Fault-Seal Analysis Using a Stochastic Multifault Approach.pdf

AUTHORS

William R. James ExxonMobil UpstreamResearch Co., P.O. Box 2189, Houston, Texas

Bill James earned his B.S. degree in geologyfrom Earlham College and a Ph.D. from North-western University in 1968. He moved on tocareers at the Corps of Engineers and the U.S.Geological Survey before starting at ExxonProduction Research (now ExxonMobil Up-stream Research) in 1979. He worked there,specializing in statistical applications in geol-ogy, assessment, and seal analysis, until hisrecent retirement.

Lee H. Fairchild 4614 Baldwin Creek Dr.,Mt. Hood, Oregon; [email protected]

Lee Fairchild has a B.A. degree in geology fromthe University of California, Berkeley, and anM.S. degree and a Ph.D. from the Universityof Washington. He joined Exxon ProductionResearch (now ExxonMobil Upstream Research)in 1985, working on structural geology andfault-seal analysis. In 1999, he moved to StarpathExploration as a geophysicist, prospecting insouth Texas. In 2001, he began an independentconsulting business.

Gretchen P. Nakayama deceasedGretchen Nakayama earned her B.S. and M.S.degrees from the State University of New York,Rochester, and her Ph.D. in geology from theUniversity of California, Davis, in 1990. Shestarted her career at Exxon Production Research(now ExxonMobil Upstream Research) imme-diately, specializing in fault-seal analysis. Weare saddened by our recent loss of Gretchento cancer.

Susan J. Hippler ExxonMobil ExplorationCompany, 233 Benmar, Houston, Texas;[email protected]

Susan Hippler has a B.A. degree in geologyfrom Augustana College and a Ph.D. from theUniversity of Leeds (1989). She then joinedExxon Production Research (now ExxonMobilUpstream Research) as an expert in fault-zonecharacterization and fault-zone migration. Shetransferred to ExxonMobil Exploration Co. in1996, specializing in applications of integratedtrap analysis to exploration, development, andproduction problems.

Fault-seal analysis using astochastic multifault approachWilliam R. James, Lee H. Fairchild,Gretchen P. Nakayama, Susan J. Hippler, andPeter J. Vrolijk

ABSTRACT

We have developed a stochastic multifault method for analysis of

the impact of stratigraphic uncertainty on cross-fault leakage at

sand-sand juxtapositions. This method assumes that all sand-sand

juxtapositions leak across the fault. Stratigraphic uncertainty is

modeled by stochastic variation of stratigraphic stacking. Struc-

tural uncertainty is addressed through variation of the input. Our

objectives were to quantitatively predict the impact of uncertain-

ties in stratigraphic and structural input and to simulate the com-

plex system of structural spills and juxtaposition leak points that

control hydrocarbon contact levels in traps with stacked reservoir

systems and many faults.

Three examples demonstrate how this stochastic multifault

method has helped us evaluate uncertainty and understand complex

leak fill-and-spill controls. The Ling Gu prospect demonstrates that

widespread cross-fault leakage on two crestal faults with throw

changes that exceed seal thickness causes only a single hydrocarbon

column to accumulate in multiple-stacked reservoirs. This column is

controlled by a juxtaposition leak point on a third, deeper fault. We

have learned from examples like Ling Gu that the relative size of

throw change and seal thickness is a fundamental control on the

probability of cross-fault juxtapositions. An example at prospect A

demonstrates the sensitivity of hydrocarbon entrapment to small

faults in a sand-prone interval with thin seals. The prospect A

analysis shows that if seals are thin, faults or channel incisions below

seismic resolution can leak hydrocarbons out of stacked reservoirs

that are interpreted as unfaulted on seismic data. This introduced

significant predrill uncertainty and risk. Guntong field demonstrates

that a thin sand in a juxtaposed seal interval can introduce large

uncertainty in the prediction of hydrocarbon columns.

These examples and many other analyses using the method dem-

onstrate how small changes in stratigraphic and structural input

to a fault-seal analysis can introduce significant uncertainty in the

predicted range of hydrocarbon volumes. Such uncertainties need to

be directly and systematically accounted for in a fault-seal analysis.

AAPG Bulletin, v. 88, no. 7 (July 2004), pp. 885904 885

Copyright #2004. The American Association of Petroleum Geologists. All rights reserved.

Manuscript received June 12, 2003; provisional acceptance August 27, 2003; revised manuscriptreceived October 23, 2003; final acceptance February 18, 2004.

DOI:10.1306/02180403059

INTRODUCTION

Our application of traditional fault-seal analysis repeatedly en-

countered problems because of uncertainty in our stratigraphic

and structural input. Furthermore, we recognized that in systems

with numerous faults and stacked reservoirs, our traditional anal-

ysis methods were unable to simulate the extremely complex sys-

tem of structural spills and juxtaposition leak points that control

hydrocarbon contact levels. This paper describes a method we de-

veloped to evaluate the impact of stratigraphic and structural un-

certainty on our fault-seal analysis and the significant lessons that

we learned as a result of applying this approach.

Our traditional fault-seal analysis approach in a clastic section

has been to deterministically identify sand-sand juxtapositions at

faults, evaluate which will leak or seal, and consequently predict

the hydrocarbon fill of the reservoirs. This traditional analysis typ-

ically starts with the construction of fault-plane sections, which are

cross sections that depict the reservoirs on both sides of each fault

(also called Allan diagrams; Allan, 1989). Because beds are offset

across a fault, reservoirs can come in contact at the fault and po-

tentially communicate with each other across the fault. This rela-

tionship is recognized on a fault-plane section by the intersection,

or juxtaposition, of the sands. We describe these fault-plane sec-

tions as deterministic because they depict a single stratigraphy that

is assumed to correctly depict the actual stacking of reservoirs and

seals. The stratigraphy is typically derived from a nearby well or

from seismic stratigraphic models or other sources if wells are not

available. Juxtapositions are important because leakage at juxta-

positions may limit the accumulation of hydrocarbons in simple

fault-block traps. For this reason, juxtapositions are commonly re-

ferred to as leak points. If hydrocarbons fill a reservoir down to a

juxtaposition leak point, leak across the fault into the sand on the

opposite side, and then migrate away from the trap, then hydro-

carbons can fill the reservoir no further, and the leak point fixes the

hydrocarbon contact. Furthermore, juxtapositions on internal faults

in a structural closure facilitate communication between reservoirs

in different fault blocks.

