Lecture 2 petroleum risks & decision analysis(4)

Lecture 2
Dr Ebenezer Sholarin, PMP®
Hydrocarbon Economics &
Project Management
PEEN4001/ECON6007
Petroleum Risk &
Decision Analysis
1

Lecture 2_Petroleum Risks and Decision Analysis Prepared by Dr Ebenezer A. Sholarin, PMP®
Hydrocarbon Economics & Project Management (PEEN4001/Econ 6007)
“If you are sure you understand everything that
is going on, you are hopelessly confused.”
• Walter F. Mondale
2

Lecture objective
The overall objective of this lecture is to develop
engineers who have critical thinking skills to
identify opportunities, solve problems and
make more confident decisions in the presence of
risks and uncertainty.
3

Lecture Outline
• Defining Risk and Uncertainty
• Identifying uncertainties in the E&P business
• Expressing and combining uncertainties
– One point analysis
– Three-point distributions
– Subjective probabilities and expected value concepts
• Tools for quantifying Risk
– Sensitivity analysis
– Decision tree analysis
– Monte Carlo simulation
• Dealing with Risk Using Bow-Tie Model
4

Characteristics of Petroleum Venture
• Oil and gas industry is prone to risks and uncertainties:
– Oil and Gas reserve uncertainty
– Exploration uncertainty (Capital & Operating Costs)
– Oil and Gas price uncertainty
– Production uncertainty (Royalty, Tax, PSC)
– Demand uncertainty
– Supply uncertainty
• Oil and gas industry is complex industry affected by:
– Global risks (political, legal, commercial and environmental)
– Element risks (construction, operation, financing and revenue
generation)
– Both risks categories affects upstream and downstream phases
5

Degrees of Uncertainty in oil and gas projects
Level of Uncertainty
Exploration appraisal Development Production
Project Management Phases
Low
High
6

Case Study(1) Varanus Island Gas Explosion
• In 2008, an export gas pipeline
ruptured near the Apache Varanus
Island gas plant, causing a fire in a
large section of the plant. The loss of
containment was caused by corrosion
and resultant thinning of the pipe wall.
• Key outcomes from the post-incident
investigation:
– Ineffective anti-corrosion coating at the
beach crossing;
– Ineffective cathodic protection of the wet-
dry transition zone; and
– Ineffective inspection and monitoring of the
beach crossing and shallow water section of
the pipeline.
7

Case Study(2): BP Macondo Oil Well Disaster 2010
• Oil rig blaze off Louisiana leaves at least
11 missing (21st April, 2010)
– http://news.bbc.co.uk/2/hi/americas/863487
4.stm
– http://www.youtube.com/watch?v=ttC7o3Jx
sxE
8

Risk perception:
People don’t think probabilistically!
• “It will happen to me” or “it will not happen to me”
• Other factors influence protective decisions: worry;
peace of mind.
• Affections and emotions play a key role in decision-
making.
• Biases: likelihood of an event is estimated by the
ease with which a person can visualise it.
9

Risk – What is it?
• “The chance or probability of a successful wildcat
discovery” (Simpson, et al 2000)
• “The potential for outcomes that are quite
different” (Schuyler & Newendorp, 2000)
• “The chance of success or an opportunity for loss”
(Megill, 1988)
10

Risked Reserves categories
• SPE/WPC
– Society of Petroleum Engineers (SPE)
– World Petroleum Congress (WPC)
Proved reserves
(Reasonable certainty)
Probable reserves
(More likely than not)
Possible reserves
(less likely than probable)
1P
2P
3P
11

Dimensions of Risk
Risk has two components:
 The probability of failing to achieve a particular
outcome.
 The consequences of failing to achieve that
outcome.
• Risk Management is a methodical approach to controlling
risk.
• Goal: Discover early, track closely
12

