Oil Exploration Portfolio Cook

8/2/2019 Oil Exploration Portfolio Cook

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DAAG 2001 Exploration Portfolio Management

An Oil Exploration

Portfolio Management Process:Why? How? Learnings? Challenges?

David M. Cook, Jr.


2/25


Why Portfolio Management Needed?

1998 SituationOil price plummetingAnnual Exploration Capital Budget slashed by 1/3Too Many Opportunities competing for limited

funding Decision-Maker ProblemNeeded mechanism to prioritize strategic

opportunities


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Exploration Management Desires

Understand relative fit of worldwide oil/gasopportunities based on economic decision-makingcriteria appropriate to Exploration uncertainties.

Improve decision-making at the strategic geologic

play oropportunity level (an aggregate ofprospects).

Evaluate multiple year, multiple well outcomesbased on unbiased probabilistic (lognormal)

estimates gathered in a consistent manner.

Recognize and model conflicting objectives andtradeoffs.


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Exploration Portfolio Management Process

Model and Data Capture/Calculation Tool

Multi-Attribute Utility (MAU) Analysis

Ranking and Budgeting


5/25


Model Calculates Risk, Volumes and Value

Probability of

Opportunity

EconomicSuccess

Probability of

Opportunity

Economic

Failure

Opportunity

Success

Value &

Volume

Failure

Cost

Two-Armed Decision Tree Estimates an OpportunitysUnrisked Value & Volume and Risked Value

Risked Expected

Monetary Value =

Ps x Success Value

- Pf x Failure Cost

Ps

Pf = 1-Ps


6/25


Tool Input & Reality Checks

Input (* lognormal range) Reality ChecksRisk Dependent Risk, P(petsys)

Independent Risk, P(prosp)

Petroleum System Database

Risks Dependent & Independent

Prospect Size Distribution

Economic Threshold

Historical Drilling Results

Actual Success Probabilities(technical and economic)

Well Costs

Historical Company %

Discovery Sizes

Discovery Value

Current Prospect Inventory

(Product Pipeline)

Prospect Risk

Prospect Size

Number of Leads/Prospects

Planning & Budget Submittals

Number Wells, Costs, Size, Risk

Oil/Gas

Volume

* Prospect Field Size Distribution(technical volume including non-

economic accumulations)

Activity * Failure Case Number ofConsecutive Dry Holes

* Success Case Number ofProspects Drilled

Cost * Well Cost

* Failure Case Exploration $

Value Prospect Economic Threshold Company %, Discount Rate, Tax %

* Success Case Net Present Valueper Barrel-Oil-Equivalent


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Ps, Probability of Opportunity Economic Success

Dependent Risk, P(petsys) Probability that Petroleum Systemexists in the defined opportunity.

Independent Risk, P(prosp) Probability of average prospectencountering technical oil & gas.

Economic Risk, P(threshold) Probability of any individual discoveryexceeding the Economic Threshold.

Failure Case Number of

Consecutive Dry Holes

Wells willing to drill before exiting theopportunity.

Ps = P(petsys) x [1-{1-[P(prosp) x P(threshold)]}#ConsecutiveDryHoles]


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Ps is Asymptotic at Dependent Risk

P(petsys) x [1-{1-[P(prosp) x P(threshold)]}#ConsecutiveDryHoles]

Independent Binomial

Probability Equation

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10

Number of "Consecutive Dry Holes"

ProbabilityofOneorMore

EconomicSuccessesAsymptotic at

Dependent Risk

P(petsys) = 0.70

P(prosp) = 0.40

P(threshold) = 0.80

Dependent

Risk


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Calculated Tool Output

Individual Prospect Opportunity( * critical output for MAU)

Probability ofProspectExceeding Economic

Threshold

Risk * Probability of Opportunity Economic Success

Prospect Economic Field SizeDistribution Oil/Gas

Volume

Success Case Technical Volume

* Success Case Economic Volume

Activity Success Case Number of Technical Discoveries Success Case Number of EconomicDiscoveries

Cost * Failure Cost

Success Case Dry Hole Cost

* Success Case Cost

Value Success Case Discovery Value * Success Case Net Present Value

* Risked Expected Monetary Value


10/25


Key Model & Tool Learnings

Management support and championing iscritical. Dedicated data gathering team for

consistency and QA.

Reality check vs. other databases. Technologists understood and bought-in. Re-interview and iterate anomalies. Data is dynamic update with new

information. Spreadsheet allowed quick, easy sensitivity

analysis.


11/25


Multi-Attribute Utility Analysis What Objectives Define the Best Exploration Opportunity?

Maximize Economic Value Maximize Economic Volume Minimize Economic Risk

Use Multi-Attribute Utility theory when $ is not the sole measure of value, or objectives not easily translated into monetary-equivalents, or objectives conflict or have trade-offs.

Utility = Goodness based on decision-makerspreferences. Define: Objectives Objective Attribute(s)Attribute Utility

Attribute Weight


12/25


Objectives Hierarchy, Attributes, Weights

Expected

Monetary Value

AttributeSuccess

NPV

Maximize Reward

Failure Cost

Success Cost

Minimize Cost

Maximize Value

SuccessEcon. VolumeMaximize Volume

Prob. of Opp.

Econ. SuccessMinimize Risk

Best

Exploration

Opportunity

45%

35%

20%

10%

15%

15%

5%

35%

20%


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Utility: Risk Neutral, Averse, Seeking

Risk Averse Utility Curve. an 800 million barrel

discovery has about same utility as a 1000 million barrels.

Utility(x)

Company Success Case Volume

1.00

0.00

Min:

1

Max:

1000

Level:

800

U(800)=0.95U(1000)=1.00

Linear Utility curveis Risk Neutral.