The second step in our traditional approach has been to eval-

uate the seal potential of fault-zone materials. Outcrop observa-

tions have shown that faults in clastic sequences typically have a

clay-prone gouge. Capillary seal by the gouge may impede the flow

of hydrocarbons across faults between juxtaposed sands. Tradi-

tional fault-seal analyses typically attempt to model this potential

using some algorithm to predict the sealing potential of gouge

(Downey, 1984; Bouvier et al., 1989; Jev et al., 1993; Gibson, 1994;

Yielding et al., 1997; Alexander and Handschy, 1998; Bretan et al.,

2003; Davies and Handschy, 2003; Davies et al., 2003; Gibson and

Bentham, 2003). In some cases, the analysis may include a predic-

tion of the potential for enhanced seal from other processes such as

cementation. Fault-zone materials can be important when they seal

a juxtaposition that otherwise would allow hydrocarbons to migrate

Peter J. Vrolijk ExxonMobil UpstreamResearch Co., P.O. Box 2189, Houston, Texas;[email protected]

Peter Vrolijk earned his B.S. and M.S. degreesfrom the Massachusetts Institute of Technol-ogy and his Ph.D. in geology from the Uni-versity of California, Santa Cruz, in 1982. In1989, he joined Exxon Production Research(now ExxonMobil Upstream Research), doingresearch on a wide range of topics, includingmost recently fault-seal analysis and faulttransmissibility.

ACKNOWLEDGEMENTS

The authors thank ExxonMobil, its Malaysian af-filiate ExxonMobil Exploration and ProductionMalaysia Inc., Petronas, Sable Offshore EnergyInc. and its partners Shell, Imperial Oil Ltd., andExxonMobil Canada for permission to publishthis paper. We thank Eric Schmidtke, Mohd.Tahir Ismail, and Stan Malkiewicz for obtainingpermission. David Reynolds and David Phelpsprovided material for the paper. Brooks Clark,Steve Davis, and Rod Meyers helped formu-late the goals and subject matter of the man-uscript. Yao Chang and Brooks Clark wereinstrumental in software development. Re-views by Eric Schmidtke, Emery Goodman,Tom Hauge, Dave Reynolds, Tom Bultman,George Ramsayer and AAPG reviewers LaurelAlexander, Terrilyn Olson, Graham Yielding,and John Lorenz improved this manuscript;their time and dedication are greatly appre-ciated. The authors are indebted to ourmany colleagues at ExxonMobil who havegreatly improved stochastic multifault analysisthrough their discussions and application ofthe technique.Please direct inquiries regarding reprints orfurther information to Peter Vrolijk.

886 Fault-Seal Analysis Using a Stochastic Multifault Approach

away from the trap because this allows hydrocarbons to

fill to a deeper level. Analyses of enhanced seal from

fault-zone materials are combined with juxtaposition

analysis to predict the level of fill of reservoirs in the trap.

We developed the stochastic multifault analysis

approach to address two key issues that we encoun-

tered with this traditional approach. The first issue is

that stratigraphic and structural uncertainty, which

causes uncertainty in the definition of juxtapositions

across faults, should be addressed systematically and

consistently. Stratigraphic uncertainty may arise from a

change in reservoir stacking patterns between the near-

est well control and the prospect of interest, or because

there are no nearby wells, so that seismic facies or strat-

igraphic models provide the only stratigraphic constraint

at a prospect. Structural uncertainty is caused primarily

by a degradation in seismic quality near faults, a re-

duction in computer-contouring accuracy near faults,

or generally poor seismic resolution, although there

can be many other sources of uncertainty. We have

observed that there is commonly enough stratigraphic

or structural uncertainty that it invalidates the use of

deterministic fault-plane sections because these sec-

tions incorrectly depict juxtapositions.

The second issue is the need to simulate the ex-

tremely complex system of structural spills and juxta-

position leak points that control hydrocarbon contact

levels in multifault traps with stacked reservoir sys-

tems. The process of hydrocarbon fill and spill can be

quite complicated, even in simple structures. For exam-

ple, Figure 1a shows a simple, faulted anticline with

three reservoirs and three fault blocks that are separated

by two faults. Reservoir A communicates with itself be-

tween blocks 1 and 2 at a leak point with an elevation of

1150 m. It then communicates with reservoir C at a leak

point on the second fault (1200 m) and with reservoir B

at a deeper leak point on the first fault (1230 m). For

simplicity, assume that hydrocarbons migrate into the

trap from the left into reservoir A. As reservoir A fills, it

first leaks to itself at the 1150-m leak point (Figure 1b),

then it connects to reservoir C at the 1200-m leak point

(dashed line, Figure 1b). Finally, it connects to reser-

voir B in block 1 at the 1230-m leak point (Figure 1c).

Ultimately, these leak points allow a common hydro-

carbon column to accumulate in these reservoirs that is

controlled by a structural spill at 1240 m in reservoir C in

block 3 (Figure 1d). Thus, if reservoirs A and B in block 1

are the drilling target, we would expect them to have a

common contact that is controlled by a structural spill

in a separate sand at the opposite end of the structure.

Communication across three leak points on two faults

creates this common hydrocarbon system. In our ex-

perience, these fill-and-spill systems become so com-

plex in multifault traps with many stacked reservoirs

that it is impractical (because of time constraints) or

impossible to identify them and predict the resulting

hydrocarbon contact using traditional fault-plane sec-

tions. A computerized method is needed to track fill

and spill at juxtaposition leak points and to determine

resulting contact levels.

STOCHASTIC MULTIFAULTANALYSIS PROCEDURE

Although it is important to generally understand the

procedure we follow to conduct an analysis, our ap-

proach is merely one of many that could be employed.

Consequently, the details of the software that we de-

veloped are less important than the issues (above) that

the software seeks to redress or the lessons that we

learned from its application.

Stochastic multifault analysis addresses the effect

of stratigraphic and structural uncertainty on cross-fault

leak where reservoirs are juxtaposed on faults. It does

not address dip leak along fault zones (instead of across

fault zones) or seal enhancement by fault gouge. Dip

leak appears to be prevalent primarily in areas where

effective stresses are conducive to tensile or shear fail-

ure in the fault zone (G. Yielding, 2003, personal com-

munication). Consequently, we can generally anticipate

the cases in which it is likely to be important and account

for the process with a separate analysis.

