AUTHORS

Zhuoheng Chen � Geological Survey ofCanada, 3303-33rd Street, NW, Calgary,Alberta, Canada T2L 2A7; zchen@nrcan.gc.ca

Zhuoheng Chen, a research scientist at theGeological Survey of Canada (GSC), obtainedhis Ph.D. from the Norwegian University ofScience and Technology in 1993 and held aposition as an associate professor at ChinaUniversity of Petroleum (Beijing) before joint-ing GSC in 1998. He has developed methodsfor resource assessment and exploration-riskevaluation. His research interests include pe-troleum resource assessment (methods andapplications), basin analysis, and gas-hydratestudies.

Kirk G. Osadetz � Geological Survey ofCanada, 3303-33rd Street, NW, Calgary,Alberta, Canada T2L 2A7; kosadetz@nrcan.gc.ca

Kirk graduated from the University of Toronto(B.Sc., 1978; M.Sc., 1983). He manages theEarth Science Sector Gas Hydrates Fuel of theFuture Program and is head of the LaboratoryServices Subdivision at the GSC in Calgary. Heis active regarding petroleum resource evalu-ation and has research interests in gas hydrates,tectonics, and thermochronology. He workedpreviously at Gulf Canada Resources Inc. andPetroCanada Resources Inc. in Calgary.

ACKNOWLEDGEMENTS

This work was supported by the GeologicalSurvey of Canada Project 950003 and thePanel for Energy Research and Development(Natural Resources Canada). Grateful acknowl-edgement is expressed to G. Stockmal of theGeological Survey of Canada, who provided acritical review of the original manuscript andmade many helpful suggestions. This articlebenefited from the suggestions and commentsof AAPG reviewers Colin Stabler, Joseph Stud-lick, and an anonymous reviewer. GeologicalSurvey of Canada Contribution Number 2005342.

Geological risk mapping andprospect evaluation usingmultivariate and Bayesianstatistical methods, westernSverdrup Basin of CanadaZhuoheng Chen and Kirk G. Osadetz

ABSTRACT

The current practice of geological risk evaluation at the play level

involves substantial subjectivity and rarely considers the spatial cor-

relations among the identified prospects. A quantitative procedure

using multivariate and Bayesian statistical methods is proposed,

which treats exploration-risk evaluation as a two-group classifica-

tion, to provide amore objective and reproducible result. The spatial

correlation among the identified prospects is considered because

the proposed approach is applied to the entire evaluation area. The

exploration-risk evaluation of the Upper Triassic–Lower Jurassic

Heiberg Group structural gas play in western Sverdrup Basin, Arctic

Canada, illustrates the method.

INTRODUCTION

A meaningful exploration decision depends not only on a reliable

estimate of resource potential, but also on a reliable geological risk

evaluation of the exploration target. Geological risk analysis estimates

the uncertainty in converting the anticipated resource potential in

an identified prospect to an identified accumulation subsequent to

exploratory drilling. Although large corporations may have propri-

etary tools for objective exploration-risk evaluation, the literature

indicates that geological risk analysis commonly involves substan-

tial subjectivity and uncommonly considers the spatial correlations

GEOHORIZONS

AAPG Bulletin, v. 90, no. 6 (June 2006), pp. 859–872 859

Copyright #2006. The American Association of Petroleum Geologists. All rights reserved.

Manuscript received March 10, 2005; provisional acceptance September 12, 2005; revised manuscriptreceived December 28, 2005; final acceptance January 16, 2006.

DOI:10.1306/01160605050

among the identified prospects (e.g., Otis and Schnei-

dermann, 1997; Rose, 2001). Risk analysis starts with

a play analysis, which examines all the essential geo-

logical elements necessary for oil and gas accumulation

in a region (e.g., White, 1988, 1993) and subsequently

assigns probability values to each essential geological

play-forming element. The probability of exploratory

success is the product of the probabilities of all the es-

sential geological elements for petroleum accumulation

at a specific prospect (White, 1988; Otis and Schnei-

dermann, 1997; Snow et al., 1997; Rose, 2001). The use

of the geographical information system (GIS) provides a

tool that enables more rigorous play assessments (Hood

et al., 2000).

Several drawbacks to the conventional geological

risk evaluation exist. First, there can be substantial sub-

jective judgments involved in the probability assign-

ments. In such assignments, favorability levels are judged

using analogies for all the geological elements, and each

favorable level corresponds to a range of probability

values. For example, the unfavorable level may have

a probability of 0.3 or lower, whereas the favorable

level may correspond to a probability of 0.7 or higher

(see table 4 of Otis and Schneidermann, 1997). Second,

subjective judgments employed in risk evaluation may

not be consistently applied or reproducible. For exam-

ple, if independent assessments were conducted by dif-

ferent people, the probability values of the risk could

vary significantly. A consistent and repeatable risk eval-

uation result is more desirable for consistent exploration

decision-making processes. Third, methods in published

literature do not explicitly consider spatial correlations

among prospects in the analysis of geological risk. The

consideration of spatial correlations among prospects

is desirable because petroleum occurrence at a specific

prospect is not an isolated event, but instead, it is a

part of the result of similar geological processes in both

the total petroleum system and the play. A better under-

standing of spatial variation of the objects and their

spatial relationship to the presence of essential geologi-

cal elements is important for geological risk analysis.

Exploration-risk evaluation is to estimate the risk that

an untested prospect could be dry (we define ‘‘dry’’ as

noncommercially productive, including both non-

petroleum-bearing prospects and minor petroleum-

bearing accumulations), which is equivalent to estimat-

ing the uncertainty in a two-group classification. Tools

that allow us to estimate this uncertainty quantita-

tively exist, such that we may infer the geological risk.

