allow a calibration of the clay smear cal-culations for several faults. Because of the heteroge-neous nature of the Brent Group reservoir, it wasdivided into eight zones (corresponding to thezones in the reservoir model) and SGR was used(equation 4). An example of one analyzed fault isgiven in Figure 10. This figure shows two strikeprojections, the first displaying the juxtaposition ofthe Brent reservoir zones, and the second showingcalculated SGR. Note that there is considerableoverlap of the gross reservoir interval (Brent

Yielding et al. 907

Figure 7Comparison of (a) shale smear factor (SSF) of Lindsay et al. (1993) with (b) shale gouge ratio (SGR) forfault K, Nun River field. For cases with noncompound smears, these two attributes are expected to have a reciprocalrelationship, and the color bars are set up to reflect this.

Group), and yet a difference in oil-water contact ofabout 100 m across the fault. Figure 11 shows thepore-pressure profile across the fault. On the upperpart of the fault where the two gas columns are jux-taposed, the pressure difference reaches 9.5 bars(about 140 psi). The SGR reaches a minimumslightly deeper on the fault, shown on Figure 10bby the yellow area just below the crest of the reser-voir overlaps. Here, the SGR drops below 20% (toabout 18%) and the across-fault pressure differenceis almost 8 bars (116 psi). As a sensitivity analysis,SGR calculations on this fault were rerun using a65-layer stratigraphic model. The results showedonly minor differences compared to the coarsemodel because the algorithm tends to reduce theeffect of stratigraphic complexity as the throwincreases. A coarser stratigraphic zonation is ade-quate, providing the zone thicknesses are less thanthe fault throw.

Figure 12 shows a summary of results from theOseberg Syd data set. Each data point representsthe critical seal on one fault segment; i.e., the maxi-mum pressure difference for small SGR values.There is a very restricted range of SGR where sealbecomes effective. At an SGR of 14%, no seal isobserved, whereas at an SGR of 18%, a pressure dif-ference of almost 8 bars (116 psi) is observed. Thishigh seal capacity is considerably greater than thatseen in the Nun River data set (cf. Figure 9b). Theseobservations are tightly constrained by good-qualityRFT data in the wells on opposite sides of thefaults. Faults where gas is trapped at the fault showhigh across-fault pressure differences, consistentwith the higher interfacial tension expected for thegas-water system. Pressure differences for trappedoil are significantly lower (points marked by filledcircles in Figure 12).

An interesting observation is that on two faultsegments the seal is holding back an aquifer pres-sure (squares in Figure 12). Thus, these faults can-not be membrane seals (Watts, 1987), because amembrane seal results from the surface tensionforces associated with a hydrocarbon phase enter-ing the water-wet seal rock. Such surface tensionforces are not present in the case of higher pres-sure water attempting to enter the seal rock; how-ever, there are two other possibilities. First, thesefaults could be regarded as hydraulic seals (sensuWatts, 1987), with a seal rock (presumably claysmear) that would remain intact until differentialstresses reach a level that would fracture the rock.Alternatively, the pressure drop may indicateaquifer flow across a fault zone of low permeabili-ty; such dynamic seals are termed hydraulic resis-tance seals by Heum (1996). Either way, these faultzones are analogous to shale beds that can vertical-ly compartmentalize a reservoir into differentaquifers. It is possible that the other (gas-sealing)


Figure 8(a) Crossplot showing relationship betweenshale smear factor (SSF) and shale gouge ratio (SGR)for fault K, Nun River field (attributes plotted in Figure7). Data points represent all areas of sand/sand contacton the fault surface; each point corresponds to onegrid node on the fault (attributes gridded at 20 10 m).Note reciprocal relationship between these attributes.(b) Crossplot showing relationship between CSP (Fig-ure 6a) and SGR (Figure 7b) for fault K, Nun River field.Although there is a broad scatter, there is an underly-ing quadratic relationship between these twoattributes.

faults in this data set are also strong enough tobehave in the same way, but it is not possible toconfirm this without fault-rock samples.