Risk versus Hazard
• Some definitions of risk focus only on the probability of
an event occurring.
– Hazard is something that has potential to lead to a situation
that we do not like (e.g. potential to cause harm to people,
damage to assets or facilities, business loss, etc.)
– Risk is the likelihood that a specific undesired event will occur
within a specified period.
• Risk is a function of both the likelihood and consequence of a specific
hazard being realized.
13

Some examples of Risky Events
• Scope Creep,
• Poor Task Duration Estimate
• Subcontractor delay/default or declaring bankruptcy (e.g.
construction, etc.)
• Vendor delay/default (e.g. unstable code; contractual/legal)
• Hydrate formation in subsea flowlines and pipelines
• Resource limitation or conflicts
• Technical issues: interfaces, pump failure, ineffective new
technology
• Political change: a war or coup in a country, where exploration is
being conducted.
• Funding/Budget overrun
• Think of any other risk event!
14

The Key Risk Questions to ask:
• What can go wrong?
– Scope creep, poor estimate, insufficient
funding, etc.
• How likely is it to happen?
– What is the likelihood that crude prices will
fall to $10/bbl in the next few years?
• What are the consequences?
– What effect will low price have on the
decisions we are now considering?
• What can we do about it?
– In high risk areas are we better off to place
our capital in a few fully owned wells or
should we take a small piece of many
exploration prospects?
?
? ?
15

What can go wrong?
Main cause
Problem
(Effect)
Cause Level 1
Cause Level 2
Cause-and-Effect Diagram

What might go wrong for doing business as usual?
17

Probability Scale
Won’t Happen
(Unknown-unknown)
Will happen
(Known-known)
Probability of occurrence
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0
ΣPi = 1
Where (1 – Ps) = Pf, i.e., the probability of getting a dry hole.
If Ps + Pf = 0, then the outcome will certainly not occur.
If Ps + Pf = 1, then the outcome will certainly occur.
18

What is Probability?
• A chance that a specified consequence will
happen.
– Calculated as the long run ratio of the number of
times the outcome has occurred divided by the
total number of times the experiment or chance
phenomenon has been repeated.
– It may also be expressed qualitatively in terms of
events that have happened in a particular industry,
organisation or location.
19

Unwanted consequences
Can you think of any?
20

What can we do about it?
• Take – Accept current severity level as is and
continue to apply responses as in place
• Treat – Accept the risk per se, but deploy
additional and/or new responses, implement
measures to decrease severity level
• Transfer – Do not accept current severity level, but
transfer all or part of it to others ( e.g. through
insurance or contracting strategy)
• Terminate – Change the business plan in order to
avoid the risk.
21
A realistic Risk Response approach often involves a 4T combination

How to deal with Risky events?
1. Identify - Risk register
2. Qualify - Probability x Impact/Consequence
3. Assess - Risk Assessment Matrix
4. Quantify - Expected Monetary Value Analysis,
Sensitivity analysis, Monte Carlo simulation)
5. Manage (respond & Control) - mitigate, avoid,
accept, insure, track, control, replan.
22

1. Identify Risks: Risk Register
23

2. Qualify Risks: Probability and Impact
Risk Event:
Good or Bad
Thing That
Might Happen
(A)
Probability
It Might Happen
(1-10)
(B)
Impact (Consequence)
If It Happens
(1-10)
PLAN
Plan what you
Will do on the
BIGones
(A) X (B)
24

Risk Equation
X
RISK =
LIKELIHOOD IMPACT
Consequence/
Impact
Risk =
Chance of
Hazard Occurrence X
25

3. Assess Risks: Risk Assessment Matrix
• Colors are indicative of level of risk:
Source: Sinclair Knight Merz, 1999:33
26

