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85Journal of Petroleum Geology, Vol. 35(1), January 2012, pp 85-98
2012 The Authors. Journal of Petroleum Geology 2012 Scientific Press Ltd
A GENERAL METHOD FOR
THE CONSISTENT VOLUME ASSESSMENT
OF COMPLEX HYDROCARBON TRAPS
A. Beha1*, J.E. Christensen1 and R.Young2
Complex hydrocarbon traps are those in which a number of different trapping elements haveto work favourably and simultaneously in order to allow access to the full hydrocarbon volumepotential of the prospect. The probability of success varies across the prospect and the volumedistribution.
A consistent assessment of the probability of success relative to the volume uncertainty ofhydrocarbon traps is essential for unbiased prospect characterisation and vital for decision
making, portfolio management and for delivering predicted value. It is relatively straightforwardto assess the probability of geological success and volume uncertainty of simple anticlines ordomal structures. Unfortunately, simple four-way closures are usually drilled early in theexploration of a basin or play and become increasingly less common with exploration maturity.As a consequence, prospects available for drilling in mature exploration areas are typicallycomplex traps, i.e. they possess a combination of different trapping elements. In such cases,trapping of the full volume potential requires that many geological elements work concurrently.
Complex traps are often perceived to carry a comparatively lower probability of geologicalsuccess. However, the introduction of additional factors in a multiplicative chance estimationmodel may not reflect the true probability of finding hydrocarbons at the prospect location.Evaluations which involve multiplying additional chance factors may lead to an under-estimationof the probability of geological success and an over-estimation of the hydrocarbon volume.
A solution to this problem is to calculate the probability of occurrence of each possible
success scenario or outcome. This paper describes a straightforward method for assessing volumeuncertainty in complex traps which is independent of the model or method used to estimatethe probability of geological success. A faulted four-way closure is used as an example for thedetailed description of the suggested workflow.
1 DONG E&P, Agern All 24-26, DK-2970 Hrsholm,Denmark.2 Rose and Associates, LLP, 4203 Yoakum Boulevard, Suite320, Houston, 77006, USA.* Corresponding author: [email protected]
Key words: complex hydrocarbon traps, prospectassessment, probability of well success, fault seal failure,risk analysis.
INTRODUCTION
The uncertainty of estimated hydrocarbon volumes
in a prospect is most commonly captured using
stochastic evaluation methods. Although the
mathematical foundations of many of these methods
are comparable, the estimated volumes of a potential
hydrocarbon trap will vary depending on factors
including the type of assessment method used, bias
rooted in a particular exploration company and, to a
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86 Volume assessment of complex hydrocarbon traps
certain degree, the bias of individual explorationists
within the company. A widely accepted technique is
to link an assessment of the probability of geological
success (POS) with the assessment of the potentialhydrocarbon volume (see for example, Young et al.,
2005; Citron et al., 2006). However, guidelines for
preferred probability distributions for all volume
parameters as well as definitions of the model for
estimating the probability of geological success may
vary from company to company. Estimates of the POS
and trap size may therefore differ significantly, even
between companies in the same joint venture evaluating
the same acreage with the same data.
A variety of mathematical modelling tools for
prospect assessment is available. A typical model
output will include the POS and a range of possible
success-case hydrocarbon volumes. The latter isusually expressed by the mean value of the uncertainty
distribution and a pre-defined subset of percentiles of
the cumulative probability curve of which P90, P50
and P10 are the most common. (Throughout this
paper, the authors use the greater than or equal to
convention, i.e. P99 is the smallest outcome and P1
is the largest).
The methodology described below focuses on a
best practice for volume assessment of complex
hydrocarbon traps. It can be used independently of a
specific model for estimating the chance of geological
success, and hence is relatively easy to implement.
The only prerequisite required is a definition of the
link between the POS and the volume uncertainty. In
this paper, the POS is defined as the probability that
there will be a minimal but sustained flow of
hydrocarbons. The POS therefore represents the
chance of equalling or exceeding the P99 of the
volume distribution, prior to any truncations related
to the minimum commercial field size. The POS of a
prospect can be determined from an assessment of a
standard set of parameters (e.g. Reservoir, Trap,
Hydrocarbon Charge, and Seal). These parameters
vary between companies and the assessment of their
probabilities can be conducted in different ways.
However, the resulting number will determine the
predicted success rate of the prospect, i.e. theprobability of a hydrocarbon-bearing trap occurring
if an infinite number of identical prospects could be
drilled.
POS assessment will not be discussed further in
this paper, but readers are directed towards Otis and
Schneidermann (1997) for further details.
Buoyancy forces cause hydrocarbons to
accumulate in the crest of a valid trap. In order to
define the test of a prospect as technically successful,
only a relatively small gross rock volume at the crest
of the prospect is required to be effectively sealed
and filled with hydrocarbons. The potential failure of
additional trapping elements down-dip from the crest
of the structure will not reduce the probability of
finding hydrocarbons at the prospect location. Rather,
they will influence the probability of deeperhydrocarbon-water contacts and hence the presence
of larger volumes of hydrocarbons. This is important
because the results of the different assessment
methods can be significant.