In recent years, attempts have been made to eval-

uate geological risk by considering the spatial charac-

teristics of exploration targets using spatial statistical

tools and improved numerical manipulations of geo-

graphical information. Chen et al. (2000a, 2001, 2002,

2004) have proposed several techniques for the charac-

terization of spatial variability of geological factors and

for the estimation of petroleum accumulation proba-

bilities at the play level. Gao et al. (2000) proposed an

object-based method for estimating the probability of

petroleumoccurrence. Rostirolla et al. (2003) proposed

the use of a Bayesian assessment of favorability for oil

and gas prospects. Chen and Osadetz (in press) have

used a geological model-based stochastic simulation

for predicting the locations of undiscovered petroleum

resources.

In this article, we present a statistical procedure

for a systematic geological risk analysis using Bayesian

theory and multivariate statistics at the play level. We

have applied the proposedmethod to theHeibergGroup

structural petroleum play in the western Sverdrup Ba-

sin of the Canadian Arctic archipelago to evaluate

regional exploration-risk variations. Combined with es-

timated prospect potentials, the untested prospects are

subsequently ranked within this play.

METHOD DESCRIPTIONS

Representation of Geological Variables

In this study, the spatial variation of a geological vari-

able is represented by a digital geological map, which is

presented using a rectangular array ofN pixels of equal

size. The pixels are labeled as i = 1, 2,. . ., N. Suppose

that there are m subsurface geological maps, m = 1, 2,

3. . . . At each pixel i on the kth geological map, the

geological attribute xk(i) represents the average value

of the geological attribute, such as formation thickness

or structural residual. The kth geologicalmap is specified

by the vector, Yk = (y (k,1),y (k,2),. . ., y (k,N )). In practice,

the elements of a map are unknown, except at the sam-

ple points, such as well locations. To distinguish the

sample points from the unknowns, we label the known

components as x (k,1),. . .,x (k,n), n < N. The remaining

points of Y, x (k,n+1),. . .,x (k,N), correspond to the pixels

where the map attribute is to be estimated. Kriging or

other interpolation methods can be used to estimate

the values at the unsampled locations. Maps, which are

commonly employed as analytical results or even as

data, are, in reality, themselves interpretations that do

not commonly display the uncertainties and subjectiv-

ity involved in their construction.

860 Geohorizons

Probability of Petroleum Occurrence

The goal of geological risk analysis is to determine to

what degree of certainty a potential drilling target may

be either productive or nonproductive, based on avail-

able geological criteria. The risk analysis problem is

equivalent to classification with uncertainty in a multi-

variate space. As such, the method is dependent on

the availability of suitable data. The uncertainty asso-

ciatedwith the classification represents the geological

risk of a prospect being dry. The basic assumption for

classification is that there are statistically significant

differences in the characteristics among the different

groups, whereas no significant differences exist with-

in each of the groups. This assumption can be used

to define, but is not limited to, the two statistical pop-

ulations considered in this analysis. Depending on the

objectives of the study, the categories of the classifi-

cation could be two or three, as long as the defini-

tions of the categories are consistently applied. For

example, the two populations could be accumula-

tions (both economic and noneconomic, including

shows) and non-petroleum bearing, or three popula-

tions such as economic accumulations, noneconomic

accumulations, and dry. In this analysis, we discuss a

classification problem with two populations as previ-

ously defined. The geological signatures that can be

used to distinguish between these two populations di-

agnostically are the results of geological processes and

conditions that are necessary for the formation of pe-

troleum accumulations.

Suppose that the nature of an area in a petroleum

play is to be classified into two categories, the pro-

ductive area, A, and nonproductive area, A, and that

the play was penetrated by n exploratory wells that

represent a sample from the play. On the basis of

n wells and other available geoscience information,

we seek to estimate the probability with which each

untested location belongs to one of the two defined

categories. This estimation may take the form of a

conditional probability upon m geological variables.

Let G(r) denote the random vector of m geological

variables containing information on the classification,

and g(r) be the assumed values for variable G(r). Theconditional probability that, for given observations

G(r) = g(r), the area at location r belongs to A can be

written as

PðAjgðrÞÞ ¼ PðA; gðrÞÞPðgðrÞÞ ð1Þ

where P(A,g(r)) is the joint probability of productive

areaA, and observations,G(r) = g(r); and P(g(r)) is theprobability of G(r) = g(r).

Mahalanobis Distance

In principle, we can use multivariate Bayesian statis-

tics to derive a conditional probability of hydrocarbon

occurrence at each untested location.However, in some

circumstances, it is more convenient to employ a mul-

tivariate statistical method to integrate all the avail-

able geological variables and then to calculate the

conditional probability. Mahalanobis distance (MD)

is a multivariate statistical measure, which has been

widely used in classification. In this study, MD, a dis-

tance in parameter space, is used to integrate the avail-

able geoscience data. The MD is defined in the follow-

ing way.

Let X denote the data consisting of p observations

and m variables. The geological favorability of a loca-

tion for petroleum accumulation is measured as the

MD (Mardia et al., 1989), which is defined as

MD ¼ ðXk � XsÞTS�1s ðXk � XsÞ ð2Þ

where Xk is the kth row vector of X, or the kth obser-

vation; Xs is the mean vector and (. . .)T is the trans-

posedmatrix of the training data set, such as the general

characteristics of the petroleum accumulation sites (i.e.,

all productive wells); and Ss is the covariance matrix of

the training data set. Graphically, theMD is a standard-

ized square distance between the geometric center of

the group of known accumulations and an untested tar-

get relative to their geological characteristics in a multi-

variate space. The applications of the MD in hydro-

carbon explorationwere described byHarff et al. (1992)

and Harbaugh et al. (1995).

TakingMD as themeasure of classification, the con-

ditional probability in equation 1 can be rewritten as

PðAjDðrÞÞ ¼ PðA;DðrÞÞPðDðrÞÞ ð3Þ

where D(r) represents the MD between the geomet-

rical center of the geological characteristics of the

productive petroleum group and the geological char-

acteristic vector at an untested location in a multivar-

iate space.

Chen and Osadetz 861

Criteria for Selecting Geological Variables in Risk Analysis

Selecting geological variables that contain useful in-

formation for differentiating between potentially pro-

ductive and nonproductive prospects is important for

geological risk evaluation. In the exploration decision-

making process, we wish to avoid two types of errors:

(1)mistakenly assigning a high probability to a dry area,

and (2) abandoning a potentially productive area with-

out knowing its true value. We use a Bayesian con-

ditional probability formulation to estimate the prob-

abilities of these two types of errors in risk analysis.

