AUTHORS

Sidnei Pires Rostirolla � Basin Analysisand Petrophysical Laboratory, Department ofGeology, Federal University of Parana (UFPR),Curitiba, Parana, Brazil; present address:Universidade Federal do Parana, Setor deCiencias da Terra, Departamento de Geologia,Centro Politecnico, Jardim das Americas,81531-990, Curitiba, Parana, Brazil;sidnei@geologia.ufpr.br

Sidnei P. Rostirolla is a professor of geology atthe Federal University of Parana. He receivedhis M.Sc. (1991) degree from the FederalUniversity of Ouro Preto (UFOP) and Ph.D.(1996) from the State University of Sao Paulo(UNESP). He worked at the Petrobras ResearchCenter as a structural geologist, where hisresearch focused on balancing cross section,syntectonic sedimentation and field work. Hiscurrent research interests are basin analysis,geomathematics, structural geology, and frac-tured reservoir characterization.

Antonio Carlos Mattana � A. C. MattanaConsultoria e Sistemas, Curitiba, Parana,Brazil; acmtt@ig.com.br

Antonio C. Mattana received his M.Sc. degree(2000) in exploration geology from the FederalUniversity of Parana. His research interests aregeographical information systems and object-oriented programming applied to geology andgeophysics.

Marcelo Kulevicz Bartoszeck � BasinAnalysis and Petrophysical Laboratory, Depart-ment of Geology, Federal University of Parana(UFPR), Curitiba, Parana, Brazil;taipataipa@hotmail.com

Marcelo K. Bartoszeck is a graduate student ofgeology in the Federal University of Parana.His research interests are reservoir modeling,seismic interpretation, and structural geology.

ACKNOWLEDGEMENTS

The authors thank John Lorenz, John Doveton,Robert Otis, Luciano Magnavita, and JohnHarbaugh for the profitable suggestions toimprove the manuscript. Sidnei Pires Rostirollathanks the Brazilian National Research Founda-tion CNPq, processes 520063/98-8 e 463002/00-8, for financial and scholarship support,and UFPR for institutional support.

Bayesian assessment offavorability for oil and gasprospects over the Reconcavobasin, BrazilSidnei Pires Rostirolla, Antonio Carlos Mattana, andMarcelo Kulevicz Bartoszeck

ABSTRACT

This paper presents a Bayesian approach to evaluate remaining po-

tential of oil and gas in the Reconcavo basin, Brazil. The purpose is

to test a new Bayesian weighting methodology and quantify the

favorability for the existence of new fields in the basin. The meth-

odology implies organization of petroleum system data in descrip-

tive models with which results from drilling are manipulated sta-

tistically, including analysis of geologic factors that are spatially

correlated with both producing and dry areas. In the first stage of

modeling, the essential elements (reservoir, seal, and overburden

rocks) that control the fundamental processes of generation, ex-

pulsion, migration, and entrapment of petroleum accumulation are

defined throughout integration of previously published data. The

petroleum accumulation models of Reconcavo basin comprise gen-

eration from Neocomian shale rocks of the Gomo Member (Can-

deias Formation), vertical migration along extensional and transfer

faults, and accumulation in tilted horsts with Upper Jurassic prerift

reservoirs (Sergi Formation) or in Neocomian turbidite reservoirs in

stratigraphic/combined traps (Candeias and Marfim formations).

Probability distributions and weights are then calculated through

Boolean operations among producing and dry areas and each diag-

nostic criterion evaluated through descriptive models such as source

bed thickness, onset of organic maturation, presence or absence of

faults and structural blocks, reservoir thickness, and seal distribu-

tion. The final stage of evaluation consists of spatial integration of

raster maps that are weighted according to their necessity and suf-

ficiency conditions, the results being presented as favorability maps.

The characterization of favorable areas and their comparison with

known fields suggest that such a Bayesian approach can contribute

to the understanding of petroleum systems as a practical approach

that considers the spatial nature of exploration variables.

Copyright #2003. The American Association of Petroleum Geologists. All rights reserved.

Manuscript received January 3, 2002; provisional acceptance June 20, 2002; revised manuscriptreceived August 5, 2002; final acceptance October 7, 2002.