METHODOLOGY
In this paper, the volume uncertainty distribution is
analyzed for a fictitious complex four-way trap. All
volume calculations were performed on a personal
computer by a Monte Carlo simulator. This method
relies on the repeated random sampling of various
parameters and computes a predefined quantity ofcombinations (trials). The key for such calculations
is the definition of uncertainty ranges for all the
relevant input parameters. The result of the simulation
is a range of possible outcomes that can be displayed
as a histogram of success cases and a corresponding
exceedance probability (cumulative probability) curve.
The basis for calculating the volume of recoverable
hydrocarbons in a trap is expressed by equation 1:
Recoverable volume =
[GRV x N/G x Porosity x HC saturation x
Recovery Factor] / FVF (1)
The gross rock volume (GRV) is calculated by
combining (i) area versus depth measurements for
the top surface of the reservoir from the crest to the
lowest closing contour; (ii) an estimate of reservoir
thickness; and (iii) the definition of possible
hydrocarbon-water contacts. Other input parameters,
such as net-to-gross ratio (N/G), porosity,
hydrocarbon saturation (HCsaturation), formation
volume factor (FVF) and recovery factor can also be
given uncertainty ranges. The determination of valid
input ranges and uncertainty distribution functions for
these parameters may be anchored in well-defined
company policies.
Modelling of possible volume outcomes aims toaddress all the uncertainties related to the amount of
hydrocarbons in a potential trap. The interpretation
of geological and geophysical data allows appropriate
uncertainty ranges to be determined for all relevant
factors influencing the volume potentially trapped. The
first step of a prospect volume simulation is the
translation of observations from the geological model
into numerical data. Parameters in the method of
estimating the probability of geological success, for
instance, are assessed for their probability of
adequacy. This can be translated into an either / or
distribution which has only two valid values: 0 for
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87A. Beha et al.
failure and 1 for success. Weighting of the two
values expresses how likely the parameter is to be
present (working) or not. Each parameter of the
chance estimation model has a range of possibleconsequences for the hydrocarbon volume associated
with it.
Migration is a good example, and is commonly
estimated during the assessment of chance of success
and volumes. The estimate of efficient migration of
hydrocarbons into a potential trap is an either / or
decision: either hydrocarbons did migrate in, or they
did not. But of equal importance is the assessment of
hydrocarbon quantity if migration into the trap
occurred. Well-calibrated petroleum systems models
can provide answers to this question. In most cases,
a range of possible scenarios needs to be included, and
inputs for the Monte Carlo model are consequentlydisplayed as a histogram and an exceedance probability
curve.
Trap complexity can be treated in a similar fashion,
and observations from the geological model need to
be translated into numerical information. This task
comprises an analysis of how likely a trap element is
to fail. Thus, specific information for each potential
trap element is required to compute the predicted
frequency of failure and the consequences of it.
For instance, a fault located down dip from the
crest requires careful investigation to assess its sealing
capability. There will be no consequences for the trap
if the fault is sealing. However, failure of the fault
results in a leak point at the shallowest intersection
between the fault plane and the top reservoir surface.
If the fault is leaking, no hydrocarbons will be trapped
below this depth. Translation of the observation into
numerical modelling language is straightforward. The
simulation input will be a weighted-values distribution
with two options: either (i) presence of a leak point at
the intersection between top reservoir surface and
fault plane at the predicted chance of fault failure
frequency; or (ii) no leak at a 1 chance of fault
failure frequency.
Theoretically, each trap element down-dip from
the crest of the structure requires an individual input
distribution. The method suggested here aims toexpress this complexity in a single uncertainty
distribution for the Monte Carlo simulation.
The workflow starts with an introduction to the
prospect example and to the geological observations
and interpretations. These will help to determine the
most appropriate modelling concept. Before the final
volume model is designed, the process will describe
the method of simplifying the trap complexity to a
single continuous uncertainty distribution as input for
the final model. The identification of leak points and
attempts to predict their frequency using stochastic
methods are dealt with in this phase.
The first model in the workflow is very simplistic
and was built for the purpose of testing the theoretical
predictions with a Monte Carlo simulator. The
assumptions were verified by the modelling, and weretherefore applied as a single uncertainty distribution
input to a more comprehensive volume model. This
final volume model contains all the uncertainty
parameters which affect the depth of the hydrocarbon-
water contact.
CASE STUDY: A FAULTED
FOUR-WAY CLOSURE
Assumptions and definitions
A possible workflow for the suggested method is
shown for a fictitious structural trap with two faults
intersecting the top reservoir grid (Figs 1, 2). Thetwo faults can be sealing or may provide pathways
for hydrocarbons to leak out of the system. The crest
of the structure is at 2000 m and the lowest closing
contour is defined by the 2150 m depth contour. A
constant reservoir thickness of 60 m is assumed over
the entire prospect area.
