Value of Seamlessly Collaborative Integrated Studies

SPE Distinguished Lecturer Program

The SPE Distinguished Lecturer Program is funded principallyThe SPE Distinguished Lecturer Program is funded principally through a grant from the SPE Foundation.

The society gratefully acknowledges the companies that supportThe society gratefully acknowledges the companies that support this program by allowing their professionals to participate as lecturers.

Special thanks to the American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) for its contribution to the program.p g

Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

Value of SeamlesslyValue of Seamlessly Collaborative Integrated Studies

Presented byYasin Senturk

Principal ProfessionalPetroleum Engineering and Development

Saudi Aramco Oil Company

Society of Petroleum Engineers

Saudi Aramco Oil CompanyDhahran, Saud Arabia

Society of Petroleum Engineers Distinguished Lecturer Programwww.spe.org/dl

OUTLINE

IntroductionObjectivePremise of Collaborative StudiesPremise of Collaborative Studies Critical Resources/Drivers

Q lifi d P f i l M (Mi d )Qualified Professional Manpower (Minds)Multidisciplinary “Know-How” (Expertise)I t t d D i i A l i (IDA) Th PIntegrated Decision Analysis (IDA) - The Process, Bases and Methodology

Conclusions : A Corporate Check List

Yasin Senturk, Principal Professional, Saudi April 2009 – Malaysia, Philippines and India 33

Conclusions : A Corporate Check List

IntroductionSequential vs. Collaborative ProcessesSequential vs. Collaborative Processes

Sequential “Asset Team” Approach (Late 1980’s)

Exploration& Delineation

ReservesAssessment

ReservoirDevelopment &

Management

ProductionOperations

Processing & Sales

Sub-Optimal Solutions

Seamlessly “Collaborative Team” Approach (During 2000’s)

ProcessingProcessing& Sales


Parallel workflows & synergies captured: Optimal Solutions. l

ObjectiveCreate Value and Maximize Shareholders’ Wealthth h S i i i d t l d d t b

j

through Synergizing minds, tools and database.

HOW?Educate, Recruit, Develop and Retain Qualified Professional WorkforceIntegrate Multidisciplinary “Know- How”* to carry out appropriate studies to help make

ti l i t t d i irational investment decisions.

*


* Defined as Core Competencies/Expertise in Technical, Business and Leadership Skills l

Collaborative StudiesThe Premise

AB

The Premise

BCD

Decisions

AB

Focus only on what mattersXX

XX

XBCD

Work in parallel with faster tools

XX X

X

Work in parallel with faster toolsABC Improved Resources Utilization &


CD

p o ed esou ces Ut at o &Substantial Savings in Time l

Collaborative StudiesKey Attributes

Multi-disciplinary / Cross-Functional Teams

Key Attributes

Multi-disciplinary / Cross-Functional TeamsDedication & FocusParallel WorkflowsFull Spectrum of AlternativespB-i-C Integrated Decision Analysis (IDA)B tt & F t R lt d D i iBetter & Faster Results and DecisionsSynergy (Minds, Tools & Database) l


Collaborative StudiesSynergies Captured: Minds, Tools & DatabaseSynergies Captured: Minds, Tools & Database

l


Critical Resources / Drivers

Qualified Professionals: MindsKey Attributes Education Learning & Development (L&D)Key Attributes, Education, Learning & Development (L&D)

Multidisciplinary “Know-How” : Expertise

I t t d D i i A l i (IDA) P ttiIntegrated Decision Analysis (IDA): Putting it all together to capture synergies of Minds, T l d D t b


Tools and Database

Qualified ProfessionalsKey AttributesKey Attributes

Successful Professional Jasper King, The Way Ahead, SPE

AmbitiousR lt O i t d

p g yPublication (Vol.3, No.1, 2007)

Results-OrientedTechnically ProficientTeam PlayerEffective CommunicatorBusiness Savvy