AAPG Bulletin, v. 87, no. 4 (April 2003), pp. 647–666 647

INTRODUCTION

This paper integrates numerical basin modeling and

statistical procedures to the mapping of success prob-

ability for oil and gas prospects in the Reconcavo basin,

Brazil. The objective is to apply weighting procedures

and Bayesian updating of the hydrocarbon favorability

in the Reconcavo basin based on isopach, structural,

lithologic, and geochemical mapping. The procedures

attempt to seek out Bayesian relationships between

major geologic variables and the presence or absence

of oil and gas based on results of previous exploratory

drilling.

Most evaluation systems depend heavily on geo-

logic factors considered essential for an oil or gas pros-

pect, such as the presence of reservoir beds, traps, seals,

hydrocarbon source beds, and so forth. Although these

geologic features are of unquestionable importance,

they are commonly entered into decision systems as

poorly constrained assumptions. The goal here is to

outline some statistical procedures that can help to min-

imize uncertainty and to provide quantitative measure-

ments of uncertainty in the form of objectively derived

probability distributions.

Many different schemes for analyzing risk in oil ex-

ploration have been proposed. Most of them attempt to

provide a systematic approach in which geologic factors

are systematically weighted to yield numerical scores

(e.g., Sluijk and Nederlof, 1984; Harbaugh et al., 1995;

White, 1993; Otis and Schneiderman, 1997; Lerche,

1997). The procedures discussed here consider the same

modeling strategy and are similar to those of Agterberg

(1989) and Bonham-Carter (1994), involving sampling

over an entire region, followed by processing of the

sample data stored in raster or pixel form or, alternatively,

in vector form as points, lines, and polygons.

Because we are interested in forecasting the out-

comes of exploratory wells, it is important to categorize

these outcomes with respect to the geologic percep-

tions of prospects before they are drilled. Schemes can

be readily devised for systematic comparison of predrill

perceptions with postdrill outcomes or ‘‘postmortems,’’

yielding frequencies that can be manipulated with Bayes’

theorem to yield conditional probabilities based on joint

and marginal frequencies. The study has involved exten-

sive search in the Reconcavo basin for Bayesian relation-

ships that relate geologic factors to producible quantities

of oil and gas, for which a large amount of published in-

formation is available.

Such an approach contrasts with forecasting ap-

proaches that assign hydrocarbon volumes subjectively,

calculation involving the multiplication of separate

probabilities for key geologic properties. These prop-

erties include thickness of the reservoir rocks, area of

closure, proportion of fill, and so forth. If these properties

can be objectively estimated before drilling, multiplica-

tion procedures have great merit, but if the properties

cannot be estimated objectively, the procedures are

likely to be misleading, thus increasing the uncertainty

instead of decreasing it.

The Reconcavo basin provides a wealth of pub-

lished data for testing favorability evaluation procedures,

including migration pathways, reservoir properties,

trapping structures, seals, and hydrocarbon-generation

windows. Although the favorability evaluation present-

ed here has been specifically calibrated for the Recon-

cavo basin, it could be adapted for other sedimentary

basins.

METHODOLOGY FOR DELINEATINGFAVORABLE AREAS

Because most statistical and mapping tools are readily

available for uncertainty assessment, the time-consuming

tasks in basin assessment involve data-set organiza-

tion. Deterministic relationships among prospects, oil

fields, and rocks can contribute to petroleum system

approaches in which all major geologic factors are

incorporated into the definition of the system, as source,

carrier and reservoir beds, traps, seals, faults, temper-

ature gradients, burial history, and fluid flow. The ex-

tent to which petroleum systems can be incorporated

into statistically based assessment systems is current-

ly unresolved. The end product of petroleum systems

modeling, however, necessarily consists of quantita-

tive measurements of favorability expressed as prob-

ability distributions. Table 1 lists the main geologic

factors and their forms.

The transformation of ‘‘point or cross section data’’

such as wells or local seismic into mapped probabili-

ties is made on a cell-by-cell basis, in which geologic

factors can be classified as present or absent, and then

weighted and probabilistically combined. The method

implies exploration modeling, statistical analysis of post-

mortems and manipulation of geographically refer-

enced variables, including the following tasks: (1) se-

lection of the geologic factors from Table 1 that are

deemed to be critical, (2) rasterization of the area into

cells for which the probabilities of new discoveries are

to be calculated, (3) calculation of the probability

distributions for well outcomes based on joint occurrences

648 Bayesian Assessment of Favorability for Oil and Gas Prospects

of the geologic factors, (4) organization of the results in

worksheet form with columns for locations of produ-

cing and dry wells, weighted geologic factors, and the

resultant favorability factors assigned to each cell, and

(5) interpolation of the favorability factors by contour-

ing over the area of interest.