Uncertainty with regard to the trapped volume is
very common when hydrocarbon prospects are
evaluated because many parameters are associated
with uncertainty ranges. When selecting a depth
versus area representation of the prospect GRV (gross
rock volume), the parameters with the most influence
on hydrocarbon volume are those which affect the
depth of the hydrocarbon-water contact. In the faulted
four-way closure example, uncertainty related to
hydrocarbons available for filling the trap will have to
be assumed because no information is available from
petroleum systems analysis. For the volume
assessment, it is assumed that the charged volumes
can range from very small to large. Leak point
uncertainty on a leaking fault also needs to be taken
into account when assessing the position of the
hydrocarbon-water contact, because failure of either
of the faults will influence the maximum depth of this
contact. If for example the NE fault has no sealing
capacity, no hydrocarbons can be trapped below 2050
m.For the sake of simplicity, no uncertainty is
assumed for the integrity of the top seal in this
example. Therefore the two independent parameters
amount of available hydrocarbons and potential
fault leak points control the distribution of the
hydrocarbon-water contact and thus the degree of
trap fill. The two parameters hydrocarbon-water
contact and leak point depth are sampled
individually during the Monte Carlo simulation, and
the shallower of the two depths will determine the
actual hydrocarbon-water contact of the individual
simulation trial.
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88 Volume assessment of complex hydrocarbon traps
The range of hydrocarbon volume available to fill
the trap will be addressed below. First, however, leak
point uncertainty is considered.
The fault to the NE (NE fault) cuts the top
reservoir at a depth of 2050 m. If the fault has nosealing capacity, hydrocarbons cannot be trapped
below this depth. The second fault (SW fault) cuts
the top reservoir at a depth of 2100 m. It is assumed
that there is no dependency between the two faults,
i.e. if one fault fails to act as a seal there is no increased
probability of failure of the second fault. Quantifying
the probability of seal integrity for both faults is critical
for volume assessment of the prospect. The probability
that the NE fault seals is estimated to be 40%, and the
probability that the SW fault seals is 70%. These
estimates are independent of the probability of
geological success (POS) estimated for the prospect.
The prospect POS is defined as the probability offinding the minimum, P99, success-case volume of
hydrocarbons which accumulates at the crest of the
structure regardless of the seal integrity of either fault.
Hence, the two estimates of sealing integrity for the
SE and NE faults will have no impact on the assessment
of the prospect POS. But the authors suggest that the
reduced probability of encountering hydrocarbons at
a greater depth down-dip from the crest is determined
by the uncertainty distributions of the potential leak
points. This requires the identification and definition
of all possible trap configurations and the assessment
of the likelihood of their occurrence.
Identification and definition
of possible trap configurations
Three different leak points can exist in this prospect
example (Fig. 2): one at a depth of 2050 m if the NE
fault allows hydrocarbons to leak out of the trap;one at 2100 m if the NE fault is sealing and the SW
fault leaks; and one at 2150 m if both faults are sealing.
For this last option, the leak point is equivalent to the
lowest closing contour of the structure, i.e. the spill
point depth.
In order to create a stochastic volume model with
a Monte Carlo simulator, the software needs to know
how often it is supposed to generate the different
leak points which determine the potential limitation
of the hydrocarbon-water contact. This information
is provided by the assessment of fault seal
probabilities. From the trap configuration, it is clear
that the deepest leak point at 2150 m depends on thesealing capacity of both faults. But it may not be
valid to assume that this is the least likely result. The
workflow may appear ambiguous and counter-
intuitive but it follows stochastic principles.
The map view of the prospect (Fig. 1) shows
that the highest point where the NE fault intersects
the top of the reservoir section occurs at a depth of
2050 m. If the NE fault is not sealing, no
hydrocarbons can be trapped below this depth. Note
however that this outcome is an aggregate of two
possible scenarios (the first where the NE fault leaks
and the SW fault leaks; and the second where the
A
B
P(fault seal) = 0.4
P(fault seal) = 0.7
N
2150
m
2100
m
2050
m
Fig. 1. Map view of a faulted four-way dip closure. Two faults cut into the reservoir below depths of 2050 m and2100 m, respectively. The fault seal probabilities are estimated to be 40% for the NE fault and 70% for the SWfault. A - B marks the line of the cross-section in Fig. 2.
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NE fault leaks and the SW fault seals). The nature of
the SW fault has no influence on the leak point depth
but both scenarios are valid and should be considered
when the probability of possible outcomes is assessed.
If the NE fault is sealing and the SW fault leaking,
hydrocarbons can potentially be trapped down to a
leak point at 2100 m. Finally, if both faults are sealing,
hydrocarbons can fill the entire trap to its lowestclosing contour at 2150 m. Fig. 3 illustrates possible
fault leak points and the lowest closing contour in
map view, highlighting the maximum area of each
potential outcome. The next section focuses on the
critical determination of leak point probabilities.
Theoretical prediction of the
scenario probabilities
Four scenarios with three different leak points have
been identified (Table 1). It is important to note
however that the probability of the scenarios occurring
is not equivalent to the probability of success (POS)
of the prospect. Stochastic combination of the two
elements NE fault sealing and SW fault sealing
results in four scenarios, as shown in Table 1.
Success here indicates that the respective fault acts
as a seal; failure indicates that the faults are non-
sealing and that hydrocarbons are leaking out of the
trap at the shallowest intersection between the fault
plane and the top reservoir surface. The two elements,and their probabilities of occurrence, contribute to
the probability calculation shown in Table 2.
In Scenario 1, both NE and SW faults are sealing; in
Scenarios 2 and 3, one fault is sealing and the other is
not; in Scenario 4 there is sealing failure of both elements.
