Decision making in the oil and gas industry

Decision-making
Decision making in the
Oil & Gas Industry –
From Blissful I
F Bli f l Ignorance to
t
Uncertainty Induced Confusion
y
Reidar B Bratvold
University of Stavanger

My Intentions Today are to

Illustrate some of the traps that
sometimes impacts our valuation and
p
decision making efforts.

Bratvold: Aberdeen - 301107 2

Background: Has the increased uncertainty modeling
g
focus confused more than it has enlightened?

The use of probabilistic methods in the oil and
gas i d t h i
industry has increased d
d dramatically over th
ti ll the
last 20 years.
In light of this focus on uncertainty quantification,
it seems appropriate to scrutinize its perceived
value.
value
Has the focus on uncertainty quantification
improved d i i making?
i d decision ki ?
Are we dealing with the uncertainties that are the
g
most critical?
How consistent and normative is the industry in
dealing with risk?

Some of the conclusions in this presentation are drawn
from a survey of close to 500 SPE members
y

Survey goal: To understand the use of
probabilistic methods and d i i making
b bili ti th d d decision ki
methodologies the oil and gas industry
Launched 23 A il 2007 and closed 5 J l 2007
L h d April d l d July
Internet based with 21 questions
Targeted SPE members
Most respondents from Europe or US
p p
62.4% with operating companies
29.2%
29 2% identified themselves as decision makers
out of which 12.3% have decision authority for
investments greater than $100 million


How is the Oil & Gas Industry Dealing with an
Uncertain World?

The Oil & Gas Industry’s Interest in and Implementation
of Formal Decision and Risk Assessment over the Last
Few Years Has Been Amazing

− “We have implemented a comprehensive
We
Decision & Risk Analysis approach in our
company.
company.”
− “We use portfolio optimization to
determine corporate capital allocation ”
allocation.
− “We never decide on any drilling location
until we have done a thorough assessment
of all relevant uncertainties.”
− “W use P10, P50 and P90 i all our
“We d in ll
evaluations.”


Saying It Doesn’t Make It True
y g
• E&P CEO to manager:
– “I want your guarantee that we will not spend
more than the P50 on this project!”

• E&P Project Manager
– “We don’t have enough information to give
We don t
ranges of possible values for costs. We’ll have
to make our best estimates and model it
deterministically.”

• Manager - Lykos Line Shipping:
– “What I need is an exact list of specific
unknown problems we might encounter ”
encounter.


Two common ways of “dealing with uncertainty” are not
really dealing with uncertainty
• What Ifs
– Wh if the time to production is longer
What f h d l
than expected?
– Wh t if the well cost is higher th expected?
What th ll st hi h than t d?
– What if first year production is less than planned?

• This approach may help to answer questions regarding
specific scenarios.
• Problems
• Easy to get swamped with numbers and buried in endless
assumptions.
• Still have no idea about probabilities of each scenario
scenario.
• Don’t know which uncertainties are your real value drivers.

The two common ways of “dealing with uncertainty” are
not really dealing with uncertainty
• The ”Base Case”
– Often together with a low and high estimates
the input parameters to build the low,
the Base Case (BC), and the high cases for the output value
of interest

• Useful for identifying the key value drivers in the p y
y g y payoffs.
• Problems
• Y have no id what th probability of th B
You h idea h t the b bilit f the Base C
Case i
is.
• Taking low, BC, and high values of the input parameters does
not give you the low BC and high values of the output
low, BC,
− A full Monte Carlo simulation is needed
• It is extremely unlikely that all of the input parameters will be low
(or BC or high) at the same time.

However, in some cases the pendulum
has swung too far in the other direction
g
• Human tendency is to focus our
efforts on th
ff t those thi
things we can d ( t t l or
do (got tools
competency) – but what is the value?
• How often do we build a detailed
uncertainty model just because we
can?
• Or use advanced stochastic algorithms just
because we can?
b ?
• We often forget that the g
g goal is to make g
good
decisions which will lead to good outcomes –
not to reduce uncertainty!


Why can’t we just keep on making our
investment decisions the way we always have?

