Stochastic Handling of Uncertainties in the Decision Making Process

SPE London, 26th October 2010

Dag Ryen Ofstad, Senior Consultant, IPRES Norway

Setting the sceneProduction Prognosis

0.0

150.0

300.0

2007 2030

MS

m3

o.e

. / Y

ea

r

Undiscoveredresources

Improvedrecovery

Discoveries

Reserves

Mature areas: Production decline and marginal discoveries

New areas: Risks and uncertainties may be high

• offshore ultra deep water• unconventional resources• use of new technology

Average Volume / Discovery

0

50

100

1969-78 1979-88 1989-98 1999-08

MS

m3

o.e

.

Averagevolume /discoveryMSm3 o.e.

NPD 2009NPD 2009

Increasing Need for Proper Decision Analyses

Technical Disciplines

Project Managers

Economic Analysts

Portfolio Management

Top Management

DECISIONS

DECISIONSITUATIONS

Basic Economics

•Drill exploration wells•Choose field development concepts•Choose drainage strategies•Rank and drill production wells •Buy/sell assets•Include/exclude projects from portfolio

Basic Probabilistics

Decision Theory

Quantifying Uncertainty

METHODOLO

GY

WO

RK PRO

CESSES

Geology, geophysicsproduction, drainagedrilling, facilities, timing

Monte Carlo simulationMean, Mode, P10, P50, P90Correlations

Systematic, unsystematic riskNPV, discount rateTax systems, price simulation

Decision parametersProject optimizationDecision treesPortfolio management

SO

FTWARE T

OO

LS

Decision Basis for Management

Decision Basis for Management

Decision analysis

Structure Problem

Structure Problem

Quantify Key Measures

Quantify Key Measures

Capture Uncertainties

Capture Uncertainties

Buy licence?

Sell? At which

price?

Develop discovery? Area Plan?

How?Drill exploration

well?

Negotiations -Licensees -Government

Strategy and

planning processes

LIF

EC

YC

LE

Production, EORRe-development projects

Project Execution

Concept Screening

ConceptOptimization

Exploration / Early

feasibilityLIF

EC

YC

LE

FE

ED

DECISION GATE 1 DG2 DG3 DG4

PDO

Facts• One existing platform• Exploration well, discovered gas with a thin oil column (>10 m)• Enough gas for development, but uncertain for oil development• Total of three discoveries and 3 prospects in the area

Discovery A

Export route A

Export route B

Prospect C

Field AWith oil rim

Discovery B

Prospect B

Prospect A

Decision Analyses - Project Examples

Area Development

& Concept Selection

Facts• 3 exploration wells• Gas-condensate + Oil leg• 3 development scenarios

Well CWell AA’A

Oil Leg ?

Well B

Field B Field A

?

• Produce oil leg?• Additional appraisal well? • Drainage strategy?

Decision Analyses - Project Examples

?

Facts• Oil + Associated gas• 2 segments, one proven• 6 development scenarios

Decision Analyses - Project Examples

Tie-in to A

Tie-in to B

FPSO1

FPSO2

FPSO3

FPSO4

2010

Differences in:Production start dateBuild-upCAPEX / OPEXLease / TariffsLiquid CapacityContract Period

Which option to choose given the uncertainty in reserves and productivity

20122014

Pro

babi

lity

NPV (10^6 USD)

Concept 1, 2, 3, 4, 5

Concept 1

2

34

5

Highest NPV, but also largest uncertainty

Decision Analyses - Methodology

Success criteria

CONSISTENCY

DECISION-MAKING PROCESS DG1 DG2 DG3 DG4 DG5

• Decision tools • Integrated work approach• Methodology

=> Need all!

DATA DECISIONSANALYSES

Method x

DECISION-MAKING PROCESS

Method y

Analysis 1

Analysis 2

Analysis 3

Analysis 4

Analysis 6

Method z

EXPERTS PROJECTS

CONSISTENCY

Tools, Work Approach and Methodology

PORTFOLIO

Economic Parameters

Semi-Deterministic work approachSub-Surface, Production, Drilling Parameters

Decision?

CAPEX / OPEX and Schedule

SENSITIVITES

CAPEX, OPEX and Schedule

Economic Uncertainties

Integrated and Stochastic work approach

Sub-Surface ProductionDrilling

MONTE-CARLO

SIMULATION

UNCERTAINTIES

AND RISKS

Portfolio risk

Portfolio effects on risk

Systematicrisk

Unsystematicrisk

Size of portfolio

Relevantrisk

Portfolio x

Unsystematic riskSystematic risk

Can be reduced in a portfolio of assets through diversification.

Exploration risks, reserves,recovery, production, drilling and operations.

Cannot be reduced by diversification.

Price, currency, inflation, material cost.

oil

gas

oil

gas

oil

gas

oil

gas

Nr. & type of production/Injection wells

ProducingReserves

Production & Transport Facilities

Process capacity

Production profiles

CAPEX schedule

CAPEX

Well CAPEX & OPEX

Well CAPEX schedule

Process & Transport EPCI timeDrill rate

OPEX

Well uptime

Productionbuild up

Well/Process Capacities

Processuptime

Oil priceTariffs Revenue,oil & gas

CO2 fee

Gas price

Tax

Market considerations

Inflation &Discount

rate

Project cash flow

Economic indicators:

EMV,NPV,IRR, etc.