Let P(HCjFav) denote the conditional probability of

petroleum occurrence given a favorable geological con-

dition and let P(DryjUnfav) denote the probability of

a tested and abandoned hole given an unfavorable con-

dition. P(HCjFav) is the indicator for a high explo-

ration success, its value indicating which prospects

should be drilled; whereas P(DryjUnfav) is the indica-

tor for the avoidance of loss of opportunity, its value

indicating which prospects should be abandoned. A bal-

anced exploration strategy for risk reduction would be to

achieve a high exploration success without compromis-

ing the loss of opportunity. The product of these two

probabilities, F(Phc,Pdry) = P(HCjFav)P(DryjUnfav),

provides a useful measure for optimizing the explora-

tion strategy. The product can also be used as a cri-

terion for selecting geological variables in the quan-

titative evaluation of exploration risk. The greater the

F(Phc,Pdry) value is, the more useful the variable could

be in terms of differentiating productive and nonpro-

ductive targets. Examples of geologic variable selec-

tion are provided in the next section, which describes

the application of this method to the Heiberg Group

structural gas play in thewestern Sverdrup Basin of the

Canadian Arctic.

GEOLOGICAL RISK MAPPING

Exploratory History and Geological Setting

The Sverdrup Basin in the Canadian Arctic region is

a frontier basin with an area of about 310,000 km2

(19,660 mi2). Discovered total in-place reserves are

294.1� 106m3 (2.16� 109 bbl) oil and 500.3� 109m3

(17.6 tcf) gas, whereas estimated total potential re-

sources include 540–882 � 106 m3 (3.96–6.47 �109 bbl) oil and 1242–1423� 109 m3 (43.9–50.2 tcf)

gas (Chen et al., 2000b). Although unfavorable con-

ditions (remote location, harsh environment, and lack

of infrastructure) prohibit exploration anddevelopment

activities currently, the growing demand for petroleum,

particularly for natural gas, may make the Sverdrup

Basin one of the most important frontier basins for fu-

ture reserve additions in North America.

The regional petroleum geology has been summa-

rized by Embry (1991) andWaylett and Embry (1993).

The stratigraphy and structural geology of the area are

described by Balkwill and Roy (1977), Balkwill and Fox

(1982), Balkwill (1983), Embry (1983), and Harrison

(1995). The Sverdrup Basin is a major extensional ba-

sin underlying theQueen Elizabeth Islands of the Cana-

dian Arctic archipelago and was a major depocenter

through much of the late Paleozoic and Mesozoic. The

stratigraphic succession, up to about 13,000m(42,000 ft)

thick, comprises Mississippian to early Tertiary marine

and nonmarine strata. The discovered petroleum ac-

cumulations all occur within a broad fairway extend-

ing from western Ellef Ringnes Island southwestward

to northeastern Melville Island (Figure 1) and are ac-

cumulated in structural traps. Oil and gas accumula-

tions found to date are present mainly in the upper-

most Triassic–Lower Jurassic porous sandstones of the

Heiberg Group beneath thick, argillaceous strata of the

Jameson Bay Formation (Figure 2).

Seismicallymapped structural closures and diapiric

structures in the western Sverdrup Basin are illustrated

in Figure 1. The amplitudes and closure areas decrease

southwestward because of lessening deformation and

thinning of the upper Mississippian and lower Penn-

sylvanian evaporites. A variety of salt structures, some

of which rise to or near the sea floor and land surface,

occur throughout the study area. These vary from cir-

cular diapiric domes to long salt walls (Balkwill, 1978,

1983; Balkwill and Fox, 1982). Salt pillows core many

of the anticlinal structures that host petroleum fields.

Two styles of structural traps are associated with ex-

isting discoveries in the Sverdrup Basin, representing

two petroleum accumulation trends separated by Loug-

heed Island (Figure 1). The first trap style consists of

a broad, low-amplitude flexure parallel to the south-

western margin of the Sverdrup Basin, which hosts two

giant gas fields, Drake Point and Hecla. Stratigraphic

pinch-out onto the southern flank of the basin appears

to be a component of trapping in theHecla field (North-

ern Oil and Gas Directorate, 1995). The other trap

style consists of high-amplitude anticlines lying largely

offshore of southwestern Ellef Ringnes Island andKing

Christian Island. They are of early Tertiary age devel-

oped or accentuated during the compressive Tertiary

862 Geohorizons

Eurekan orogeny and aligned following the orogenic

trend. Discoveries in this trap style include Kristoffer,

Sculpin, Thor, Jackson Bay, Wallis, King Christian,

Cape Allison, Cape MacMillan, Char, and Balaena.

These structures are partly filled to about 10% of their

closure, likely the consequence of surface seepage

through extensive crestal faulting (Waylett and Embry,

1993). The discoveries at Skate and Maclean, offshore

of eastern Lougheed Island, and the Whitefish gas

discovery and the Cisco oil and gas discovery show a

structural style in between the two described groups,

where trap fill in these intermediate structures is in

excess of 50%.

Data

Ninety wells have been drilled to the Heiberg Group

in the play area. Several types of geological data are

collected from published literature and our own anal-

ysis for this study. The available geological data have

been grouped according to their significance for pe-

troleum accumulation. The source rock data come pri-

marily from published organic geochemistry analysis

of handpicked cuttings and core samples from recog-

nized source rock intervals (Powell, 1978; Goodarzi

et al., 1989, 1993; Brooks et al., 1992; Gentzis and

Goodarzi, 1993) and biomarker maturity parameters

of discovered oils (Curiale, 1992). Because no strong

geographical correlation is evident between individ-

ual elements of source rock data and petroleum occur-

rence in the basin (Chen et al., 2002), a petroleum

expulsion intensity index from the source rock data

calculated using Pepper’s (1990) model is used in this

study. For details of how such an index can be estimated,

see Pepper (1990) and Pepper and Corvi (1995). The

reservoir quality data include reservoir thickness,

net sand/shale ratio, and average reservoir porosity,

which were derived from core analysis and well-log

interpretations. A regional structural map of seismic

reflection time to the top of Heiberg Group and an

isopach of the thickness of top seal (Jameson Bay

Figure 1. Location of the study area and exploration results in the western Sverdrup Basin, Canadian Arctic Islands. Locations aredescribed using latitude and longitude. Subsequent maps (Figures 7–9) display the same region using a Lambert projection (thecentral meridian of the projection is 111.5jW; base latitude is 49jN; Lambert lower standard latitude is 49jN; Lambert upperstandard latitude is 77jN). Black = discoveries; dark gray = untested structures; hatched = salt diapirs; light gray = drilledstructures.

