IPA - Monte Carlo Simulation

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IPA 90-227

PROCEEDINGS INDONESIAN PETROLEUM ASSOCIATION

Nineteenth Annual Convention, October

199

RISK ANALYSIS AND ECONOMIC EVALUATION IN CAPITAL INVESTMENT

USING SIMULATION TECHNIQUES

Hayu Susilo Prabowo

Entang H adisasmita

ABSTRACT

Calculating prospect or well economics and using

profitability criteria is a step that decision makers have

taken. These criteria provide a measure of prospect or

well profitability such as rate of return, payout time,

and net present value. When the analyst has a good

understanding of his data and there is very little

uncertainty, these profitability indicators can be used

confidently to evaluate and select prospects or projects.

However, when uncertainties in the data are high, these

criteria are a poor measure of investment worth because

they do not provide a quantitative estimate of the

chance or probability of achieving a certain economic

value. The next logical step in evaluating investments

which exhibit a high degree of uncertainty in the data is

to add statistical techniques to the evaluation process.

Simulation techniques provide an extremely yet

conceptually straightforward, way to solve problem.

The basic idea of simulation consists of building a

conceptual model of the uncertain real-world problem

being studied combined with economic sensitivity

analyses to produce a statistical estimate of the

profitability of a certain prospect or project. The results

of a simulation study, a profit distribution, provide a

convenient way to graphically portray to management

all of the possible outcomes of the decision option.

ENGINEERING ECONOMIC YARDSTICK

The engineering appraisal must provide answers

to

at

least several questions. Is the rate of cash flowback

commensurate with ,the needs of the investor and the

risk involved? How long will it take to return the capital

invested? How much new capital will the investment

generate? What is the probability of a successful

investment?

Virginia Indonesia Company

Rate of return calculation basically provides the answer

for the first question. It uses generally accepted time

value of money concepts and provides numbers for use

in the inevitable judgement decision.

Payout time

of

a project

is

defined as the length of the

time required to receive accumulated net revenues

equal to investment. It tells the decision maker nothing

about the rate of earnings after payout time and does

not consider the total profitability of 'the investment

opportunity. Consequently, it is not a sufficient

criterion in itself to judge the worth

of

an investment.

The profit criterion of net present value is similar to rate

of return except that a single, previously specified

discount rate is used for all economic analyses. The

single discount rate is usually called the average

opportunity rate, and presumably represents the

average earnings rate at which future revenues can be

reinvested.

All of the measures of investment worth that have been

discussed thus far are no-risk parameters. That is they

do not include explicit statements about the degree of

risk of the investment. The need to consider risk arises

whenever more than one outcome

is

possible from a

given investment decision. Drilling investments certain-

ly fall into this category. In fact, we will see that risk is

probably the most critical factor in the decision.

RISK ASSESSMENT

Decision to invest capital in oil business always include

risk and uncertainty. Virtually all of . the important

investment decisions we have made over the years were

taken without knowing exactly what the outcome would

be in terms of profits and losses. In most of these

decisions the element of risk was probably recognized,

but the usual practice has been to more or-less account

for risk by adjectives and/or use

of

minimum acceptable

profitability levels (rates of return, payout, etc.) where

© IPA, 2006 - 19th Annual Convention Proceedings, 1990sc Contents

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the profit criteria were computed on a no-risk basis as if

the well were certain to be a discovery.

Typically, we computed rates of return to four or five

place accuracy and then assessed risk with an adjective.

These rather intuitive approaches for decision making

have been, in general, acceptable because historically

th e level of risks and capital requirements have been

relatively low.

But times have changed. The shallow, easy-to-define

structures have all been drilled, and we are now

exploring for oil and gas in deeper, harder-to-find types

of traps involving much greater levels of risk and

uncertainty.