The scenario probabilities are calculated by
multiplying the probabilities of the respective
conditions. The probability of Scenario 1 (Table 2) is
therefore the product of all success probabilities
(0.4*0.7 = 0.28).
In Scenario 2, the failure probability of the SW
fault is (1- the probability of success), i.e. (1.0 - 0.7
A B
2050 m
2000 m
2100 m
2150 m
max contact 'NE fault' leaking
max contact 'NE fault' sealingAND 'NW fault' leaking
max contact 'NE fault' sealingAND 'SW fault' sealing
Fig. 2. Cross-section view of the faulted four-way dip closure showing the maximum possible hydrocarbon-water contact depths for the three possible outcomes.
Sealing
Leaking
N
Sealing
Sealing
N
Leaking
Leaking OR
Sealing
N
a) b) c)
2150
m
2150
m
2150
m
2050
m
2050
m
2050
m
2100
m
2100
m
2100
m
Fig. 3. Map view of the three possible outcomes of the prospect example. (a) The small volume outcome(dark grey) is applicable if the NE fault is leaking. The probability of the scenario where the NE fault is leakingand the SW fault seals is different from the probability of the scenario where both faults are leaking at thesame time. However, whether the SW fault seals or leaks has no influence on the leak point depth if the NEfault leaks. (b) The medium volume outcome (medium grey) is applicable in the scenario where the NEfault is sealing and the SW fault leaks. (c) The large volume outcome (light grey) is only used for thescenario where both faults are sealing.
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90 Volume assessment of complex hydrocarbon traps
= 0.3). The probability of Scenario 2 is therefore
(0.4*0.3 = 0.12). Similarly, the probability of Scenario
3 is calculated to be 0.42.
The probability of Scenario 4 is determined by
multiplying the failure probabilities of both faults ((1.0
- 0.4) * (1.0 - 0.7)) = 0.18. Note that the sum of the
four scenarios is 1.0.
The final column in Table 2 summarises leak point
depths for each scenario. As noted above, if the NE
fault fails, then the failure or success of the SW fault
has no implication for the leak point. Therefore a 2050
m leak point is assigned to both scenarios 3 and 4 in
which the NE fault is not sealing. The sum of the
probabilities of Scenario 3 and 4 determine the
probability of the leak point at 2050 m; the probability
of the leak point at 2100 m is given by the probability
of Scenario 2 only. Finally, the probability of thedeepest leak point at 2150 m is equal to the probability
of Scenario 1. The probability of the leak point depths
equals the frequency at which the Monte Carlo
simulator is required to determine the identified leak
points for the calculation of the prospect volume
uncertainty.
Fig. 4 shows the calculation of weighting
probabilities in the form of a scenario tree, a type of
display which is commonly employed when various
different scenarios are being evaluated. The POS of
the prospect is included in the tree; however, the POS
only determines the success rate of the prospect and
therefore the rate at which the Monte Carlo simulator
calculates successful trials. Once a success has been
identified, the POS neither influences the scenario
weighting nor the volume calculation. In other words,
the weighting of scenarios is normalised to the success
rate of the prospect.
In Fig. 4, all valid combinations of trapping
elements below the crest of the prospect are depicted
as individual branches. The weight of each branch
is calculated by multiplying the probabilities of the
events leading to the particular branch after passing
the initial POS criterion. To the right, the applicable
leak point depth is attached to each branch. A
reference to the scenario determination method
described above and shown in Tables 1 and 2 is given
by the scenario names.
Simulation of the predicted scenario weighting
In the next step, a test model is created in the Monte
Carlo software for the purpose of replicating and
validating the theoretical probability predictions and
testing the implications of each scenario on the leak
point depth.
The calculation of volumes with a Monte Carlo
simulator requires the definition of uncertainty ranges
for various input parameters. The gross rock volume
(GRV) of the example prospect is defined by the depth
versus area data in Table 3 and an estimate of reservoir
thickness in Table 4. For the sake of simplicity, the
Scenario name NE fault sealing SW fault sealing
Scenario 1 x x
Scenario 2 x -
Scenario 3 - xx Success
Scenario 4 - -- Failure
Table 1. All possible combinations (scenarios) for the two independent prospect trapping elements are
identified in this table. Note that the failures or successes of the elements have no impact on the geologicalsuccess, but on the uncertainty of the leak points.
Scenario name NE fault sealing SW fault sealing P(scenario) Leak point depth
Scenario 1 0.4 0.7 0.28 2150 m
Scenario 2 0.4 (1.0 - 0.7) 0.12 2100 m
Scenario 3 (1.0 - 0.4) 0.7 0.42 2050 m
Scenario 4 (1.0 - 0.4) (1.0 - 0.7) 0.18 2050 m
Table 2. The probability calculation of all valid scenarios combined with the implication for the leak point depth.The basis for the scenario probability calculations is the assessment of adequacy of the two identified trappingelements. The NE fault sealing has a probability of 0.4 and the SW fault sealing is estimated to be valid at
a rate of 0.7.
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reservoir thickness has been kept constant as have theareas at the individual depths (this therefore assumes
no uncertainty regarding the mapped structure).