Chen and Osadetz 863

Formation) are available, as well as the exploration re-

sults, including locations of the oil and gas discoveries

(Figure 1), the size of discoveries (Chen et al., 2000b),

as well as the locations and sizes of the untested known

prospects (Figure 1). Other exploration information,

such as borehole temperature, water salinity, and pres-

sure measurements from drillstem tests, are also avail-

able publicly. Secondary information derived from the

original data consists of a structural map, in depth, de-

rived from seismic traveltime using Bayesian cokriging

(Doyen et al., 1996) with formation depth observed in

the wells; pressure gradient; hydraulic potential; mag-

nitude of postgeneration uplift (estimated from geo-

chemical data); and structure residuals. All well data are

from theGeological Survey of Canadawell information

database. The geological variables used for the geologi-

cal risk analysis are listed in Table 1.

Because the Sverdrup Basin is a frontier basin and

no commercial petroleum production occurs, the pa-

rameters for determining the commercial accumula-

tion size are unknown for the existing discoveries. The

in-place sizes of the discovered accumulations were

estimated by the Northern Oil and Gas Directorate

(1995), although the details of those calculations are not

available publicly. In this application example, we use

the terms ‘‘discovery’’ instead of ‘‘productive,’’ ‘‘accu-

mulation’’ instead of ‘‘field’’ or ‘‘pool’’, and ‘‘tested and

abandoned’’ instead of ‘‘dry’’ in exploration-risk evalua-

tions to avoid the confusion of commercial or geological

success. The probability of hydrocarbon occurrence in

the following case study is presumably relatable to a yet-

to-be-determined probability of commercial success.

Geological Variable Selection

To illustrate how a geological variable is selected for

the geological risk analysis, we use the reservoir po-

rosity of the Heiberg Group. The frequency distribu-

tions of the reservoir porosity from the tested and

abandoned holes and discovery wells are displayed in

Figure 3. Most of the discoverywells encountered in the

reservoir have an average porosity greater than 10%.

In contrast, the tested and abandoned holes generally

have reservoir porosities less than 10%. We set 10%

reservoir porosity as a threshold value for defining a

favorable reservoir (>10%) or an unfavorable reservoir

Table 1. Conditional Probabilities and Favorability Function

Values of the Geological Variables

Variable P(HCjFav) P(DryjUnf) Favorability

Porosity 0.60 0.78 0.47

Net thickness 0.49 0.73 0.36

Net/gross 0.54 0.70 0.38

Cap thickness 0.81 0.55 0.45

Estimated uplift 0.72 0.64 0.46

Effective source rock 0.70 0.60 0.42

Pressure gradient 0.60 0.67 0.40

Salinity gradient 0.68 0.51 0.35

Structure 0.55 0.63 0.35

Structure residuals 0.74 0.63 0.46

Gross Thickness 0.43 0.50 0.22

MD 0.93 0.89 0.83

Figure 2. Stratigraphic distribution of discovered oil and gas reserves (right, oil � 106 m3, unshaded; gas � 106 m3 oil equivalents,shaded), and generalized stratigraphic chart (left), the western Sverdrup Basin, Canadian Arctic Islands.

864 Geohorizons

(�10%) for petroleum accumulation. The calculated

P(HCjFav), P(DryjUnfav), and F(Phc,Pdry) using equa-

tion 1 are listed in Table 1. P(HCjFav) = 0.6 implies that

if an exploration target has a favorable reservoir

condition, the chance of petroleum occurrence in that

target could be 60%, whereas P(DryjUnfav) = 0.78

means that if the reservoir is unfavorable, the chance of

it being tested and abandoned is 78%. In other words,

the chance of success is 22%. Comparing the past aver-

age exploration success rate of 47% suggests that con-

sideration of reservoir porosity in exploration target

selection could improve exploration success. Therefore,

reservoir porosity contains useful information for

differentiating the location of accumulations from dry

prospects.

In the same way, all available geological variables,

representing the essential geological elements necessary

for petroleum accumulation in the play, were analyzed

using the Bayesian statistical measures discussed above.

The distribution characteristics for some of the vari-

ables are shown in Figure 4. The reservoir porosity,

net/gross ratio, and thickness of cap rock and uplift ap-

pear to be good indicators for separating discoveries

and tested and abandoned prospects. The gross forma-

tion thickness does not provide such a discriminating

power. Our understanding of the petroleum system,

with its source rocks in Middle Triassic bituminous

strata, and the migration pathways into Upper Triassic

and Lower Jurassic reservoirs, in the vicinity of the

petroleum kitchen, is consistent with the results of the

Bayesian analysis. The most important elements con-

trolling significant accumulations are reservoir quality

and cap-rock integrity. Therefore, we infer that pro-

spect characterization, instead of petroleum system and

migration pathway, provided the diagnostic geological

parameters to be analyzed statistically.

To calculate the Bayesian statistics, different thresh-

old values for the geological variables are determined

based on the frequency distribution characteristics, so

that the likelihood of classification errors is minimized,

which is equivalent to maximizing the value F(Phc,Pdry).

Table 1 lists all geological variables and associated Baye-

sian conditional probabilities and F(Phc,Pdry) values. We

set the F(Phc,Pdry) value of 0.3 as a cutoff and take all the

geological variables with F(Phc,Pdry) value greater than

0.3 into consideration in the geological risk mapping.