RISK QUANTIFICATION
3 4 6 8 12
4 6 9 13 20
7 10 14 20 32
11 16 21 27 40
18 26 36 42 50
Value Rating
< 12
12 - 20
> 20
LOW RISK
MEDIUM RISK
HIGH RISK
RISK EVALUATION
LIKELIHOOD
IMPACT
Risk Level Likelihood
a
b
c
d
e
Not Likely
Uncertain
Likely
Highly Likely
Nearly Certain
LIKELIHOOD
LIKELIHOOD factored with
IMPACT equals…
Risk
Level
Impact on Scope/
Quality/technical performance
Impact on Time/
Schedule
Impact on
Cost/Budget
1
2
3
4
5
Minimal or no impact
Minor performance shortfall
Moderate delivery shortfall
Unacceptable, with alternative
Unacceptable, no alternative
Additional tasks required to meet key dates.
Minor schedule slip; will miss critical milestone
Impact to critical path.
Impact on project delivery date > 10%
< 1 % of budget
< 5% of budget
< 10% of budget
>10% of budget.
IMPACT
a
b
c
d
e
1 2 3 4 5
27

4. Quantify Risks: Risk Analysis Approaches
a. Deterministic Approach
I. Single Point Estimation
II. Expected Value Concept
III. Sensitivity Analysis
IV. Decision Tree Analysis
b. Stochastic Approach
I. Probability Distribution Function (PDF)
II. PERT Analysis
III. Monte Carlo Simulation.
28

Deterministic Vs Stochastic Approach to
dealing with Uncertainty
Range of Uncertainty
Stochastic (Probabilistic) Approach
TOTAL
GAS-INITIALLY-IN-PLACE
GAS PRODUCTION
Reserves
Proved Probab Possib.
Unrecoverable
Contingent Resources
Unrecoverable
Prospective Resources
1-P 2-P 3-P
1-C 2-C 3-C
LOW
EST
BEST
EST
HIGH
EST
Range of Uncertainty
Increasing
Chance
of
Commerciality
Deterministic Approach
What can go wrong?
How likely is it to happen?
What can we do about it?
What are our reserves estimates?
What is our production target?
What is our range of uncertainty?
Not to scale
29

a. Deterministic Approach
1.1 Single Point Estimation
30
5 HP Pumps
Base Estimate
5 x 2.0 = 10 M USD
Then adding deterministic
contingencies
 Data assumed to be known with
certainty
Strengths: Insert the most likely or EV in
the spreadsheet to determine outcome;
Quick and easy.
Weakness: Misleading
Target
Acceptable

Deterministic Approach (cont.)
1.2 Expected Value (EV) Concept
1.3 Decision Analysis
• Decision framing or structuring the problem, using a
Decision Tree.
• Modelling the alternatives
• Assessing the alternatives
31

Expected Value Concept
• Decision alternative – an option, or choice, open to the
decision-maker.
• Outcome or Event – something which could occur once a
decision is made.
• Conditional value of an outcome – the value of an event, if it
occurs.
• Expected Value of an outcome – conditional value of the
outcome multiplied by the probability that it will occur.
• Expected Monetary Value – used when we are referring to the
EV of an event which has a conditional monetary value.
32

The Power of choice
"It is our choices ... that show what we truly are,
far more than our abilities".
- J.K. Rowling
“The art and science of decision-making
is applying decision policy to make rational
choices".
What if a decision is sensitive to probability?

An EV Table for G5 Gas Injection Option
Possible
Outcome Probability
Outcome
(mln.)
EV
($mln.)
No effect 0.01 $22.6 $0.3
Low 0.33 $32.4 $10.7
Medium 0.33 $40.8 $13.5
High 0.33 $71.1 $23.5
1.00 EMV = $47.8 million
34

Normative Decision Analysis Tools
• Traditional normative decision analysis aids:
– Linear/Goal programming
– Queuing theory
– Simulation
– Stochastic models
• Modern normative decision analysis aids
– Geological interpretation models
– Reservoir models
– Monte Carlo Simulation
– Decision Trees

An Example of Decision Modeling
Drill/
No drill
Capital
Expenditure
Operational
Expenditure
Market
Share
R&D
Costs
Exploration
Costs
Oil/Gas
Revenue
Number
of Barrels
Market Size
Unit
Price
NPV
Legend:
Value Measure
Probabilistic Variable
Deterministic Variable
Decision/Choice