Prospect(s)

Oil/gas priceforecast

CAPEX & OPEX Market prognosis

Discovery?

Field development planning

Provide clear insight into complex projects

Res

ult

sCapturing the Uncertainties

Capacity ConstraintsFacilities & Wells, Schedule

Oil and Gas Reserves / Resources

Production Profiles

CAPEX OPEX Tariff

P&AAbandonment

Cut off

Cash flow

Rock & Fluid Characteristics

Rock VolumeParameters

RecoveryFactor

Revenue

Fiscal Regime

Probability Plots

Decision Trees

Summary Tables

Tornado Plots

Time Plots

NPV

Cash Flow

PR

OB

AB

ILIT

Y

RESERVES

PR

OD

UC

TIO

N

TIME

Prod.start

Project descriptionResponsibilitiesChange Records

Model initialisationSystem set-up

Exploration risks

Reserves calculationsMay include different:-Geological scenarios-Seismic interpretations-Several sediment.models etc.

Production profilesProduction constraintsAvailable capacityProfile preview

Economics input(Oil price, gas price, discount rate, fiscal regime)

Run simulation

Inspect resultsComparisonsExport to STEA

Generate reports

New / Open / CloseSave / Save As / Exit

Drilling cost and timingRisk factors and cost implications

CAPEX / OPEXPhasingTransportation and tariffsLogistics and insurance

Separate analyses of field projects, concepts and sensitivites

Analysis A Analysis B Analysis C Analysis D Analysis E

Integrated Field Development Model

Integrated Field Development Model

HIGHESTEMV

E EE’

Compare and rank

Optimize andupdate

H

GF

BC

DE

A

CONCEPTS

Analyses

Optimum path basis for decisions

Compare and rank

BACK-UP SLIDES

Deterministic vs. probabilistic approach

How can input risk and uncertainty be quantified?

DETERMINISTIC PROBABILISTIC

• Full range of possible outcomes

• True expected NPV

• True P90

• True P10

• Correct comparison and ranking of options

PARAMETER 1 ’high’ ’base’ ’low’PARAMETER 2 ’high’ ’base’ ’low’PARAMETER 3 ’high’ ’base’ ’low’PARAMETER 4 ’high’ ’base’ ’low’PARAMETER 5 ’high’ ’base’ ’low’

• Three discrete outcomes

• Base Case Expected for the project

• High case and low case are extremely unlikely to occur

PARAMETER 1 DistributionPARAMETER 2 DistributionPARAMETER 3 DistributionPARAMETER 4 DistributionPARAMETER 5 Distribution

SimulationBase caseHigh case

Low case

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

-1000 -500 0 500 1000 1500 2000 2500 3000

NPV (10^6 USD)

Why use "Mean" for decision-making ?

PRO: The mean:• Performs right "in the long run"

– Decisions based on the mean

has the lowest expected error• Caters for occasional large

surprises• Is additive across reservoirs,

fields and portfolios• Maximises the value of the portfolio

The mean is most companies’ preferred basis for decisions !

CON: The mean:

• Is possibly more complicated tocomprehend and explain

• May give "infeasible" values

– Mean number of eyes of a dice is 3.5

– Sum of 100 dice: Makes sense

Statistical Measures

Mode

P50Mean

Mean The same as expected value. Arithmetic average of all the values in the distribution. The preferred decision parameter.

Mode Most likely value. The peak of the frequency distribution. Base case?

P50 Equal probability to have a higher or lower value than the P50 value. Often referred to as the Median.

PR

OB

AB

ILIT

Y

DETERMINISTIC BASE

STOCHASTIC MEAN

DRILLING TIMEPER WELL

n EQUAL WELLS

DETERMINISTIC BASE

STOCHASTIC MEAN

P90

P10

# WELLS

TIM

E

n

Deterministic base: Underestimates drilling costOverestimates # wells drilled per yearOverestimates production first years

Drilling campaign example

Courtesy of IPRES

PRODUCTIONDEV.COST

RESERVESDRILLING

GRV

Ø

N/G

Rc

Sw

Bo

NEX

T T

AR

GET

Probabilistic approach SIMULATION

Presents full range of possible outcomes

Key factors contributing to overall uncertainty

SIMULATION

Presents full range of possible outcomes

Key factors contributing to overall uncertainty

Example Contact Uncertainties - Cases

2731

2577

26472625

2688

2800

Non-communication

2731

2577

26472625

Communication

OPTIMISTICPESSIMISTIC EXPECTED CASE???

Monte Carlo - Principle

Probability ofGas-Cap

GRV N/G Ø Sw Bg RfRandom Number Generator

Probability forCommunication

Fault location adjustment

Depth conversion adjustment

GOC OWC

Development scenarios

(1) Pure depletion

– Long curved horizontal producer

(2) Water injection

– Short horizontal producer

– Vertical injector

(3) Gas injection

– Long horizontal producer

– Vertical gas injector

(4) WAG injection

– Short horizontal producer

– WAG injector

Reserves

P