Northern North Sea, Data Set 2

A second example from a Brent province data setis shown in Figure 13. As with the Oseberg Sydexample, this data set is based on a depth-convertedseismic interpretation. For one fault, the pore-pressure profile in both walls is constrained by welldata. In this case, the fault compartments have a

shared aquifer and also a continuous gas cap; theoil rims, however, are of different thicknesses (seeFigure 13a). This fluid distribution is diagnostic of alack of fault seal on the upper and lower parts ofthe fault, allowing across-fault equalization of pres-sure in the gas cap and in the aquifer. At the level ofthe oil there is sufficient seal capacity to support apressure difference of up to 3 bars (about 43 psi)(see Figure 13b).

This distribution of fluids could be interpretedas implying that the fault leaks for gas but seals foroil. However, such an interpretation would be con-trary to the likely relative values of the gas and oil

Yielding et al. 909

Figure 9Plots for fault K, Nun River field, illustrating the relationship between across-fault pressure difference and(a) clay smear potential (CSP), (b) shale gouge ratio (SGR), and (c) shale smear factor (SSF). Each point corresponds toone grid node on the fault surface (attributes gridded at 20 10 m). Nodes where gas is trapped against the fault (redin Figure 5a) are shown as crosses, those where oil is trapped against the fault are shown as filled circles. The lineslabeled seal capacity indicate the maximum pressure difference sustainable by a particular value of the attribute.

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interfacial tensions (see previous discussion).Instead, we favor an explanation based on spatialvariation of the fault-zone properties as a result oflithological layering in the Brent Group wall rocks.SGR was calculated for the fault surfaces in thisdata set using a Brent Group zonation of 15 layers.A characteristic feature of many of the faults is thatthey display areas of low SGR (

each data point represents one reservoir top, withobservations from many different faults. The distri-butions of points are similar to those for Nun River(Figure 9b, c), although with slightly different posi-tions for the bounding lines. In comparison withthe other data sets discussed, seal appears to bemore readily developed; i.e., pressure support isinitiated at slightly lower SGR.

Calibration Summary

Figures 914 illustrate fault-seal capacity in termsof one or more of the attributes defined in the pre-vious section. With each of the attributes, there is atrend or threshold that would allow likely sealcapacity (static pressure difference) to be predictedfrom the value of the attribute. In general, similar

Yielding et al. 913

Figure 13Fault-seal calibration for North Sea data set 2. (a) Schematic cross section across the juxtaposed BrentGroup fault blocks. GOC-1 and OWC-1 are the gas-oil and oil-water contacts, respectively, for the hanging wall(dashed lines); GOC-2 and OWC-2 are the gas-oil and oil-water contacts, respectively, for the footwall (solid lines).(b) Pore-pressure profiles for the two sides of the fault (dashed and solid lines, respectively). Aquifer and gas pres-sure gradients are equal on both sides of the fault, but there is seal between the oil legs, which have different thick-nesses. (c) Crossplot of shale gouge ratio (SGR) against pressure difference for parts of the fault that separate thehigher pressured oil leg from the other side of the fault.

predictions about a fault would be made from anyof the attributes; i.e., there are strong correla-tions between them (Figure 8). In deciding whichattribute to use for fault-seal prediction in a particulardata set, the main factor should be the format of theavailable lithological data. In simple, blocky sand-shale sequences, either smear factors or gouge ratiowould be appropriate. Where the sequence is hetero-geneous and lithological information is available on a

zone-by-zone basis, the gouge ratio calculation isbetter suited.

Comparison of a calculated attribute betweendata sets shows strong similarities in the inferredrelationship between the attribute and seal capaci-ty. The relationship between seal capacity and SGR,for example (Figures 9b, 12, 13b, 14b), shows thatfor trapped oil the seal threshold is of the order of1020% SGR. Higher values of SGR can supportprogressively higher pressure differences. The sup-portable pressure difference is higher for gas as thetrapped phase (e.g., Figure 12), in keeping with thehigher interfacial tension for the gas-water system.The similarity of the plots is encouraging in thatthey represent data from different sequences in dif-ferent areas. This implies that attributes such asSGR can be used predictively across a range of envi-ronments. Detailed differences in the calibrationsin different areas are probably due to factors suchas shale lithology and degree of consolidation,among others.