However, uncertainty distributions have been applied
to all other volume parameters such as net-to-gross ratio,
porosity, hydrocarbon saturation, formation volume
factor, gas-oil-ratio and recovery factor. These
parameters are not discussed in detail but are the same
for all volume models in this study, and contribute to
the calculation of the volume uncertainty distribution of
each model. Input ranges and the distribution type of
the volume parameters (whether constant, normal or
uniform) are defined in Table 4.
In the first simulation, the complexity of the trapis reflected in two input distributions, one for each
of the two faults. Potential leak points are identified
and the failure frequency is predicted from analogue
data. For the simulator to know how many trials it
should create for a sealing or leaking fault, the
observations need to be translated into probabilistic
language. Fig. 5 shows the prospect in a depth
versus square-root-of-area display on the right of
the diagram, and the translation of fault failure
probabilities and their implication on the leak point
on the left. One input distribution is assigned to
each potentially leaking fault. The assessment of
Prospect
Failure
Success
NE fault
Success
SW fault
Success
SW fault
Failure
NE fault
Failure
SW fault
Success
SW fault
Failure
0.6
0.40.3
0.7
0.7
0.3
1-POS
2050 m0.42
0.12
0.28
2050 m
2100 m
2150 m
0.18
1.0
Scenario 3
Scenario 4
Scenario 2
Scenario 1
P(scenario) Leak point depth
Sum of P(scenario) =
Scenario nameScenario tree
POS
Fig. 4. A scenario tree showing all possible outcomes as branches, the weight of each branch and the impliedleak point depth for the example prospect. Reference to the scenarios in Tables 1 and 2 is established by thescenario names.
Depth [m] Distribution type Area [km2]
2000 Constant 0.00
2025 Constant 0.41
2050 Constant 1.34
2075 Constant 2.82
2100 Constant 4.90
2125 Constant 6.93
2150 Constant 8.90
Depth vs. Area definition
Table 3. Depth versus area input data for the description of the prospect shape. For simplicity, no areauncertainty has been modelled.
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the NE fault translates into a weighted-values
distribution where the random number generator will
produce 40% trials with a leak point depth of 2150 m
(NE fault sealing) and 60% trials with a leak point
depth of 2050 m (NE fault leaking). The distribution
of possible outcomes for the SW fault looks very
similar but has different frequencies and
consequences (leak points). Thus, in 70% of the trialsthe simulator generates a leak point depth of 2150 m
(SW fault sealing), and in 30% of the trials the leak
point depth is generated at a depth of 2100 m (SW
fault leaking). The Monte Carlo simulator samples both
distributions simultaneously and independently for
each trial.
The hydrocarbon-water contact input distribution
is kept constant at the deepest closing contour (2150
m) for this first modelling exercise, and no uncertainty
on available hydrocarbons is therefore considered.
This initial model is designed only to validate the
theoretically predicted consequences of the observed
trap complexity.Despite the fact that the input of the hydrocarbon-
water contact in the model is constant at the lowest
closing contour of the prospect, variation of the
resulting hydrocarbon-water contact can be expected
due to differences in the fault leak points and in their
probability of occurrence. Both fault leak distributions
will be sampled simultaneously, and the shallowest
leak point determines the resulting hydrocarbon-water
contact of the individual trial. For example, if the
simulator draws a 2150 m deep leak point from the
NE fault distribution (replicating a sealing NE fault in
this trial), and in the same trial simulates a failure of
the SW fault (leak at 2100 m), the hydrocarbon-water
contact of the system cannot exceed a depth of 2100
m even though the input hydrocarbon-water contact
distribution has a constant value at 2150 m. The
resulting hydrocarbon-water contact of this trial is
therefore 2100 m. All resulting hydrocarbon-water
contacts of the simulation are displayed in a histogram
in the centre of Fig. 5. The resulting distribution ofhydrocarbon-water contacts differs significantly from
the input hydrocarbon-water contact distribution and
allows for the validation of the predicted outcomes
through the following procedure.
The Monte Carlo simulator was utilised to produce
10,000 successful trials. The starting point of each
trial is a filled-to-spill trap with a constant
hydrocarbon-water contact at 2150 m, and only the
definition of leak point depths and their probability of
occurrence will force the contact to deviate from this
depth in the simulation.
The purpose of the first Monte Carlo simulation is
to verify the predicted probabilities of the differentscenarios. Four different scenarios were described.
Two have the same leak point depth and hence will
produce the same hydrocarbon-water contact.
Scenarios 3 and 4 will both limit the maximum
possible hydrocarbon-water contact to a depth of 2050
m (Fig. 4). The sum of the predicted probabilities of
Scenarios 3 and 4 (0.42 + 0.18 = 0.6) is the estimated
frequency at which this will occur. Thus, out of the
10,000 simulated samples, ca. 6000 are expected to
have a contact depth of 2050 m. Scenario 2 would
allow for a deeper leak point at 2100 m and the
predicted probability is 0.12 (Fig. 4). Thus, of 10,000
Parameter Distribution type Mean Minimum P90 P50 P10 Maximum
Reservoir thickness [m] Constant 60.0 60.0 60.0 60.0 60.0 60.0
Net/Gross ratio [%] Normal 65.0 40.0 52.8 65.0 77.2 90.0
Porosity [%] Normal 19.0 13.0 16.1 19.0 21.9 25.0
HC Saturation [%] Normal 55.0 45.0 50.1 55.0 59.9 65.0
Formation Factor [bbl/STB] Normal 1.25 1.10 1.18 1.25 1.32 1.40
Gas Oil Ratio [scf/STB] Uniform 1200 1000 1040 1200 1360 1400
Recovery Factor [%] Normal 47.5 35 41.4 47.5 53.6 0.6
Volume parameter input distributions
Table 4. Uncertainty ranges and distribution shapes of all contributing volume parameters for the stochastic
calculation of the prospect potential. Note that all distribution shapes are basic functions such as normal,uniform or constant. More complex shapes may be appropriate in many parts of the world where moreavailable data allows for fine tuning of the histograms.