Initially, we chose not to address the more com-

plex problem of seal by fault-zone materials, because

we felt that we needed to establish whether it was

useful to incorporate uncertainty into a juxtaposition

analysis before undertaking this more difficult prob-

lem. Although we recognize that this is a shortcoming

of the approach, we concluded that it is prudent to

first test the utility of uncertainty analysis for the sim-

pler juxtaposition problem before attacking the much

more complex problem of uncertainty in seal by fault-

zone materials. We incorporated into the software the

ability to seal any chosen set of leak points, which

provided a procedure to evaluate the potential effects

of sealing gouge. We have been surprised by the suc-

cess that we have had using only juxtaposition analysis

that incorporates uncertainty, to such a degree that it

has caused us to undertake a reassessment of our anal-

ysis of gouge seal. This issue will be addressed in detail

in the discussion section.

James et al. 887

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We chose two approaches to stochastic multifault

analysis: one that is one-dimensional (1-D) and another

that is fully three-dimensional (3-D). One-dimensional

models attempt to describe complex 3-D relationships

in a simple 1-D manner by having laterally uniform

stratigraphy and vertically uniform structure. The ben-

efit of these simplifications is speed. Models can be

generated rapidly (typically in minutes to 1 hr) and

modified quickly for sensitivity analysis. The limita-

tions of this approach are that complex structural and

stratigraphic relationships such as significant down-

dip fault throw gradients or channelized stratigraphic

bodies are simulated by indirect constructs, instead

of geometrically correct definitions. Intelligent appli-

cation of the 1-D method can commonly minimize

the impact of these simplifications. A fully 3-D ap-

proach employs a faulted geologic model with non-

uniform stratigraphy that requires more knowledge and

requires considerably longer to build or modify for sen-

sitivity analysis. We favor the 1-D approach for most

exploration problems where detailed facies variability

is commonly poorly known and deadlines are short;

the benefit of speed generally outweighs the limita-

tions of the 1-D model. The 3-D approach is com-

monly preferable for production problems where greater

precision is required and more information and time

are generally available. In this paper, we will focus on

the 1-D approach.

Assumptions for One-Dimensional Analysis

Two assumptions were made to employ 1-D models.

The first is that sands and shales are laterally uniform

and continuous, which allows us to greatly simplify the

input of both stratigraphic and structural data. Our

specific requirement is that each sand represented in a

model is sufficiently continuous to reach important

leak points and structural spills in the trap. Clearly, this

assumption may be incorrect in traps with narrow

channel sands. In these cases, our models may have

fewer, more continuous sands than the prospect we are

modeling. The effect that this assumption has on our

results will be addressed later in the discussion section.

The second assumption is that structure remains uniform

vertically over each interval analyzed, and consequently,

that faults are vertical over that interval. Vertical struc-

tural variation is accommodated by subdividing the trap

vertically. For example, an upper structure map will

be applied to an upper stratigraphic interval and a

lower map to a lower interval that has different struc-

tural relationships. This is done for as many intervals as

the structural variation requires. Results are then com-

bined for a full prospect summary.

Analysis Procedure

Step 1: Stratigraphic Model

Stochastic multifault analysis represents stratigraphy as

a stack of leak and seal beds (Figure 2). If a suitable

analog well is available, then leak and seal beds are dis-

tinguished on the basis of log analysis of a clay fraction

curve such as a V-shale curve. Leak beds have a clay

fraction below a specified cutoff value of 0.40. They

are beds that, based on the cutoff, are sufficiently rich

in sand that they leak when juxtaposed across faults.

We use the term leak instead of sand to empha-

size the fact that this cutoff may include sands that

have poorer quality than reservoir sands. Seals have a

clay fraction above the same cutoff and typically

represent silty shales to high-quality clays that will not

allow cross-fault leakage when juxtaposed. The soft-

ware blocks the log by computing the tops and bases of

leak and seal beds based on the calibrated cutoff value.

The cutoff value was determined by calibration to

approximately 30 fields where the hydrocarbon accu-

mulations were known well. During the calibration, po-

tential cutoff values between 0.30 and 0.50 were used

to predict hydrocarbon accumulations in each field.

We found that predictions were generally accurate for

cutoff values between 0.35 and 0.45, but that cutoff

values outside of this range led to a significant degra-

dation in prediction accuracy. Based on this result, we

use a standardized cutoff of 0.40, but we routinely vary

the cutoff to test sensitivity to this value. In basins with

a high percentage of sediments with V shale values veryclose to 0.40, predictions can vary significantly with a

small change in the cutoff. This introduces a significant

additional uncertainty that must be addressed.

Stochastic multifault analysis allows either a deter-

ministic or stochastic analysis, and the process used to

create stratigraphic models differs depending on which

analysis is conducted. A deterministic analysis is iden-

tical to a single trial in a stochastic analysis; both use a

single model of stacked seal and leak beds to represent

the stratigraphic stacking. If a deterministic analysis is

done, then commonly, an analog well is selected, and it

is blocked into a sequence of leak and seal beds to

provide the stratigraphic model.

If a stochastic analysis is conducted, then the stra-

tigraphy is divided into multiple, broad stratigraphic

packages from which the program can create a unique

stacking of leak and seal beds for each trial. If a well is

James et al. 889

available, the interpreter examines the stratigraphic

stacking in the well and subdivides the stratigraphy into

packages (typically tens to hundreds of meters thick),

each with relatively uniform leak percent and bed

thickness. In Figure 2, the stratigraphy has a reservoir

section bounded by two seals. Each of these three in-

tervals is treated as a separate package. The well log is

blocked to determine the percentage leak, percentage

seal, average leak thickness, and average seal thickness

for each package. The complete stratigraphic model is a

stack of these packages, each of which is defined by

these basic parameters. In rank exploration settings,

with no wells nearby, the interpretation team works

together to generate a model stratigraphy, which may

be based on seismic facies, seismic inversion, regional

interpretation, or other methods. Stratigraphic pack-

ages are defined, along with the anticipated percent leak

and seal and average bed thickness of each package, but

these packages are defined based on the teams inter-

pretation of alternative input data.

Step 1A

To create a trial stratigraphy from this stochastic mod-

el, mathematical distributions of leak and seal thick-

ness are computed from the average leak thickness

and leak percentage provided as input. A Monte Carlo

Figure 2. Chart showingthe procedure followed bystochastic and determi-nistic multifault analysis.


process is then used to randomly select from these dis-

tributions to create a model with average leak thickness

and leak percentage properties that are similar to the

original input package, but with a different stacking

pattern of leaks and seals. This is done for each package,

and then the results for the packages are stacked to

create a complete trial stratigraphy.

Step 2: Structural Model

The structural model is very simple in the 1-D approach.

