MAKING DECISION IN OIL FILED

VIETNAM NATIONAL UNIVERSITY, HO CHI MINH CITY
HO CHI MINH CITY UNIVIERSITY OF TECHNOLOGY
FACULTY OF GEOLOGY & PETROLEUM ENGINEERING


Petroleum Project
MAKING DECISION IN OIL FIELD LIFE CYCLE

Advisor. Assoc. Prof. Dr. Tran Van Xuan
Students. Nguyen The Vinh 31204550
Bui Nhat Thinh

31203602

Project Committee :
1.
2.
3.
4.

..........................................................................
..........................................................................
..........................................................................
..........................................................................

HCMC, 1/2016
ACKNOWLEDGEMENT


Petroleum Project

Making Decision In Oil Field Life Cycle

This project consumed huge amount of work, research and dedication. Still,
implementation would not have been possible if we did not have a support of many
individuals. Therefore we would like to extend our sincere gratitude to all of them.
First of all we are sincerely grateful to Assoc. Prof. Dr Tran Van Xuan for provision
of expertise, and technical support in the implementation. Without his superior
knowledge ,experience and uncountable enthusiasm, the paper would like in quality
of outcomes, and thus his support has been vital.
We also give our thanks to Department for assistance with lots of precious
document which moderated this paper and in that line improved the manuscript
significantly.
Nevertheless, we express our gratitude toward our families and my friends for their
kindness and encouragement which help us in completion of this project.

ABSTRACT
22


Petroleum Project

Making Decision In Oil Field Life Cycle

The research presented in this project is about how to make a decision in phases of
the field life cycle. Making a good decision is extremly important, because it will
make profit, reduce the risk and uncertainty, it is safe for people and enviromenet.
There are many factors that impact to the decison such as the data of discovery,
exploration, appraisal, the reserves can recover from the reservoir, the economic of
exploration, appraisal, and development; and the affect to enviroment. Decison
maker need to analysis each factors and their risks an uncertainties to choose the

best choice for the project.
In this project, we present four problems. Firstly, the study indentifies what is
decision analysis, the tools are used to make decision, and sensitive analysis.
Secondly, we present the technique fators that influence

making decision –

reserves. Thirdly, we analyze economic factors impact to the decision in oil filed,
especially the exploration and appraisal phase. And the last, we introduce the EIA
report.

33


Petroleum Project

Making Decision In Oil Field Life Cycle

CONTENTS

LIST OF TABLES

44


Petroleum Project

Making Decision In Oil Field Life Cycle

LIST OF FIGURES

ABBREVIATION
AV
Annual Value
DA
Desicion Analysis
EV
Expected Value
EMV
Expected Monetary Value
GIIP
Gas Initial In Place
MMstb
Million Stock Tank Barrel
OPEX
Operating Expenditure
POS
Probability of success
RF
Recovery Factor
UR
Ultimate Recovery

CAPEX
Capital Expenditure
DCF
Discounted Cash Flow
EVPI
Expected Value of Perfect Information
FV

Future Value
HCIIP
Hydrocarbon Initially In Place
NPV
Net Present Value
PDF
Probability Density Function
PV
Present value
STOIIP
Stock Tank Oil In place

55


Petroleum Project

Making Decision In Oil Field Life Cycle

66


Petroleum Project

Making Decision In Oil Field Life Cycle

CHAPTER 1 DECISION ANALYSIS
1.1.

Decision Analysis (DA)