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simulations, the model will produce around 1200
samples with a hydrocarbon-water contact at 2100
m. Similarly, the predicted probability of Scenario 1
(0.28) indicates that the simulator will produce ca.
2800 samples with a hydrocarbon-water contact at
2150 m. In these ca. 2800 cases, the output
hydrocarbon-water contact will be the same as the
input depth.The resulting (output) hydrocarbon-water contact
is therefore a suitable criterion for filtering the
successful trials of the test model and validating the
prediction. Consequently, three sample groups are
defined. Sample group 1 filters all 10,000 trials for
outcomes with a hydrocarbon-water contact at 2050
m. The software identified 6005 samples in this group
and therefore confirms the predicted frequency (6005
10,000 = 0.6005) of this outcome. Some 1192
samples were identified in the second group with the
filter set to a hydrocarbon-water contact of 2100 m,
likewise confirming the predicted frequency. The
remaining 2803 trials were assigned to the third samplegroup, which filtered the trials for a hydrocarbon-
water contact at 2150 m. The absence of duplicates
or remaining unclassified samples indicates that no
scenario was left out of consideration.
In this first Monte Carlo simulation, one input
distribution was required to model the frequency and
consequences of each leaking fault. The resulting
hydrocarbon-water contacts of the model confirmed
the stochastically-determined predictions, permitting
a simplified input of leak point uncertainty in
subsequent volume modelling calculations. The
resulting hydrocarbon-water contact distribution in
the centre of Fig. 5 is a proxy for the leak point
frequency, and can be defined by a single weighted-
values distribution. Thus, one input distribution can
simulate two (or any number of) faults. This is
especially valuable because not all simulation software
provides the option of defining as many leak point
uncertainty distributions as required. In order to
simplify the input of potential leak points at variousdepths and frequencies, the workflow allows for the
translation of discrete geological events (inefficient
fault seal at specified frequency of occurrence) and
their consequences (leak points at specific depths)
into a single uncertainty distribution.
For the realistic assessment of prospect volume
potential, more complex modelling is usually necessary.
So far, all the calculations started with a filled to
spill condition. However, potential limitations in
charge require the additional uncertainty of the
hydrocarbon-water contact.
Advanced prospect assessment software provides
the option of defining the amount of available chargeas an input. In this case, the simulator can determine
the amount of hydrocarbons for each sample from a
given range of possible hydrocarbon charges. This
feature is not considered in the present workflow.
Instead, the uncertainty of the hydrocarbon-water
contact, as a result of potentially limited hydrocarbon
charge, is applied to the model.
Final volume modelling
of the complex four-way trap
Uncertainty modelling of the hydrocarbon-water
contact may be performed differently in different
HC Water
contact [m]
Result
2000
2120
2040
2080
2160
Depth[m]
Square root of area
2050
2100
2150
2000
4000
6000
Number of
trials
NE fault seal
probability
and failure
consequence
SW fault seal
probability
and failure
consequence
Depth[m]
Fig. 5. The results of the test model for the validation of the predicted likeliness of the leak point depths. Onthe left, the observations from the fault seal analysis (frequencies and consequences of fault seal failure) havebeen translated into Monte Carlo simulator input distributions for both faults. The output hydrocarbon-watercontacts and their frequencies as proxy for the three possible outcomes are displayed next to the depth versusarea representation of the example prospect.
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94 Volume assessment of complex hydrocarbon traps
companies which use the depth versus area
methodology to assess the GRV. This element of the
analysis is usually critical in any hydrocarbon volume
analysis and requires particular attention. Here, a
specific methodology for estimating the range, the
anchor points or the shape of the hydrocarbon-water
contact distribution is not suggested. Rather, a single
distribution shape is applied to two volume models as
an example. One model is constructed without the
elements of complexity of the structure, and the other
includes the potential leak points and their predicted
frequencies. Finally, differences in the results of the
calculations are compared and discussed.
The minimum geological success case is defined
by a hydrocarbon-water contact of 2020 m. At this
depth the example prospect consists only of the four-
way element, and it is assumed that a sustainable flow
of hydrocarbons to the surface will be guaranteed if
hydrocarbons are detected at or below this depth.
Hence, the success case definition allows for theassessment of probability of geological success
independent from fault seal probabilities. The
maximum hydrocarbon-water contact depth is
equivalent to the depth of the lowest closing contour
at 2150 m. For the sake of simplicity, a uniform
distribution between minimum and maximum possible
hydrocarbon-water contacts is assumed. Other
distribution shapes can be used to describe the
uncertainty of the hydrocarbon-water contact without
affecting the described method.