Probability Mapping

In a multivariate space, the selected geological vari-

ables define the statistical characteristics of the sam-

ples, whereas each sample represents a vector in this

Figure 3. Frequency distributions of reservoir porosity of thediscovery wells and tested and abandoned holes.

Figure 4. Distribution characteristics of some of the geologi-cal variables at the locations of discovery wells and tested andabandoned holes.

Chen and Osadetz 865

space. Becausewe know exactlywhich population each

sample belongs to, the statistical characteristics of each

population can be represented by its mean vector. The

MDs between the mean vector of the known discovery

wells and the sample vectors of all wells drilled both

discoveries and tested and abandoned were calculated.

Figure 5 illustrates the frequency distribution charac-

teristics of the MDs of the tested and abandoned wells,

as well as the MDs of the discovery wells to the mean

vector of the petroleum-bearing wells. In Figure 5,

most of the discoveries show MD values less than 2.5.

In contrast, most of the tested and abandoned wells

appear to have MD values greater than 2.5. Defining

an MD of 2.5 as the threshold value for classifying

petroleum-bearing and tested and abandoned prospects,

P(HCjFav), the conditional probability of petroleum

occurrence given favorable conditions (MD < 2.5) is

0.93 (Table 1), whereas P(DryjUnfav), the probability

of a tested and abandoned prospect given unfavorable

geological condition, is 0.89.With P(HCjFav) = 0.93, it

means that, for those prospects at the locations where

MD values are less than 2.5, the exploratory drilling

success rate could be 0.93, more than 46% higher than

the average success rate (47%) in the play. The con-

ditional probability for a tested and abandoned hole

given unfavorable conditions, P(DryjUnfav) = 0.89,

means that if a prospect lies at the location where

the MD value is greater than 2.5, it is very likely that

this prospect will be tested and abandoned (there is

<12% chance it could be a discovery). The F(Phc,Pdry)

value 0.83 suggests that MD is a very good indicator

for distinguishing the categories in the classification:

the discovered accumulations versus the tested and aban-

doned holes. Comparing it with single geological vari-

ables in Table 1, the conditional probability values for

MD are higher than any of the probability values from

a single geological variable. The favorability function

value of the MD is about 35% better than the best

single geological variable (the value of 0.47 associated

with reservoir porosity), suggesting that by combining

several geological variables, the MD contains substan-

tially more useful information for classifying discov-

eries and tested and abandoned holes in this play.

The geological maps of the variable employed for

the calculation ofMD can be converted to anMDmap.

Figure 6 is an MD map, representing the geological

condition for petroleum accumulation based on infor-

mation from previous exploration. On this map, most

of the well locations that were discoveries have MD

values smaller than 2.5, whereas most of the locations

that proved to be tested and abandoned holes show

MD values greater than 2.5. For geological risk anal-

ysis, the uncertainty of a prospect containing hydro-

carbons at a specific location is commonly expressed as

the probability of hydrocarbon occurrence. The MD

map can be converted into a conditional probability

map based on Bayesian probability formulation in equa-

tion 3 and the frequency distributions in Figure 5.

Figure 7 illustrates the probability of petroleum oc-

currence conditioned on the estimated MD values in

Figure 6. The probability of petroleum occurrence of

a prospect can be approximated using the average value

of all probability values in the pixels covering the areal

extent of that prospect and can be directly read from

the map, representing the expected success rate at the

prospect. Figure 8 illustrates the distribution of petro-

leum occurrence probability for the 68 identified pros-

pects that have not yet been tested by drilling. Con-

sidering available geoscience data, about 75% of the

untested prospects display high exploration risk (proba-

bility of petroleum occurrence < 0.25), whereas only

about 11% of the prospects show low exploratory risk

(probability of petroleum occurrence > 0.5).

Prospect Evaluation and Ranking

Two essential estimates for the evaluation of an ex-

ploration prospect exist: the size of the potential re-

source and the exploration risk of encountering this

size or greater. The exploration risk was calculated

based on the probability map, which is 1 minus the

probability value of the prospect, and the resourceFigure 5. Comparison of the distribution of MDs from testedand abandoned holes (dry) and the discovery wells.

866 Geohorizons

potential for each prospect can be estimated based on

the volumetric variables in the previous petroleum

resource assessment (Chen et al., 2000b). Otis and

Schneidermann (1997) defined the exploration risk as

1 over the probability of hydrocarbon occurrence,which

is amarkedly different expression of risk. Figure 9 is the

Figure 7. Conditional probability map of petroleum occurrence based on the MD in Figure 6 and the classification of discovery wellsand tested and abandoned holes in Figure 5.

Figure 6. An MDmap indicates the distance between known discoveries and unknown location (represented by pixel) in multivariablespace, calculated using the pixel values on maps of selected geological variables as compared to the characteristics of knownhydrocarbon discoveries.

Chen and Osadetz 867

gas field size distribution in the Heiberg Group struc-

tural play for the 68 mapped but untested prospects

(see Chen et al., 2000b for the whole gas field size

distribution). In this estimation, the distributions of net

pay and saturation were assumed as lognormal, and the

parameters were estimated from a multivariate discov-

ery process model (Chen et al., 2000b). Other volu-

metric parameters, such as closure area, porosity, reser-

voir temperature, and initial reservoir pressure, were

directly read from relevant geological maps. The for-

mulation of the reservoir volumetric equation for cal-

culating the gas resource potential is presented in Chen

et al. (2000b). The estimated mean field size is 8.4 �109 m3 (296.6 bcf) of gas in place, and the mean value

of the largest untested risked field size is 28.5� 109 m3

(1 tcf) of gas in place.

The untested prospects can then be ranked by dif-

ferent criteria based on the assessor’s belief or the com-

pany’s risk tolerance and exploration strategy. Usually,

the prospects are ranked by risked resource potential

(Rose, 2001) or exploration costs (Joubert et al., 2001).