Summary: Possess Technical Business & Leadership Skills


Summary: Possess Technical, Business & Leadership Skills

Qualified Professionals…University Education: Rethink & Reengineer

Economic Evaluation of a Typical Oil Producing Property16 000 1 400

University Education: Rethink & Reengineer

12,000

14,000

16,000

1,000

1,200

1,400

MM

$)

Cum Costs

The Key Hypothesis*

8,000

10,000

ate

(ST

B/D

ay)

600

800

nd C

ash

Flow

s (MRate

Cum NCF @ 0%Cum NCF @ 10%

Q vs. t

l

2 000

4,000

6,000

Oil

Ra

0

200

400

Cum

Cos

ts a

n l

0

2,000

1 3 5 7 9 11 13 15 17 19 21 23 25

Years

-200

0


* How learning the topic like porosity (e.g. reserves) is going to contribute to this bottom line must be emphasized.

Qualified Professionals…L&D – Competency Development Cycle

Create Personal Skill InventoryDefine

Set / Review

L&D Competency Development Cycle

E i t

Create Personal Skill Inventory(Discipline Specific)

Set / ReviewPersonal Goals &Business Needs

R i Assess

l

EnvironmentConducive for

Quality Learning &

ReviewAssess Effectiveness

& Create PersonalSkills Inventory

AssessIdentify Development Needs & Skills GAP

Development (L&D)Plan

Prepare/Modify

y

Execute Prepare/ModifyDevelopmentAction Plan

ImplementDevelopment

Actions

CoursesPlanned Work ExperiencesSpecial Job Assignments


Adapted from “ Oxy’s input to PetroSkills (2004 Conclave, Houston, TX)”

Qualified Professionals…L&D - Career Progression & Value Added

Principal

L&D Career Progression & Value Added

Staff

Consultant

VALUE(Performance

Delivery)

Specialist

SeniorDelivery)

ResultsScope ,Impact

Discipline Junior

p , p& Influence

Measures Progress & Sets Expectations for Performance Delivery l

Core Competencies


Adapted from “ Unocal’s input to PetroSkills (2004 Conclave, Houston, TX)”


Qualified Professionals - Minds

Multidisciplinary “Know-How” - ExpertiseTechnical, Business and Leadership Skills

Integrated Decision Analysis (IDA)


Multi-Disciplinary “Know-How”

Three (3) Core Competencies / Expertise:

p y

Technical Specialties/SkillsEarth Sciences – Geology & GeophysicsEarth Sciences Geology & Geophysics

Petrophysics

Engineering - Drilling & Completion, Reservoir, Production & Facilitiesg g

Statistics & Decision Analysis

Maintenance & Operations, etc.

Business Skills - Economics & Finance

Leadership Skills K l d Sh i C i ti


Leadership Skills – Knowledge Sharing, Communication, Impact & Influence, etc l


Qualified Professionals - Minds

Multidisciplinary “Know How” E tiMultidisciplinary “Know-How” - Expertise

Integrated Decision Analysis (IDA)S i t d th Mi d D t b & T lSynergies captured thru Minds, Database & Tools


Integrated Decision Analysis (IDA) The Process – A Pictorial View

Geological Geological M d lM d l

Data AcquisitionData AcquisitionSimulation

The Process A Pictorial View

ModelModel

Database

Simulation Model

Database

Interpretation & ModelingInterpretation & Modeling

Technical Analysis D i i A l i

Interpretation & ModelingInterpretation & Modeling

Technical Analysis• Multiple Realizations• Reserves• Development Options

Decision Analysis Plans for Execution


Development Options

Business Modell

Integrated Decision Analysis (IDA)The Process – A Conceptual PremiseThe Process A Conceptual Premise

Reservoir Characterization,Modeling & Simulation

(Multiple Realizations – Volumes & Profiles)