Volume assessment model 1 ignores the influence
of the potentially leaking faults on the hydrocarbon-
water contact, and therefore only the uniform
distribution of the contact is applied. Model 2
incorporates added complexity in terms of the down-
dip trapping elements. In addition to the uniform
uncertainty of the contact, a weighted-values
distribution for the leak points is applied. This
uncertainty distribution reflects the previously
predicted scenarios, their frequency of occurrence,
and the implications on the leak points. Fig. 6 illustrates
the input distributions of the two volume assessment
models in relation to the depth versus area
representation of the prospect. The graph also shows
the resulting hydrocarbon-water contacts of both
models which can be extracted after the Monte Carlo
simulator finishes the calculation. The distribution of
the resulting hydrocarbon-water contacts of model 1
is, as expected, very similar to the input contact
distribution. However, the fault leak probabilities have
a significant impact on the resulting contact depths in
model 2. This model recognises the trap complexity,and calculates a contact of 2050 m in 4593 of the
10,000 samples; these are visible as a prominent peak
in the result histogram of the hydrocarbon-water
contact of model 2 (Fig. 6). This peak can be explained
in terms of the stochastic interaction of the two
independent input uncertainties (hydrocarbon-water
contact, and leak point depth). The independent
behaviour of these uncertainties will lead to many trials
where the simulator picks a contact from the
hydrocarbon-water contact input distribution at a
depth below 2050 m. This depth (2050 m) is the P77
of the input hydrocarbon-water contact and therefore
Minimum HC
column length
of 20 mHCWatercontact/leakpoint
uncertainty[m]
2000
2150
2050
2100
Depth[m]
Square root of areaModel 2(incl. trap
complexity)
Model 1
(ignoring trap
complexity)
Uniform HC
contact
distribution
Weighted
value leak
point
distribution
Model 2Model 1
Uniform HC
contact
distribution
Model 2Model 1
2020
2150
2050
2100
Input Result
Fig. 6. This diagram displays the input and output uncertainty distributions in relation to the depth versus arearepresentation of the prospect. Two models are constructed to evaluate the significance of the trapcomplexity on the volume distribution. Model 1 ignores the fault leak possibilities and hence only the uniformhydrocarbon-water contact is applied to this model. As expected, the resulting hydrocarbon-water contactdistribution looks very similar to the input. In model 2, the weighted values distribution is applied to the spill-point parameter in order to include the trap complexity in the volume calculation. This has a considerableimpact on the resulting hydrocarbon-water contact.
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95A. Beha et al.
a deeper contact is selected in 76% of the trials. At
the same time, the simulator draws a leak point at
2050 m (60% of the leak point input distribution). A
potentially large column will then be truncated, and
the resulting hydrocarbon-water contact of the
particular trial is 2050 m. Thus the shallower depth
of the two independently sampled input distributions
determines the hydrocarbon-water contact of the trial.
The difference between models 1 and 2 withrespect to the amount of recoverable hydrocarbons
is shown in Table 5, which shows the mean volume
and the five most commonly used percentiles of the
cumulative probability curve. Numbers in the table
are given in million barrels of oil equivalent [MMboe]
and the conversion factor to million standard cubic
metres [MMSm3] is 0.159. The resource histogram
and cumulative probability curve of both models are
shown in Fig. 7. The significant difference of the
resource distribution shapes is solely caused by the
different effective hydrocarbon water contact
distributions.
If trap complexity in model 1 is ignored (i.e.
ignoring whether the NE fault and SW fault may
or may not seal), this is equal to assuming permanent
access to the full GRV of the structure. Table 5 shows
that the result of this volume uncertainty calculation
gives a mean volume of 27.5 MMboe compared to
12.7 MMboe when trap complexity is incorporated,
but a key reminder is that the probability of geological
success (i.e. flowing hydrocarbons at a minimal butsustained level) is the same for both.
DISCUSSION
A consistent definition of the geological success of
an exploration project is vital for the volume
assessment of the trap, especially when prospects
are aggregated within a portfolio and forecasts or
commitments are made. Whether the prospect
generator is requested to assess the probability of a
mean volume case or the minimum economic volume
of hydrocarbons or the minimum success-case
Model 1(ignoring trap complexity)
Model 2(including trap complexity)
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
00 20 40 60 80 100 120
Accumulation size Total Resources [1e6 STB OE]
Cumulativefrequency
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
00 20 40 60 80 100 120
Accumulation size Total Resources [1e6 STB OE]
Cumulativefrequency
Fig.7. Resource histogram and cumulative probability curve for both volume models. Model 1 ignores trapcomplexity while model 2 includes the fault seal assessment of both faults. The effect of the potential fault sealfailures on the hydrocarbon water contact determines the significant difference of the results.
Volume model Mean P99 P90 P50 P10 P1
Model 1 ignoring trap complexity 27.5 0.7 1.8 19.7 65.7 98.4
Model 2 including trap complexity 12.7 0.7 1.8 5.6 36.9 82.3
Recoverable hydrocarbon volume [MMboe]
Table 5. Monte Carlo simulation results of the two volume models of the example prospect. Mean volume andmost commonly communicated percentiles of the recoverable hydrocarbon volume distribution are displayedin million barrels of oil equivalent [MMboe].