Both of the criteria are meaningful if used for the for-

mulation of different exploration strategies. Table 2 lists

the top 36 prospects, whose risked gas resource po-

tentials are greater than 109 m3(35 bcf) in place.

DISCUSSION

On the estimated petroleum generation intensity index

map (Figure 10), two major generation centers are ap-

parent, one in the southern part of Ellef Ringnes Island

and the other lying between the Sabine Peninsula and

the Mackenzie King Island, separated by the Lougheed

Arch. The calculation is based on kinetic petroleum

generation models and estimated absorption (Pepper,

1990; Pepper and Corvi, 1995). Because no volume

measurements of the source rock interval are available,

the calculation ends with an index of generation in-

tensity, which is a measure for the spatial distribution

characteristics of generation capacity. The lack of com-

plete geographical correspondence between generation

intensity and known petroleum occurrence indicates

that lateral secondary migration is a significant factor in

final petroleum entrapment. The discovered oil and gas

reserves are almost equally distributed between the

two sides separated by the arch, 408 � 106 m3 (2566 �106 bbl) oil equivalents in the west and 386 � 106 m3

(2428 � 106 bbl) oil equivalents in the east. On the

western side of the arch, however, the discovered gas

reserve is approximately 300� 109m3 (1897� 106 bbl)

oil equivalents, but only 203� 109m3 (1277� 106 bbl)

oil equivalents on the eastern side. The existing dis-

coveries indicate two oil-collecting centers: the area

surrounding Lougheed Arch and the area offshore of

southern Ellef Ringnes Island. Given the thermal mat-

urity pattern, we expect that petroleum-bearing traps

in the western part of the Hecla gas field, around the

Lougheed Arch, as well as in the southeastern margin

of the basin, are more likely to contain oil than gas.

The locations of the top five prospects ranked by

the risked natural gas potential are marked by dashed

circles in Figure 7. The number beside the circle in-

dicates the rank. As can be seen from the conditional

Figure 8. Cumulative distribution of the estimated conditionalprobability for the untested 68 prospects in the mapping area.

Figure 9. Predicted gas pool size distribution for the mappedbut yet-to-be-tested prospects in the mapping area.

868 Geohorizons

probability map (Figure 7), the probability of explo-

ration success for the Heiberg Group structural targets

in the northwest of the basin is low (probability < 0.2)

because of the lack of favorable reservoir intervals.

The chances of exploration success in the northeast of

the basin and the eastern part of the northeast margin

of Ellef Ringnes Island are also small (probability <

0.2) because of unfavorable preservation conditions

caused by the latest extensive tectonic activity and poor

reservoir quality because of diagenesis caused by deep

burial and magmatism. The low-risk areas (probability

> 0.5) on the probability map are concentrated in three

zones: a northwest zone lies offshore of southwestern

Ellef Ringnes Island and King Christian Island (zone I),

coincident with the area where the existing discoveries

in high-relief anticlines are situated; a second zone

parallels the southwest basin margin (zone II), where

the low-relief and high trap fill discoveries occupy its

Table 2. Untested Prospects Ranked by Different Criteria*

Pool Size 106 m3 Probability Risked Size 106 m3 Easting Northing Ranking 1 Ranking 2 Ranking 3

44,100 0.65 28,478 �277024 3014905 1 3 1

24,252 0.97 23,577 �96987.5 3177853 2 16 2

27,910 0.64 17,858 �164334 3079116 3 34 6

17,734 0.96 16,972 �89801 3168400 4 12 3

11,112 0.96 10,697 �93237.4 3170331 5 13 5

54,176 0.15 8300 �22152.8 3253659 6 26 8

9714 0.77 7450 �153177 3059005 7 30 14

10,534 0.6 6302 �163264 3091754 8 32 18

37,235 0.16 6027 111082 3114202 9 1 4

32,574 0.17 5448 57138.03 3213275 10 24 10

23,305 0.23 5262 �192549 3063931 11 35 29

6745 0.74 4978 �84752.2 3078704 12 25 13

25,233 0.19 4677 46059.09 3103082 13 5 7

5956 0.75 4478 �109714 3085796 14 29 20

21,697 0.2 4424 34169.22 3104496 15 8 9

4787 0.91 4365 �135830 3170647 16 23 11

31,666 0.11 3428 �90713.5 3098553 17 33 32

5014 0.64 3197 �78774.5 3062494 18 14 15

5026 0.61 3059 �79240.1 3088949 19 28 27

5026 0.6 3029 �138661 3036111 20 18 16

3981 0.72 2883 �143503 3048105 21 27 26

8994 0.31 2782 �33356.8 3086195 22 4 12

7977 0.29 2307 �86310.1 3134568 23 20 24

8194 0.28 2270 33897.5 3194154 24 19 21

5447 0.4 2193 �38642.2 3076814 25 6 17

8084 0.26 2088 �189411 3052508 26 36 36

4492 0.42 1909 �164298 3100722 27 31 35

4362 0.42 1816 �247170 3047499 28 21 30

2990 0.6 1805 �53064.5 3070207 29 10 22

2991 0.59 1756 �55237.4 3066027 30 9 23

2921 0.59 1738 �48376.1 3076212 31 11 25

4480 0.37 1659 �343151 3031542 32 2 19

15,945 0.1 1617 151371.8 3246187 33 22 31

2893 0.48 1383 �51052.7 3062498 34 7 28

2335 0.46 1069 �54420.3 3148462 35 15 33

1506 0.68 1032 �113898 3053247 36 17 34

*Ranking 1 = based on risked potential; ranking 2 = based on unit well cost; ranking 3 = based on reserve and well cost.

Chen and Osadetz 869

eastern portion; a third zone trending north-northeast–

south-southwest connects the two zones described

above across Lougheed Island (zone III). Among the

three low-risk zones, zones I and II contain nearly

the same volume of discovered natural gas, 223 and

233 � 109 m3 (7875 and 8228 bcf) in place, respec-

tively. In zone III, only 77 � 109 m3 (2719 bcf) of gas

was found in place. Areas with modest exploration risk

are present (0.2 � probability � 0.5), which include

the southeast margin of the basin and the western ex-

tent of zone III.