TECHNICAL ANALYSIS

Evaluation of Alternatives, including Risk Analysis

Data & Knowledge

M t

Evaluation of Alternatives, including Risk Analysisincluding Risk Analysis

(Reserves, Development Plans and Producing Strategies)

DECISION ANALYSIS

Management(Acquisition & Learning)

DATABASE

including Risk Analysis(Reserves, Development Plans

and Producing Strategies)

Planning and Field

Implementation

DECISION ANALYSISPlanning and Field

Implementation

DECISION ANALYSIS

l


ACTION PLANACTION PLANl

Integrated Decision AnalysisTechnical Issues – Key Parameters

With Significant Impact on Results:

Technical Issues Key Parameters

Proper PhysicsDetails - Grid Size, Number of Layers eta s G d S e, u be o aye sPetrophysical-based Rock TypingINPUT DATA Quality Static and DynamicINPUT DATA Quality – Static and DynamicInitialization – How to propagate & distribute data?data?Simultaneous Nodal Analysis (Reservoir, Completion Wellbore & Surface) l


Completion, Wellbore & Surface) l

Integrated Decision Analysis Technical - Location of Trapped Oil Example

Water Cut and Pressure Match

Sw Profile

Technical Location of Trapped Oil Example

lWater Cut and Pressure Match

Well Log

Simulator

l

New Well

Delta Sw in 30 Years(14 Layer Model; 53,000 cells)


Delta Sw in 30 Years(128 Layer Model; 1.4 million cells)SPE 71628 Pavlas

Integrated Decision AnalysisTechnical - Quantifying UncertaintyTechnical Quantifying Uncertainty

100% 5%

80%

100%

4%

5%

bilit

y ≥ Mean=28

Average or Expected Value (EV)

40%

60%

2%

3%

babi

lity

ePr

obab

50% l

20% 1%

Prob

mul

ativ

e

SD-Variability/Risk0%

-40 -20 0 20 40 60 80 1000%

Reserves (MMSTB) / Value (MM$)

Cu 16 28


Risk Profile & Expectation Curve

Integrated Decision AnalysisTechnical - Reserves Assessment (Static)

Volumetric Equation: EUR = [A x h x Ø x (1- Swi) / FVF] x RFModeMode Most likely (Mode)Most likely (Mode)

Technical Reserves Assessment (Static)

Random Variables(Probability Distributions) Pr

obab

ility

Swi

Log-Normal

Mode

Mean

Prob

abili

ty

Swi

Log-Normal

Mode

Mean

Prob

abili

ty

AhTriangular

Most likely (Mode)

Min Max

Prob

abili

ty

AhTriangular

Most likely (Mode)

Min Max

RFUniform

Prob

abili

ty Min Max

RFUniform

Prob

abili

ty Min Max

Prob

abili

ty

Mean & Mode

PorosityNormal

Prob

abili

ty

Mean & Mode

PorosityNormal

Fixed ParametersVolumetric Model

g Ng N TriangularTriangular

lity

Mode

Mean

lity

Mode

Mean

Expectation Curve1.00.9

babi

lity ≥

Prob

abil

Prob

abil

OOIP0.1

0.5

1P 2P3PC

um. P

rob


ReservesReserves Reserves0

l

Integrated Decision AnalysisTechnical - Reserves Assessment (Dynamic)

Production Profile Relaizations

Technical Reserves Assessment (Dynamic)

12,000

13,500

15,000

60

75

TB

)

7 500

9,000

10,500

STB

/Day

)

45

uctio

n (M

MST

l

4,500

6,000

7,500

Oil

Rat

e (S

30

mul

ativ

e Pr

od

Exaggerated Scales

0

1,500

3,000

0

15 CumExaggerated Scales


1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

Years

Integrated Decision AnalysisTechnical - Reserves Assessment (Dynamic)