In the ‘‘weights of evidence’’ method devised by

Agterberg (1989), the favorability indexes are repre-

sented as spatially interpolated cells or pixels. Agter-

berg’s method is based on the probabilities, odds (being

defined as the ratio of occurrence probability to non-

occurrence probability), necessity, and sufficiency of

each variable considered. All variables must be mapped

and stored as spatial information.

The Bayesian method employed here is a simpli-

fication of Agterberg’s method. Each variable is weighted

Rostirolla et al. 649

Table 1. Exploration Data that can be Represented in Raster Maps and Used for Uncertainty Analysis, Considering Diagnostic

Criteria Based on Petroleum System Modeling*

Diagnostic Factor Variable Type Criterion

GENERATION

Source potential (TOC, S1 + S2, SPI) Continuous Cutoff

Thickening or volume of the source bed Continuous Cutoff

Area distribution and continuity Continuous Cutoff

Kerogen type Discrete Adequate/inadequate

RESERVOIR

Lithology Discrete Adequate/inadequate

Depositional model Discrete Adequate/inadequate

Lateral continuity Continuous Cutoff

Thickening and vertical variability Continuous Cutoff

Volume Continuous Cutoff

Fluid transmissibility Continuous Cutoff

Heterogeneity Discrete Adequate/inadequate

CHARGE

Drainage system Continuous Cutoff

Thermal system Continuous Cutoff

Migration pathway Discrete Adequate/inadequate

Entropy Discrete Adequate/inadequate

RETENTION

Trap type (anticline, fault, pinch-out, etc.) Discrete Adequate/inadequate

Trap quality (method, reliability) Discrete Adequate/inadequate

Seismic resolution Continuous Cutoff

Lateral closure Discrete Adequate/inadequate

Seal type (bed, fault) Discrete Adequate/inadequate

Seal lithology and plasticity Discrete Adequate/inadequate

Source bed continuity and thickening Continuous Cutoff

Impedance Discrete Adequate/inadequate

Fluid flow (favorable or restrictive) Discrete Adequate/inadequate

EFFICIENCY (+ Preservation)

Timing Discrete Adequate/inadequate

Post-trapping tectonism Discrete Adequate/inadequate

Biodegradation Discrete Adequate/inadequate

Thermal cracking Discrete Adequate/inadequate

*Modified from Rostirolla, 1999.

by a probability that represents the multiplication of its

‘‘necessity and sufficiency’’ values. The necessity of a var-

iable is proportional to its absence in a dry hole and can be

measured by the conditional probability pðV j AÞ, where

A represents nonaccumulation in a particular drilled site.

Its sufficiency, in turn, is proportional to its presence in

producing well contexts, measured by p(V j A). Ideal

variables would show high values for both pðV j AÞ and

p(V j A) probabilities (i.e., their presence accounts for

petroleum accumulation over a given area, whereas their

absence in turn accounts for nonaccumulation of petro-

leum over the same area). The spatial relationships be-

tween variables and drilled sites are outlined in Figure 1.

Weighting Procedure

The first step of weighting is to select the appropriate

variables, which are then grouped into diagnostic fac-

tors following the definition of a petroleum system in

Magoon and Dow (1994). The presence and absence

of each variable is then evaluated over different parts

of a control area. The resultant frequencies define the

cutoffs to be attributed to each variable for estimating

its pðV j AÞ and p(V j A) probabilities. The product

below represents the probability of success (p s) to be

assigned to each variable

ps ¼ pðV j AÞ � pðV j AÞ or ps ¼V \ A

A� V \ A

Að1Þ

Based on its presence or absence in a given lo-

cation, a variable is ranked weak (0.0–0.2), regular

(0.2–0.4), good (0.4–0.6), very good (0.6–0.8), or

excellent (0.8–1.0). The success probability of a new

discovery (D), considering the adopted model (which

can be analyzed in the same way as a single prospect in

the traditional approach), is calculated by p smodel(D j Fi),

with i = 1,n diagnostic factors (Fi), as the product of

the success probabilities of all diagnostic factors (only

one variable can be chosen for each generation, res-

ervoir, charge, retention, and efficiency functions)

psmodelðD j FiÞ ¼

Yn

i¼1

pðVi j AÞ � pðVi j AÞ" #

ð2Þ

The model’s favorability is then given by the sum

of p smodel(D j Fi) plus the success ratio (sr), the latter

assigning the same value for the entire area. The suc-

cess ratio represents the previous probability, being

calculated as the ratio between the number of known

fields and the total number of previous drilled wells.