Booz Allen Hamilton, Inc Report 2006:
Capital Project Execution in the Oil and Gas Industry.

“…
“ more than half of the executives said
th h lf f th ti id
they are dissatisfied with their companies’
overall project performance citing the costly
performance,
budget and schedule overruns that plague
40 percent of their projects ”
projects.


Industry Performance:
Taking on a Cult of Mediocrity
• “The last 10 years might be called ‘a decade of
unprofitable growth’ for many upstream
growth
companies.”
Ed Merrow, Independent Project Analysis (
, p j y (IPA)
)
– Based on the analysis of more than 1000 E&P projects:
2/3 offshore, average $
, g $1Million – $3Billion
$
Average CapEx = $670MM

– One in eight of all major offshore developments in the
g j p
last decade falls into the ‘disaster’ category.
Failed on two out of three metrics:
>40% cost growth, >40% time slippage,
growth slippage
produced < 50% than 1 st year plan

– Record even worse for mega-projects
CapEx of $1 billion or more
Source: UPSTREAM, 23 May 2003

Improving Uncertainty Quantification

IPA Study: A large number of E&P projects do
not deliver on th i promises wrt schedule,
t d li their i t h d l
costs, and 1st year production.
Survey result:
− Little support for improving the level of uncertainty
modeling to better capture key uncertainties,
including uncertainties in:
o Schedule,
S h d l
o Costs, and
o Production


Decision analysis is providing us with a set of
g
fundamental insights
• Distinction between
good decisions and
good outcomes.
• Clear and concise rules for good
decision making
• Methods for how to ensure clarity of thinking (sound
reasoning).
• Consistent and logically correct ways to account for
individual and corporate attitudes toward risk.
p
• Distinguishing between constructive and wasteful
information gathering
gathering.
• ... Bratvold: Aberdeen - 301107 15

Questions

1. Are oil & gas companies risk averse?

2.
2 Should oil & gas companies be risk averse?

3. If the answer to (1) or (2) is yes, do they
implement their risk-aversion in a consistent
risk aversion
way?

Risk Tolerance Study – Walls (1995)

GROUP: Log RT vs. Log SMCF: 1983 - 1995
vs
ANADARKO
CHEVRON
8.5 CONOCO
EXXON
8.3
83 MOBIL
8.1 PHILLIPS
SHELL
7.9 AMOCO
7.7 RT = 2.71 + 0.47 * SMCF
Log RT

7.5

7.3

7.1

6.9

6.7

6.5
9 9.2 9.4 9.6 9.8 10 10.2 10.4 10.6
Log SMCF


Questions

1. Are oil & gas companies risk averse? Yes
2.
2 Should oil & gas companies be risk averse?

risk aversion
way?

Shareholders of public companies are entitled to the
protection of fiduciary principles [Easterbrook and Fischel 1985]

• While employees, debt holders, and other stakeholders are
p y , ,
protected by contracts and other applicable law, shareholders,
as the holders of the residual claims on the firm, receive few
explicit promises.
• Instead they get the protection of fiduciary principles:
– The “duty of loyalty” and “the duty of care”
• Duty of care:
– Managers must act for shareholders as a prudent person would in the
management of their own affairs
• Duty of loyalty:
– Requires managers to make decisions in the interest of shareholders
rather than their own interest or in the interest of other constituencies
• If we take this loyalty seriously, we must face some thorny
issues in making this concept of shareholder-based preferences
operational.
operational


Corporate finance starts with the premise that the
corporate objective is to maximize shareholder value

• Then based on the capital asset pricing
Then,
model (CAPM) and the distinction between
systematic and unsystematic (or diversifiable)
y y ( )
uncertainties, corporate finance concludes
that corporations should
– use a market-determined rate to discount
systematic uncertainties and
– value diversifiable uncertainties at their expected
value, discounted at the risk-free rate.
• Brealey and Myers: ”This implies that to have
the firm adopt a risk averse policy is at best
useless and at worst wasteful ”
wasteful.

