03 - uncertainty

7/27/2019 03 - Uncertainty

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IntroductionProbability, Distributions and CorrelationEstimating Under UncertaintyTight Clastics / Carbonate AssessmentShale AssessmentReservoir FlowValuation Techniques

Risk, Uncertainty & Economic Analysisfor Resource Assessment and Production

Forecasting in Shale and Tight Reservoirs

Have Formal Training In

MathematicsPhysical SciencesComputer Science

But Who Has Ever Had A Course In

EFFECTIVE ESTIMATING?

Geologic PrinciplesScientific MethodEngineering

Rose & Associates, LLP 1 Ch 3 - UncertaintyAAPG Cartagena 2D course, Sept. 2013


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P10

P50

P90

P99

P01P99

P90

P50

P10

P01

1 10 100 1,000 10,000

80 % Confidence Interval

SMALL CHANCETHE OUTCOME IS MORE

THAN P10 VALUE

LARGE CHANCETHE OUTCOME IS MORE

THAN P90 VALUE

Estimating With Probabilistic Ranges

EstimatingP10 - P90 Ranges

Provide your ranges forquestions 1- 5 .

80% confidence

tofrom



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Pre-drilling geological estimates of the size of prospect-targets

are real-life exercises in estimating under uncertainty. Thefollowing exercise is a practical experiment in assessinguncertainty. Even though you probably will not know the exactanswer to each of the ten questions below, you should be ableto make an estimate as to the correct answer. One way to dothis is to try to bracket the correct answer between an upperand a lower limit. Try to set the upper and lower limits suchthat you are 80% sure that the range you have selected willcontain the correct answer.

For example, you might be 80% sure that Columbusdiscovered Latin America sometime between 1450 and 1550A.D.

Estimating With Probabilistic Ranges

What is the air distance from Ushuaia,Argentina to Anchorage, Alaska, in KM?

What was the population ofBogota in 1912?

When was the earliest permanentsettlement established (as proved byceramic dating) in South America?

P90 P10

When was the epic poem Cantar deMio Cid written in Spain?

How many hotel / apartment rooms didBrazil agree to have for the 2016 RioOlympics to meet IOC requirements?



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P90 P10

As of 1998 how many exploration wells weredrilled in the 379,357 sq km of the SaoFrancisco basin?

How much shale gas (BCF) in the U.S. was

produced in 2009?

According to YPF what is the areal extent(sq km) of the Vaca Muerta formation?

How much oil (barrels) was producedfrom the Bakken Formation in NorthDakota in 2011?

What was the 2009 average daily oilproduction from Colombia (b / d)?

P90 P50 P10

Practice

Slide A

Slide B

Slide C

EXERCISE 3-2

Estimating With An 80% Confidence Range



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INTUITIVE DECISION MAKING

Involves no formal analysis or process.

Impossible to audit for completeness and quality.

Based upon past experiences.

Everyone processes information differently.

Danger of poor decision making is greater whena manager is under stress.

Must Know How To Play The Odds

Level Of Uncertainty Generally Unknown

Biases In Assessing Uncertainty:

Overconfidence

Representativeness

Availabi lity

Anchor ing

Implicit Conditioning

Motivational

Tversky &Kahneman (1981)

Estimating Under Uncertainty

Biases



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Overconfidence We Think We Are Smarter Than We Actually Are!

Setting Predictive Range Too Narrow

Representativeness Small Sample Size; Examples May Not Be Truly

Analogous

Availability Tendency To More Heavily Weight The Spectacular

Examples; Limited Imagination

Estimating Under UncertaintyBiases

Tversky &Kahneman (1981)

Percentage of Misses

Type of PeopleTested

Type of Quest ionAsked

IdealTarget

ActuallyObserved

Harvard MBAs Trivial facts 2% 46%

Employees of achemical company

Chemical industry andcompany-specific facts

10%50%

50%79%

Managers of acomputer co.

General business factsCompany-specific facts

5%5%

80%58%

Physicians Probability that a patienthas pneumonia

0-20% 82%

Physicists Scientific estimates likethe speed of light

32% 41%

Overconfidence

Biases In Assessing Uncertainty

RussoandSchoemaker (1989)



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We have little comprehensionof comparative levels of confidence

We Think We Are Smarter Than We Actually Are!

Setting Predictive Range Too Narrow

Biases in Assessing UncertaintyOverconfidence

Capen (1976)

Biases in Assessing Uncertainty

RepresentativenessA certain town is served by two hospitals. In the larger hospital, about 45babies are born each day, and in the smaller hospital, about 15 babies areborn each day. Although the overall proportion of boys is about 50% theactual proportion at either hospital may be greater or less than 50% on anygiven day.

QUESTION:

At the end of one year, which hospital is likely to have the greater numberof days on which more than 60% of the babies born were boys? (Checkonly one answer)

The large hospital The small hospital Neither hospital - The number of days will be about the

same (within 5% of each other)



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Biases in Assessing UncertaintyAvailability

For the following pair, choose the one you think causes more deaths in theUnited States each year:

Lung Cancer Motor Vehicle Accidents

RussoandSchoemaker (1989)

Anchoring Reluctance to Move Far From Some Initial High or

Low Side Value

Implicit Conditioning We Estimate Based on Our Specific Experience or

Expertise - Seek the Input of Others!

