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WET AND DRY COMPLETION PROJECTS COMPARISON THROUGH RISK CASH FLOW ANALYSIS

Authors: BASTOS, Glucio A.; JACINTO, Carlos Magno C.; TORRES S.Jr, Flodoaldo

ABSTRACT

After globalization, businesses do not have more borders. The decision maker

strategic positioning demands information. Ideal decisions, if existent, are

intimate closer on (almost) perfect information attainment, analysis and interpretation. To hold reliable information in correct time became the great

differential for companies.

Exploration and production projects are complex and there is a lot of variables that need to be considered in decision process.

Those projects can be analyzed through discounted cash flow, but would not be real if risks on different variables are not considered. Therefore, statistical

models are fundamental on viability analysis.

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September, 2008.

SUMMARY

1-INTRODUCTION 03

2-METHODOLOGY 03

3-APPLICATION 04

3.1-Basis Development 04

3.2-Deterministic Analysis 06

3.3Sensitivity Analysis 08

3.4Probabilistic Analysis 08

4-ANALYSIS OF OUTCOMES 11

4.1-Measures for outcome evaluation considering Risk 11

5-CONCLUSIONS 19

6-BIBLIOGRAPHY 19

4

1-INTRODUCTION

The current scenario of oil & gas fields development in Brazil is a consequence of high efforts and investments made in last decades in search of self-sufficiency in providing care to internal demand for oil derivatives, activity that today boasts a RESERVE / PRODUCTION RELATION of 19 years that is high compared with 10 years world average, indicating the need for new projects deployment aiming available reserves disposal. This urgency guides the project management activity seeking outcome maximization in the possible shortest time. To do this effective techniques application is indispensable for treating RISK in projects. By the way, oil fields development projects demand high investments in the long term, reflecting in project RISK, which can be quantified in terms of technology, policy and marketing and depends on technology choice and assessment of its impact on business.

In the calculation model being presented in this paper, the following considerations were adopted as a strategy for its implementation:

in two completion alternatives comparison, deployment costs are considered to be inherent to reliability (availability, failure rate, etc.);

monetary outcomes determination considers the "costs at risk"; and

political risks on production flow, market uncertainties and so on are not covered because their impact on the two technologies are similar.

The alternatives included for comparison include technical and financial models of the following three technological scenarios:

a Wet Completion: with Wet Christmas Trees and Standing Production Unit (SPU) of FPSO type;

b Dry Completion with all wells interconnected to Mini-TLP (Scenario "A"): with Dry Christmas Trees installed in a SPU of Tension Leg Platform (TLP) type interconnected to wells through rigid risers, where wells / SPU distance restraint can be offset using one or more TLPs, preferably of Mini-TLPs (TLWP) type - a smaller one in size and capacity but that otherwise provides benefits such as CAPEX reduction, less SPU construction time and consequently early wells production start, anticipating the revenue flow as compared with conventional TLP installation and thus reducing the main disadvantage of TLP as compared with FPSO operation;

c Dry Completion with production wells interconnected with the Mini-TLP and injection wells linked to FPSO (Scenario "B"): here Mini-TLP size and capacity are reduced as compared with previous alternative situation, besides directional drilling becomes less complex as long as injection wells are not connected by rigid riser to Mini-TLP, although underwater lines are kept for this arrangement.

2-METHODOLOGY

The data source was accessed from the Brazilian Agency for Oil and Natural Gas (ANP) internet site - www.anp.gov.br treated with industry average costs and then assembled in a spreadsheet showing revenue and development costs information to a technical and financial cash flow model considering alternative projects according to their different strategic scenarios.

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The mathematical model was constructed as described and solved using @RISK - a software for business and technical analysis under "risk" exposure which is a MS-EXCEL applicative that incorporates decision analysis techniques. The combination of these two products allows cash flow modeling with "risk" considerations and improves key variables understanding on decision impact.

In this study analyses were conducted using @RISK generated responses with the purpose of oil and gas production projects comparison, in search of financial resource allocation to maximize the return on investment, regarding business risks.