1.1.1. Definition of Decision Analysis (DA)

The historical origins of decision analysis can be partially traced to
mathematical studies of probabilities in the 17th and 18th centuries by Pascal,
Laplace, and Bernoulli. However, the applications of these concepts in business and
general management appeared only after the Second World War (Covello and
Mumpower, 1985; Bernstein, 1996). The problem involving decision-making under
conditions of risk and uncertainty has been notorious from the beginnings of the oil
industry. Early attempts to define risk were informal.
Decision Analysis a methodology based on a probabilistic framework which
facilitates high-quality, logical discussions, leading to clear and compelling actions.
Decision analysis is a scientific and practical method for making important
decisions. It was introduced in the 1960s. Decision analysis involves identification,
clear representation, and formal assessment of important aspects of a decision and
then determination of the best decision by applying the maximum expected value
criterion. Decision analysis is suitable for a wide range of operations management
decisions where uncertainty is present. Among them are capacity planning, product
design, equipment selection, and location planning.
1.1.2. Reasons to use Decision Analysis
DA is a prescriptive approach, based on Decision Science, aimed at helping
people to deal effectively and consistently with difficult decisions. Carefully
applying DA techniques will lead to better decisions and better outcomes
DA is an information source, providing insight about the situation, uncertainty,
objectives and trade-offs.which should help the decision-maker arrive at a
compelling to avoid procrastination and “paralysis by analysis”
1.1.3. Procedure of DA
Determine the goal or objective, e.g., maximize expected profit or net present
value, or minimize expect cost.

77

Petroleum Project

Making Decision In Oil Field Life Cycle

Develop a list of possible alternatives for the decision in order to achieve the
goal.
Identify possible future conditions or states of nature for each random variable
(e.g.,demand will be low, medium, or high; the equipment will or will not fail; the
competitor will or will not introduce a new product) that will affect the goal.
Determine or estimate the payoff (or value) associated with each alternative for
every possible future condition.
Estimate the likelihood of each possible future condition for each random
variable. Evaluate the alternatives according to the goal or decision criterion, and
select the best alternative.
1.1.4. Advantages of DA
- DA has many advantages , such as its comprehensiveness and vitality as a
model of the decision and its ability to place a dollar value on uncertainty.
- DA offers the operations research profession the opportunity to extend its
scope beyond its traditional primary concern with repetitively verifiable operations.
- DA encourages meaningful communication among the members of the
enterprise because it provides a common language in which to discuss decision
problems. Thus, engineer and marketing planners with quite different jargons can
appreciate one another’s contributions to a decision. This is the most important
advantage of DA.
1.1.5. Ability of a good decision maker
- Separating the actual problem from its symptoms.
- Clearly articulating the problem to others.
- Knowing the timing and sequence of decisions which must be made.

- Knowing what objectives the decision is designed to achieve, & their relative
importance.
- Using limited information and managing uncertainty effectively.
- Understanding the true risk and consequences.
- Identifying opportunities and creating alternatives.
88


Petroleum Project

Making Decision In Oil Field Life Cycle

- Dealing with complexity and ambiguity.
1.1.6. Apppplication of DA in life
Decision analysis is widely used in business and government decision making.
Following is a non-exhaustive list of most common applications:
Business
- Airline and hotel yield management
- Oil exploration
- Quality assurance and control
- Reliability and maintenance
- Crop protection
- Credit and loan portfolio management
- Project selection
- New product development
- New venture launching
Government
- Emergency management
- Environmental risk management
- Choice of new energy sources

- Research and development programs
Common
- Medical diagnosis and treatment
- Bidding and negotiation
- Litigation

99


Petroleum Project

1.2.

Making Decision In Oil Field Life Cycle

Decision analysis tool
1.2.1. Decision tree

A decision tree is a graphical representation of the decision variables, random
variables and their probabilities, and the payoffs. The term gets its name from the
tree like appearance of the diagram
Decision trees are particularly useful for analyzing situations that involve
sequential or multistage decisions. For instance, a manager may initially decide to
build a small facility but she has to allow for the possibility that demand may be
higher than anticipated. In this case, the manager may plan to make a subsequent
decision on whether to expand or build an additional facility.
A decision tree is composed of a number of nodes that have branches
emanating from them. Square nodes denote decision points, and circular nodes
denote chance events. Read the tree from left to right. Branches leaving square
nodes represent alternatives; branches leaving circular nodes represent the states of

nature.
After a decision tree has been drawn and necessary data are determined, it is
analyzed from right to left; that is, starting with the last decision that might be made
it is “rolled” back. For each decision, choose the alternative that will yield the
greatest return (or the lowest cost). For each chance node, calculate the expected
value of the payoffs of its states of nature. If chance events follow a decision,
choose the alternative that has the highest expected value (or lowest expected cost).
The dollar amounts at the branch ends indicate the estimated payoffs if the
sequence of decisions and chance events occurs. For example, if the initial decision
is to build a small facility and it turns out that demand is low, the payoff will be $40
(thousand). Similarly, if a small facility is built, and demand turns out high, and a
later decision is made to expand, the payoff will be $55. The figures in parentheses
on branches leaving the chance nodes indicate the probabilities of those states of
nature. Hence, the probability of low demand is 0.4, and the probability of high

1010


Petroleum Project

demand

is

Making Decision In Oil Field Life Cycle

0.6.