The risks associated with estimating the reserves and

value of a hydrocarbon-producing property are divided

into three classifications: technical, economic, and

political (Garb 1988). Technical uncertainty relates to

whether the hydrocarbon volumes estimated by the

geologists and engineers do exist in the ground and

whether the reserves and recovery rates will be as

projected by the engineers. Technical risk is a function

of howloqg the property has produced and the maturity

and quality of the data base from which the reserves

determinations were developed. The reserves estimates

depend strongly on the accuracy of measurements from

tools that too frequently are inexact. Log- determined

porosity, for example, might truly measure the pore

space of a rock, yet today's logs cannot identify how

much of that pore space is interconnected.

Until recently, technical uncertainties (oil and/or gas,

area, porosity, net pay thickness, water saturation,

producing mechanism, recovery factor

,

and producing

rate) dominated the concern of our industry, and

determination of the technical uncertainty factor was

usually assigned to engineers and geologists.

In the economic risk, prices can effect the uncertainty in

an exploitation property and also capital and operating

costs. Inflation and interest rates on borrowed capital

also add to our uncertainty. Last, but not least, there is

the subject of market. Even though we may have

substantial reserve and are willing to sell it at a going

price, if there is no market

or

if the market is extremely

variable, it is difficult to account for the value of money

received in the future during an evaluation process.

Political uncertainty includes local and national taxes,

environmental regulations, and global concerns, e.g.

international instability that could disrupt imbalance

levels of imports. Efforts to, stabilize oil prices may take

on the form of import taxes or OPEC production

regulations. If a property being studied is an

international one, there is further political risk of

nationalization, operational restrictions, a royalty

revision, or political unrest in the host country.

In sutnmary (Fig. l ) , the total uncertainty factor is the

product of the technical, economic, and political

subfactors. The future net revenue one might project

from production of the estimated reserve must be

reduced to an expected value using the total uncertainty

factor.

ECONOMIC SIMULATION

MODELS

In 1964, Hertz proposed a new approach for the

analysis of risk in capital expenditures called economic

simulation. Within a few years numerous articles began

to appear

on

the application of simulation to many

diffwnt types of petroleum engineering and explora-

tion analyses. Today the method is being used to

analyze risk and uncertainty on major decisions

(offshore bids, decisions to go into new exploration

areas, etc.) by virtually all major oil companies. And as

more engineers, geologists, and management become

aware of its logic and application, the method is gaining

increasing use in day-to-day decision analyses.

Briefly stated, simulation allows the analyst to describe

risk and uncertainty (variability) in the form of a

probability distribution of possible values each random

variable could have. The simulation method of risk

analysis (Fig.

2)

can be described best as a sequence of

steps (Newendorp 1967).

1 .

Estimate the range and distribution of possible values

of each independent variable that will affect ultimate

profitability. This may require the judgement of

several people-the geologist, engineer or economist.

Each would describe the distribution of the variable

about which he is the almost knowledgeable.

2.

From the distributions of each variable select at

random one value. Compute the profitability

(discounted net profit, for example) using this

combination of variables. This determines one point

in the final distribution of probability. Select at

random a second value from the distribution of each

variable. Again compute the resulting profitability.

This is a second point in the distribution of

profitability.

3.

Repeat the process again and again, each time with a

set of values selected at random from the distribution

of each variable. Enough combinations of variables

should be considered to describe adequately the

shape and range of the distribution of ultimate

profitability.


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4. The ultimate profitability data can be arranged to

determine the probability of obtaining various ranges

of net profit. Or, by re-arranging on a cumulative

frequency basis, the probability of obtaining at least

a specific profit can be determined.

A random variable is here defined as a parameter

affecting ultimate net profit whose exact numerical

value cannot be measured or determined at the time of

the analysis. Examples in drilling prospect analysis

include net pay thickness, initial well potentials, drilling

costs, future prices, etc. These factors all have an

important bearing on profit, but we are not able to

determine their exact numerical value at the time the

decision is being made to drill the prospect. Simulation

is a useful evaluation tool in these instances because the

entire range of possible values for these parameters can

be included in the analysis of ultimate net profit of the

investment.

