The Campos Basin

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José Diamantino de A. Dourado. UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO. CENTRO DE TECNOLOGIA E CIÊNCIAS. FACULDADE DE GEOLOGIA.
Grupo de Pesquisa

PETROUERJ

UNIVERSIDADE DO ESTADO DO RIO DE JANEIRO CENTRO DE TECNOLOGIA E CIÊNCIAS FACULDADE DE GEOLOGIA

MASTERS DISSERTATION (2009):

CLEVELAND M. JONES APPLICATION OF THE CONCEPT OF AREA EXHAUSTED TO THE REGION OF SHALLOW WATERS OF THE CAMPOS BASIN, UTILIZING EXPLORATORY PROCESS MODELING TOOLS Line of Research: Geoeconomic Assessment of Energy Mineral Resources Adviser: Prof. Hernani A. F. Chaves Co-Adviser: Prof. José Diamantino de A. Dourado 2

INTRODUCTION

The objective of any exploratory investigation in a certain geologic province which may contain economically recoverable hydrocarbon resources is to be able to determine, with greater precision, where to explore and what is the potential of the discoveries which may yet be found (yet-to-find-oil).

The objective of the original Masters Dissertation was to propose an assessment methodology for determining the potential of new discoveries, using both the concept of area exhausted by previous exploratory efforts, as well as commercial tools available for modeling the exploratory process. 3

INTRODUCTION In this presentation we wanted to show one of the main results of that research project. We hoped to answer the question: Are the shallow waters of the Campos Basin really mature? What is the size and the corresponding chances of the total resources still expected to be found?

What are the chances for new discoveries of various sizes? 4

Why choose the region of Shallow Waters of the Campos Basin? Ever since the discovery of Albacora, in 1984, the discoveries in deeper waters of the Campos Basin redirected the focus of exploratory activities, from the region of shallow waters towards deeper waters.

Expansion of the exploratory frontiers (Petrobras, Plano de Negócios 2006-2010)

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Why choose the region of Shallow Waters of the Campos Basin? 1. The possibility that significant discoveries are being ignored may represent a strategic mistake.

2. There are technical and economic issues which favor exploration efforts in shallower waters. 3. The region of shallow waters contains geologic structures and horizons which suggest that there is still a potential for additional discoveries.

4. Exactly when that area seems to be relatively ignored, it would be interesting to apply a methodology which could indicate and quantify this potential.

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The Campos Basin The Mesozoic sedimentation of the Brazilian continental margin started with the rifting and separation of the Gondwana supercontinent, in the late Jurassic/Early Cretaceous (apx. 150 MY). The subsequent phases of subsidence and sedimentation in the Western portion of Gondwana (South-American plate) correspond to the continental (or syn-rift), transitional, and marine (or post-rift) megassequences.

Rifting phases of Gondwana, from the late Aptian (apx. 121 MY) to the early Campaniano (apx. 80 MY) (Fainstein, 2002)

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Megassequences of the Campos Basin Clastic sediments from fault-controlled subsidence were deposited (130-140 MY) over the base of the continental megassequence. Three lithological facies predominate: sandstone fans/deltas; lime mudstone (calcilutitos); and coquinas. The Lagoa Feia Formation lime mudstone is the main source rock in the Campos Basin.

Seismic section of the Campos Basin (Modified from Rangel and Martins, 1998)

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The Campos Basin

Mapped horizons on the stratigraphic chart (Fainstein, 2002) Stratigraphic chart of the Campos Basin (Rangel, 1994)

Geological Favorability of the Campos Basin The Campos Basin has all the elements necessary to establish a prolific petroleum system with great potential:

1. Source rocks; 2. Favorable conditions for generation; 3. Migration ducts; 4. Reservoir rocks; 5. Traps and Seals; 6. Synchronism; 7. Favorable preservation conditions (despite some biodegradation). 10

Geological Favorability of the Campos Basin 1. There are significant structural elements which offer opportunities for new exploratory efforts. 2. Many of the large fields discovered in deep waters, such as Roncador (1996), have sequences which also occur in shallow waters, such as Cretaceous turbidites. 3. Hydrocarbon accumulations in the stratigraphic column of the Campos Basin occur from the Neocomian to the Miocene (apx. 140-20 MY). 4. Various types of oil bearing rocks: fractured basalt, coquinas, carbonates, and sandstones.