Payoffs

in

parentheses

indicate

losses

Figure 1.1. Decision tree fot a facility building
Analyze the decisions ( Figure 1.1) from right to left:
1. Determine which alternative would be selected for each possible second
decision. For a small facility with high demand, there are two choices: do nothing,
or expand. Because expand has higher payoff, you would choose it. Indicate this by
placing a double slash through do nothing alternative. Similarly, for a large facility
with low demand, there are two choices: do nothing or reduce prices. You would
choose reduce prices because it has the higher expected value, so a double slash is
placed on the other branch.
2. Determine the product of the chance probabilities and their respective
payoffs for the remaining branches:
+ Build small
Low demand .4($40) = $16
High demand .6($55) = 33
+ Build large
Low demand .4($50) = 20
High demand .6($70) = 42
3. Determine the expected value of each initial alternative:
1111


Petroleum Project

Making Decision In Oil Field Life Cycle

Build small $16 + $33 = $49
Build large $20 + $42 = $62
Hence, the choice should be to build a large facility because it has higher
expected value than the small facility.
1.2.2. Influence Diagram
Influence diagrams (see example below) can graphically represent complex
decision problems that have many random variables (chance events) and one or
more decision variables.
Influence diagrams are more concise than decision trees because they do not
show the alternatives branches coming out of the decision nodes and the states of
nature branches coming out of the chance nodes.
Constructing and validating an influence diagram improves communication and
consensus building at the beginning of the decision modelling process.
The following is an example of the influence diagram representing the decision
of whether or not to introduce a new product.
The green circles show the random variables (chance events) and the rounded
yellow squares show the payoff or part of it. This influence diagram for a new
product decision also involves a pricing decision.
The uncertainties (i.e., random variables) are units sold, which are affected by
the pricing decision, fixed cost, and variable cost. Profit is the ultimate payoff,
which is influenced by the total cost and revenue.

1212


Petroleum Project

Making Decision In Oil Field Life Cycle

Figure 1.2. Influence Diagram of introducing a new product
1.3.

Expected Value (EV)
1.3.1. Calculation Expected Value (EV)

The expected value for an uncertain alternative is calculated by multiplying
each possible outcome of the uncertain alternative by its probability, and summing
the results. The expected value decision criterion selects the alternative that has the
best expected value. In situations involving profits where “more is better" the
alternative with the highest expected value is best, and in situations involving costs,
where “less is better" the alternative with the lowest expected value is best.
Example : ( Figure 1.3)
Product decision. The expected values for the Special Instrument Products
decision are designated by “EV" . These are determined as follows:
1) For the temperature sensor alternative:

0,5 x $900.000 + 0,5 £ ( - $100.000) = $400.000,

1313


Petroleum Project

Making Decision In Oil Field Life Cycle

2) for the pressure sensor alternative:
0,8 x $390.000 + 0,2 x ( - $10.000) = $310.000

3) for doing neither of these $0.
Thus, the alternative with the highest expected value is developing the
temperature sensor, and if the expected value criterion is applied, then the
temperature sensor should be developed.