The trap is subdivided into compartments that are typ-

ically the equivalent of fault blocks. For each compart-

ment, elevations of the crest, structural spill, and a well

location are specified. The crest and spill are used to

define block closure, and the spill is also a possible control

for hydrocarbon contacts. The well location constrains

how hydrocarbon contacts are counted: only hydrocar-

bon columns that fill below the level of the well are

counted because only these would be encountered by

the well. With this capability, predictions can be made

at a specified well location, and different well locations

can be tested. Systematic shifting of the well location can

also reveal a key elevation where substantial leak point

controls are concentrated if a small downflank shift in

the well location yields dramatically different results.

The geologist also provides the name of each fault

and identifies which compartments are located on the

upthrown and downthrown sides. Fault-plane sections

are defined by offset depth pairs along the fault. This

information is sufficient to calculate elevations of leak

points throughout the trap and to define the reservoirs

and compartments that are juxtaposed at each leak

point.

Step 3

The analysis proceeds by convolving the stratigraphic

model with the structural model. It creates fault-plane

sections, builds an array of all leak and structural spill-

points, and then determines hydrocarbon contact levels

for each leak bed in each fault block. In the case of a

deterministic analysis where only one input stratigra-

phy is used, the analysis is complete at this point, and a

summary of all hydrocarbon contact and column data

are provided for each sand and each fault block. The

analyst can then process these results using a variety

of tools, including the ability to query any predicted

contact to determine which leak point or spill controls

that contact, its elevation, the fault on which it occurs,

and which reservoirs are juxtaposed. This information is

invaluable in determining exactly where key leak points

or spill controls occur.

In a stochastic analysis, the results for the first trial

stratigraphic model are recorded, and then the process

is repeated for a specified number of trials (commonly

500). The software returns to the input stochastic strat-

igraphic model and randomly creates a new trial strat-

igraphic model, which has a different stacking than the

previous models. This new model is then convolved

with the structure model to generate and record results

for that trial. After the final trial, the software records

the statistical summary of results. For each stratigraphic

package in each fault block, the following information

is provided:

Chance of success (fraction of trials having one ormore columns at a specified well location)

Average number of columns, average column height,average pay thickness, and total pay thickness for the

success cases

For these latter parameters, both the average of all

runs and the full probability distribution from P99 to

P01 are provided.

A wide range of outcomes has been observed in

many of our analyses. To evaluate the causes of this

variability, we included the capability to examine the

results of any of the trials as a single, deterministic

model. For instance, the analyst may decide to look at

trials that are similar to the average result or look at the

trials with the most or least trapped hydrocarbons. By

comparing trials with different results, they can deter-

mine what geologic changes are responsible for the

variation in the prediction. This has commonly helped

us focus on certain stratigraphic or structural charac-

teristics that have the greatest impact on the potential

of the prospect. We can then reevaluate our confidence

in these elements or reinterpret them if appropriate.

LESSONS LEARNED

During the application of stochastic and deterministic

multifault analysis, we have recognized new concepts

that have improved our understanding of the effects

of fault juxtapositions on hydrocarbon accumulations.

These concepts will be illustrated by examples in the

following section.

Delta Throw vs. Seal Thickness

Delta throw is defined as the magnitude of throw

change along a specified fault segment. A schematic

James et al. 891

fault-plane section illustrates the relationship of delta

throw to the probability of juxtaposition (Figure 3).

This fault offsets several evenly spaced reservoirs. The

throw relationship is emphasized by the dark blue

bed, which shows that the fault has a zero-throw tip

at the right end of the profile. Throw remains small

across the right-hand third of the profile, so delta throw

is small there. Delta throw is large where the amount

of throw increases dramatically across the middle third

of the profile. On the left-hand side, throw is very

large, but again, the delta throw is small. The profile

shows clearly that virtually all juxtapositions occur in

the area of high delta throw; the probability that a bed

will encounter a juxtaposition leak point is higher in

segments along the fault with high delta throw. It also

shows that there is no correlation between the mag-

nitude of throw and juxtaposition likelihood in an in-

terval with evenly spaced reservoirs.

The ratio of delta throw to seal thickness is the

fundamental control on juxtaposition probability. The

importance of this ratio can be understood by fol-

lowing the downthrown blue bed across the segment

with high delta throw. Juxtaposition must occur every

time the blue bed crosses another bed, and this occurs

every time the throw increases by approximately the

thickness of the seal bed. If the delta throw is five times

the seal thickness, then approximately five juxtaposi-

tions will occur. If seals are thicker, then the same delta

throw will create fewer juxtapositions. As the ratio of

delta throw to seal thickness increases, the probability

of juxtaposition leak points increases. Note that deltathrow is used instead of the throw gradient because the

length of the fault segment is irrelevant. The number of

juxtapositions is the same in the large delta throw

segment whether it is 100 or 1000 m (330 or 3300 ft)

wide; what matters is how many bed intersections

there are, which is defined by the relative magnitudes

of delta throw and seal thickness. In addition, because

most stratigraphic intervals have variable seal thickness,

delta throw is typically compared to the average seal

thickness.

This concept can be exploited as a quick-look tool

to identify areas in a prospect with high leak probability

or potential map errors. Fault segments with high delta

throw can be identified by simply annotating a map

with fault throws and identifying segments with large

changes in throw. If the average seal thickness can be

estimated, any fault segment where delta throw signif-

icantly exceeds seal thickness can then be highlighted;

these will be the areas with the highest probability for

juxtapositions. High delta throw may arise from a map

or interpretation error. For instance, high delta throw

can occur if an interpreter fails to recognize an inter-

secting fault. For this reason, and because delta throw

Figure 3. Schematic fault-plane section showing the relationship of the ratio delta throw/seal thickness to the likelihood of faultjuxtaposition leak points. Highside beds are yellow, lowside beds are light blue, except for reservoir A, which is highlighted in darkblue on both sides of the fault.


is such an important control on juxtaposition, we rou-

tinely focus more careful interpretation on the segments

with high delta throw, and we attempt to rectify any

errors or interpretation uncertainty.

Importance of Small Faults

If the average seal thickness is small, then a small delta

throw can create significant probability of juxtaposi-

tion. In high net/gross settings, average seal thickness

is commonly less than 20 m (66 ft). In this case, faults

with as little as 20 m (66 ft) delta throw (or with as

little as 20 m [66 ft] of maximum throw if one or both

fault tips are in the trap) can introduce significant fault

leak potential (see the prospect A example below). Con-

versely, thick seals require much larger delta throw to

generate high juxtaposition probability, and small faults

are of little concern.