5. Complex but promising petroleum province. 11

Geological Favorability of the Campos Basin Given the geological favorability and the size of the region of shallow waters, we need to determine whether it is truly mature, and the expected return from future exploratory efforts in the region as a whole, and in its various plays.

Campos Basin, showing large areas of shallow waters (CPRM)

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Other assessments of yet-to-find-oil in Campos Basin: World Petroleum Assessment 2000 – WE2000 Given the strategic importance of identifying and quantifying new discoveries of oil and gas reserves (and other non-conventional energy sources) the USGS conducted a study of these resources throughout the world: World Petroleum Assessment 2000 (WE2000). Their objective was to apply an assessment model for yet-to-find-oil, in all known petroleum provinces of the world, and to present a table with the probabilities of new discoveries in each province. The USGS adopted the Total Petroleum System (TPS) approach in order to assess the occurrence of hydrocarbon accumulations, and within each TPS, Assessment Units (AU) are considered. That approach is also employed by the exploratory process modeling tool which we used GeoX® (from GeoKnowledge). 13

World Petroleum Assessment 2000 – WE2000 Based on a statistical analysis of the information collected, the USGS presented an estimate of the distribution and probabilities of the sizes of the fields which may yet be discovered (Undiscovered Resources), in the form of tables for each TPS and AU. In Brazil, the AU Late Cretaceous-Tertiary Turbidites, in the TPS Lagoa Feia-Carapebus, in the Campos Basin petroleum province, covers a good part of the shallow waters of the Campos Basin, closely matching the area in which we focused. Another AU, Cretaceous Carbonates, in the same TPS, is also in the region of shallow waters of the Campos Basin.

Those results (10,3Bboe and 0,9Bboe, respectively, at F50 confidence) represent a base for comparisons with the results of our work. 14

World Petroleum Assessment 2000 – WE2000

Assessment Units of the Lagoa Feia-Carapebus TPS (USGS, 2002)

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Estimates Generated by the USGS Model

Table with results from the WE2000 for the Lagoa Feia-Carapebus TPS (USGS, 2002) 16

Assessment method using FSD – Field Size Distribution and Discovery Sequence The two main concepts involved in the science of analyzing and predicting yet-to-find-oil in petroleum basins are: 1. The distribution of the sizes of the accumulations – Field Size Distribution 2. The order of discovery of those accumulations - Discovery Sequence.

All accumulations discovered in a given play represent a sample of the universe of existing accumulations, so their size distribution must follow that of the universe of all existing accumulations, including those which have not yet been made. On the other hand, the exploratory experience strongly suggests that the largest accumulations will be discovered first. That implies an ordered aspect in the sequence of expected future discoveries. 17

Probabilistic Modeling of the Historical Sequence of Discoveries The geology of the basin defines the behavior of the discovery of accumulations. This concept allows one to use the sequence of those discoveries to predict the sequence of future discoveries (Kontorovich, Dyomin e Livshits, 2001).

The two main geological uncertainties are related to the distribution of the sizes of accumulations, and to the number of those accumulations (Divi, 2004). Thus, the assessment of an individual play consists of three main steps: 1. Determining the FSD; 2. Estimating the number of possible accumulations; 3. Quantifying the exploratory risk.

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Field Size Distribution Ever since Arps and Roberts (1958) studied the Denver Basin and plotted the frequency of occurrence of the discovered fields in a log histogram, it is known that the FSD shows that smaller fields are not necessarily be more numerous, since many are not considered economical and thus may not even be counted. Given a model of the FSD and the extremities of that curve (maximum and minimum size of accumulation to be discovered), we can determine the future discoveries of different sizes of accumulations.

