Wet x dry completion with risk

2

WET AND DRY COMPLETION PROJECTS COMPARISON THROUGH RISK CASH FLOW ANALYSIS

Authors: BASTOS, Gláucio 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.

3

September, 2008.

SUMMARY

1-INTRODUCTION 03

2-METHODOLOGY 03

3-APPLICATION 04

3.1-Basis Development 04

3.2-Deterministic Analysis 06

3.3–Sensitivity Analysis 08

3.4–Probabilistic 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.

5

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$ 10

3) 132,535

seismic 3,000

exploration and appraisal 121,470

others 8,065 Wells development (US$ 10

3/well) 59,631

drilling 39,754

completion 19,877

Table 1

3.1–Basis Development

The specific data to each scenario may thus be compared:

6

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$ 10

3) 2,478,668

Table 2

DRY COMPLETION – Scenario “A”



TLWP 400,000

FPSO 800,000

pipes 150,500



3) 2,268,524

Table 3

DRY COMPLETION – Scenario “B”

Subsea equipment (US$ 103/well) 16,000



TLWP 300,000

FPSO 800,000

subsea 100,800

pipes 150,500



3) 2,404,270

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

7

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

Bo

e

Production FPSO + TLWP

0

10

20

30

40

50

60

70

80

90

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

Year

M

Bo

e

8

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$ 10

3)

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

9

Graph 5

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

3.3–Sensitivity 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$10

3/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

10

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)


• 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



• Year 3



• Year 4



e) Total investment in wells development, according to Costa (2003, p102)


• 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:

11

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

12

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)

TO

TA

L IN

VE

ST

ME

NT

IN

W

EL

LS

D

EV

. (

10

3 U

S$/W

EL

L)

13

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$ 10

3) 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)

Sta

nd

ard

devia

tio

n (

US

$ 1

03)

14

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)

Sta

nd

ard

de

via

tio

n (

US

$ 1

03)

15

• 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

0

1

2

3

4

5

6

7

8

9

0 0,5 1 1,5 2 2,5 3 3,5

NPV (US$ 103)

pdf

Valu

es x

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

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9

1

0 0,5 1 1,5 2 2,5 3 3,5 NPV (US$ 10

3)

cdf Wet

Dry Scen. "A" RISKview Trial Version











16

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

0

1

2

3

4

5

6

7

8

0 0,5 1 1,5 2 2,5 3 3,5

NPV (US$ 103)

pd

f V

alu

es

x 1

0^-

7

Dry Scen."A" Dry Scen."B"

RISKview Trial Version For E valuation Purposes Only










Dry Complet ion Scenario"A" X Dry Completion Scenario"B"

30% Tax

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9

1

0 0,5 1 1,5 2 2,5 3 3,5 NPV (US$ 10

3)

cdf Dry Scen."A"

Dry Scen."B" RISKview Trial Version











17

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":


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

cdf Wet

Dry Scen."A" RISKview Trial Version












0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

pdf

Valu

es x

10^-

6

Wet Dry Scen."A"

RISKview Trial Version For Evaluation Purposes Only










18

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

0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

pdf

Valu

es x

10^-

6

Wet Dry Scen."B"











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

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

cd

f Wet Dry Scen."B"











19

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$ 10

3) 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

0

0,1

0,2 0,3

0,4

0,5

0,6 0,7

0,8

0,9 1

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

cd

f Dry Scen."A" Dry Scen."B"











Dry Completion Scenario"A" X Dry Completion Scenario"B"

60% Tax

0

0,2

0,4

0,6

0,8

1

1,2

1,4

-0,4 -0,2 0 0,2 0,4 0,6 0,8 1 1,2 1,4

NPV (US$ 103)

pd

f V

alu

es x

10^

-6

Dry Scen."A"

Dry Scen."B" RISKview Trial

For Evaluation Purposes Only RISKview Trial










20

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. Quantificação do Impacto de Incertezas e Análise de Risco no

Desenvolvimento de Campos de Petróleo. 2003. D.Sc. thesis. Faculdade de Engenharia

Mecânica. Universidade Estadual de Campinas.

DALLOLIO, Valério Machado. Análise de Viabilidade Econômica de Projetos. 2006.

Paperback of MBA in Oil and Gas Management course. Capital Humano – Fundação Getúlio

Vargas. Niterói.

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.

21

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.

Disponível em www.rigzone.com.

SAMANEZ, Carlos Patrício. Matemática Financeira. 3ª ed., São Paulo: Pearson-Prentice

Hall, 2005.

SENE, Eustáquio de; MOREIRA, João Carlos. Espaço Geográfico e Globalização. Ed.

Scipione, 1997.

STEAGALL, Daniel Escobar. Análise de Risco nas Previsões de Produção com Simulação

Numérica de Fluxo – Exemplo de um Campo na Fase de Delimitação. 2001. M.Sc. thesis.

Faculdade de Engenharia Mecânica. Universidade Estadual de Campinas.

ABOUT THE AUTHORS:

Bastos, Gláucio A. has MBA title from Rio de Janeiro Fundação Getúlio 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 UFRJ’s 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 Fundação Getúlio Vargas

(FGV) where has just finished an Oil and Gas Management Course.

Wet x dry completion with risk

Business

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