The inferred relationship between capillary sealand shale gouge ratio is strongly supported by mea-sured properties of natural gouges. Gibson (inpress) presents entry and breakthrough pressures(from capillary injection tests) for a global data setof fault-gouge samples, with the results expressedin terms of the effective pore-throat radius. Thosedata are replotted in Figure 15, with the pore-throatradius converted to a capillary pressure using typi-cal oil-water properties under reservoir conditions(see figure caption for details). There is a strongtrend of increasing capillary pressure with increas-ing percentage of phyllosilicates in the gouge sam-ple. This compositional parameter would beexpected to correspond in a general way with theupscaled parameter, shale gouge ratio. Insofar as afault seal is only as strong as its weakest point, wewould expect the lower bound of the data range inFigure 15 to be relevant to evaluation of seals in thesubsurface. When the gouge contains

and typically involves grain breakage and comminu-tion. This results in a significantly reduced grain sizein the fault zone and increased grain packing. Thepore-throat radii are consequently constricted, andthe fault rock can support a pressure differencebecause of the increased capillary entry pressure.

Knipe (1992) reviewed microstructural studies offault-zone rocks and noted that cataclastic faultgouge may have pore-throat radii less than 0.001mm, capable of supporting an oil column height ofup to 300 m. Observations of permeability acrosscataclastic slip bands (Antonellini and Aydin, 1994;Heath et al., 1994) showed that cataclasis mayreduce the permeability by up to four orders of mag-nitude in high-porosity sandstones, even with only afew centimeters of displacement. Mandl et al. (1977)noted in an experimental study that gouge formationwas restricted to initial displacement and did notincrease thereafter. Crawford (1995) also showedexperimentally that initial displacements producedrastic reductions in permeability.

There is some difficulty relating the microstruc-tural observations to field-scale fault geometries. Anumber of studies (e.g., Robertson, 1983; Scholz,1987; Evans, 1990; Knott, 1994) indicated thatthickness of the zone of deformation around a fault

correlates approximately linearly with displace-ment, such that the thickness is 0.010.1 of the dis-placement. If permeability continued to decreaseas the fault zone became thicker, measured faultdisplacement could be used as a guide to the per-meability reduction. However, this seems unlikely,because the thicker fault zone is composed of anas-tomosing and en echelon strands that each have alower displacement than the total zone. Reservoirmodeling studies of this geometry (Omre et al.,1994) indicated that the transmissibility of the zonemay be preserved by virtue of flow around individ-ual fault strands; therefore, cataclasis in a faultedsand probably has a significant effect on seal capac-ity at low displacements and then only increasesslowly as displacement continues to increase.Fulljames et al. (1996) suggested that the continu-ity of the fault zone tends to increase with increas-ing displacement, and therefore there may be a crit-ical displacement at which the transmissibilitydrops to zero (i.e., the fault zone becomes a seal).This threshold is likely to be dependent on detailedhost-rock lithology (e.g., porosity and subsequentdiagenesis).

A major control on the development of a cata-clastic gouge is the magnitude of the stress on thefault plane during movement (Watts, 1987). Thus,generation of cataclastic gouge should be favoredby greater depth and reverse and strike-slip faultingrather than extensional faulting. However, experi-mental studies (Mandl et al., 1977) showed thatsome grain breakage can occur at low overburdenpressures corresponding to only 100 m of burial.There is a common observation of cataclastic webstructure in DSDP/ODP cores from active margins.This structure occurs even in poorly consolidatedsands with maximum burial depths of a few hun-dred meters (e.g., Lucas and Moore, 1986).

Cataclastic deformation bands commonly haveassociated with them features of clay smear or dia-genesis. Fractures in clay-bearing sandstones showincreased packing and a strong alignment of clayparticles, enhancing their seal potential (e.g., Kentet al., 1995; Fristad et al., 1996). Zones of cataclasisare commonly affected by cementation, and quartzovergrowths develop on the fractured grains,reducing the pore-throat sizes (or, in extreme cases,blocking the pore throats), again leading toenhanced seal potential.