That is why geologists, geophysicists and engineers work to determine the extremities of those log-normal distributions which describe the accumulations in a play, since with that information they can determine the probability of occurrence of any accumulation size (Cartwright, 2007). 19

Curve Fitting

When the play is more explored and there are sufficient discoveries, the FSD can be modeled by a log-normal curve which adjusts itself to at least part of the discoveries.

This is the process of curve fitting –adjusting an ideal FSD curve to the data available. Given the maximum and minimum size of accumulations, a corresponding curve can be chosen, and then Monte Carlo simulations can describe the expected individual accumulations to be discovered (White, 1992)

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Probability of fields larger than the size indicated on the horizontal axis

Curve Fitting – White Method White, 1994

Size in any units (bbl, m3, etc.)

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Examples of FSD Curves FSD for fields discovered in the Southern Rift of Sudan.

Rodgers, 2002

Rodgers, 2002

The extremities are given by the maximum (328Mbbl) and minimum (0,2Mbbl) size expected for fields in that play.

Cumulative FSD Curve of the discoveries in the Southern Rift of Sudan, plotted on a linear graph. 22

Examples of Discovery Sequence Curves

Cumulative discoveries made

The information in a FSD curve can be transformed into a curve of cumulative discoveries. The result is a curve with a tendency to flatten, representing the expected Discovery Sequence. In the example below, the points in red represent the theoretical perfect curve, and the points in yellow the real discoveries, closely matching the theoretical curve.

Campbell, 2002

Cumulative number of exploratory wells

Exploratory Maturity The Discovery Sequence curve furnishes an indication of how much oil is yet to be discovered: that amount is represented by the discoveries along the curve, between the current stage and the point where the last (and smallest) discoveries are expected (Campbell, 2002).

Campbell, 2002

In the region where the Discovery Sequence curve flattens, the play is said to have reached exploratory maturity, since only small additional discoveries are expected after that point. 24

Examples of areas which exhibit Exploratory Maturity

Rodgers, 2002

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Monte Carlo Simulations The study of a system can be made through direct observations or utilizing a representative model of the system.

The Monte Carlo method consists of the substitution of the study of a real, physical process, by a probabilistic model which can treat deterministic problems by random sampling (Oliveira, Barros and dos Reis, 2007). The Monte Carlo simulation transforms the initial entry uncertainties into output uncertainties, through a function which describes the behavior of the parameter which is being studied (Metropolis, 1987). Exploratory process modeling tools also utilize Monte Carlo simulations.

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Monte Carlo Simulations In order to do a simulation it is necessary to have a model – a probability density function which describes the system (Camponogara, 2004). It is also necessary to generate random (or pseudo-random) numbers, to be used in generating the large number of simulations required. The algorithms utilized should not generate repetitions or known sequences in order to ensure good results, which is not a trivial requirement.

In general, the precision of the result depends on the number of simulations. If only an approximate solution is sought, then a Monte Carlo method with less simulations can be fast and practical. The Monte Carlo method is considered simple and flexible, but the main disadvantage used to be the number of simulations required in order to reach na acceptable error in the solution, which entailed intensive computational demands, less of a problem nowadays. 27

Example of Monte Carlo Simulations in the WE2000 In the World Petroleum Assessment 2000 (WE2000 ), Monte Carlo simulation tools were used, which basically involved the following steps (Charpentier and Klett, 2002): 1. Take a sample of the number of fields yet to be discovered, from the distribution curve for the total number of fields expected in all size classes; 2. Convert that number into a whole number and use it to define the number of samples to be taken from the expected FSD; 3. Add those sampled sizes in order to get a value for the total accumulations to be discovered; 4. Repeat that process 50,000 times, in order to create a probabilistic distribution of the total volume of accumulations to be discovered. 28

Example of Monte Carlo Simulations in the WE2000 Take a sample of the number of fields yet to be discovered, from the distribution curve for the total number of fields expected in all classes. Use that number to define the number of samples to be taken from the expected FSD. Add those sampled sizes in order to get a value for the total accumulations to be discovered. Repeat that process 50,000 times.