@RISK provides two simulation techniques as user options, based on different data sampling, which are Monte Carlo and Latin Hypercube (LH). The LH technique was chosen considering the following advantages as compared with conventional Monte Carlo technique:

LH attaches equal weights to all chance tracks during iterative process avoiding trends; and

LH reduces the total number of iterations on about 30%. The number of iterations adopted in each simulation was 10,000 since was experimentally determined that for a larger number, differences among each simulation outcomes were negligible.

3-APPLICATION

This study deals with exploration and development of SAGATIBA prospectus, block BMS-69, considering 869 MMSTB of annual oil production through pre-salt layer and the following additional data common to all three scenarios:

Production time (years) 24 Location to shore distance (km) 215 Wells number 22

production 15 injection 7

Exploratory expenditure (US$ 103) 132,535 seismic 3,000

exploration and appraisal 121,470 others 8,065

Wells development (US$ 103/well) 59,631 drilling 39,754

completion 19,877

Table 1

3.1Basis Development

The specific data to each scenario may thus be compared:

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WET COMPLETION Subsea equipment (US$ 103/well) 16,000 Development CAPEX (US$ 103) 2,994,111

directional drilling 1,111,523 FPSO 800,000

subsea 304,000 pipes 150,500

onshore plant 50,000 others 578,088

Development OPEX (US$ 103) 2,478,668

Table 2

DRY COMPLETION Scenario A Development CAPEX (US$ 103) 3,534,720

directional drilling 1,556,132 TLWP 400,000 FPSO 800,000 pipes 150,500



Table 3

DRY COMPLETION Scenario B Subsea equipment (US$ 103/well) 16,000 Development CAPEX (US$ 103) 3,374,278

directional drilling 1,394,890 TLWP 300,000 FPSO 800,000

subsea 100,800 pipes 150,500



Table 4

Conclusions after tables 2, 3 and 4 follows:

Wet Completion: presents the lowest CAPEX and highest OPEX;

Dry Completion Scenario "A": presents the lowest OPEX, as long as all X-Trees are dry, positioned on TLWP at the surface thus making workovers more cheaper besides there is no subsea lines because wells are connected to Dry X-Trees through rigid risers which otherwise makes directional drilling more expensive;

Dry Completion Scenario "B": presents the lowest CAPEX among Dry Completion scenarios as long as injection wells are linked to FPSO through subsea lines and the TLWP that links production wells is lower and therefore cheaper than that scenario A one, besides OPEX is lower than that of Wet Completion because workovers are

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more efficient and therefore cheaper as long as production wells Dry X-Trees are positioned on TLWP.

3.2-Deterministic Analysis

Specific data for this phase follows:

WET DRY Scen.A

DRY Scen.B

Estimated production (M Boe) 948 1,080 1,080 Oil price - no taxes (US$/b) 35 35 35 Income revenue tax (%) 30 30 30 Capital cost (%) 15 15 15 Depreciation (years) 24 24 24

Table 5

Dry Completion has the highest production thanks to ease and efficiency of operations through Dry X-Trees which increase the wells recovery factor.

Below, production curves for both completion types are shown:

Graph 1

Graph 2

Production FPSO Wet Completion

0

10

20

30

40

50

60

70

80

90

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034

Year

M

B

oe

Production FPSO + TLWP

0

10

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30

40

50

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70

80

90

2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034

Year

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The three project scenarios are mutually exclusive, since the deployment of one of them requires the removal of other alternatives. In this case, according to Samanez (2005, p260) a comparison of their cash flows of Internal Rate of Return IRR method, as long as the first one assumes that cash flows will yield the opportunity cost when reinvesting the generated cash flows. Hence, in this situation project selection using IRR yields inconsistencies and contradictions with NPV method and therefore the NPV was adopted in this study as an indicator of mathematical calculation model outcomes.