The proposed method requires a database contain-

ing sufficient numbers of discoveries and tested and

abandoned holes to treat geological risk analysis as

an estimation of classification errors. The inferences of

geological risk and petroleum resource potentials of

the mapped prospects herein were based on current

data and understanding. Additional data and informa-

tion from future exploration will improve our under-

standing and change the geological maps. Risk and re-

source potential evaluations must therefore be updated

to reflect improvements in geological models as a dy-

namic process.

Determining the major geological factors in a play

is essential for a successful evaluation of geological risk.

The selection of geological variables for the probability

calculation depends, to a large degree, on an under-

standing of the key geological factors controlling the

petroleum accumulation. The proposed Bayesian tech-

niques and criteria provide quantitativemeans that find

the essential geological controls and, thus, guide the

selection of geological factors for theMDcalculation. It

is not important which geological variables should be

included in the evaluation data matrix, but instead, the

discriminating power of that set of variables that sep-

arates petroleum accumulations from the tested and

abandoned prospects. Although risk evaluation results

using different combinations of geological variables

may vary, by minimizing the classification error using

the proposed Bayesian techniques and criteria applying

to the training data set, the variation is expected to be

limited.

CONCLUSIONS

The proposed approach to geological risk mapping re-

sults in a conditional probability map that depicts the

spatial variation of comprehensive regional geological

conditions favorable for petroleum accumulation, pro-

viding a systematic and consistent basis for risk analysis

and prospect ranking.One can use both the conditional

probability map and the estimated resource potentials

of the untested prospects to highlight areas with high

Figure 10. A petroleum generation intensity map derived from the analysis of organic geochemical variables. The higher values ofthis index, shown by higher numbers, indicate a greater intensity of petroleum generation.

870 Geohorizons

resource potential and relatively low exploration risk,

as illustrated by the analysis of the Heiberg Group

structure play.

Several advantages to the proposed method exist,

including the following:

1. It considers spatial relationships among geological

variables and prospects. The exploration-risk eval-

uation for the occurrence of petroleum accumula-

tion in a specific prospect is related to the others in

the same play where hydrocarbon accumulations

were governed by the same geological processes.

2. All prospects are evaluated consistently and simul-

taneously using the same method, criteria, and data,

resulting in reproducible and easily updateable re-

sults, which clearly reflect relative prospectivity.

3. The use of established, mathematically valid clas-

sification methods produces an objective result not

affected by the bias of the assessor.

REFERENCES CITED

Balkwill, H. R., 1978, Evolution of Sverdrup Basin, Arctic Canada:AAPG Bulletin, v. 62, p. 1004–1028.

Balkwill, H. R., 1983, Geology of Amund Ringnes, Cornwall andHaig Thomas islands, district of Franklin: Geological Survey ofCanada Memoir 390, 76 p.

Balkwill, H. R., and F. G. Fox, 1982, Incipient rift zone, westernSverdrup Basin, Arctic Canada in A. F. Embry and H. R.Balkwill, eds., Arctic geology and geophysics: Canadian Societyof Petroleum Geologists Memoir 8, p. 171–187.

Balkwill, H. R., and K. J. Roy, 1977, Geology of King ChristianIsland, district of Franklin: Geological Survey of Canada Mem-oir 386, 28 p.

Brooks, P. W., A. F. Embry, F. Goodarzi, and R. Stewart, 1992,Geochemical studies of Sverdrup Basin (Arctic Islands)—Organic geochemistry and biological marker geochemistry ofthe Schei Point Group (Triassic) and recovered oils: Bulletin ofthe Canadian Petroleum Geology, v. 40, p. 173–187.

Chen, Z., and K. G. Osadetz, in press, Undiscovered petroleum ac-cumulation mapping using model-based stochastic simulation:Mathematical Geology, v. 38, no. 1.

Chen, Z., K. Osadetz, H. Gao, P. Hannigan, and C. Watson, 2000a,Characterizing spatial distribution of the undiscovered hydro-carbon resource, Keg River reef play, the West Canadian sed-imentary basin: Bulletin of the Canadian Petroleum Geology,v. 48, no. 2, p. 150–161.

Chen, Z., K.G.Osadetz,A. Embry,H.Gao, and P.Hannigan, 2000b,Petroleum potential in western Sverdrup Basin, Canadian Arc-tic archipelago: Bulletin of Canadian PetroleumGeology, v. 48,no. 4, p. 323–338.

Chen, Z., K. G. Osadetz, H. Gao, and P. Hannigan, 2001, Improvingexploration success through uncertainty mapping, the Keg Riverreef play, Western Canada sedimentary basin: Bulletin of theCanadian Petroleum Geology, v. 49, no. 3, p. 367–375.

Chen, Z., K. G. Osadetz, A. Embry, and P. Hannigan, 2002, Geo-logical favorability mapping of petroleum potential using fuzzyintegration, example from western Sverdrup Basin, Canadian

Arctic archipelago: Bulletin of the Canadian Petroleum Geol-ogy, v. 50, no. 4, p. 492–506.

Chen, Z., K. G. Osadetz, H. Gao, and P. Hannigan, 2004, SuperSD:An object-based stochastic simulation program for modeling lo-cations of undiscovered petroleum accumulations: Computerand Geosciences, v. 30, p. 281–290.

Curiale, J. A., 1992, Molecular maturity parameters within a singleoil family: A case study from the Sverdrup Basin, ArcticCanada, in J. M. Moldowan, P. Albrecht, and R. P. Phip, eds.,Biological markers in sediments and petroleum: EnglewoodCliffs, New Jersey, Prentice Hall, p. 275–300.

Doyen, P. M., L. D. den Boer, and W. R. Pillet, 1996, Seismic po-rosity mapping in the Ekofisk field using a new form of col-located cokriging: Annual Technique Conference and Exhib-ition, Society of Petroleum Engeers, SPE Paper 36498, 8 p.