50.000 repetitions

Create a probabilistic distribution of those totals, generating a distribution curve for the total accumulations expected. Charpentier and Klett, 2002

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Information about the Area Studied The information about the area studied was obtained from the BDEP (well database) site of ANP. Only data about the shallow water area was considered:

• • • •

water depth; depth reached; date of completion of the well; and result of the well drilled (dry well, discovery well, producing well, etc.).

In that same area, information about the fields discovered was researched: name of the field, date of discovery, volume of accumulations, and area of field. Two types of information were organized: information about wells drilled and information about fields discovered.

ArcGIS® SIG tools were used to map information about valid wells and discoveries. Then, spatial information was extracted, area calculations made, investigation area delimited by bathymetry and borders, etc. 30

Preparation of the Information Available

The region of shallow waters with valid wells mapped.

Discovered fields in shallow waters, with corresponding discovery wells.

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Application of the Concept of Area Exhausted to the Region Studied After determining a volume/area relation for the Campos Basin fields, corresponding areas of influence for each well were calculated.

The areas of influence for each probability level (F10, F50 and F90) were 12.13Km2, 3.67Km2 and 0.90Km2, respectively. Then the radius of influence of each well was calculated based on the equivalent radius of each area of influence. Those radii were, respectively: 1.97Km, 1.08Km, and 0.53Km. Finally, the total area exhausted by all wells drilled, whether discovery wells or not, was calculated, based on the individual areas of influence covered, less overlaps.

The total area of the shallow waters, 33,884Km2 , was adjusted for this total area exhausted (3,466Km2). 32

Application of the Concept of Area Exhausted to the Region Studied

Area exhausted, considering the area of influence of each well, at the confidence level of F90 (smaller area, with higher probability), giving a total of 478Km2, or less than 1% of the area investigated.

Area exhausted, considering Area exhausted, considering the area of the area of influence of each influence of each well, at the confidence well, at the confidence level of level of F10 (larger area, with lower F50 (median area of fields), probability), giving a total of 3.466Km2, giving a total of 1.554Km2, or or about 10% of the area investigated. 5% of the area investigated. Detail shows how individual areas of 33 influence are consolidated

Modeling of the Exploratory Process Given the more conservative estimate (larger area exhausted – 3,466Km2), the GeoX® tool was used to model the exploratory process. GeoX® considers entry parameters related to volume, technical parameters of the reservoirs, and parameters related to the independent or joint uncertainties for the various occurrence factors between prospects of the area analyzed and of the play. All information thus gathered was inserted in the GeoX tool. 34

Data Entry in the GeoX® Tool Since only information about discovered fields was used to run the simulations, it was possible to directly insert information about volumes discovered into the tool.

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Results

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Discovery Sequence for the Region Studied The GeoX® tool produced a Discovery Sequence graph without a pronounced flattening of the curve. The conclusion is that the area of Shallow Waters of the Campos Basin does not appear to show Exploratory Maturity.

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Discovery Sequence for the Region Studied Another obvious observation is that the Discovery Sequence curve has at least three distinct segments, probably due to technological innovations driving new exploratory cycles. These cycles could roughly correspond to exploratory periods which initially focused on shallow waters, then carbonate reservoirs, and finally turbiditic sandstones. Note: not all discoveries were utilized to input information into the modeling tool, only 24 of 57. Other simulations using information from additional discoveries, could result in a flatter Discovery Sequence curve.

There is also ambiguity regarding what are considered fields, since some discoveries in extension areas of discovered fields are considered new discoveries. Another difficulty in obtaining information is due to the fact that many fields still appear as exploratory areas without field names, per 38 the convention which applies to discovered fields.

FSD for the Region Studied Only after determining a maximum and minimum field size, does the modeling tool determine the FSD curve.