Risk Tolerance Study – Walls (1995)

GROUP: Log RT vs. Log SMCF: 1983 - 1995
ANADARKO
CHEVRON
8.5 CONOCO
EXXON
8.3 MOBIL
8.1 PHILLIPS
SHELL
7.9 AMOCO
7.7 RT = 2.71 + 0.47 * SMCF

Log RT
7.5

L
7.3

7.1

• Walls: 6.9

6.7

6.5
9 9.2 9.4 9.6 9.8 10 10.2 10.4 10.6
Log SMCF

”E&P firms in the high risk tolerance category
E&P
demonstrate significantly higher returns than
those that are less willing to take on risk”
risk


Questions

2.
2 Should oil & gas companies be risk averse? No
risk aversion
way?

The three main approaches to – knowingly or not –
implementing risk aversion in oil & gas companies are
1. The use of hurdle rates
– This involves the superimposing of
hurdle rates in the metric used
for selecting p j
g projects
For example - Any project with a reserves potential
less than, say, 400 MMBOE is rejected

2. The use of increased discount rates
– Many companies use increased discount rates for
projects in ”risky” countries or projects requiring novel
technology.
3.
3 The use of an artificially low corporate planning
price


Hurdle Rates and the Optimizer’s Curse
[Brown,1974, Smith 2005]

• Using hurdle rates for project
selection will lead to inevitable
disappointments.
• In real life we don t know the outcomes (NPV
don’t (NPV,
reserves, production, RoR, ...) and must use
estimates.
• In the face of uncertainty these estimated values
are subject to error.
j
• Even when the estimated project returns are
unbiased, E [ xi* − Vi* ] = 0 , we should
expect to be disappointed on average
when comparing actual outcomes to
value estimates
estimates.

A Simple Example

• Three investment alternatives that all have true values = 0
• The value of each alternative is estimated and the
estimates are independent and normally distributed with
mean equal to the true value of zero and standard
deviation
d i i one - N(0 1)
N(0,1).

True Value

Estimated Value

E [ xi* − Vi* ] = 0

-3
3 -2
2 -1
1 0 1 2 3


Examples

Distribution of each Distribution of max
values estimate values estimate
(EV = 0) (EV = .85)

E [Vi* − μi* ] = 0.85

‐3 ‐2 ‐1 0 1 2 3

• The expected disappointment will be 85% of the
standard deviation of the value estimates.


Let’s look at an example where we use a hurdle rate of
IRR ≥ 15% for project selection [From Horner 1980]

Estimated IRR (%)
70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % ‐10 % ‐20 % ‐30 %

50 % 1 2 4 2 1
40 %
40 % 2 4 8 4 2
%)
Actual IRR (%

30 % 5 10 20 10 5
20 % 10 20 40 20 10
10 % 20 40 80 40 20
0 % 50 100 200 100 50
‐10 %% 100 200 400 200 100

Number of projects
p j



180 projects with actual IRR ≥ 15%
Average IRR = 26.7%
Estimated IRR (%)
Estimated IRR (%)
70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % ‐10 % ‐20 % ‐30 %

50 % 1 2 4 2 1
40 %
40 % 2 4 8 4 2
Actual IRR (%)

30 % 5 10 20 10 5
20 % 10 20 40 20 10
10 % 20 40 80 40 20
0 % 50 100 200 100 50
‐10 % 100 200 400 200 100

255 projects with estimated IRR ≥ 15%
Estimated IRR = 27.3%
ED 9.1%
ED = 9.1%
Actual IRR = 18.2%
l


Excluded but attractive

Estimated IRR (%)
70 % 60 % 50 % 40 % 30 % 20 % 10 % 0 % ‐10 % ‐20 % ‐30 %

50 % 1 2 4 2 1
40 % 2 4 8 4 2
al IRR (%)

30 % 5 10 20 10 5
20 % 10 20 40 20 10
Actua

10 % 20 40 80 40 20
0 % 50 100 200 100 50
‐10 % 100 200 400 200 100

Included, but unattractive

Using hurdle rates (optimizer’s curse) will
g ( p )
lead to inevitable disappointments.