Embry, A. F., 1983, The Upper Triassic and Lower Jurassic Heibergdeltaic complex of the Sverdrup Basin, in A. F. Embry andH. R. Balkwill, eds., Arctic geology and geophysics: CanadianSociety of Petroleum Geologists Memoir 8, p. 189–218.

Embry, A. F., 1991, Mesozoic history of the Arctic Islands, in H. P.Trettin, ed., Innuitian orogen and Arctic platform: Canada andGreenland: Geological Survey of Canada, Geology of Canada,no. 3, p. 369–433.

Gao, H., Z. Chen, K. G. Osadetz, P. Hannigan, and C. Waterson,2000, A pool-based model of the spatial distribution of undis-covered petroleum resources: Mathematical Geology, v. 32,no. 6, p. 725–749.

Gentzis, T., and F. Goodarzi, 1993, The source rock potential andthermal maturity of the sedimentary succession in the Drakeand Hecla hydrocarbon fields, Melville Island, Canadian Arcticarchipelago, inT.O.Vorren, E. Bergsager,O. A.Dahl-Stamnes,E. Holter, B. Johansen, E. Lie, and T. B. Lund, eds., Arctic ge-ology and petroleum potential: Norwegian Petroleum Society(NPF) Special Publication 2, p. 159–172.

Goodarzi, F., P. W. Brooks, and A. F. Embry, 1989, Regional matu-rity as determined by organic petrography and geochemistry ofthe Schei Point Group (Triassic) in the western Sverdrup Basin,Canadian Arctic Archipelago: Marine and Petroleum Geology,v. 6, p. 290–302.

Goodarzi, F., T. Gentzis, A. F. Embry, K. G. Osadetz, D. N.Skiboand, and K. R. Stewart, 1993, Evaluation of maturity andsource rock potential in the Lougheed Island area of the centralSverdrup Basin, Arctic Canada, in T. O. Vorren, E. Bergsager,O. A. Dahl-Stamnes, E. Holter, B. Johansen, E. Lie, and T. B.Lund, eds., Arctic geology and petroleum potential: NorwegianPetroleum Society (NPF) Special Publication 2, p. 147–157.

Harbaugh, J. W., J. C. Davis, and J. Wendebourh, 1995, Computingrisk for oil prospects: Principles and programs: New York,Pergamon, p. 93–96.

Harff, J., J. C. Davis, and R. A. Olea, 1992, Quantitative assessmentof mineral resources with an application to petroleum geology:Nonrenewable Resources, v. 1, no. 1, p. 74–83.

Harrison, J. C., 1995, Melville Island’s salt-based fold belt, ArcticCanada: Geological Survey of Canada Bulletin, v. 472, 325 p.

Hood, K. C., B. C. South, F. D. Walton, O. D. Baldwin, and W. A.Burroughs, 2000, Use of geographic information systems inhydrocarbon resource assessment and opportunity analysis, inT. C. Coburn and J. M. Yarus, eds., Geographic informationsystems in petroleum exploration and development: AAPG,Computer Applications in Geology, v. 4, p. 173–185.

Joubert, T., G. Cain, and B. Ireland, 2001, A finding and devel-opment (F&D) model for making money by finding gas whereit is: Abstracts of Technical Talks, Posters and Core Displays,the Canadian Society of Petroleum Geologist Annual Con-vention, 2001, June 18–22, 2001, Calgary, Alberta, Canada,p. 183–191.

Chen and Osadetz 871

Mardia, K. V., J. T. Kent, and J. M. Bibby, 1989, Multivariateanalysis, 7th ed.: New York, Academic Press, p. 213–254.

Northern Oil and Gas Directorate, 1995, Petroleum exploration innorthern Canada: A guide to oil and gas exploration and po-tential: Indian and Northern Affairs Canada, p. 83–88.

Otis, R. M., and N. Schneidermann, 1997, A process of evaluatingexplorationprospects:AAPGBulletin, v. 81, no. 7, p. 1087–1109.

Pepper, A. S., 1990, Estimating the petroleum expulsion behaviorof source rocks, a novel quantitative approach, in W. A. En-gland and A. J. Fleet, eds., Petroleum migration: GeologicalSociety (London) Special Publication 59, p. 9–31.

Pepper, A. S., and P. J. Corvi, 1995, Simple kinetic models ofpetroleum formation: Part I— Oil and gas generation: Marineand Petroleum Geology, v. 12, no. 4, p. 291–320.

Powell, T. G., 1978, An assessment of the hydrocarbon source rockpotential of the Canadian Arctic Islands: Geological Survey ofCanada Paper 78-12, 82 p.

Rose, P. R., 2001, Risk analysis and management of petroleumexploration ventures: AAPGMethods in Exploration Series 12,164 p.

Rostirolla, S. P., A. C.Mattana, andM.K. Bartoszeck, 2003, Bayesianassessment of favorability for oil and gas prospects over theReconcavo Basin, Brazil: AAPG Bulletin, v. 87, no. 4, p. 647–666.

Snow, J. H., A. G. Dore, and D. W. Dorn-Lopez, 1997, Risk anal-ysis and full-cycle probabilistic modeling of prospect: A proto-type system developed for the Norwegian shelf, in A. G. Doreand R. Sinding-Larsen, eds., Quantitative prediction and eval-uation of petroleum resources: Norwegian Petroleum Society(NPF) Special Publication 6, p. 135–166.

Waylett, D. C., and A. F. Embry, 1993, Hydrocarbon loss from oiland gas fields of the Sverdrup Basin, Canadian Arctic Island,in T. O. Vorren, E. Bergsager, O. A. Dahl-Stamnes, E. Holter,B. Johansen, E. Lie, and T. B. Lund, eds., Arctic geology andpetroleum potential: Norwegian Petroleum Society (NPF)Special Publication 2, p. 195–204.

White, D., 1988, Oil and gas play maps in exploration and assess-ment: AAPG Bulletin, v. 72, no. 8, p. 944–949.

White, D., 1993, Geological risking guide for prospects and plays:AAPG Bulletin, v. 77, no. 12, p. 2048–2061.

872 Geohorizons