100% Distribution fitSi l t d li ti

P90

Technical Reserves Assessment (Dynamic)ba

bilit

y Simulated realization

P50B t T h i l S i

ve P

rob

Mean

Best Technical Scenario

50% Production Profile Relaizations

12,000

13,500

15,000

60

75

B)

Production Profile Relaizations

12,000

13,500

15,000

60

75

B)

mul

ativ

P10Model Results

0

1,500

3,000

4,500

6,000

7,500

9,000

10,500

12,000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

Oil

Rat

e (S

TB

/Day

)

0

15

30

45

60

Cum

ulat

ive

Prod

uctio

n (M

MST

B

0

1,500

3,000

4,500

6,000

7,500

9,000

10,500

12,000

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

Oil

Rat

e (S

TB

/Day

)

0

15

30

45

60

Cum

ulat

ive

Prod

uctio

n (M

MST

B

l

0%0 20 40 60 80 100 120 140 160

Cu YearsYears


Oil Recovery (MMSTB)

Integrated Decision Analysis Business Issues - Sustainable Earnings Growth

Growth Objective

Business Issues Sustainable Earnings Growth

($)

Net Earnings

jfor

Corporate Earnings(with New Investments)

Ear

ning

s (

P j d E i

Net Earningsrequired to meet

Objectives in year Ti

Ann

ual E Projected Earnings

from Existing Projects(no further Investment)

Time (Years)

A

TiNow 20 40l


Earnings Gap Analysis (Newendorp & Schuyler, 2000)

Integrated Decision Analysis Business - Corporate ObjectivesBusiness Corporate Objectives

Maximize shareholders’ value (SHV) whileminimizing exposure to loss; andensuring earnings growth at a stipulated rate

Preferable when the Corporationmakes Profit each year,makes Profit each year,

stabilizes Profit and sustainable Growth,

maintains Liquidity and Solvency andmaintains Liquidity and Solvency, and

is a good Corporate Citizen. l


Integrated Decision Analysis Business - How to Maximize SHV?

Maximize Market Value Added (MVA)

Business How to Maximize SHV?

( )MVA = Market Value (MV) – Book Value (BV)

M i i (MV BV) R ti th ki ti dMaximize (MV:BV) Ratio thru making continuous and sound new investments

8

Balance Sheet explains 15%(1/6) of Value Added only.

4

6

8

V) R

atio

6 to 1

1 to 1

S&P 500

Remaining 85% (5/6) byTalent & Intelligent Assets l

0

2

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

(MV/

BV


1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Years

Integrated Decision Analysis Business - Valuation/Appraisal Model

Has Appropriate Business Model

Business Valuation/Appraisal Model

Accounts for the Time Value of Money ConceptQuantifies Judgments about Uncertainty andQuantifies Judgments about Uncertainty andincorporates the Risks in project cash flows.Uses realistic Company Discount Rate (or MARR)Uses realistic Company Discount Rate (or MARR)Has a Figure of Value, the best value measure:

NPV @ MARR % (Deterministic Analysis)NPV @ MARR % (Deterministic Analysis)EMV @ MARR % (Probabilistic Analysis)

D i i R l A t if P j t NPV EMV≥ 0 l


Decision Rule: Accept if Project NPV or EMV≥ 0 l

Integrated Decision Analysis Business - Cash Flow based Valuation Model

Net Cash Flow (NCF)

Business Cash Flow based Valuation Model

Income Taxes

Costs Revenue

OPEX CAPEX

• Drilling Platforms & Wells• Fixed Costs (Labor)

ProductionTaxes

• Drilling – Platforms & Wells• Well Flowlines• Production & Injection Facilities• Oil & Gas Pipelines l

• Fixed Costs (Labor)• Variable Costs

- Lifting Costs- Electricity

Etc

Royalty

Production


- Etc.Profiles

Integrated Decision Analysis Business - Evaluation Methods (How?)

Deterministic Methods

Business Evaluation Methods (How?)