favmodel ¼ psmodelðD j FiÞ þ sr ð3Þ

The favorability index for each cell can be cal-

culated as the sum of the j individual probabilities in

each cell, normalized by the sum of all k individual

probabilities over the whole area multiplied by the fa-

vorability of the model

favcellðDÞ ¼ favmodelðD j FiÞ �

Pnj

ps

Pnk

ps

ð4Þ

given i = 1,n diagnostic factors, j = l,n variables in the

cell, and k = l,n variables over all the area. An example

of the calculation of necessity, sufficiency, and success

probability of a specific variable is presented in Figure 2.

FAVORABILITY EVALUATION OF THERECONCAVO BASIN

Geology and Exploration Modeling of the Reconcavo basin

The Reconcavo basin spreads over 11,500 km2 in

northeastern Brazil (Figure 3). The sediment infilling

processes are typical of a rift aborted during the Late

Jurassic and Early Cretaceous opening of the Atlantic

Ocean. The overall sedimentary sequence is as much

650 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figure 1. Venn diagram representing the Bayesian spatialcorrelation of variables and drilled sites.

Rostirolla et al. 651

Figure 2. Schematic representationof spatial analysis, by which informa-tion is evaluated for each cell, andsufficiency (s), necessity (n), andprobability of success (p s) are calcu-lated. Maps (a) and (b) contain thelocation of the same drilled areas(producing versus dry) having differ-ent shades representing hypotheticaldistribution of two different variables.Map (c) represents the final interpola-tion of the favorability over the area.The formulas on the right displayfrequencies that are based on the welloutcomes and their joint frequencieswith the different variables. Favor-ability is calculated cell by cell andcontoured considering the node atthe center of each cell or pixel.

as 6000 m thick and consists of three sequences, which

in ascending order consist of (1) prerift fluvial and arid

alluvial deposits, (2) synrift deltaic and fluvial deposits,

and (3) postrift alluvial deposits. The major structural

controls during basin filling include extensional faults,

such as the Salvador fault, that define the main border

of the basin. In turn, transverse faults that include the

Mata Catu and Itanagra Araca faults separate the basin

into northeast, central, and southeast compartments

(Figueiredo et al., 1994).

According to Magnavita (1992), the Reconcavo ba-

sin is a half graben tilted eastward against the Salvador

652 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figure 3. Location mapand regional structuralmap of the Reconcavobasin (modified fromPenteado, 1999).

fault, being structurally characterized by a 30jN–40jE-

trending system of extensional faults that define platforms

and structural lows (Camacari, Miranga, and Quiambina).

The northeast fault system is interrupted in places by

the northwest-trending transfer zones (Mata Catu and

Itanagra Araca faults) that divide the basin into three

compartments, each compartment exhibiting character-

istic stratigraphic and structural architectures.

The exploration variables were selected according

to their availability in literature and considering the fol-

lowing accumulation models for the Reconcavo basin

(Figueiredo et al., 1994; Figure 4): (a) structural traps

represented by tilted horsts formed by extensional faults,

having fluvial prerift reservoirs charged laterally from

shales deposited in the deepest parts of the basin; (b)

traps including turbidite reservoirs of the Candeias For-

mation and the Caruacu Member (Marfim Formation),

which are directly connected to the source rocks; (c) struc-

tural rollovers in the deepest parts of the basin, related

to listric faults in the synrift sequence, whose deltaic res-

ervoirs of the Pojuca and Marfim formations are charged

from vertical migration along regional faults.

The selection and inclusion of diagnostic criteria

(and their cutoffs) in the delineation of favorable areas

were based on spatial relationships among the variables

available in literature (Table 2; Figures 5–7) and known

Rostirolla et al. 653

Figure 4. Stratigraphicchart of the Reconcavobasin (above), havingaccumulation models(below): (a) prerift horsttraps; (b) stratigraphicand combined traps; (c)synrift rollovers traps(modified from Figueiredoet al., 1994). The curvingarrows labeled (a), (b),and (c) represent mi-gration paths and arecorrespondent to theletters in the models(adapted from Figueiredoet al., 1994).

producing and dry wells (histograms in Figure 8).

Although the database is not representative, because the

known exploration variables of Reconcavo basin were

intentionally omitted in previous published papers, the

available data could validate a first test of the evaluation

system.