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96 Volume assessment of complex hydrocarbon traps
volume has a significant impact on the total
assessment. The definition of the probability of
geological success used here permits the use of a
relatively straightforward methodology for estimating
the potential hydrocarbon volume in a trap. Whether
or not trap elements fail in the deeper part of the
structure will not influence the probability of finding
the minimum success-case volume since this is usually
located in the shallowest part of the structure.
The methodology presented will determine the
volume distribution of complex traps, but will also
influence subsequent analyses such as calculating the
probability of success of a particular well location if
the well is not located in the most crestal position.
For example, a location may be chosen to test theminimum economic hydrocarbon volume of the
prospect. Once a Monte Carlo simulation has been
completed to assess the prospect volume uncertainty,
the shallowest reservoir entry point to test a minimum
economic volume of hydrocarbons can then be back-
calculated. The probability of success of the well
location then becomes the POS (the probability of
finding the minimum accumulation) multiplied by the
percentile associated with the shallowest reservoir
entry point to test a minimum economic volume on
the cumulative probability curve of the hydrocarbon-
water contact.
Fig. 8 illustrates the concept of determining the
probability of well success multiplier for a well that is
planned to enter the reservoir at a depth of 2075m.
Two multipliers are determined: one is based on model
1 which ignores the trap complexity, and the second
is based on model 2 which includes the potentially
failing trap elements. For model 1, the probability that
the hydrocarbon water contact is at 2075m or deeper
amounts to some 57%. This implies that in 43% of
the geologically successful cases, the contact will be
between 2020 m and 2075 m. These cases cannot be
proven by the well, and hence the probability of well
success is equal to the probability of technical success
(POS) multiplied by 0.57, i.e. the fraction of the
contact distribution that is at or below 2075 m. Thesame technique is applied to model 2; in this case,
only 23% of the technically successful cases can be
tested by a well that is designed to enter the reservoir
at 2075 m. In other words, the multiplier determined
from model 2 (0.23) is significantly smaller than the
multiplier of model 1 (0.57). The likelihood of deeper
hydrocarbon-water contacts is reduced in cases
where the trap is dependent on additional trapping
elements down-flank. Therefore, the probability of
well success can be considerably less if the well is
designed to test a section that is potentially affected
by the failure of a deeper trap element.
1.0
0.0
0.5
0.23
P(HC water contact at
or deeper than
reservoir entry depth)
Model 2
(incl. trap
complexity)
1.0
0.0
0.57
P(HC water contact at
or deeper than
reservoir entry depth)
Model 1
(ignoring trap
complexity)
2000
2150
2050
2100Depth[m]
Square root of area
2075
Projectedwell
trajectory
Fig. 8. The probability of well success is different than the probability of technical success i f the well is notdesigned to test the prospect in the most crestal position. The graph shows a well that is planned to enter thereservoir at a depth of 2075 m. Contact depths of 2075 m and deeper result in a successful well. Thehydrocarbon-water contact probability of exceedance curve of model 1 shows that this is the case in some 57%of the technically successful trials. Hence, the probability of well success is equal to the probability of geologicalsuccess (POS) multiplied by 0.57. However, in model 2 which includes the potentially leaking faults, theprobability of a successful well is significantly reduced to 23% of the geological success cases due to thecomplexity of the trap.
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97A. Beha et al.
The probability of success for any well location
can be calculated in the same way.
CONCLUSIONS
The assessment of complex hydrocarbon traps can
be challenging and requires more attention to detail
compared to simple four-way dip closures. All elements
which can potentially reduce the probability of deeper
hydrocarbon-water contacts need to be assessed and
their implications for the volume model evaluated. The
stochastic combination of all elements allows for an
adequate volume assessment of the prospect.
It is evident from the example presented that the
assessment of complex traps may be a counter-
intuitive process. Without determining all possible
scenarios, it is not intuitively obvious that a deep leakpoint can be statistically more likely than a leak point
higher up the structure, although the deeper leak point
requires more elements to seal simultaneously.
ACKNOWLEDGEMENTS
The authors wish to express their gratitude to Gary
Citron, David Cook and James MacKay for their
reviews of an earlier draft of this article. Their
comments were very much appreciated and helped to
improve the manuscript. They would also like to thank
the referees Ren O. Thomsen and Glenn McMaster
for their very constructive suggestions during journal
review.
REFERENCES
CITRON, G.P., MACKAY, J.A. and ROSE, P.R., 2006. AppropriateCreativity and Measurement in the Deliberate Search forstratigraphic traps. In: Allen, M.R., Goffey, G.P.,Morgan, R.K.and Walker, I.M., (Eds), The Deliberate Search for theStratigraphic Trap Where Are We Now? Geol. Soc. Lond.Spec. Publ., 254, 27-41.
OTIS, R.M. and SCHNEIDERMANN, N., 1997. A process forevaluating exploration prospects.AAPG Bull., 81(7), 1087-1109.
YOUNG, R., McINTYRE, S., MCLANE, M.A., COOK, D.M.,MACKAY, J.A. and GOUVEIA, J. 2005. Complex Traps: Amethod for calculating the chance-weighted valueoutcomes for a prospect with multiple trapping styles.(Abstr)AAPG Bull.
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