A Decision Analysis for a secondary oil
recovery project
G5 W1
22.6 21.6
0.01 0.01
0.33 32.4 0.33 25.6
NPV 47.8 0.33 40.8 32.0 0.33 29.1
0.33 0.33
71.1 41.7
22.6% 21.9%
0.01 0.01
0.33 27.6% 0.33 24.0%
IRR 34.8% 0.33 27.0% 0.33 25.7%
31.7%
0.33
0.33
45.5% 31.6%
• Based on the above information, which option would you choose, and why?
• Do you know how the EMV for G5 and W1 was calculated?
37

EV of example drilling decision
• Suppose we have the opportunity to drill a well on a prospect which, if successful, is
expected to lead to a development with an estimated NPV of $100 million. Suppose
the well costs $5 million and the estimated POS is 10%.
• The different outcomes and their conditional values are shown below:
Drilling decision
Well cost = $5MM
Drill
Don’t Drill
Discovery
(10%)
Dry Hole
(90%)
$0.00
-$5MM = Outcome 2
+$100MM = Outcome 1
EV
=
($100MM * 10%) + (-$5MM * 90%)
=
($10MM - $4.5MM) = $5.5MM
EV
=
Conditional Value of Success * Ps
+
Conditional Value of Failure * Pf
38

Expected Value Balances all Risk & Reward Factors
• Field size
• Production rate
• Markets
• Cost of development
• Cost of operation
• PSC effects
• Royalties
• Taxes
• Discount rate
Reservoir depth,
Location, etc
NPV(Dev)*Ps
Reward
EV =
+
Geology
NPV(Well)*Pf
Risk
39

Decision Tree
• A graphical representation of
complex decisions and with
provisions for the calculation
of decision paths.
• Represents various events
with unique symbols.
• A square node and a circular
node generally represent a
decision and an uncertainty.
40

Using Decision Tree to Resolve
a Complex Problem
• If you put a small coin into an empty
bottle and replace the cap, how would
you get the coin out of the bottle without
taking out the cap or breaking the bottle?
41

Decision Tree Analysis
Decision Definition Decision Node Chance Node Outcome Node
Decision to be made Input: Cost of each decision
Output: Decision made
Input: Scenario probability,
reward if occurs.
Output: EMV
Computed:
Payoffs minus costs
along paths
TIME
$80M
($30M)
Build new plant
(invest $120M)
Upgrade Plant
(invest $50M)
Build or
Upgrade?
Strong Demand
($200M)
60%
40%
60%
40%
Weak Demand
($90M)
Strong Demand
($120M)
Weak Demand
($60M)
$70M
$10M
$80M = $200M - $120M
$(30M) = $90M - $120M
$70 = $120M - $50M
$10 = $60M - $50M
$36M = .6 x 80M + .4 x ($30M)
EMV of “Build new plant”
$46M = .6 x 70M + .4 x $10M
EMV of “Upgrade plant”
Decision EMV = $46M
(the largest of $36M and $46M)
Decision node
Chance node
Outcome node
42

Sensitivity or “What-if” Analysis
• A method for determining how “sensitive” your
model results are to parameter values.
– Sensitivity of NPV, sensitivity of policy choice.
• Methodically entering even increments of values to
view the projected outcomes.
• Results in a mountain of data indicating the range of
possible outcomes, but no associated probabilities.
43

Sensitivity Analysis Tool
• Spider Diagram
0
40
80
120
160
200
-50 -40 -30 -20 -10 0 10 20 30 40 50
Percentage change
NPV
($
mln)
Capex
Opex
Oil Price
Production
44

Sensitivity Analysis Tool
• Tornado Diagram
Sensitivity analysis will tell you what is important and
the main key value drivers where you should be focusing your attention.
45

b. Stochastic Approach
1. Probability Distribution Function (PDF)
2. Managing Uncertainty – The PERT Approach
3. Monte Carlo Simulation Technique
46