The Gomo and Taua members (Candeias Forma-

tion) represent the main source rock units of the Recon-

cavo basin, having an average total organic carbon (TOC)

of approximately 1% and a source potential of 5 kg HC

(hydrocarbons)/ton in the deepest parts of the basin,

reaching 10 kg HC/ton in certain places. The Pojuca

Formation also contains carbon-rich shales, but has not

fallen in the generation window in most of the basin.

Fluvial sandstones of the prerift sequence (Sergi Forma-

tion) are the main reservoir rocks, a coarsening-upward

sequence having porosity ranging from 10 to 25% and

permeability from 20 to 1200 md (Figueiredo et al.,

1994). The turbiditic reservoirs of the Taquipe For-

mation are components of the synrift Ilhas system, the

second most important one in the basin. Their porosities

range between 18 and 24% and permeability from 30

to 300 md. Turbidite sandstones of the Pitanga Mem-

ber, belonging to the Candeias Formation according

Figueiredo et al. (1994), are associated with fractured

shales and represent a third reservoir system, having po-

rosity ranging from 15 to 18% and permeability in the

best intervals around 30 md and reaching 200 md in

places. Intercalated and overlying pelites form the seals

to the reservoir sandstones.

Figures 5–7 present generalized maps of the gen-

eration, migration, reservoir, and trap variables in the

Reconcavo basin. The Gomo Member (Figure 5a) has

a general hydrocarbon source potential trend that is

thicker in the southeastern part of the basin. Despite

this general trend, its distribution suggests that anoxic

environments are poorly correlated to depocenter areas

because there is no coincidence between the principal

depocenter and the hydrocarbon richer areas. A gen-

erative variable, defined by vitrinite reflectance (Figure

5b), is controlled by the thermal distribution in deeper

parts of the basin. The Sergi Formation (Figure 6a) has

a rather uniform thickness throughout the basin, but

thickens gradually to the southeast, whereas the Pitanga

654 Bayesian Assessment of Favorability for Oil and Gas Prospects

Table 2. Exploration Variables Used in the Favorability Evaluation of the Reconcavo Basin*

Variable Authors Cutoff Figure in this Paper

CHARGE

Generation

Isopach map of the Gomo Member Figueiredo et al. (1994) >500 m Figure 5a

Vitrinite reflectance Magnavita et al. (1994) >1500 m Figure 5b

Migration

Extensional faults Milani and Davison (1988) Presence Figure 3

Extensional faults (buffer of 2.5 km) Milani and Davison (1988) Presence Figure 3

Transfer faults Milani and Davison (1988) Presence Figure 3

Transfer faults (buffer of 2.5 km) Milani and Davison (1988) Presence Figure 3

Contour map of basement in the Jurassic Carozzi et al. (1976) <600 m and >1000 m Figure 6d

ENTRAPMENT

Reservoir

Isopach map of the Sergi Formation Figueiredo et al. (1994) 100–200 m Figure 6a

Isopach map of the Pitanga Member Figueiredo et al. (1994) <300 m Figure 6b

Isopach map of the Caruacu Member Figueiredo et al. (1994) >200 m Figure 6c

Seal

Pojuca and Candeias shales (No restrictions)

Structure

Top of the Sergi Formation Magnavita (1992) >2500 m Figure 7a

Structural lows and highs Magnavita (1992) Presence Figure 7b

Platforms and hinge zones Magnavita (1992) Presence Figure 3

*Only published data were used in the favorability analysis. First, second, and third columns indicate type, corresponding authors, and cutoffs used for classification ofraster maps. The last column indicates the figures where the variables are displayed.

(Figure 6b) and Caruacu members (Figure 6c) tend to

be thicker in the southwestern part of the basin, which

indicates influence of more subsiding areas in the basin.

The relationship between the oil generation and the

formation of structures was indirectly considered by the

contour map of the basement during the Jurassic (Figure

6d). Based on the burial history of the Gomo Member,

hydrocarbon generation should have occurred by the

Early Cretaceous. According to Daniel et al. (1989), the

Gomo source rock began to generate oil in the lows during

the early Aptian at about 115 Ma. Traps developed as

the rift formed, and trap formation was essentially com-

plete when source-bed deposition ceased. Because trap

formation ceased by the Early Cretaceous, the critical

moment of the petroleum system was during the Early

Cretaceous (Mello et al., 1994). The contour of the

basement during the Jurassic was used to restore partially

the hydrocarbon drainage trend, because the migration

occurred after the principal deformation phases affecting

the basin, during the Late Jurassic and Early Cretaceous.