Figure 1.3. Expected value decision in developing temperature sensor
1.3.2. Expected value of perfect information (EVPI)
Expected value of perfect information (EVPI) is the difference between the
expected payoff with perfect information (under certainty) and the expected payoff
without the information (under risk).
Expected value of perfect information = Expected payoff under certainty Expected payoff under risk

1414


Petroleum Project

Making Decision In Oil Field Life Cycle

Table 1.1. Possible future demand in building a facility
Alternatives
Small facility
Medium facility
Large facility

Low
$10*
7
(4)

Possible Future Demand
Moderate
$10
12
2

High
$10
12
16

Using the expected value method, identify the best alternative for the following
payoff table ( Table 1.1) given these probabilities for demand: low = .30, moderate
= .50, and high = .20
Find the expected value of each alternative by multiplying the probability of
occurrence for each state of nature by the payoff of the alternative for that state of
nature and summing them:
EVsmall = .30($10) + .50($10) + .20($10) = $10
EVmedium = .30($7) + .50($12) + .20($12) = $10.5
EVlarge = .30(−$4) + .50($2) + .20($16) = $3
First, calculate the expected payoff under certainty. To do this, identify the best
payoff under each state of nature. Then combine these by weighting each payoff by
the probability of that state of nature and adding the amounts. Thus, the best payoff
under low demand is $10, the best payoff under moderate demand is $12, and the
best payoff under high demand is $16. The expected payoff under certainty is, then:
.30($10) + .50($12) + .20($16) = $12.2
The expected payoff under risk, as calculated in Example S-1, is $10.5. The
EVPI is the difference between these:
EVPI = $12.2 − $10.5 = $1.7
EVPI indicates the upper limit on the amount the decision maker should be

willing to spend to obtain information. Thus, if the cost exceeds EVPI, the decision
maker would be better off not spending additional money and simply going with the
alternative that has the highest expected payoff.

1515


Petroleum Project

Making Decision In Oil Field Life Cycle

1.4. Sensitivity analysis
Sensitivity analysis provides the range of probability over which an alternative has
the best expected payoff. The approach illustrated here is useful when there are two
states of nature. It involves constructing a graph and then using algebra to determine
the range of probabilities for which a given alternative is best. In effect, the graph
provides a visual indication of the range of probability over which various
alternatives are optimal, and the algebra provides exact values of the endpoints of
the ranges
Table 1.2. State of Nature

Alternative

A
B
C

4
16
12

State of Nature
#1
#2
12
2
8

Example ( Table 1.2) :
- First, plot the expected payoff of each alternative relative to P2. To do this, plot the
#1 payoff on the left side of the graph and the #2 payoff on the right side. For
instance, for alternative A, plot 4 on the left side of the graph and 12 on the right
side. Then, connect these two points with a straight line. The three alternatives are
plotted on the graph as shown below
- The graph shows the range of values of P2 over which each alternative is optimal.
Thus, for low values of P2 (and thus high values of P1, since P1 + P2 = 1.0),
alternative B will have the highest expected value; for intermediate values of P2
alternative C is best; and for higher values of P2 alternative A is best

1616


Petroleum Project

Making Decision In Oil Field Life Cycle

Figure 1.4. Sensitivity analysis of 3 alternatives
- From the graph, we can see that alternative B is best from the point P2 = 0 to the
point where the alternative B line intersects the alternative C line. To find that point,
solve for the value of P2 at their intersection. This requires setting the two equations

equal to each other and solving for P2. Thus,
16 − 14 P2 = 12 − 4 P2
- Rearranging terms yields
4 = 10 P2
- Solving yields P2 = 0.40. Thus, alternative B is best from P2 = 0 up to P2 = 0.40.
Alternatives B and C are equivalent at P2 = 0.40.
- Alternative C is best from that point until its line intersects alternative A’s line. To
find that intersection, set those two equations equal and solve for P2. Thus,
4 + 8 P2 = 12 − 4 P2
- Rearranging terms results in 12 P2 = 8

1717


Petroleum Project

Making Decision In Oil Field Life Cycle

- Solving yields P2 = 0.67. Thus, alternative C is best from P2 > 0.4 up to P2 = 0.67,
where alternatives A and C are equivalent. For values of P2 greater than 0.67 up to
P2 = 1.0, alternative A is best.
- Table 3 is the summary of equations above
Table 1.3. Equations of 3 alternatives
A
B
C

#1
4
16

12

#2
12
2
8

Slope
12-4=+8
2-16=-14
8-12=-4

Equation
4+8P2
16-14P2
12-4P2

1818


Petroleum Project

Making Decision In Oil Field Life Cycle

CHAPTER 2 RECOVERY FACTOR AND RESERVES
2.1. Estimate the recovery factor
The recovery factor for oil is a target for how great a proportion of the oil can be
recovered.
Re covery factor =