Motivational Overestimate to Sell the Deal; or Underestimate to

Err on the Side of Conservatism

Estimating Under Uncertainty

Biases

Tversky andKahneman (1981)



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Biases in Assessing UncertaintyAnchoring

Thats my story and Im sticking to it.

Dont confuse me with the facts.


Biases in Assessing Uncertainty

Implicit Conditioning

Sourceof Risk

SmokingAlcoholic BevMotor VehiclesHandgunsElectric PowerMotorcyclesSurgeryX-RaysRailroadsNuclear Power

BusinessClubMembers

4531

1929

24208

ActuarialEst Deaths

150,000100,00050,00017,00014,000

3,0002,8002,3001,950

100

LeagueWomenVoters

4623

185

1022241

CollegeStudents

3752

196

1117231

Ranking Order

Moore(1983)



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Overestimate to sell the deal; or underestimate toerr on the side of conservatism; often driven bythe rewards system and unwritten rules in place!

Written Unwritten

1 1

2 2

3 3

45

Rules for Advancement

Biases in Assessing UncertaintyMotivational


How Do we Overcome Bias?

Require every estimate to Include a Level ofConfidence

Provide feedback and training to help peoplecalibrate

Ask Disconfirming Questions about your ideas andsources

Expose the hidden sources of future problems Limit yourself to the information you can handle

Develop Multiple Working Hypotheses



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Ruling Theory

Working Hypotheses

Multiple Working Hypotheses

Progression of Methods

How Do We Overcome BIAS?

Chamberlin(1931)


Single, Dogmatic Opinion

Theory Often Formed Prematurely

Press New Facts To Fit Theory

If Questioned, Owner Takes Personally

Ruling Theory



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Working Hypothesis Better than Ruling Theory (a single,dogmatically-held opinion)

Used as a vehicle to suggest and guide inquiry,not to find facts to fit the theory: If this is true,then this other fact should follow.

Although a working hypothesis may (afterappropriate investigation and consideration)legitimately evolve into Ruling Theory, it is all tooeasy for it to slide into Ruling Theory withoutsuitable support.


Multiple Working Hypothesis

Pursues simultaneously, none championed

Helps pinpoint critical elements common to allpossible scenarios

Promotes thoroughness

Requires discipline and imagination

Allows later adjustments

What could hurtme here?

Make more

than one map !

What are thealternativescenarios?



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Multiple Working Hypothesis More than one map is necessary to often

characterize the various scenarios you mayinterpret

This is the crux of composite mapping so critical tounconventional play assessment

What are thealternativescenarios?

What could hurtme here?

Make morethan one map !

Where are thesweet spots?

Pressures Against the Use of

Multiple Working Hypothesis

May be seen as wasteful or irresolute Belief that management cant handle uncertainty Difficult to sell a Project if it isnt clear cut Need to justify Project expenditures May need to act decisively before enough data are

available



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1, 2, 4 _____

1, 2, 4 _____

While You Are Doing This, Pay AttentionTo What Is Going On In Your Head!

Numerical Logic:

1, 2, 4 _____

1, 2, 4 _____

Multiple Working Hypotheses

1, 2, 4 _____

ConsistentCan apply a similar process to different trends

UnbiasedMiss the range on the high side about as often as the low side

PracticalStarting with baseline or analog data, craft the estimate approximatewith the amount of uncertainty present

ResourcefulShould make maximum use of the information contained in the data

CalibratedTrack results against forecasts as a basis for improvement

A Professional Estimator Should Be



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Most Geotechnical EstimatesAre Made Under Uncertainty!

Source rock type, volumes and richness

Productive areaAvg. net payEffective PorosityHC saturation

Geological Chance Factors

Productive rates: IP, initial decline, b, terminal decline and rate

Pilot duration and completion designCosts: land, drilling, completion, developing, operatingPrices, timing

Ro, perhaps densityTOC, Gas content (from lab work)% recovery (dont estimate in UCR)Bg, Bo

More prevalent withunconventionalreservoirs

This part of the Basic Equation is expressed as a cash-flowschedule incorporating net revenue interest (NRI), production

decline, time-value of money, and anticipated inflation.

PcTOTALEUR

WELLHEADPRICE

NET FINDING,DEVELOPING, &OPERATING COSTS

NETTAXES

X ++NRI * -

(1- Pc)NET AFTER -TAX FAILURE

COST=

PROJECT EXPECTEDNET PRESENT VALUE

@ X%

The Basic Equation For Project Evaluation



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Key Points To Remember

Patterns of estimating bias underuncertainty are endemic, but usuallytreatable

By recognizing this, we can take steps toovercome the biases

A good estimator should be:Consistent, Unbiased, Practical, Resourceful &

Calibrated

Effective estimating skills arise from

Utilizing the expectation of lognormality; Appropriate Ranges; Plausibility and reality checks; The power of independent multiple estimates

ESTIMATING IS$ERIOUS BUSINES$ !!

!!! Learning Opportunity !!! Normally each $1.00 or more each

A larger contribution will make a larger prize.

Winner takes all!



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P99

P01

P98

P02

P95

P05

P90

P10

P80

P20

P70

P30

P60

P40

P50

P50

P40

P60

P30

P70

P20

P80

P10

P90

P05

P95

P02

P98

P01

P99

10

100

1,0

00

10,0

00

03 - uncertainty

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