A Simple Correction

• Calibration through Bayesian updating
– Model the uncertainty in the value estimates
explicitly
– Use Bayesian methods to interpret these values;
i.e., rank projects based on the posterior
expectation

E [ μi | V ] , for i = 1, K , n
,


Commodity Price Assumptions when
valuing investment opportunities
g

80
70
60 Historical Price in ???
nt Spor ($/bbl)
$2006
50
40
30
Bren

20
10
0
1975 1985 1995 2005 2015 2025 2035

Year


The survey indicates a lack of support for increasing
the level of detail used in price modeling.
Rate the following sources of To what degree are improvements
uncertainty in terms of impact on
y p warranted to increase the level of
investment performance. (scale 1-5) detailed used to quantify
uncertainty. (scale 1-5)
More than Minor
Uncertainty Average Important/ Uncertainty Average Improvements
Source Score Significant Source Score Warranted
Subsurface 4.4 82% Subsurface 3.5 47%
H. Carbon Prices
H C b Pi 4.3
43 78% Reserves 3.5 45%
Reserves 4.1 71% Schedule 3.4 41%
Drilling 3.9 67% Drilling 3.4 41%
Capital 3.9 66% Capital 3.3 36%
Schedule
Sched le 3.6
36 57% Production
P d ti 3.3
33 36%
Production 3.5 53% Op Costs 3.2 34%
Facilities 3.5 52% Facilities 3.2 30%
Operating Costs 3.5 51% H. Carbon Prices 3.1 29%
Fiscal Terms 3.4
34 46% Geopolitical 2.9
29 24%
Geopolitical 3.2 43% Fiscal Terms 2.8 20%

Hydrocarbon prices are recognized as a significant source of
Hydrocarbon prices are recognized as a significant source of
uncertainty, but little energy exists for modeling them in greater
detail. Bratvold: Aberdeen - 301107 33

What oil price do oil & gas companies use for
investment valuation?

Total says sticking to $25/bbl for long-term
price assumptions London (Platts)--21Sep2005

Total, Europe's third largest oil company, said Wednesday it plans to
stick to a long-term oil price assumption of $25/bbl to assess new
upstream projects, less than half the current price of benchmark spot
c udes
crudes.

"I think we need to keep a relatively conservative price scenario for
deciding developments today " Total chairman and CEO Thierry
today,
Desmarest said at an oil conference in London. "We keep for development
decisions a long term oil price scenario of around $25/bbl in real terms."

At this time the spot price was around $45
(forward curve in contango).

Commodity Price Assumptions when
valuing investment opportunities
g

80 Historical Price in
70 $2006

60 Corp Planning Price 1
Historical Price in

nt Spor ($/bbl)
$2006
50
Corp Planning Price 2
40
30
Bren

20
10

0
1975 1985 1995 2005 2015 2025 2035

Year


The arguments for using a conservative,
and fixed, corporate planning price
1. ”We need to make it simple for our
managers to understand ”
understand.
– Are managers less smart or less capable than
non managers?
non-managers?
– If they don’t understand a relatively simple stochastic
process model for the oil or gas price, how come they
understand the infinitely more complex models being
f
used to characterize the subsurface and production?
2. Determining
2 ”Determining the corporate planning price is an
essential element of the executives’ need to
have control.”
– Why not let the executives determine the parameters
in the stochastic model?


The arguments for using a conservative,
and fixed, corporate planning price

3.
3 ”We are not really looking for the absolute
value of any given investment. The main
reason for doing valuation is to be able to rank
g
projects. A fixed oil/gas price will ensure proper
ranking.”
– This is flawed reasoning
– Our valuation models are not linear and hence using
more representative price models will result in
different rankings (and different sequencing)


Questions

2.
2 Should oil & gas companies be risk averse? No
3. If the answer to (1) or (2) is yes, do
they implement their risk-aversion in a
consistent way?
risk aversion No

There are a number of ways to include risk
y
aversion in a normative and consistent way
Utility theory

Stochastic dominance
S h i d i

Expected shortfall (Conditional Value-at-Risk
(CVAR))