Deterministic MethodsDCF Analysis and NPV ProfilesSensitivity AnalysisSensitivity Analysis

Probabilistic MethodsDecision Tree AnalysisMonte Carlo Simulation


Deterministic ApproachComparing Projects by NPV ProfilesComparing Projects by NPV Profiles

800Key Prof it abilit y Measures Sum m ary

600

700 Project A Increm ent alProject A Project B Project (A - B)

Investment Capital (MM$) 300 200 100NPV @ 10% (MM$) 416 205 211DCF - ROR (%) 46% 53% 39%

300

400

500C O (%)

l

100

200

300

RORB = 53%

RORA = 46%

ROR(A-B) = 39%Project B

MARR=10%

-100

0

100Incremental Project


-1000 10 20 30 40 50 60

Nominal Discount Rate (%)

Deterministic ApproachSensitivity Analysis – “Project A” NPVy y j

Tornado Diagram for a Typical Oil & Gas Producing PropertyS i i i f P j NPV Ch i h

Oil Price

Sensitivity of Project NPV to Changes in the Key ParametersParameter

+30%-30%

Discount Rate

$28/bbl $52/bbl$40/bbl

13% 7%10%

-30%+30%

l

CAPEX +30% -30%

13% 7%10% l

OPEX +30% -30%416 MM $


250 300 350 400 450 500 550 600Net Present Value, NPV (Million $)

Decision Tree Analysis“Project A” Expected Value EstimatesProject A Expected Value Estimates

NPV @ 10% EMV @ 10%Reserves Size

(MMSTB)

0.380

NPV @ 10% EMV @ 10%

$200 MM $60 MM

(MMSTB)

1200.6$370 MM $400 MM $240 MM

2000.1

$700 MM $70 MM

$370 MMEV of Reserves

EMV of the Project A

116 MMSTBEMV = $370 MM (probabilistic) versus

NPV = $416 MM (deterministic)


NPV $416 MM (deterministic)

Decision Rule: Higher the EMV better the project profitability. l



( )




Monte Carlo Simulation“Project A” NPV Risk Profile

y

Mean & Mode

y

Mean & ModeMode Mode

y

Most likely (Mode)

y

Most likely (Mode)

ty

Mean & Mode

ty

Mean & Mode

y

Mean & Mode

y

Mean & Mode

NCFt = Volumet x Pricet – Royaltyt – OPEXt – CAPEXt - Taxest

Project A NPV Risk ProfilePr

obab

ility

Volume

Prob

abili

ty

Volume

Prob

abili

ty

CAPEX

Mean

Prob

abili

ty

CAPEX

Mean

Prob

abili

ty

Prices

Min Max

Prob

abili

ty

Prices

Min Max

Prob

abili

t

OPEX

Prob

abili

t

OPEX

Prob

abili

ty

Taxes

Prob

abili

ty

Taxes

Cash Flow Model Fixed Parameters(Deterministic)

100% 50%Mean & SD≥

60%

80%

ve P

roba

bilit

y

30%

40%

babi

lity

Mean & SDEMVA = $385 MM

SDA = $200 MM

50%

≥

Profile with an EMV of $385 MM as compared to l

0%

20%

40%

Cum

ulat

iv

0%

10%

20%Pr

o

350

tnt

∑=

pSingle NPV Estimate of $416 MM

l


%-200 0 200 400 600 800 1000

Net Present Value, NPV @ 10% (MM$)

% t

t

t MARRNCFNPV )1/(%10@

0

+=∑=

Probabilistic AnalysisComparing and Ranking Projects

50%

Comparing and Ranking Projects

40%

y

EMVB = $450

EMVA = $385

20%

30%

Prob

abili

ty

0%

10% SDA=$200 SDB=$100

0%-200 0 200 400 600 800 1000



Decision: Project B is clearly superior to Project A. l

Probabilistic AnalysisComparing and Ranking ProjectsComparing and Ranking Projects

50%

EMVA = $385 30%

40%

ty

EMVC = $190

20%

30%

Prob

abili

SDA=$200SDC=$80

0%

10%

0%-200 0 200 400 600 800 1000



2) For a risk-averse investor, it is not clear & further analysis required. lDecision: 1) For a risk-neutral investor, Project A is better than Project C.