Highly Sensitive Traps

In some traps, a small change in stratigraphy or fault

throw yields an extremely large change in the pre-

dicted size of hydrocarbon columns (see the Guntong

example later). An example is shown schematically in

Figure 4, where a slight thickening of an intermediate

seal eliminates a leak point near the structural crest of

a trap (case B). Because there are no other leak points,

the sand then fills to spill, creating a much larger accu-

mulation that includes multiple sands. In these situa-

tions, stochastic analysis typically predicts a large range

of outcomes. In contrast, other traps (see the Ling Gu

example later) are very insensitive to stratigraphic or

structural uncertainty. This is commonly because delta

throw on the faults is either much larger or much smaller

than seal thickness. A small change in either variable

induces little response in predicted outcomes.

EXAMPLES

Ling Gu-1 Well Postdrill Evaluation

Ling Gu is a simple, faulted anticline (Figure 5) with

two reservoir intervals, which are designated the A and

Figure 4. Schematic fault-plane section for a fault that dies out near the crest of an anticline. The map at the base shows the faulton the south flank of the anticline (defined by one offset contour). The fault-plane section extends across the anticline and includesthe unfaulted north flank. Dashed lines indicate beds on the downthrown side of the fault, and solid lines indicate the upthrown side.Assume that beds continue upward, allowing leakage out of the upper sands. (A) Case with a thinner intermediate seal, which causesa leak point near the crest in the underlying sand (see arrow). (B) A slightly thicker seal causes this sand to fill to spill by eliminatingthe shallow leak point.

James et al. 893

B sands. When Ling Gu was drilled, expectations were

high because most anticlines in the area trap large hy-

drocarbon columns in many stacked reservoirs. How-

ever, the Ling Gu-1 well found only 1 gas column in

6 sands in the A interval and 1 gas column in 13 sands

of the B interval. Multifault analysis was performed to

test whether the analysis would correctly replicate the

poor result and to understand why Ling Gu-1 had failed

to meet expectations.

There are four faults that could affect the accu-

mulation, each with maximum throw between 40 and

60 m (130 and 200 ft). Throw on two of the faults (C

and D) dies near the anticlinal culmination, creating

large delta throw near the crest of the trap. Gas can

exit the trap either at a saddle on the east, where it mi-

grates to the next structure in the trend, or by leaking

across the western fault and migrating west. The Ling

Gu-1 well is located approximately 70 m (230 ft) be-

low the crest of the trap. Gas charge comes from sources

interbedded with the reservoirs and at deeper strat-

igraphic levels. Charge is clearly adequate in the area

because similar nearby anticlines are filled with large

volumes of gas that migrated from the same source

areas. The map on the lower sands is very similar to the

map on the upper sands, but closure heights are suf-

ficiently different that the upper sand map was used

for predictions in the upper sands, whereas the lower

sand map was applied to the lower sands.

The stratigraphic model, which was derived from

Ling Gu-1, comprises the A and B sands, which are

packages of coastal plain to deltaic, moderately contin-

uous to channelized sands bounded by seal intervals

(Figure 6). The quality of the seals in this area is ex-

cellent, but sparse thief sands are interbedded with

thick shales, as shown by the statistics for the model

stochastic packages. The sand packages have relatively

high percentages of thin leak beds, so that the average

seal thickness in these intervals is quite small.

Stochastic multifault analysis was performed using

these structure and stratigraphic models as input. The

results correctly indicate that Ling Gu-1 should not

have encountered many gas columns (Figure 7). The

Figure 5. Depth structure map of Ling Gu trap on the top of the A sands. Arrows show important juxtaposition leak point controls. Thesolid red line labeled GWC is the gas-water contact as observed in the uppermost (mapped) sand. The dashed lines approximate theaverage gas outline in deeper sands that were limited by shallow juxtaposition leak points. The map of the lower sands is very similar tothis map, except the closure amplitude is larger on the lower sands, and closure east of fault B has more steeply dipping flanks.


Figure 6. Stochastic stratigraphic model for Ling Gu-1. The V shale well trace for the Ling Gu-1 well is shown on the left. The yellowbars in the middle panel denote the leak % in each stratigraphic package used in the stochastic model. Statistical data for thepackages are tabulated on the right.

Figure 7. Stochastic multifaultanalysis results for columns inthe Ling Gu-1 well. The grayvertical bar shows the observedvalues in the Ling Gu-1 well,which is the value that thepostdrill analysis is attemptingto replicate. The horizontal blackline shows the average stochas-tic multifault prediction, andthe vertical black line showsthe predicted P95-P05 range.All realizations for the A sandspredicted one column, so thereis no vertical bar shown. Allcolumn heights are measuredfrom the crest of the structure.COS = chance of success, or thepercentage of trials that encoun-tered hydrocarbons in the well.

James et al. 895

predicted number of columns and column heights rep-

licated the outcome extremely well, although the chance

of success for the A sands is slightly pessimistic. This

result is especially encouraging, considering that the pre-

dicted (and observed) number of columns is so small

compared to the large number of potential hydrocarbon-

bearing sands, 6 in the A interval and 13 in the B interval.

Trials were examined, and the well location was

varied to determine the factors that cause this trap to

hold an uneconomic accumulation. The primary prob-

lem at Ling Gu is that there is a high probability of

juxtaposition on fault D because seals are thin in the

reservoir intervals (5 m [16 ft] in the A sand interval

and 16 m [52 ft] in the B sand interval) compared to

delta throw of 45 m (150 ft) on fault D. The effect of

these juxtapositions is magnified by the crestal loca-

tion of fault D. To demonstrate the effect of these

juxtapositions on hydrocarbon contacts, we will focus

on the B sands, where the key relationships are best

demonstrated. Figure 8 is a fault-plane section on fault

D for one realization in the B sands. On first inspec-

tion, the accumulations of hydrocarbons look quite

promising because of a large common contact beneath

the top seal. However, the problem is that despite this

common system, only two of the sands actually fill past

the tip of fault D. Consequently, the area of gas in

most sands is quite small (dashed red line in Figure 5).

Most trials were similar to this, although the number

of sands varies within a small range.

The juxtapositions on fault D are responsible for

this result. Juxtapositions shown by black circles in

Figure 8 allow communication between the upper

sands, which establishes the common system between

these. Without these juxtapositions, each sand could

have had a column as large as that in the uppermost

sand, resulting in much more total gas. Deeper juxta-

positions (arrows in Figure 8) drain the lower reservoirs

and limit accumulations to extremely small sizes.