Other palinspastic restoration maps are not published.

The contour surface of the top of the Sergi Formation

(Figure 7a) and the regionalized structural map (Figure

7b) show evidence of the importance of normal and trans-

fer faults in the tectonic evolution of the Reconcavo basin

and consequently in the migration pathways around it.

Delineation of Favorable Areas

The locations of oil and gas wells used in this study

were compiled from diverse sources of information,

and each cell was classified as containing or not con-

taining a producing well or a dry hole. The frequency of

distribution of the variables in drilled sites can be vi-

sualized as a first approximation in Figure 8, in which

the histograms are obtained overlaying variable raster

maps with drilled sites. The joint occurrences of each

variable were then presented in the form of graphics

that were sensitive to the different distributions of val-

ues as positive (producing) or negative (dry). The dif-

ferences in variable importance (weights, Table 3)

control the final favorability score distribution in the

Reconcavo basin.

Rostirolla et al. 655

Figure 5. Source-bed distribution (a) and depth to the onset of maturity of organic matter defined by vitrinite reflectance of 0.6%;(b) maps used in the probability assessment (source of information is Table 2; coordinates referenced to the meridian 47j).

656 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figure 6. Isopach maps of reservoir units (a, b, and c) and structure map of the basement during the Jurassic (d) used in theprobability assessment (source of information is Table 2; coordinates referenced to the meridian 47j).

Rostirolla et al. 657

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658 Bayesian Assessment of Favorability for Oil and Gas Prospects

The structural map of the top of the Sergi Forma-

tion and its relationship with wells (Figures 7, 8) can

be used to exemplify the weighting definition. Consid-

ering only this variable, an area for prospecting could be

defined as the area of potential accumulation whose

values are satisfied by its presence or absence as a diag-

nostic criterion. For example, the same contour map of

the top of Sergi Formation (Figure 7a) could represent

two different exploratory variables depending on the

cutoff used. In Figure 9, the contour of the top of the

Sergi Formation was discretized in two ways: (1) be-

tween 1000 and 2000 m deep and (2) deeper than 2500

m. The success probabilities for those cases are 0.1383

and 0.3278, respectively (Table 3). These two different

scenarios for the same variable demonstrate the sen-

sitivity of the weighting method to the cutoffs and cor-

respondent frequency distributions.

To investigate the variability of petroleum potential

in the basin, six favorability maps have been constructed

with different sets of variables (Figures 10–12). Much

of the remaining potential of the oil and gas can be

delineated, considering the combination of variables of

higher weights (Figure 10a): Gomo Member thickness,

structural lows, paleobasement contour map, top of the

mature zone, Sergi Formation thickness, and platforms.

Otherwise, a series of small leads can be delineated if we

consider only diagnostic criteria derived from the struc-

tural maps: structural lows, paleobasement contour

map, platforms, normal faults (having 2.5-km buffers

around them), and the top of the Sergi Formation

(Figure 10b).

To achieve better constraints about generation

factors, Figure 11a presents a set of geochemical var-

iables, Gomo Formation thickness and vitrinite reflec-

tance. The results are also tendentious to structural

lows, confirming the idea of bulk petroleum expulsion

and hydrocarbon saturation controlled by hotter sites in

the basin. In the Figure 11b, the result of a combination

Rostirolla et al. 659

Table 3. Ranked Results of Bayesian Probabilities Assigned in the Reconcavo basin Evaluation