The outcome ranking after cash flow modeling and solving follows:

COMPLETION RANK NPV (US$ 103)

DRY Scen.B 1 2,049,420 DRY Scen.A 2 1,993,648 WET 3 1,867,134

Table 6

The Accumulated Present Value curves for each scenario are as follows:

Graph 3

Graph 4

FPSO Wet Completion : PV

-1.500.000

-1.000.000

-500.000

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

2004 2007 2010 2013 2016 2019 2022 2025 2028 2031 2034

Year

US$ 1

03

FPSO + TLWP Scenario"A" : PV

-2.000.000 -1.500.000

-1.000.000 -500.000

0 500.000

1.000.000 1.500.000

2.000.000 2.500.000

2004 2007 2010 2013 2016 2019 2022 2025 2028 2031 2034

Year

US$ 1

03

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Graph 5

Note that the Discounted Payback is 11 years for all alternatives.

3.3Sensitivity Analysis

Sensitivity Analysis applied to the deterministic model indicated the relevant variables ranked as shown below including each respective outcome impact:

RELEVANT VARIABLE UNIT RANK OUTCOME IMPACT

Capital cost MRI % 1 -

Oil price US$/b 2 + Annual estimated production M boe 3 +

Tax % 4 -

Annual production flow % 5 +

Total investment in wells development US$103/well 6 -

Table 7

Note: positive impact indicates that variable increasing leads to outcome increasing and variable decline to outcome decreasing while negative impact, on the contrary, indicates that increasing or decreasing in variable leads to an opposite direction outcome.

Conclusions of Sensitivity Analysis for the three scenarios can be presented in graphic form, through either tornado or spider graphs, as for indicating the influence percentage on outcome variables.

3.4-Probabilistic Analysis

After definition of variables that significantly impact the model, their PROBABILITY DISTRIBUTION FUNCTIONS are established according to the bibliography

FPSO + TLWP Scenario"B" : PV

-2.000.000

-1.500.000

-1.000.000

-500.000

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

2004 2007 2010 2013 2016 2019 2022 2025 2028 2031 2034

Year

US$ 1

03

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presented at the end of this study according to experiments that were carried out to determine each distribution theoretical type that best fits to historical data as analyzed by the Chi-Square Test which indicates the best probabilities theoretical distribution through the lower Chi-Square Index boasted.

According to Costa (2003, p211), Mian (2002, p318) and Steagall (2001, p24), except for TAX, all other relevant variables show Probability Distribution Frequency Curves or PDFs (which are frequency distributions for each observed value of variable) compatible with the TRIANGULAR DISTRIBUTION which is a CONTINUING one and whose PARAMETERS are: the lowest possible value, the more likely value and the highest possible value. For each relevant variable, the following Triangular Distribution parameters were used, where the most likely value MLV is that one considered in deterministic analysis:

a) Capital cost - MRI, according to Costa (2003, p102) lowest value: - 15% of MLV; highest value: + 15% of MLV;

b) Oil price, according to Costa (2003, p102) lowest value: - 30% of MLV; highest value: + 30% of MLV;

c) Annual estimated production, according to Steagall (2001, p61) lowest value: - 20.0% of MLV; highest value: + 12.5% of MLV;

d) Annual production flow, according to Steagall (2001, p74) Year 1

lowest value: - 4.0% of MLV; highest value: + 1.6% of MLV;

Year 2 lowest value: - 7.5% of MLV; highest value: + 3.4% of MLV;



e) Total investment in wells development, according to Costa (2003, p102) lowest value: - 20% of MLV; highest value: + 20% of MLV.

For TAX, according to Costa (2003, p115) and Dallolio (2006, p72), as long as it is considered a DISCRET variable, two separate simulations were launched, each one for a different scenario, as follows:

30% tax rate in case of Income Tax imposed just on revenue; 60% tax rate in case of total tax applied on the venture, that also includes

Governmental Participations fraction incident on the project under a more conservative viewpoint which makes the model more robust.