The modeling tool produces an FSD curve with the discovered fields indicated, leaving spaces where new discoveries are expected. The FSD curve produced is presented in log-log form, with inverted vertical axis. The result is an approximately straight line, with ends limited by the maximum (288Mm3) and minimum (1Mm3) accumulation expected. 39

Other Results Screen showing the expected distribution of the size of fields (accumulations) in the area: F90: 4.2Mm3 F50: 17.2Mm3 F10: 56.9Mm3

Screen showing the expected distribution of the total number of fields (accumulations) in the area: F90: 63 F50: 91,5 F10: 118 40

Calculating yet-to-find-oil for the Region Studied As in the method used by the WA2000, GeoX® makes Monte Carlo simulations based on those two results (number and size of fields), to arrive at a distribution for the total accumulations expected. Discounting the discoveries made, the modeling tool shows the distribution for the total accumulations which can be expected to be discovered (yet-to-find-oil): F90: 1.604Bm3 F50: 2.118Bm3 F10: 2.775Bm3

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Size-by-Rank of the expected accumulations From the FSD curve the modeling tool makes Monte Carlo simulations in order to come up with a table with the sizes of each field expected (first screen seen here). There are 124 lines, corresponding to the maximum number of accumulations expected, including those already discovered, which are indicated in their respective position in the table. 42

Size-by-Rank of the expected accumulations From the prior table, a graph is built, showing the sizes of the discovered fields, along with points corresponding to accumulations which are still expected to be found. These are points where no discoveries are yet marked, but exactly where such discoveries are expected. 43

Rosy Future Graph Finally, the GeoX® tool builds a graph (rosy future graph), showing the likely evolution of the discoveries which are expected to be made in the investigated area, as new exploratory efforts (wells) are made.

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Conclusions

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Conclusions The methodology employed, applied to the region of shallow water of the Campos Basin, presents an optimistic picture, with implications for new exploratory campaigns in this area. Even so, it must be remembered that this evaluation may be too optimistic, since many of the known discoveries made in this area were not considered, due to incomplete data. Including such discoveries might have suggested a higher exploratory maturity, and, consequently, the pink region of the rosy future graph could be smaller. Observing the results in light of what is known today about several additional discoveries not considered in this study, many of them could perfectly well fit within the projections generated, as they could be considered to be some of the discoveries projected before they were actually made.

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New Assessments with the Methodology Used, and Utilizing Exploratory Process Modeling Tools Based on Historical Data This type of analysis is useful for making predictions about the potential for new discoveries of conventional resources of oil and gas, and other researchers have also used it to assess natural gas hydrates, tight gas, and other unconventional energy resources. In fact, this type of assessment continues to be employed currently, in order to carry out evaluations of the potential for new discoveries in other petroleum provinces around the world, not only by the USGS, but also by other researchers.

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Number of Accumulations and Total Accumulations In relation to the size of accumulations and the total yet-to-find-oil, the results are quite favorable. At probability confidence levels of F10, F50 and F90, total yet-to-findoil of approximately 2.8Bm3, 2.1Bm3, and 1.6Bm3, respectively, is expected. In relation to the results of the WE2000 (apx. 1.9Bm3 for both AUs together, at F50), the results of this study do not seem exaggerated.

Accumulations raging in size from 4.2Mm3 (F90), to 56.9Mm3 (F10) are expected, sufficient to attract at least smaller &P companies, if not the majors.

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Strategic Implications The chances of new discoveries in areas which had been relatively ignored or considered to be at exploratory maturity, have strategic importance, as they represent the possibility of realizing value from accumulations currently ignored or considered unviable.

Accumulations which require lesser technical sophistication for effective exploitation, and which require lower cash outlays over their entire life cycle, can be considered valuable “finds”, and particularly attractive to certain players of the oil industry.

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New Studies Projected While not considered in this study, new studies are planned, limiting the study area so as to make it coincide with the AUs defined by the WA2000, for example (Cretaceous Carbonates - AU 60350102, or Late Cretaceous-Tertiary Turbidites – AU 60350101). This would allow a closer comparison with prior assessment approaches. Assessments are also considered for other geological areas:

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Grupo de Pesquisa

PETROUERJ

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