Estimate of recoverable oil
Estimate of in-place oil

The in-place volumes and the volumes assumed to be recoverable are both used to
calculate the recovery factor. Uncertainty is attached to both these quantities,
especially in the early phase of a project. The various oil companies, moreover,
often calculate the in-place volume differently, thus making it difficult to compare
the recovery factor from one field to another. Changes in the recovery factor over
time are, nevertheless, an indicator of the effort made by the licensees to enhance
recovery.
Drive mechanism has the greatest geological impact on recovery factor. Narrowing
the range in recovery factor is a matter of estimating how much difference pore type
and reservoir heterogeneity impact the efficiency of the drive mechanism. To
estimate the recovery factor, use the procedure below:
-

-

Decide which drive mechanism is most likely from the geology of the
prospective reservoir system and by comparing it with reservoir systems of
nearby analog fields or analog fields in other basins.
Multiply STOIIP or GIIP by the recovery factor for the expected drive.
Narrow the recovery factor range by predicting the thickness of the reservoir
by port type. Port type affects recovery rate. For example, in a reservoir with
strong water drive and macroporosity, recovery will be up to 60%,
mesoporosity recovery will be up to 20%, and microporosity recovery will
be 0%.

The main techniques for estimating the recovery factor are:
-

Field analogue
Analytical models
Reservoir simulation

1919


Petroleum Project

Making Decision In Oil Field Life Cycle

RF = RFP + RFS
The primary recovery factor, RFP, is estimated from the type of drive mechanism
Table 2.1. Estimation of primary recovery factor
Drive mechanism
Depletion
Solution gas
Expansion gas
Gas cap drive
Water drive
Bottom
Edge
Gravity

Primary recovery factor drive mechanism (%)
18–25
2–5
20–40
20–40

35–60
50-70

The secondary recovery factor, RFS, equals
RF = ED ×

Soi − Sor
×100%
Soi

where
ED = Macroscopic Displacement Efficiency
Soi − Sor
× 100%
Soi

: Microscopic sweep efficiency

2020


Petroleum Project

Making Decision In Oil Field Life Cycle

Figure 2.1. Estimating recovery factor by analogue
These efficiency terms are influenced by such factors as residual oil saturation,
relative permeability, reservoir heterogeneity, and operational limitations that
govern reservoir production and management. Thus, it is difficult to calculate the
recovery factor directly using these terms, and other methods, such as decline

curves, are often applied.
2.2. Reserves estimation
2.2.1. Definition of oil reservers
2.2.1.1. Oil Reserves
Oil reserves are the amount of technically and economically recoverable oil.
Reserves may be for a well, for a reservoir, for a field, for a nation, or for the world.
Different classifications of reserves are related to their degree of certainty.
Because of reservoir characteristics and limitations in petroleum extraction
technologies, only a fraction of this oil can be brought to the surface, and it is only
this producible fraction that is considered to be reserves.

2121


Petroleum Project

Making Decision In Oil Field Life Cycle

Because the geology of the subsurface cannot be examined directly, indirect
techniques must be used to estimate the size and recoverability of the resource.
While new technologies have increased the accuracy of these techniques, significant
uncertainties still remain. In general, most early estimates of the reserves of an oil
field are conservative and tend to grow with time. This phenomenon is called
reserves growth.
2.2.1.2. Classification
All reserve estimates involve uncertainty, depending on the amount of reliable
geologic and engineering data available and the interpretation of those data. The
relative degree of uncertainty can be expressed by dividing reserves into two
principal classifications—"proven" (or "proved") and "unproven" (or "unproved")

Figure 2.2. Schematic graph illustrating petroleum volumes and probabilities
a)

Proven Reserves

Proven reserves are those reserves claimed to have a reasonable certainty
(normally at least 90% confidence) of being recoverable under existing economic
and political conditions, with existing technology. Industry specialists refer to this
as P90 (that is, having a 90% certainty of being produced)
b)