The model takes into account the probabilistic dependence possibility among your variables. According to Mian (2002, p335), there are basically four types of probabilistic dependence patterns among variables that are defined by Scattering Diagrams whose vertical and horizontal lines represent respectively dependent and independent variable, which are presented as follows:

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Graph 6

We can describe each Scattering Diagram type as follows: Total Linear Positive Dependency (graph 6.a) - in this case, independent

variable increase or decrease causes the same effect on dependent variable; Total No Linear Negative Dependency (graph 6.b) - independent variable

increase or decrease causes the opposite effect on dependent variable; Diffuse Positive Dependency (graph 6.c) this one can be positive or negative

and indicates that the variables are correlated but in a not so perfect way as occurs in previous types;

Without Dependency (graph 6.d) - in this case, there is no correlation between variables which therefore are each other independent.

Among model relevant variables studied in this work was found a positive correlation of Total Linear Positive Dependency type (as in Fig. IV.1.15.a) between independent variable - OIL PRICE and dependent variable - TOTAL INVESTMENT IN WELLS DEVELOPMENT presenting a Correlation Coefficient of 0.92102 which as being positive and around 1.0 indicates that correlation is strong and positive. This analysis was made after the following historical data, extracted from RigLogix (12/15/2007), which include oil price influence on certain types of offshore development investments:

Graph 7

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Graph 8

For such correlation the following Scattering Diagrams were established:

Graph 9

4-ANALYSIS OF OUTCOMES

NPV PDFs calculated for each scenario and tax level, according to Chi-square Test and consistently with Mian (2002, p318), keep pace with NORMAL DISTRIBUTION, which is a CONTINUING type distribution and whose PARAMETERS are the mean and the NPV standard deviation. Also of relevance for the Analysis of Outcomes are Cumulative Distribution Frequency Curves or CDFs which are frequency distributions of values that a variable can show less or equal to each observed variable value.

About Sensitivity Analysis made after probabilistic model solution, the relevant input variables can be ranked as follows for each tax level, in descending order of influence on the outcome:

1st. Oil price; 2nd. Capital cost - MRI; 3rd. Annual estimated production; 4th. Total investment in wells development; 5th. Annual production flow.

4.1-Measures for outcome evaluation considering Risk

The Performance Index - PI incorporates NPV and Risk to the extent of feasibility of an investment under restrictions either on Risk or on the investor's decision to choose the investment that maximizes economic outcomes and is calculated as follows:

Correlation between OIL PRICE x TOTAL INVESTMENT IN

WELLS DEVELOPMENT

0 20 40 60 80

100 120 140 160 180

0 20 40 60 80 100 Oil Price (US$/b)

TOTA

L IN

VEST

MEN

T IN

W

ELLS

DE

V.

(10

3 US

$/WEL

L)

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In decision making the investor sets a minimum value for PI according to his attitude to Risk. Projects with PI higher than this minimum desired amount are selected as economically viable.

The PI is a measure of the type "mean-standard deviation" where alternatives of higher mean (maximization of the outcome) and / or lower standard deviation (minimization of risk) - and therefore of higher PI - dominate others. NPV outcomes calculated for each scenario and tax level considering Risk have the following parameters:

Table 8

After investor options of minimum PIs (e.g. PI values of 3200 and 1700 respectively to tax levels of 30% and 60%) were drawn, curves that represent efficiency borders for three points of standard deviation are recorded in the three scenarios for each tax rate. The three points of "NPV mean-standard deviation" from the three scenarios simulation for each tax rate are located below the line, indicating that they are within the efficiency border as long as for each NPV mean, the standard deviation found was lower than expected at the limit and should therefore be considered as valid option for the analysis of outcomes next step, as shown below:

Graph 10

COMPLETION TAX Average NPV (US$ 103) Standard Deviation (US$ 103) PI Wet

30% 1,784,145 500,714 3,563

Dry Scenario A 1,902,577 572,622 3,323 Dry Scenario B 1,912,366 572,079 3,343

Wet

60% 606,119 287,752 2,106

Dry Scenario A 564,927 324,501 1,741 Dry Scenario B 577,040 324,264 1,780

Performance Index = mean standard deviation

Selection by mean-deviation Tax = 30%

500000 510000 520000 530000 540000 550000 560000 570000 580000

1600000 1700000 1800000 1900000 2000000

Average PV (US$ 103)