The critical step in simulation is to identify the key

variables in the evaluation. Most articles recommend o r

imply that we should describe a distribution for each

random variable, and sample from each distribution

separately on each pass. The mistake here is that

sampling each distribution separately on each pass

implies that each variable is independent of all other

random variables. In the analysis of drilling prospects,

however, there are several important random variables

which are not independent of one another. Available

statistical tests should be used on a data sampling to

determine whether the variables are independentie.,

do not depend on one of the other variables being

analyzed. Testing for independence is usually per-

formed by plotting one variable vs. another (Fig. 3) .

Totally independent variables will show a complete

random scatter when so plotted. Completely dependent

variables will form a well-defined trend. Partially

dependent variables will plot in an envelope, having

some scatter but showing definite trend. One of the

easiest ways to treat dependent variables is to integrate

them where possible into a single independent building

block.

A

distribution of recovery factor, for instance,

may intergrate oil gravity, porosity, water saturation,

viscosity, and gas in solution.

It is usual to array the raw data in some manner and to

reduce the amount of raw data, if combersome, so that

they may be segmented into representative groups. A

typical arrangement would be ascending order, with the

total array divided into sufficient groups, so that the

same practical statistical results can be achieved with

the classes instead of with each data point separately.

When classes have been selected and the ordered

elements divided into classes, a frequency curve and a

cumulative frequency curve can be prepared. This is a

plot of the percentage of all observations possesing

values greater or less than the class boundary vs. the

class mark for that group. This distribution curve

defines -the population of that variable.

If insufficient data are available to develop a distribu-

tion curve from historical information, one is forced to

use a standard curve found from past experience to

represent the variable being studied. Types of

probability distributions frequently used in petroleum

engineering are symmetrical or bell- shaped curves;

skewed curves, either positively or negatively shifted;

and rectangular curves, where no central tendency is

observed. A simple way of establishing a distribution

curve, if none are available, is to use probability paper.

Maximum and minimum values may be plotted on the

probability paper at 99 and 1 cumulative percentile

points, respectively, and draw a stright line connecting

these points used to define a distribution. If most likely

values have been estimated, triangular distributions can

be processed into a cumulative distribution curve by

simple equations (Garb 1988).

When the variables have been ordered, studied, and

analyzed into distribution curves and profiles, the data

can be entered into the model chosen. After execution

of the model, the answers should be compared with

reality to ensure the success is no better than observed

in the past. Finally, the results of a probabilistic study

should be expressed in the form of a frequency distri-

bution.

FIELD CASE

Virginia Indonesia CompanyRertamina operate two

major gas fields in East Kalimantan. The Badak and

Nilam gas fields provide an average daily supply of over

1.0 BSCF to the Bontang

LNG

plant and Kaltim

Fertilizer plants. The peak demand of the process

facilities is in excess of 1.6 BCSF.

In this dynamic environment, field development

planning must consider deliverability requirements to

assure steady gas supplies are maintained and efficient

utilization of gas reserves is achieved. The gas reserves

in these fields are found in a geologically complex

deltaic environment. There are in excess of

3

individual gas reservoirs, ranging in depth from

4,000

feet to 15,000 feet. The reservoirs have a wide range of

size, permeability, and compositional make-up.

The stratified nature of the fields results in a lesser cost

development approach of producing the deeper

reservoirs during the early years and the shallow

reservoirs in the later years. This approach is referred to

as a bottoms-up depletion strategy. The advantages


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412

of this approach are: (1) through the life of the field, a

single wellbore can be\ used for depletion of several

reseqvoirs; and 2) diilling hazards in the later life of the

field are minimized. The disadvantages of this approach

are that the deeper reservoirs tend to have lower

productivity and poor sand continuity development,

and therefore, the development of these reservoirs

require high capital investment besides having a high

degree of uncertainty.

To meet the peak demand conditions, drilling to

shallow (A, B, C, D E) , high permeability, zones

provides gas deliverability at minimum cost, on

condition that there are some deviation in reservoir

management and possible drilling hazards in the later

life of the field.

Many of wells drilled earlier are not deep enough to

penetrate the F,

G,

and H sands. The few wells that

have penetrated these deeper sands indicate that

substantial gas reserves are present.