Unproven Reserves

Unproven reserves are based on geological and/or engineering data similar
to that used in estimates of proven reserves, but technical, contractual, or regulatory
uncertainties preclude such reserves being classified as proven. They are subclassified as probable and possible.
2222


Petroleum Project

Making Decision In Oil Field Life Cycle

Probable reserves are attributed to known accumulations and claim a 50%
confidence level of recovery. Industry specialists refer to them as "P50" (i.e., having
a 50% certainty of being produced).
Possible reserves are attributed to known accumulations that have a less
likely chance of being recovered than probable reserves. This term is often used for
reserves which are claimed to have at least a 10% certainty of being produced
("P10"). Reasons for classifying reserves as possible include varying interpretations

of geology, reserves not producible at commercial rates, uncertainty due to reserve
infill (seepage from adjacent areas) and projected reserves based on future recovery
methods.
2.2.2. The methods are used to estimate reserves
Estimating hydrocarbon reserves is a complex process that involves integrating
geological and engineering data. Depending on the amount and quality of data
available, one or more of the following methods may be used to estimate reserves:
-

Volumetric
Material balance
Production history
Analogy
Table 2.2. The methods are used to estimate reserves

Method
Volumetric

Application
STOIIP, GIIP, recoverable reserves.
Use early in life of field.

Material
balance

STOIIP, GIIP (assumes adequate
production history available),
recoverable reserves
(assumes STOIIP and GIIP known).
Use in a mature field with abundant

geological, petrophysical, and
engineering data.
Recoverable reserves. Use after a
moderate amount of production data

Production
history

Accuracy
Dependent on quality of
reservoir description.
Reserves estimates often high
because this method does not
consider problems of
reservoir heterogeneity.
Highly dependent on quality
of reservoir description and
amount of production data
available. Reserve estimates
variable.
Dependent on amount of
production history available.
2323


Petroleum Project

Making Decision In Oil Field Life Cycle

is available.

Analogy

STOIIP, GIIP, recoverable reserves.
Use early in exploration and initial
field development.

Reserve estimates tend to be
realistic.
Highly dependent on
similarity of reservoir
characteristics. Reserve
estimates are often very
general.

2.2.2.1 Volumetric estimation
Volumetric estimates of STOIIP and GIIP are based on a geological model
that geometrically describes the volume of hydrocarbons in the reservoir. However,
due mainly to gas evolving from the oil as pressure and temperature are decreased,
oil at the surface occupies less space than it does in the subsurface. Conversely, gas
at the surface occupies more space than it does in the subsurface because of
expansion. This necessitates correcting subsurface volumes to standard units of
volume measured at surface conditions.

Figure 2.3. Paremeter of HCIIP
N = Ah

N
φ (1 − Sw ) / Boi
G

where
2424


Petroleum Project

Making Decision In Oil Field Life Cycle

N = STIIP (STB)
A = area of reservoir (ft2) from map data
h = height or thickness of pay zone (ft) from log and/or core data
N/G = net to gross ratio
Φ = porosity (decimal) from log and/or core data
Sw = connate water saturation (decimal) from log and/or core data
Boi = formation volume factor for oil at initial conditions (reservoir bbl/STB)
from lab data; a quick estimate is Boil = 1.05+(N x 0.05), where N is the number of
hundreds of ft3 of gas produced per bbl of oil [for example, in a well with a GOR of
1000, Boi = 1.05 + (10 × 0.05)]
Another basic volumetric equation is
G = Ah

N
(1 − Sw ) / Bgi
G

where
G = GIIP (SCF)
Bgi = formation volume factor for gas at initial conditions (RES ft3/SCF)
Recoverable reserves are a fraction of the STOIIP or GIIP and are dependent
on the efficiency of the reservoir drive mechanism. The basic equation used to

calculate recoverable oil reserves is
Recoverable oil reserves or Ultimate recovery (STB) = HCIIP x RF
2.2.2.2. Material balance estimation for oil
The material balance technique mathematically models the reservoir as a tank.
This method uses limiting assumptions and attempts to equilibrate changes in
reservoir volume as a result of production. Aquifer support and gas cap expansion
can be accounted for by using this method.
One general equation is:

2525