U(800)=0.80

Risk Seeking Utility curveshows gambling tendency.

Only big payoffs have bigUtility.

U(800)=0.50

Calculate weighted-average composite utility for each opportunity.


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0

2

4

6

8

10

12

14

16

18

20

$0 $400 $800 $1,200 $1,600 $2,000 $2,400

Cum. Expl. Cost (BT) over Five Years

Cu

m.U

tility

Utility Ranking Shows Impact

Cumulative Failure Cost vs. Cumulative Utility

Linear expected a curve.No relationship betweenFailure Cost and Utility.

Constrained capital budget requires prioritized ranking

Rank order on descending Utility

Fund the highest Utility projects


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0

2

4

6

8

10

12

14

16

18

20

$0 $400 $800 $1,200 $1,600 $2,000 $2,400

Cum. Expl. Cost (BT) over Five Years

Cum.U

tility

Efficiency Ranking Shows Bang-for-Buck

By ranking on Efficiency can attain:-the same Utility for $400 million less or-a 20% higher Utility for same dollars.

Efficient Frontier

Efficiency = Utility / Failure Cost Rank order in terms of Efficiency

Fund the most Efficient projects


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Dialog and Understanding of S/W/B/C

015304560

Utility Ranking

EfficiencyRankin

g

High Utility &High Efficiency:Fund As-is

4Q 3Q 2Q 1Q

1Q

2Q

3Q

4Q

High Utility &

Low Efficiency:Decrease &Control Costs

Low Utility &Low Efficiency:Reject &Farm-Out

Low Utility &High Efficiency:More fundingto increaseUtility?

Sunk CostsQuestions?


17/25


Key MAU and Ranking Learnings

Management support and championing critical. Defining objectives, attributes, utilities, weightsis key. Too many attributes led to regression to the mean

so that hard to differentiate middle-of-the-pack.

Rankings are not the final answer. Facilitate dialog and understanding.

Sensitivities can easily and quickly:

Address management concerns. Identify ways to optimize opportunities.

Portfolio Management is learning process. Be willing to modify and enhance.


18/25


Challenges and Concerns

Management Championship Communication

Understanding

Opportunity Granularity Consistency

Compositing Sunk Costs Or Costs-Forward Only?

Political, Regulatory, Environmental Risks

Ranking on Utility or Efficiency? Before-Tax or After-Tax

Multi-Year Budgeting

Sticking to Failure Case Models

Enhance with Monte Carlo and Linear Programming


19/25


Backup


20/25


Exploration Portfolio Management Process

M

odel and Data Capture/Calculation Tool Input: Interview, Capture, Reality Check

Output: Calculate, Gather, QC, Re-interview

Multi-Attribute Utility (MAU) Analysis

Objectives Hierarchy Attributes and Weights

Utility Curve for Each Attribute

Ranking and Budgeting

Calculate Weighted-Average Utility for EachOpportunity

Rank Order All Opportunities in Portfolio

Facilitate Dialog about Opportunity S/W/B/C


21/25


Utility Curves: Risk Neutral,Risk Averse

Risk Averse Utility Curve. an 800 million barrel

discovery has about same utility as a 1000 million barrels.

Utility(x)


1.00

0.00

Min:1

Max:1000

Level:800

U(800)=0.95U(1000)=1.00

A linear Utility curveis Risk Neutral.

U(800)=0.80


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Utility Curves: Risk Seeking

Risk Seeking Utility curveshows a gambling tendency.Only big payoffs have big Utility.

$ Utility curves shouldbe Risk Neutral.

U(800)=0.50

U(800)=0.80

Utility(x)


1.00

0.00Min:

1Max:1000

Level:800

Calculate weighted-average composite utility for each opportunity.


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Success Arm Economic Volumes & Value

On the Success Arm of the Decision Tree,

Number of Economic Discoveries =

Number of Wells Drilled x P(local) x P(threshold)

Conditional Field Size Distribution =

Range of Volumes in excess of Economic Threshold

Company Economic Volumes =Number of Economic Discoveries x Conditional FSD x Company %

Discovery Value =Company Economic Volumes x NPV/Barrel-Oil-Equivalent

(note that NPV/BOE is for a successful development,

excluding exploration dry hole costs)

Success Arm Dry Cost =

(Number of Wells Drilled - Number of Discoveries) x Co. Well Cost

Success Value =Discovery Value - Success Arm Dry Cost


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Failure Arm Cost and EMV

On the Failure Arm of the Decision Tree,

Failure Cost =

Number of Consecutive Dry Holes x Co. Well Cost +Overhead + Seismic & Geology Costs + Land Cost

(Discounted at Co. Discount Rate.Before-Tax and After-Tax Costs also calculated)

The risked ExpectedMonetary Value is:

Expected Monetary Value =

Ps x Company Success Value -Pf x Company Failure Cost


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Quartile Ranking: Utility vs. Efficiency

4Q 3Q 2Q 1Q

1QEDWNG

KPS

CD

AKC

KLO

CS

S

ISA

AKT TPS

NH

ACG

BC

PF

ACO

2QM

IMDWC

UFSB

IB

NCVGT

LOE

LG

LM

CFP

BCT

LSE

PAGC

BES

SPF

3QIMRT

KLO

IS

NCVGJ

UHRFB

PAO

BDW

UMBQP

VCLB

EDWNO

IDWKG

SAS

G

NAM

RS

STP

4QVM

UAS

UMBPM

URT

VE A

SW T

UMBPM

CDNS

CA

SMCT

NDW

KFSO

IEJC

SLRFBEfficienc

y(Utility/Failure

Cost)

Utility (Volumes, Value, Risk)

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Oil Exploration Portfolio Cook

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