To demonstrate the economic simulation techniques in

evaluating uncertainties of drilling one well completed

as a dual gas commingle completion (4 perforated

reservoirs in one well) in the deeper zones, a very

simple simulation procedure was developed. Maximum

and minimum values for gas reserves, initial gas

production rate, and drilling cost were entered with the

most likely values to establish a triangular distribution

of each variable, exponential decline curve was then

applied assuming economic limit rate of 300 MCFD.

Other variables, such as: gas price, operating

expenditure, etc. are considered constant. Then a

number of simulation passes were done, randomly

solving for Rate Of Return and Net Present Value at

20% discount rate. Based on this set of executions,

assuming maximum, minimum, and most likely values

as shown in Table 1, the distribution of answers resulted

in the profit distribution curves (Fig. 4).

The two curves show probability distribution of NPV at

20% discount rate and ROR of the company share.

Both curves were constructed from the results of the

5000 cases run in the simulation.

It shows that the range of possible expected results from

-1

to 2.5 million dollars of NPV at 20 discount rate

and -67% to 23,375% of ROR.

Further examination on the cumulative probability

distribution curve indicates that there is a 90% chance

the drilling program will result in a significant financial

profit.

The simulation analysis provides an important piece of

information that single-case calculations lack: the range

of uncertainty of the answer with respect to the

expected ranges of variables that determine the answer.

CONCLUSIONS

The advantages of the economic simulation analysis in

capital investment decisions presented in this paper may

be summarized as follows:

1. The simulation techniques offers additional

information of the probabilities of occurance of each

level of profit.

2.

Each variable affecting ultimate profitability is

systematically combined into a single probabilistic

description and all of the possibility combinations of

outcomes are included in the analysis.

Hertz summarized the problem very well with the

observation that,

” ..

the courage to act boldly in the

face of apparent uncertainty can be greatly bolstered by

the clarity of portrayal of the risks and possible

rewards.” The method described herein appears to be a

significant step towards this objective.

ACKNOWLEDGMENTS

The authors wish to express their appreciation to the

management of Virginia Indonesia Company and

Pertamina-BPPKA for their permission to publish this

paper.

REFERENCES

Garb, F.A. 1988. Assessing Risk in Estimating

Hydrocarbon Reserves and in Evaluating Hydro-

carbon-Producing Properties. J Pet. Tech. June,

765-778.

Hertz, D.B. 1964. Risk Analysis in Capital Investment.

Harvard Business Review.

Newendorp, P.D. 1975. A Method for Treating

Dependencies Between Variables in Simulation Risk

Analysis Models. paper

SPE

5581.

Newendorp, P.D. and Root, P.J. 1967. Risk Analysis in

Drilling Investment Decisions. paper

SPE

1932.

Newendorp, P.D. and Campbell, J.M. -1970. Decision

Methods For Petroleum Investments. John

M

Campbell and Company Norman, Oklahoma.

Surtiwa and Ellenberger, C.W. 1986. Depletion

Planning for Multi-Reservoir Gas Fields: Development

Techniques and Technical Challanges. Proceedings of

Fifteenth Annual ZP Convention.


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413

TABLE

1.

Data Used

in

The Example

Min. Most Likely

Max

Init. Prod, Rate, MMCF/D 0.0

1

o 2.0

Gas Reserves, BCF 0.0 1

o

4.0

Drilling Cost, Million$ 3.0

3 . 3

4.0

Condensate Yield = 10 bbl/MMCF

Gas Price

= $ 2.2YMCF

Cond. Price

=

$ 18.07/bbl

Operating Cost Gas = $ O.OS/MCF

Operating Cost Cond. = $ 1.23/bbl


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PRODUCTION PROFILE GENERATOR

UNCERTAIN VARIABLES

I

FIXED VARIABLES

i RGIP DRL OST

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GAS

PRICE

OPERATING COSTS

CND. PRODUCTION

RANDOM NUMBERS GENERATOR

\

ECONOMIC EVALUATION

FREQUENCY DISRIBUTION

\I/

RESULTS

FIGURE - Schematic for economic simulation through value.


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