Variable Sufficiency Necessity Success Probability

Isopach map of the Gomo Member 0.5436 0.6494 0.3530

Top of the Sergi Formation (deeper than 2.5 km) 0.5331 0.6149 0.3278

Structural lows 0.5122 0.6063 0.3105

Contour map of basement in Jurassic 0.7143 0.4339 0.3099

Isopach map of the Caruacu Member 0.3693 0.7787 0.2876

Vitrinite reflectance 0.4146 0.6092 0.2526

Isopach map of the Sergi Formation 0.3031 0.7385 0.2238

Platforms 0.3101 0.5920 0.1836

Extensional faults (buffer of 2.5 km) 0.2160 0.8218 0.1775

Isopach map of the Pitanga Member 0.1672 0.9224 0.1542

Top of the Sergi Formation (1–2 km) 0.1568 0.8822 0.1383

Transfer faults (buffer of 2.5 km) 0.0836 0.9253 0.0774

Extensional faults 0.0279 0.9655 0.0269

Transfer faults 0.0139 0.9799 0.0136

Structural highs 0.0139 0.9598 0.0133

Figure 8. Histograms summarizing the joint occurrences of variables and drilled sites as obtained by classification of raster maps ofFigures 5–7. Frequencies are obtained by overlaying variable raster maps with a well control map. Data are grouped according theirclassification into charge, reservoir, or migration/entrapment variable types. The different frequency distributions corroborate theimportance of the variables as diagnostic criteria. Compare the histogram results with weights stored in Table 3 (for example,thickness of the Gomo Member, top of the Sergi Formation, and basement contour during the Jurassic correspond to high weightedvariables). Frequency histograms with no or small differences between data sets, comparing producing wells and dry holes (e.g.,depth of the onset of maturity, thickness of Sergi Formation, Caruacu Member, and Pitanga Member) demonstrate the limitations ofmaking only a qualitative selection of diagnostic factors. This situation is well demonstrated in the histograms obtained for the depthof onset of maturity based on vitrinite reflectance of 0.6, which could be considered a more important exploration variable in asubjective selection.

of reservoir variables is presented: Sergi Formation

thickness, Caruacu Member thickness, and Pitanga Mem-

ber thickness, with results representing the areas favor-

able to reservoir existence, according to devised accu-

mulation models.

The favorability maps of Figure 12 consider the

two different plays of Reconcavo basin. The favor-

ability map for accumulations related to the prerift

play integrates Gomo Member thickness, top of the

mature zone, Sergi Formation thickness, and top of

the Sergi Formation (Figure 12a). In turn, the favor-

ability map for synrift play uses Gomo Member thick-

ness, structural lows, top of the mature zone, Caruacu

Member thickness, and Pitanga Formation thickness

(Figure 12b).

All favorability maps assign higher favorability

scores in the deepest parts of the basin, reflecting the

control of structural lows and Gomo Member thickness

in the evaluation. Clearly, the estimation of favorable

sites is influenced by the boundaries of the structural

compartments of the basin, the more prospective areas

being restricted to the Camacari and Miranga structural

lows, which present direct correlation with reservoir

thickness, thickness of source rocks, and petroleum gen-

eration (Figure 13).

The exploration variables were selected according

their availability in literature, with consideration of their

importance as geologic factors to accumulations. If avail-

able, additional data useful to increase the test of the

methodology include (1) generation— source potential

660 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figure 9. Comparisonbetween two differentdiscretization modes forthe top of Sergi Forma-tion. Success probabilitiesare p = 0.14 for the areasbetween 1000- and 2000-mdepth, and p = 0.33 forthe areas deeper than2500 m, respectively(coordinates referencedto the meridian 47j).

Rostirolla et al. 661

Figu

re1

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avor

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aps

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rent

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ofva

riab

les.

(a)

Best

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ofva

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les:

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omo

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662 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figu

re1

1.

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map

sfo

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vari

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(TOC, S1 + S2), source-bed distribution, and kerogen

type, (2) reservoir— predominant lithology and facies

mapping, depositional environments, petrophysical char-

acteristics of sand reservoirs, fluid transmissibility, and

heterogeneity, (3) charge— drainage area, geothermal

distribution around the basin, and migration pathways,

(4) trapment and retention— structural and stratigraphic

trap classification and positioning, trap quality, ambi-

guity and reliability, seismic resolution, lateral closure

of known fields and prospects, seal type and plasticity,

source bed continuity and thickening, and impedance,

and (5) efficiency— timing, post-trapping tectonism,

biodegradation, and thermal cracking. Our suggestion

is to organize geologic information in georeferenced data-

bases, obviously considering petroleum system modeling,

and to overlay variable raster maps with drilled sites to

define the necessity and sufficiency conditions of each

variable. The appropriate classification of these vari-

ables could establish good constraints in previous de-

scriptive and genetic modeling, leading to an adequate

subsequent evaluation. The idealized steps can be syn-

thesized in the following sequence: (1) data mapping

and storage in a geographical information system; (2)

development of descriptive and genetic petroleum sys-

tem models; (3) interpolation of unknown values; (4)

variable weighting; (5) integration of weighted maps;

(6) test of models. The correct test could only be made

with a complete database; however, we stress that the

Bayesian weighting is powerful enough to define favor-

able sites around the basin.

The final results confirm the constraints placed on

results achieved from a restricted database. In this case,

the most important variables have not been available. If

key exploration data, as reservoir properties, heat flow,

efficiency of the migration process, detailed maps of

traps, etc., had been included in the database, favor-

ability maps would better reflect the numerical assess-

ment of the hydrocarbon potential of the basin.