Probabilistic AnalysisComparing Projects - Risk Neutral vs AverseComparing Projects Risk Neutral vs. AverseRisk Adjusted Value, RAV =EMV – σ2

NPV/(2B)50%

Project A40%

y

RAV = $330

EMV = $385

Project A

20%

30%

Prob

abili

ty

0%

10%SD=$200SD=$150

D i i R l F i B d t (B) th hi h th RAV b tt i

0%-200 0 200 400 600 800 1000



Decision Rule: For a given Budget (B), the higher the RAV, better is the project profitability. l

Portfolio OptimizationExample Investment PortfoliosExample Investment Portfolios2500

$)

NM

IK

KN

D2000

Gen

erat

ed ($

NM

IK

KN

DA

BD

H

AP

JO

F

HE

G

1000

1500

ativ

e N

PV G

BD

H

AP

JO

F

HE

G

l

JC

GD

H

CF

GO

EB

IM

BC

J

500

1000

Cum

ula

$1,100 MM

Started with 16 ProjectsCF

GO

EB

CG

DH

IM

BC

J

M

JL

J P LI

00 200 400 600 800 1000 1200

$400 MMWith NPV of $2,070 MM

(requires $1,165 MM Capital)LJ

M

JP L

I


Funds Available for Investment ($)

Investment Portfolio OptimizationIdeal vs. Actual

(%)

Ideal vs. Actual

NPVo(max.)Rate of Return on

Incremental Investment (%) Marginal Costof Capital (%)

t of C

apita

l(

erat

ed ($

) NPVLRaider's Gap(NPV Loss)

Increasing Sub-optimum MARRL (%)

turn

orC

ost

NPV

Gen

e

Optimum MARRo (%)

gNPV ($)

p L ( )

Ann

ual R

et

No. ofP j t 0

Cum

.

3 4 16 NN-115 M21

OptimumBudget

LimitedBudget

Funds Available for Investment ($)

Projects00 3 4 16 NN 115 M21

Maximizing Shareholder Wealth (or MV ) requires:Optimized Capital Structure with Optimal D/E Ratio & minimized MARR

$O$L


Optimized Capital Structure with Optimal D/E Ratio & minimized MARRMaximized Return on Existing Projects (Capital)Maximized Value Added by the New Investment Portfolio ($A w/ $Y) l

Seamlessly Collaborative StudiesPutting it all Together: Maximize Shareholders’ WealthPutting it all Together: Maximize Shareholders Wealth

ertis

e Business

Skills Integrated IDA(Business Impact

& E ti )

ow”/

Exp

& Execution)

l

Kno

w H

o

Technical

Skills

Leadership

Skills

Degree of Collaboration

“K


Degree of Collaboration

CONCLUSIONSVALUE of Collaborated Studies

Q lifi d P f i l W kf Ed t R it

VALUE of Collaborated StudiesA Corporate Check list, Do You HAVE?

Qualified Professional Workforce – Educate, Recruit, Develop & RetainOptimized Capital Structure and Minimized Raider’s Gap

– Survival of the fittestRanked Risk Adjusted Inventory of Investment OpportunitiesppContinuous Development & Execution of Optimal Business Plan Investment Portfolios (Repeatability)Sustainable Earning GrowthSustainable Earning GrowthDistinct Competitive Advantage (Intelligent Assets)

END RESULT: Maximized Shareholders’ Value because of


VALUE CREATION thru Synergies Captured via Integration, Technology & Scale l

Thank you…

Value of Seamlessly Collaborative Integrated Studies

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