Because juxtapositons on fault D cause such small

accumulations, most sands are water wet on the down-

thrown side of fault C. This in turn creates the iden-

tical problem at fault C, where juxtapositions play the

same role, leading to significant hydrocarbon accumu-

lation only in the uppermost sands in the fault block

tested by Ling Gu-1. As in the crestal fault block,

most sands in this block have only small accumula-

tions (dashed red line in Figure 5) that were too small

to be observed in Ling-Gu-1. A well that is drilled far

Figure 8. Fault-plane section on the Dfault showing one realization of the lowerB sands. Yellow lines are highside beds,and orange are lowside. Juxtaposition leakpoints occur where the beds cross. Redindicates the accumulations of hydrocar-bons predicted for that realization. Blackcircles indicate juxtapositions that facili-tate communication between sands in thecommon hydrocarbon system. Red arrowsshow leakage that limits accumulationsin deeper sands. The vertical arrow showsa hypothetical downflank well in a positionsimilar to that of Ling Gu-1.


downflank in either fault block (for example, arrow in

Figure 8) would encounter gas only at the top of the

interval, as was observed in Ling Gu-1.

The uppermost sands in each sand interval fill with

gas because they are juxtaposed against their respective

top seals, where the probability of being juxtaposed

against a thief sand is relatively small. The chances of

success in our model indicate that this seal is good in a

large majority of the trials, particularly for the B sands.

In trials where the uppermost sand is sealed, it can fill

to deeper leak points. At the A sand level, three leak

points at almost the same elevation (leaks 2, 3, and 4)

control the contact in different trials. These leak points

establish one column at the top of the interval that is

deep enough to be encountered by the well. At the B

sand level, slight changes in the trap geometry cause

leak 3 to dominate. In both cases, the range of leak

point elevations is extremely small, which accounts for

the very narrow range of predicted column heights.

Ling Gu is an example of a trap where stratigraphic

uncertainty introduces very little uncertainty in the

prediction of fault-sealed hydrocarbons because the

fault throw relationships are such that there is very little

sensitivity to this uncertainty. Specifically, delta throw

is so much larger than seal thickness on the crestal faults

that reasonable variation in stratigraphy yields little

change in juxtaposition risk. In summary, the analysis

replicated the observations at Ling Gu-1 well and pro-

vided an explanation for the limited volume of gas.

Prospect A

Prospect A is a small gas prospect located immediately

north of a significant gas discovery. The Res-E reser-

voir interval, which is the primary focus of this dis-

cussion, is uppermost of many objective intervals in a

very sand-prone, marginal marine to coastal-plain in-

terval with both continuous and channelized sands.

Because of the small area of this trap, it can be com-

mercial only if a large number of stacked sands fill to

structural spill.

On first inspection, there appears to be no fault-

seal issue at the Res-E level because the trap has 30 m

(100 ft) of fault-independent closure (Figure 9). How-

ever, the sand-prone nature of the sediments at pros-

pect A introduces a significant potential risk (Table 1).

Prospect A has the unusual situation that the overbur-

den was expected to contain as much sand as the ob-

jective interval. The overburden was not considered a

viable target more because of a likely lack of top seal

adequacy than because of the lack of sand expected in a

top seal. Both intervals have very thin bedding. As a

consequence, the anticipated average shale thickness

is only approximately 10 m (33 ft). Any fault with only

10 m (33 ft) of delta throw can create juxtapositions

that have the potential to drain sands and significantly

reduce the number of columns. Furthermore, with shales

this thin, channel incisions could connect sands verti-

cally. The seismic data in this area do not have suffi-

cient resolution to identify faults or channel incisions

of this size. Thus, the danger exists that unresolved

faults or channels could significantly reduce the num-

ber of stacked gas columns in prospect A and render

the trap uneconomic.

This risk was evaluated by conducting an analysis

with the unfaulted structure and an analysis that

included a small, hypothetical fault at the crest of the

structure with a maximum of 10 m (33 ft) of throw

(Figure 9). Alternatively, this modeled fault could be

envisioned to represent an area with numerous, verti-

cally connected channel incisions. In the unfaulted mod-

el, it was assumed that each bed had access to charge,

which is reasonable given the large fault just west of the

trap and the fact that sources are interbedded with res-

ervoir. The stratigraphic model (Table 1) was derived

using seismic inversion to extrapolate from wells in

the nearby gas discovery. The overburden was given

the same properties as the reservoir interval to reflect

the uncommonly sand-prone section above the reser-

voir. One concern was that the most likely model with

41% leak beds might underestimate the sand likely to

be present at prospect A, so a model was also run with

60% leak beds.

The results indicate that a subseismic fault or wide-

spread channel incisions could significantly reduce the

number and size of potential accumulations at pros-

pect A, particularly if the higher net/gross model proved

to be correct (Figure 10). In the unfaulted anticline

case, the number of columns was predicted to be be-

tween 20 and 28; every bed is filled to spill with the

number of beds, depending the number of reservoirs

in the interval. If the fault or stacked channel com-

plex is present, there could be as few as one column

if the interval proved to have the higher net/gross

sand. Chance of success (the fraction of trials with gas

columns) and column height did not vary significantly

between scenarios.

When the well was drilled, no gas columns were

found in the Res-E interval and the interval had a leak

percent near 50%. One possible explanation for even

fewer columns than predicted in the faulted scenarios

is that a fault is present that has more than 10 m (33 ft)

James et al. 897

of offset but still is too small to be resolved by the

relatively poor seismic data. Alternatively, unresolved

channel incisions may have had a similar effect or may

have combined with a small fault to drain the trap. By

focusing attention on the potential problem introduced

by an undetected fault, the analyst correctly highlight-

ed the irreducible risk in this interval.

Guntong, Malay Basin

Guntong is a producing oil and gas field on a large

faulted anticline in the Malay Basin (Figure 11). Res-

ervoirs are relatively channelized coastal-plain sands.

On a plot of column height against reservoir, oil col-

umns are distributed in a distinctive sawtooth pattern

Table 1. Summary of Stratigraphic Input Used for Prospect A

Interval Interval Thickness (m) Sand (%) Average Sand Thickness (m) Average Seal Thickness (m)

Overburden 100 41.0 6.62 9.52

Res-E 320 41.0 6.62 9.52

Figure 9. Depth structure mapon the Res-E horizon at pros-pect A. The contour interval is10 m (33 ft). The hypotheticalcrestal fault used in the sensi-tivity model is shown.