Why, then, are oil & gas companies risk
averse?
1. Lack of understanding
Executive management often believe they act in the
shareholders’ best interest by being risk averse
Executive management often do not know how to
implement a consistent and normative risk attitude

2. The risk averse attitude represents the interests
p
of the firms executive management rather than
its shareholders
Observed risk tolerances are consistent with those of
a manager who has 100% of his wealth invested in
the company


Individuals often exhibit a level of risk aversion for small isolated
investments that implies absurdely severe risk aversion

How people How people
make decisions should make
naturally decisions to get
Behavioural Normative more of what they
Decision Making Decision Making want

Risk-averse Consistent and logically
decision-making in oil & correct ways to
gas companies. account attitudes
toward risk (if at all).
d k(f ll)

Questions

1. Is more information always valuable?

2. Will reducing uncertainty always create value?


Uncertainty quantification creates value only to the extent that it
holds the possibility of changing a decision that would otherwise
have been made differently
Uncertainty without a decision is simply a worry
Once the decision is clear, further quantification of
uncertainty is a waste of resources and only serves to
obfuscate the situation
Oil
Example Drill P=.2 $100

Dry
$10
P=.8

$0
Walk
The outcome is highly uncertain, but
uncertain
The decision to drill is clear
Reducing this
R d i thi uncertainty cannot alter the best course of
t i t t lt th b t f
action

There are four criteria that information (or a test) must
meet in order to be worthwhile (or value creating)
1. Observable. You must be able to Test
view the results of the test before
Result
deciding.

Test Actual
2. Relevant. The information must have
f Result Event
the ability to change your beliefs
about another uncertainty. Up
“Up”
3. Material. The information must have Down
the bilit t h
th ability to change decisions you
d i i Up
U
would otherwise make. “Down”
Down
4. Economic. The cost of the
Cost
information must be less than its Value
value.
l


We often forget that the goal is to make good decisions
which will lead to good outcomes – not to reduce uncertainty
• Quantifying uncertainty creates no value in its own right.
– In fact, it only has value to the extent that it holds the potential to
fact
change decisions that might otherwise be made differently.
– If the best course of action is clear, it is a waste of resources to further
improve uncertainty estimates
estimates.

• Reducing uncertainty c eates no value in a d o itself.
educ g u ce ta ty creates o a ue and of tse
– Reducing uncertainty only creates value to the extent that it changes
decisions.
– The goal is not to reduce uncertainty. Rather, the goal is to make good
decisions.
– This could imply that no further modeling to reduce uncertainty is
warranted even though it is possible.

Have we moved from a state of Blissful
Ignorance to Uncertainty Induced Confusion?

“Taking on a cult of mediocrity” – Are we
learning from our mistakes?
g
• Prof Daniel Kahneman
– Winner of 2002 Nobel P i i
Wi f N b l Prize in
Economics
– Psychologist and D i i A l t
P h l i t d Decision Analyst

■ ” The thing that astonishes me when I talk to
businesspeople i th context of d i i analysis i
b i l in the t t f decision l i is
that you have an organization that’s making lots of
decision and they’re not keeping track. They’re not
they re They re
trying to learn from their own mistakes; they’re not
investing the smallest amount in trying to actually
figure out what they’ve done wrong. A d th t’ not an
fi t h t th ’ d And that’s t
accident: They don’t want to know.”

Let’s stop being mediocre

• There is plenty of room for improved
performance in the oil & gas industry
industry.

• Today we are surfing on the high commodity
price wave.

• This “luck” may not last forever:
y
– Costs seem to be increasing more rapidly than
commodity prices.
– How much of a dent into the Chinese economy
will it take before the prices fall back down below
$50/bbl?


Don’t buy these arguments

• No time
– N ti
No time t i
to improve your company’s performance?
’ f ?
– No time to generate competitive advantage?

• It’s a no brainer – reducing uncertainty is
always valuable

• Too difficult


From Blissful Ignorance to Uncertainty
Induced Confusion

…the real problem in decision analysis is not
making analyses complicated enough to be
comprehensive, but rather keeping them simple
enough to be affordable and useful.
‐‐ Ron Howard


Decision making in the oil and gas industry

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