Stan

dard

de

via

tion

(U

S$ 10

3 )

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Graph 11

With NPV outcomes approved for all the scenarios under the Analysis of Mean-Standard Deviation through PI, the analysis of outcomes next step is to check the Stochastic Dominance comparing directly distributions through their PDFs and CDFs for each scenario, as long as distributions mean and standard deviation of each alternative, by themselves, do not completely describe them.

The three possible dominance rules that compare PDFs and CDFs for each alternative, according to Mian (2002, p176), are presented below:

Graph 12

The Stochastic Dominance rules can be described in this way: Deterministic Dominance (graph 12.a) - in this case, the corresponding PDFs

and CDFs of the two competitive scenarios do not intersect each other and one of the alternatives has higher mean than the other and therefore its PDFs and CDFs are far right. Therefore this is the dominant alternative. Under this type alternatives are ranked according to their maximum NPV, regardless of decision maker attitude to Risk;

First-Degree Stochastic Dominance (graph 12.b) - here, the PDFs of the two alternatives intercept each other but the corresponding CDFs do not. In this case, the chosen alternative is the one whose dominant CDF is far right and therefore boasts higher economics and lower risk;

Selection by mean-deviation Tax = 60%

280.000 290.000 300.000 310.000 320.000 330.000 340.000 350.000 360.000

480.00 0

500.00 0

520.00 0

540.00 0

560.00 0

580.00 0

600.00 0

620.00 0

Average PV (US$ 103) St

anda

rd de

via

tion

(U

S$ 10

3 )

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No Explicit Dominance (graph 12.c) - occurs when both PDFs and corresponding CDFs of the two alternatives intercept each other. In this case, you must determine the areas between CDFs before and after the intersection points. The dominant alternative is the one with the largest area where its CDF is far right and is therefore the least Risky one.

Afterwards, the scenarios are compared in pairs under each tax level for dominant alternative determination for each one of the two tax rates:

Income Tax = 30%

a) Wet Completion X Dry Completion Scenario "A":

Graph 13

Graph 14

Observations for this case are taken from the two curves above:

PDFs crossed up but CDFs did not => FIRST DEGREE STOCHASTIC DOMINANCE case;

Dry Completion Scenario "A" PDF presents more chances of higher NPV occurrence than Wet Completion PDF;

Dry Completion Scenario "A" CDF, for the same cumulative frequency percentage, presents higher NPV than Wet Completion CDF, therefore Dry Completion Scenario "A" is the LEAST RISKY;

CONCLUSION => Dry Completion Scenario "A" is better.

Wet Completion X Dry Completion Scenario"A" 30% Tax

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NPV (US$ 103)

pdf

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10^-7

Wet Dry Scen. "A" RISKview Trial Version For Evaluation Purposes Only

RISKview Trial Version For Evaluation Purposes Only









Wet Completion X Dry Completrion Scenario"A" 30% Tax

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cdf Wet

Dry Scen. "A" RISKview Trial Version For Evaluation Purposes Only RISKview Trial Version

For Evaluation Purposes Only RISKview Trial Version








For Evaluation Purposes Only

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b) Dry Completion Scenario "A" X Dry Completion Scenario "B":

Graph 15

Graph 16


PDFs and also CDFs crossed up but are practically coincident => case of NO EXPLICIT DOMINANCE;

Dry Completion Scenario "B" presents overall and individually higher NPV and lower standard deviation than Dry Completion Scenario "A", so it is the BEST DETERMINISTIC and the LEAST RISKY option;

CONCLUSION => Dry Completion Scenario "B" is the best option for 30% Income Tax.