(Figure 12). The pattern is characterized by a large

column in the top sand followed by a steady decline

in column height in the upper group I, a sharp in-

crease in column height in the lower group I, again

followed by a decline, and another sharp increase in

the J group. The largest columns occur immediately

below significant regional seals that are thicker than

the other intrareservoir seals. The objective of this

analysis was to explain the origin of this column height

pattern using deterministic multifault analysis. The anal-

ysis also highlights the possible effect of trap sensitiv-

ity on our results.

In a deterministic analysis, a single stratigraphic

model is provided. The model was generated by block-

ing the Guntong-4 well, which is located closest to

an important fault system (Figure 13). Because fault

Figure 11. Depth structure map on the I-40 (upper group I) at Guntong field. The contour interval is 20 m (66 ft). Arrows denotesegments of the faults where key leak points are located.

Figure 10. Stochastic multi-fault analysis predictions ofthe number of columns in theRes-E interval at prospect A.

James et al. 899

Figure 13. Deterministic and stochastic stratigraphic models for Guntong, based on the Guntong-4 well. The log has been blockedinto leak (yellow) and seal (red) intervals to define a deterministic model of the stratigraphy. The arrow points to the critical thin thiefsand in the I-68 seal. The right-hand panel shows the stochastic packages that were defined. These packages are based in part on anunderstanding of the regional stratigraphic packages.

Figure 12. Observed columnheight distribution in the group Iand group J sands at Guntong.The vertical bar shows the heightof column in each sand.


throws vary vertically, a separate structure model was

used for each group of declining columns. The struc-

ture model was based on a map from a reservoir in

each column group (the map for the upper interval is

provided in Figure 11). The results from the three

models were then combined to derive the predicted

contact pattern.

The initial analysis reproduced the upper I and J

columns successfully, but predicted only small col-

umns in the lower I (Figure 14A). Investigation of the

small column in the I-68A sand revealed that this sand

was leaking to a small thief sand in the overlying I-68

seal at leak point near the arrow labeled A in Figure 11

(the thief is highlighted by an arrow in Figure 13).

The thief, in turn, leaked into the upper I at a leak point

on the same fault segment. This prevented fill past

this leak point and limited the I-68A column to 70 m

(230 ft). The deeper sands in the lower I interval were

also leaking at juxtapositions on this segment of the

fault. Investigation of other wells revealed that this

thief sand was present only in the Guntong-4 well.

Therefore, we removed the sand from the deterministic

model to simulate either the absence of the sand at the

fault leak point or seal of the leak point by fault gouge.

With the thief sand removed, large columns form be-

neath the much thicker I-68 seal (Figure 14B). The

prediction now simulates the observed pattern well.

An analysis of controls on these columns shows

that where intrareservoir seals are thin, leakage occurs

near arrow A. The thickness of the regional seals ex-

ceeds the delta throw on this fault segment, so that the

sand beneath each of these seals does not leak at this

location. These sands continue to fill down, until they

encounter leak points on a second fault segment at a

significantly deeper level where delta throw exceeds

these thicker seals (arrow B in Figure 11). These leak

points control the larger oil columns. The interplay of

the delta throw/seal thickness ratio with this combi-

nation of leak points accounts for the very distinctive

pattern of oil columns.

The effect that this single thief sand in the I-68

seal had on our predicted column heights is an ex-

cellent example of a highly sensitive trap, depending

either on the efficacy of fault gouge seal or the pinch-

out of the sand. Recognition of this sensitivity led to a

concern about the influence that it would have on a

stochastic prediction. A stochastic model was made in

which the I-68 seal was represented by a package that

Figure 14. Column height distributions predicted by deterministic multifault analysis. (A) The initial prediction with the thief sandpresent in the I-68 seal. (B) Prediction without the thief sand.

James et al. 901

included the thief sand. In a stochastic analysis, this

sand will be absent in some trials and, when present,

could occur anywhere in the seal package. The results

for the lower I (the interval below the I-68 seal) are

displayed in Figure 15. The average column height

observed in the lower I is 207 m (680 ft). The deter-

ministic prediction using the Guntong-4 well is 55 m

(180 ft). In this case, the deterministic model predicts

the hydrocarbon accumulation very poorly because

the thief sand is present in the top seal. The predic-

tion improves considerably to 170 m (560 ft) if the

thief sand is removed from Guntong-4, but the analyst

may not anticipate this issue in a predrill situation and

thus may rely on the 55-m (180-ft) prediction. The

average column height in the stochastic prediction is

160 m (525 ft), which underestimates the observed

average somewhat, but the predicted range of out-

comes captures the observed column height. The more

significant result is the very large range in possible

outcomes, from 65 to 260 m (210 to 850 ft). An exam-

ination of trials indicates that this large uncertainty

arises both from the effect of the thief sand in the I-68

seal and, to a lesser degree, from the stacking of sands

in the lower I interval. Sensitivity to a stratigraphic or

structural input creates a wide range of outcomes in a

stochastic prediction, and this effectively communi-

cates the large uncertainty introduced by the sensitiv-

ity. The predicted uncertainty range (P95 to P05) does

not include the deterministic outcome because the

deterministic thief sand is located in a very unlikely,

yet very critical place in the seal interval.

DISCUSSION: RECONSIDERING THEASSUMPTIONS AND SIMPLIFICATIONS OFSTOCHASTIC MULTIFAULT ANALYSIS

After gaining experience with stochastic multifault

analysis, we reconsidered the potential importance of

the key simplifying assumptions we made to facilitate

the stochastic treatment of uncertainty. One assump-

tion of the 1-D approach is that beds are laterally uni-

form across the trap. Reservoirs commonly are more

discontinuous laterally than our simple 1-D approach

assumes, particularly in fluvial or deep-water channel

systems. A possible effect of assuming lateral sand con-

tinuity is that our models will be more leak prone

than the actual prospect because continuous sands

are more likely than a narrow sand to intersect a fault.

To some unknown degree, the effect of greater con-

tinuity is counterbalanced by the fact that our models

will have fewer sand bodies than a model with dis-

continuous sands and an equivalent net/gross. Because

Figure 15. Stochastic multi-fault analysis prediction com-pared to the deterministic pre-dictions. The vertical bars showthe average column height ofall columns in the lower group Iinterval. Deterministic predic-tions are shown by a horizontalline because only one averagecolumn height is predicted in adeterministic analysis. The firstline is for the model with thethief sand present in the I-68seal; the second line is for themodel in which the thief sandwas removed. For the stochasticprediction, the horizontal lineshows the predicted averagecolumn height, and the verticalbar shows the P95P05 rangefor 500 trials.


of these competing factors, the effect of the uniform

bed assumption is difficult to quantify. In our experi-

ence, tested predictions and postdrill calibrations match

observations relatively well, even in traps with chan-

nelized coastal-plain sands where the majority of the

calibration was conducted. This suggests that the effect

may be small enough to neglect for most exploration

applications.