Probability Distribution Function
• Statistical concepts
47
Mean or Average
• 8, 25, 7, 5, 8, 3, 10, 12, 9 -> Mean is 9.67;
Mode or Most Likely
• 8, 25, 7, 5, 8, 3, 10, 12, 9 -> -> Mode is 8;
Median or P50
• 3, 5, 7, 8, 8, 9, 10, 12, 25 -> Median is 8.
Standard Deviation (SD or σ):
• shows how much variation or dispersion from the average exists.
2
1
)
(
1
1
, ∑
=
−
−
=
N
i
i X
x
n
SD σ 33
.
6
1
9
0001
.
320
, =
−
=
σ
SD

How to calculate Mean, Mode,
Median and Standard Deviation
Score (x) Mean (M) Score - mean (D) D₂
8 9.67 -1.67 2.7889
25 9.67 15.33 235.0089
7 9.67 -2.67 7.1289
5 9.67 -4.67 21.8089
8 9.67 -1.67 2.7889
3 9.67 -6.67 44.4889
10 9.67 0.33 0.1089
12 9.67 2.33 5.4289
9 9.67 -0.67 0.4489
87 320.0001
N = 9 40.00001
9.666667 Stand Deviation = 6.324556
48

Discrete Probability Distributions
• Discovery with several possible outcomes
Outcome
10%
20%
30%
20%
10%
10%
NPV = $5MM = Outcome 1
30%
10%
20%
40%
50%
Probability
5 10 15 25
20 30
Outcomes ($MM)
60%
20%
40%
80%
100%
“Less than” Cumulative
Probability Distribution
5 10 15 25
20 30
Outcomes ($MM) 49

Frequently used Probability Distributions
1. Triangular Distribution
(Porosity)
2. Uniform/Rectangular /Even
Distribution (Oil Saturation)
3. Normal Distribution
(Net Pay, NPV)
4. Lognormal Distribution
(Reserves)
{Mode} = {Median} = {Mean}
F(x) = height = 1/(b – a)
{Mode} < {Median} < {Mean}
{Mode} = {Median} = {Mean}
F(x) = (min + 4mode + max)/6
50

PERT Analysis
• Program Evaluation and Review Techniques (PERT)
• PERT Analysis is often used to determine three level
estimates:
– To – Optimistic (low) estimate
– Tm – Most Likely (medium) estimate
– Tp – Pessimistic (high) estimate
NB: We shall discuss more on PERT Analysis in Lecture 3 51

P50/P10/P90 Estimates
• P50 (50/50) or Most Likely Estimate
– Estimate which has equal probability of over or under-run.
• P10 (10/90) or Pessimistic Estimate
– Estimate which has a 10% probability of under-run and
90% of over-run.
• P90 (90/10) or Optimistic Estimate
– Estimate which has a 90% probability of under-run and
10% of over-run.
52

Monte Carlo Simulation Technique
• What is it?
– A stochastic technique, which randomly generates values
for uncertain variables over and over to simulate a
model.
– It uses PDF as the inputs to estimate a distribution of
possible outcomes for an output variable (in Excel, a
formula)
– It explores the range of possible outcomes and the
probability of their occurrence..
53

Monte Carlo Simulation
54
How do you do it?
1. Calculate the cash flow (or other relationships) for the
picked values.
2. Develop Excel Function.
3. Define probability distribution for each input variable
(assumption cells).
4. Calculate the desired parameter – NPV, Profit, ROI etc
(forecast cells).
5. Pick any item of forecast cells and click on forecast button
to simulate the outcome (Crystal ball starts to run and at
the end shows the mean and SD for the output
distribution).

Triangular Distribution Function
55
5 HP Pump
Monte Carlo Run:
1.8 M US$
2.45 M US$
2.25 M US$
1.95 M US$
2.7 M US$
One outcome:
11.15 M USD.
Run consists of
10,000 simulations
so 10,000 possible
outcomes.