Total Tax = 60%

a) Wet Completion X Dry Completion Scenario "A":

Dry Complet ion Scenario"A" X Dry Completion Scenario"B" 30% Tax

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Dry Complet ion Scenario"A" X Dry Completion Scenario"B" 30% Tax

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Graph 17

Graph 18


PDFs and CDFs crossed up => NO EXPLICIT DOMINANCE case;

Wet Completion PDF boasts more chances of higher NPV occurrence than Dry Completion Scenario "A" PDF;

largest curves intersection area occurs in segment where Wet Completion CDF is far more right than Dry Completion Scenario "A" CDF therefore Wet Completion is the LEAST RISKY;

CONCLUSION => Wet Completion is better.

b) Wet Completion X Dry Completion Scenario "B":


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NPV (US$ 103)

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Graph 19

Graph 20


PDFs and CDFs crossed up => NO EXPLICIT DOMINANCE case;

Wet Completion PDF boasts more chances of higher NPV occurrence than Dry Completion Scenario "B" PDF;

largest curves intersection area occurs in segment where Wet Completion CDF is far more right than that Dry Completion Scenario "B" CDF therefore Wet Completion is the LEAST RISKY;

CONCLUSION => Wet Completion is the best option for assumption of 60% Total Tax.

c) Dry Completion Scenario "A" X Dry Completion Scenario "B":

Wet Completion X Dry Completion Scenario"B" 60% Tax

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f Va

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^-6

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Wet Completion X Dry Completion Scenario"B" 60% Tax

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Graph 21

Graph 22


PDFs and also CDFs crossed up but are practically coincident => NO EXPLICIT DOMINANCE case;

Dry Completion Scenario "B" boasts overall and individually higher NPV and lower standard deviation than Dry Completion Scenario "A" therefore it is the BEST DETERMINISTIC and the LEAST RISKY option;

CONCLUSION => Dry Completion Scenario "B" is the second best option for assumption of 60% Total Tax.

Then the final scenarios ranking is defined as below:

Table 9

COMPLETION TAX RANK Average NPV (US$ 103) Standard Deviation (US$ 103) PI Dry Scenario B

30% 1 1,912,366 572,079 3,343

Dry Scenario A 2 1,902,577 572,622 3,323 Wet 3 1,784,145 500,714 3,563 Wet

60% 1 606,119 287,752 2,106

Dry Scenario B 2 577,040 324,264 1,780 Dry Scenario A 3 564,927 324,501 1,741

Dry Completion Scenario"A" X Dry Completion Scenario"B" 60% Tax

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NPV (US$ 103)

cdf Dry Scen."A"

Dry Scen."B" RISKview Trial Version











Dry Completion Scenario"A" X Dry Completion Scenario"B" 60% Tax

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f Va

lue

s x

10^-6

Dry Scen."A" Dry Scen."B" RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only RISKview Trial For Evaluation Purposes Only

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The selection between the two tax rates depends on strategic and conservative policies applied by the company when evaluating its project portfolio which demands specific analysis.

5-CONCLUSIONS

Considering the decision making methodology that this work proposed to develop, we can conclude that supportive tools application in this task improves the decision making process, particularly in oil and gas business management context.

The combination of experts knowledge and tools application provides a considerable improvement on the understanding level of possible scenarios for the analysis undertaken by the decision maker. Under this perspective by application of mentioned before techniques, analysis improvement was attained.

Outcomes for 30% tax rate showed that the Dry Completion Project economic yields are better than that of Wet Completion Project ones as long as the former project higher production overcomes the higher CAPEX demanded by the Wet Completion one besides its very positive outcome contribution, considering that OPEX variable influence was found to be irrelevant for this comparison.

Otherwise, as verified in the Sensitivity Analysis under 60% tax rate the Wet Completion Project boasts better cash flow indicators than that of Dry Completion one because former technique outcomes are less sensitive to higher tax rates which have a more negative impact on the lower NPV determined to Wet Completion Project cash flow.

For any tax rate, the Dry Completion Scenario "B" Project outcomes are better than that presented by the Dry Completion Scenario "A" one as long as the technical configuration of injection wells connected to FPSO, which occurs in former alternative, reduces directional drilling cost and also Mini-TLP size. Therefore, Scenario "B" lowest CAPEX contributes more positively to a better outcome than that one from Scenario "A".