CONCLUSIONS

Observations of clay/shale smears indicate thattheir geometries are dependent on the thickness ofthe source bed and on the fault throw or smear dis-tance. These relationships have been used to definea number of fault-surface attributes that describe

Yielding et al. 915

Figure 15Plot of capillary entry and breakthrough pres-sures for fault gouge samples, using data from Gibson (inpress). Gibsons effective pore-throat radius has beenconverted to capillary pressure using Pc = 2 g cos q /R, taking g = 40 mN/m (Firoozabadi & Ramey, 1988) and cos q= 1. Cataclastic deformation bands consist of fractureddetrital grains, solution deformation bands have weld-ed grain contacts implying pressure solution, and com-plex deformation bands are cataclastic bands overprint-ed by pressure solution. Note that the increasing percentof phyllosilicates corresponds to the increasing mini-mum capillary pressure.

the likelihood of smears being developed at a par-ticular point on the fault surface. These attributesare as follows.

(1) Clay smear potential (CSP) (Bouvier et al.,1989), which is the sum of (thickness2/distance)for shale beds.

(2) Generalized smear factor, which is the sum of(thicknessn/distancem) for shale beds, with optionalexponents for both thickness and distance.

(3) Shale smear factor (SSF) (Lindsay et al.,1993), equal to throw/thickness.

(4) Shale gouge ratio (SGR), which is netshale/clay percentage in the slipped interval.

Applying these algorithms to fault surfaces in avariety of data sets shows that they can be relatedto the pore-pressure difference supported locallyby the fault; i.e., to the seal capacity. Threshold val-ues of the attributes that differentiate between seal-ing and nonsealing parts of the fault can be identi-fied. For example, the threshold value for SGR isabout 1520% (cf. 47 for SSF). A progressiveincrease in seal capacity is observed with increas-ing gouge ratio or CSP (or decreasing SSF). Thechoice of which particular algorithm to use forfault-seal prediction in a particular data set shouldbe based on the available data. Smear factor meth-ods require defining individual shale beds, whereasthe gouge ratio method can also be applied tozoned reservoir sequences.

Ideally, any study that uses such attributes infault-seal prediction needs to be locally calibratedby appropriate pressure data, as described for theexamples in this paper. However, even withoutsuch calibration in a particular study, useful resultscan still be obtained. For example, in an explo-ration context it may be possible to rank a series offault-bound prospects on the basis of their relativevalues of a fault-seal attribute, although the localrelationship between attribute and seal capacitymay be unknown.

There is a matter-of-fact tendency among geolo-gists to refer to faults either as sealing or nonseal-ing. This is an oversimplification, because the com-bination of lithologic and throw variation ensuresthat the fault rock will be heterogeneous. Themethods described in this paper predict that a sin-gle fault may seal over some regions of its surfaceand leak over others.

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Bouvier, J. D., C. H. Kaars-Sijpesteijn, D. F. Kluesner, C. C.Onyejekwe, and R. C. Van der Pal, 1989, Three-dimensionalseismic interpretation and fault sealing investigations, NunRiver field, Nigeria: AAPG Bulletin, v. 73, p. 13971414.

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Yielding et al. 917

Graham Yielding

Graham Yielding received a B.A.degree in natural sciences fromCambridge University in 1979, fol-lowed by a Ph.D. in geophysics in1984. He then worked for Britoil inGlasgow as a seismic interpreter,before joining Badleys in 1988. Hiscurrent interests include fault sealanalysis and fault populations.

Brett Freeman

Brett Freeman graduated fromLondon University in 1980 with aB.Sc. degree and from NottinghamUniversity in 1983 with a Ph.D. Hetaught structural geology at theUniversity of Newcastle-upon-Tynefor five years prior to joining Badleysin 1988. His current interestsinclude fault geometry and the seal-ing behavior of faults.

Tim Needham

Tim Needham received his B.Sc.degree in geology from ImperialCollege, London (1981) and aPh.D. from the University of Leeds(1984). After postdoctoral researchat Durham University and a lecture-ship in structural geology atGoldsmiths College, London, hejoined Robertson Research Inter-national in 1988. He joined BadleyEarth Sciences as a structural geolo-gist in 1992. His recent interests have centered on faultseal analysis, fault populations, and analysis of naturalfracture systems.

ABOUT THE AUTHORS

quantitative fault seal prediction

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