How a Risk system works in
Petroleum Ventures?
INPUT SHEET
Source Parameters
Trap Area
Reservoir Thickness
Porosity
Water Saturation
Recovery Factor
Etc.
MONTE CARLO
TECHNICAL
CONFIDENCE
RESOURCES
HISTOGRAMS
 =
56

Oil Prospect Reserve Calculation
• 1 ITERATION
Minimum
Most Likely
Maximum
57

Monte Carlo Simulation – output distributions
Cumulative Chart
Certaintyis30.80%from2.000to+InfinityUS
$m
illion
Mean=1.557
.000
.250
.500
.750
1.000
0
250
500
750
1000
-1.000 0.250 1.500 2.750 4.000
1,000Trials 1O
utlier
Forecast:N
PV@10%discount rate
58

59
Comparison of Value Outcomes
of Two Petroleum Ventures

Risk Attitude
Risk Averse – You’re willing to pay a premium or
penalty to avoid risk.
Risk-Neutral – You are rational person and your
decisions are based solely on expected monetary
value (EMV) concept.
Risk-Seeker – You’re willing to pay a premium or
penalty to accept risk.
60

Applying EMV Concept to Determine the
Risk Attitude of a Decision Maker
Preferred Value Risk Attitude
Petroleum Engineer A $3.5 million Risk Neutral
Petroleum Engineer B $1.0 million Risk Averse
Petroleum Engineer C $2.0 million Less Risk Averse
Petroleum Engineer D $5.0 million Risk Seeker
Adapted from Walls, M. R., Combining decision analysis and portfolio management to improve
project selection in the exploration and production firm, JPS&E, 2004
To Farm out
To Drill
Probable
P1 = 0.6
P2 = 0.4
Failure
EV = $3.5 million
$7.5 million
$2.5 million
$0
NPV
Adapted from www.projectdecision.org
61

5. Manage Risks:
Risk Response Approach
• Take – Accept current severity level as is and
continue to apply responses as in place
• Treat or Mitigate – Accept the risk per se, but
deploy additional and/or new responses,
implement measures to decrease severity level
• Transfer – Do not accept current severity level, but
transfer all or part of it to others ( e.g. through
insurance or contracting strategy)
• Terminate – Change the business plan in order to
avoid the risk.
62
A realistic Risk Response approach often involves a 4T combination

Risk Response Technique
63
Bow-tie Model
Hazard
Avoid
The
Hazard
Threats
Proactive measures
- Prevent
- Mitigate
- Transfer
(take preventive measures to reduce
Chance of top events occurring
Top event
Consequences
Re-active measures
- Remediate
-Recover (put remediation in
place to control the damage)
Lessen
The
Threats
RECOVERY
PREPAREDNESS
MEASURES
BARRIERS
Likelihood Top Event Effect/Impact
Conduct Risk Analysis

Lecture Key Points
• Risk is often measured by the variance or semi- variance in
the return on the investment or the probability of loss
(negative present worth).
• Sensitivity analysis determines which parameters are most
critical to a project’s worth. These are the parameters that
must be examined closely in any ensuing economic analysis.
• A random variable takes on different values (or ranges of
different values) with different probabilities. The variance
provides some measure of the spread of a random variable.
• Monte Carlo simulation is a technique used to evaluate cash
flows or cashflow inputs defined by a variety of distributions.
64

Questions for self-assessment
1. What are the four risk questions to ask when planning a project or
making an investment decision?
2. Distinguish between Risk and Hazard. How do you perform a Risk
assessment matrix (RAM)?
3. Mention four basic statistical concepts used for developing probability
distribution function. What does PERT means? What are the level
estimates used for calculating PERT? How is EV calculated in PERT?
4. Mention two tools that are used for performing sensitivity analysis in oil
and gas risk assessment.
5. Define decision tree. What are the three nodes you require to perform a
decision tree analysis? How do you distinguish the nodes from one
another?
6. Mention four common types of probability density distributions used for
performing Monte Carlo simulations in the oil and gas industry.

Project Planning &
Scheduling Techniques
Lecture 3
66

Lecture 2 petroleum risks & decision analysis(4)

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