General Method for the Consistent Volume Assessment of Complex Hydrocabon Traps

7/29/2019 General Method for the Consistent Volume Assessment of Complex Hydrocabon Traps

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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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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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89A. Beha et al.

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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= 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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91A. Beha et al.

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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93A. Beha et al.

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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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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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.

General Method for the Consistent Volume Assessment of Complex Hydrocabon Traps

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