We have also been concerned that stochastic mul-

tifault analysis does not address seal by fault-zone ma-

terials. In general, however, the predicted range of

outcomes predicted by stochastic multifault analysis

has generally captured the ultimate outcome. We have

not consistently predicted much smaller hydrocarbon

accumulations than are observed, as we might expect

if we were not accounting for significant seal by fault-

zone materials. We have identified two possible reasons

why this juxtaposition-based analysis that neglects

seal by fault-zone materials could effectively predict

hydrocarbon column heights. First, it is likely that we

are incidentally accounting for the effect of seal by

fault-zone materials to some unknown degree in our

calibration of the V shale cutoff value. A concern aboutthis is that one would not expect general success if a

method does not account directly and accurately for the

primary control on leak or seal. However, because both

juxtaposition risk and leak by fault-zone materials tend

to be covariant to some degree (both increase with in-

creasing sand in an interval), it is possible that our

analysis could be successful even where gouge seal is

important.

A second explanation is that in many cases, seal by

fault-zone materials may be a secondary factor com-

pared to cross-leak at juxtapositions. In pursuing this

hypothesis, we have made two observations that we

consider significant. First, uncertainty greatly affects the

analysis of seal by fault-zone materials. When we re-

examined the calibration data set for the ExxonMobil

equivalent of shale gouge ratio (SGR), we found that

stratigraphic and structural uncertainty affected gouge

analysis in two ways. First, there is uncertainty in the

calculated SGR value. Second, there is commonly great

uncertainty about whether two sands with different

contacts or pressures are actually juxtaposed. We found

that because of uncertainty, it was much more difficult

to prove individual examples of seal by fault gouge

in our data set than we previously believed. There

clearly are examples of seal by fault-zone materials,

but because of uncertainty, we have considerably fewer

proven examples in our data set than we had pre-

viously. Recognizing this uncertainty has made us less

confident about our conclusions regarding the im-

portance of seal by fault-zone materials. Our second

observation is based on outcrop studies where we

commonly have observed that fault-zone materials

have discontinuities and would not be continuous

enough to hold a hydrocarbon column over geologic

time. Even a small number of discontinuities would

allow the fault to leak given enough time (Doughty,

2003) and could lessen the role that seal by fault

gouge plays. If this is true, then analysis of fault-zone

materials should focus on the probability that gouge

discontinuities are present over each juxtaposition,

and on the uncertainty associated with predicting this

probability, building on an analysis of juxtaposition

uncertainty.

At this point, we continue to assess the relative

importance of juxtaposition risk and seal by fault-zone

materials, but evaluation of this issue has raised sig-

nificant issues that deserve consideration. In particu-

lar, we believe that a systematic treatment of uncer-

tainty in the analysis of seal by fault-zone materials,

particularly one that addresses discontinuities in fault

gouge, is beneficial. We continue to explore these issues

and encourage further evaluation by others.

SUMMARY

We have developed a stochastic multifault method for

analysis of the impact of stratigraphic uncertainty on

cross-fault leak at sand-sand juxtapositions. This meth-

od simulates the complex system of structural spills

and juxtaposition leak points that control hydrocar-

bon contact levels and quantitatively predicts the im-

pact of uncertainties in stratigraphic and structural

input.

The ability to simulate the extremely complex

system of structural spills and juxtaposition leak points

has helped us understand the controls on hydrocarbon

contact levels in multifault traps with stacked reser-

voir systems. For example, leakage at juxtapositions

on the crestal fault at Ling Gu dictated the behavior

of a second fault lower on the structure, which con-

trolled the contacts in the drilled fault block. A shift

from leak points on one fault to much deeper leak

points on another fault created the large change in hy-

drocarbon column height at Guntong. Furthermore,

we have found many cases where small faults have

exerted great influence on the size of hydrocarbon col-

umns. Prior to our ability to simultaneously evaluate

James et al. 903

leak points on all faults and calculate resulting hydro-

carbon columns, we may have neglected these faults to

make the visual interpretation of deterministic fault-

plane sections tractable.

Understanding the key controls on hydrocarbon

contact levels commonly focuses our stratigraphic and

structural interpretations. For instance, critical leak

points commonly are concentrated on a particular fault

segment, such as the crestal faults at Ling Gu. Because

this typically was not known at the time of initial in-

terpretation, the interpreter commonly has not taken

any additional care interpreting this crucial area. A re-

examination can lead to either a better understanding

of this critical control or a refinement of the inter-

pretation in that area. Similarly, the analysis may iden-

tify some stratigraphic characteristic that is particularly

important, such as the small thief sand in the Guntong-4

well. A reconsideration of the stratigraphic model may

lead to additional effort that could refine the model or

provide greater confidence in the model. This im-

proved focus also has facilitated more effective post-

drill analyses.

Our applications of stochastic multifault analysis

have demonstrated that uncertainty commonly has a

significant impact on fault-seal predictions. For ex-

ample, the Guntong example shows the wide range of

possible hydrocarbon column heights that depend on

whether a single thief sand is present or sealed by

fault-zone materials. The possibility of a small fault at

prospect A introduced large, irresolvable uncertainty.

It is therefore critical to recognize, analyze, and com-

municate this uncertainty in a fault-seal analysis. It is

beneficial to consider the effect of fault geometries on

the probability of leak in an interval or the relationship

between shale thickness and the probability of juxta-

positions being present. Stochastic multifault analysis

offers one of many possible approaches to addressing

uncertainty. The important conclusion is that uncer-

tainty is sufficiently important that it should be sys-

tematically addressed in an analysis of leak at fault

juxtapositions and in all aspects of fault-seal analysis.

REFERENCES CITED

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Gibson, R. G., and P. A. Bentham, 2003, Use of fault-seal analysisin understanding petroleum migration in a complexly faultedanticlinal trap, Columbus Basin, offshore Trinidad: AAPG Bul-letin, v. 87, p. 465478.

Jev, B. I., C. H. Kaars-Sijpesteijn, M. P. A. M. Peters, N. L. Watts,and J. T. Wilkie, 1993, Akaso field, Nigeria: Use of integrated3-D seismic, fault slicing, clay smearing, and RFT pressure dataon fault trapping and dynamic leakage: AAPG Bulletin, v. 77,p. 13891404.

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