Thus we can say after those alternative technological scenarios comparing that introducing Risk to the model through relevant variables values mean dispersion, it was not enough to change deterministic outcomes.

6-BIBLIOGRAPHY

COSTA, Ana Paula A. Quantificao do Impacto de Incertezas e Anlise de Risco no Desenvolvimento de Campos de Petrleo. 2003. D.Sc. thesis. Faculdade de Engenharia Mecnica. Universidade Estadual de Campinas.

DALLOLIO, Valrio Machado. Anlise de Viabilidade Econmica de Projetos. 2006. Paperback of MBA in Oil and Gas Management course. Capital Humano Fundao Getlio Vargas. Niteri.

HOBOKEN, New Jersey/2001. Ed. John & Sons Inc.

JACINTO, Carlos Magno Couto. Workshop on Reliability and Risk Analysis in Well Technology Enginering. PETROBRAS / CENPES / PDP / TEP. Rio de Janeiro.

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MIAN, M.A. Project Economics and Decision Analysis Volume II: Probabilistic Models. Tulsa, Oklahoma: PennWell, 2002.

MOCH, Stephen J.; KUNEUTHER, Howard C. Wharton on Making Decisions: Stephen J. Moch, Howard C. Kunreuther. The Wharton School with Robert E. Hunther.

PALISADE Corporation. Guide to using @RISK. Risk Analysis and Simulation Add-In for Microsoft Excel. Version 4.5. 2002. Newfield, New York.

RIGLOGIX. Weekly Offshore Rig Review: Day Rate Driver. Houston, Texas. 15/11/2007. Disponvel em www.rigzone.com.

SAMANEZ, Carlos Patrcio. Matemtica Financeira. 3 ed., So Paulo: Pearson-Prentice Hall, 2005.

SENE, Eustquio de; MOREIRA, Joo Carlos. Espao Geogrfico e Globalizao. Ed. Scipione, 1997.

STEAGALL, Daniel Escobar. Anlise de Risco nas Previses de Produo com Simulao Numrica de Fluxo Exemplo de um Campo na Fase de Delimitao. 2001. M.Sc. thesis. Faculdade de Engenharia Mecnica. Universidade Estadual de Campinas.

ABOUT THE AUTHORS:

Bastos, Glucio A. has MBA title from Rio de Janeiro Fundao Getlio Vargas (FGV) where has just finished an Oil and Gas Management Course, and a Chemical Engineering degree from Rio de Janeiro Federal University (UFRJ) and a post-graduate diploma from UFRJs Coordination of Engineering Post-Graduation Programs (COPPE/UFRJ). He is a former PETROBRAS trader.

Jacinto, Carlos Magno C. is an Electrotechnical from CEFET - Campos, Graduate in Economics from Universidade Federal Fluminense, Master in Production Engineering in the area of Quantitative Methods, Fluminense Federal University and PhD in Civil Engineering, in the area of Computer Systems for the Coordination of Postgraduate Programs Engineering ( COPPE ), Federal University of Rio de Janeiro. Member of the SRA - Society of Risk Analysis and Society of Petroleum Engineers SPE. He is currently Professor of Petrobras University School of Science and Technology Exploration and production coordinator of the research group in Reliability Engineering and Risk Analysis in Engineering from Wells CENPES - Centre for Research and Development of Petrobras. Acts as a collaborator / researcher of the research group CEERMA - Centre for Studies and Essays in Risk and Environmental Modeling UFPe and research group ARCADE - Risk Analysis , Reliability and Decision Support UFF. Has experience in Modeling, Simulation and Optimization Systems, working mainly in Risk Analysis and Reliability Engineering in the Area of Oil and Gas.

Torres S.Jr., Flodoaldo has MBA title from Rio de Janeiro Fundao Getlio Vargas (FGV) where has just finished an Oil and Gas Management Course.

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