Rostirolla et al. 663

Figure 12. Favorability maps for the two plays in the Reconcavo basin. (a) Prerift play: isopach map of the Gomo Member, vitrinitereflectance, isopach map of the Sergi Formation, and top of the Sergi Formation. (b) Synrift play: isopach map of Gomo Member,structural lows, vitrinite reflectance, and isopach maps of the Caruacu Member and the Pitanga Member (source of information isTable 2; coordinates referenced to the meridian 47j).

DISCUSSION

The Bayesian favorability quantification serves as an aux-

iliary method for petroleum-resource assessments for

the following reasons: (1) it offers a practical approach

that standardizes the spatial character of exploration

variables; (2) the essential elements of a total petro-

leum system can be scientifically tested through map

correlation; (3) the criteria used to identify and map

total petroleum systems can test previous models with

664 Bayesian Assessment of Favorability for Oil and Gas Prospects

Figure 13. Favorableareas delineated fromdifferent sets of variables,superposed to the struc-tural map of the Recon-cavo basin. The shadedarea represents the regioncovered by all favorableareas. The producing re-gions are represented bysolid areas (oil fields) andline-filled areas (gas fields),with the respective fieldnames. Note the correla-tion between gas fieldsand higher favorabilityscores in the deepestparts of the basin, whichreflects control in theCamacari and Mirangastructural low.

extensive databases; (4) new information that im-

proves the understanding of the petroleum system can

be incorporated into the assessment.

Although not optimal, the resultant favorability

maps demonstrate the applicability of the Bayesian ap-

proach in the conceptual modeling of petroleum systems

and uncertainty assessment of the Reconcavo basin. One

important point is that the methodology reflects the

subjective reasoning in petroleum exploration, because

simple joint relationships between variables and pro-

ducing and dry areas are quantified and expressed as

weights. This situation equals that of the traditional

decision-making process, in which separate probability

distributions assigned to key geologic factors are mul-

tiplied. Besides, the proposed strategy is probabilistic

and based on a systematic approach that relies on sev-

eral calibration routines prior to drilling decisions.

The method implies possibility of updating when

new information is available, through Bayesian combi-

natorial procedures yielding conditional probabilities.

The final favorability scores are regarded as a function

of the behavior of variables, forecasts being represented

by a combination of subjective probability assessments

of these variables. Assessments are predictive appraisals

of diagnostic criteria filtered in the petroleum system

modeling of the basin, which depends strongly on ex-

ploration history data.

The Reconcavo basin has been developed over the

past three decades and has provided information rel-

atively relevant to the industry, including seismic sur-

veys, well logs, and geochemical and lithologic data.

Such a continuing succession of widespread reassess-

ments would only be feasible through the use of sys-

temic procedures consistently applied in the next stages

of Reconcavo’s industrial development.

Applying favorability evaluation, the late-stage ex-

ploration drilling of the Reconcavo basin can be op-

timized. These procedures should still be useful in

assessing the favorability of those parts of the basin that

remain unknown regarding production potential for

gas because most of the oil fields of the basin have

already been outlined. The more important aspect of

the method is its simplicity and its close relationship

with the traditional reasoning in exploration, in which

available prospective guides are also weighted in a sub-

jective way by cognitive associations among variables

and tested prospects. The approach is spatial in essence

because the Bayesian analysis is linked to geographic

information systems. The scores calculated can be con-

sidered as an indirect measure of posterior probabil-

ities, which are conditional probabilities representing

the sum of the probabilities derived from variable occur-

rence over the basin plus previous success ratios (i.e.,

the joint probability that both a producer and the geo-

logic factors will coexist at the drilling site).

Despite the fact that petroleum exploration in the

Reconcavo basin is in retrenchment, there are several

reasons to consider the favorability results, such as (1)

the Reconcavo basin is in an advanced stage of matu-

rity for oil prospects, but its favorability for gas-only

prospects is still underevaluated; (2) most of the tested

prospects are in shallow sections of the basin (i.e., con-

sidering that depths below the basin have not been ex-

plored, possibilities are good for new accumulations as

demonstrated by favorable area delineation); (3) the

Reconcavo basin is a training region, and any further

knowledge will be important in the delineation of pros-

pects in analog rift basins in Brazil.

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666 Bayesian Assessment of Favorability for Oil and Gas Prospects