Stochastic Handling of Uncertainties in the
Decision Making Process
SPE London, 26th October 2010
Dag Ryen Ofstad, Senior Consultant, IPRES Norway
Setting the scene
Production Prognosis
Average Volume / Discovery
100
NPD 2009
NPD 2009
Undiscovered
resources
Improved
recovery
150.0
Discoveries
MSm3 o.e.
MSm3 o.e. / Year
300.0
Average
volume /
discovery
MSm3 o.e.
50
Reserves
0.0
2007
0
2030
1969-78 1979-88 1989-98 1999-08
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
Increasing
Need for
Proper
Decision
Analyses
DECISIONS
Decision Theory
Decision parameters
Project optimization
Decision trees
Portfolio management
Top Management
Basic Economics
Systematic, unsystematic risk
NPV, discount rate
Tax systems, price simulation
Portfolio Management
Basic Probabilistics
Monte Carlo simulation
Mean, Mode, P10, P50, P90
Correlations
Quantifying Uncertainty
Geology, geophysics
production, drainage
drilling, facilities, timing
DECISION
SITUATIONS
Project Managers
Economic Analysts
Technical Disciplines
•Drill exploration wells
•Choose field development concepts
•Choose drainage strategies
•Rank and drill production wells
•Buy/sell assets
•Include/exclude projects from portfolio
Decision analysis
Quantify Key
Measures
Decision Basis
for Management
Structure
Problem
DECISION
GATE 1
LIFE CYCLE
CYCLE
LIFE
Develop
discovery?
Area Plan?
How?
Exploration /
Early
feasibility
Negotiations
DG2
DG3
Concept
Screening
-Licensees
-Government
Buy
licence?
Sell?
At which
price?
FEED
Drill
exploration
well?
Capture
Uncertainties
Strategy
and
planning
processes
DG4
Concept
Optimization
PDO
Project
Execution
Production, EOR
Re-development projects
Decision Analyses - Project Examples
Discovery A
Export route B
Prospect A
Discovery B
Field A
With oil rim
Prospect C
Prospect B
Area
Development
&
Concept
Selection
Export route A
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
Decision Analyses - Project Examples
A
Well B
Well A
Well C
A’
Field A
Field B
?
?
Oil Leg ?
Facts
• 3 exploration wells
• Gas-condensate + Oil leg
• 3 development scenarios
• Produce oil leg?
• Additional appraisal well?
• Drainage strategy?
Decision Analyses - Project Examples
2012
Tie-in to A
2014
2010
Tie-in to B
FPSO1
FPSO2
FPSO3
FPSO4
Facts
• Oil + Associated gas
• 2 segments, one proven
• 6 development scenarios
Differences in:
Production start date
Build-up
CAPEX / OPEX
Lease / Tariffs
Liquid Capacity
Contract Period
Which option to choose
given the uncertainty in
reserves and productivity
Decision Analyses - Methodology
Concept 1
2
3
4
2,
3, 4, 5
Probability
Concept 1,
5
NPV (10^6 USD)
Highest NPV, but also
largest uncertainty
Success criteria
• Decision tools
• Integrated work approach
• Methodology
=> Need all!
DECISION-MAKING PROCESS
DG1
DG2
DG3
DG4
CONSISTENCY
DG5
Tools, Work Approach and Methodology
EXPERTS
PROJECTS
DATA
ANALYSES
DECISIONS
Method x
Analysis 1

Method y






Analysis 2
Analysis 3
Method z
Analysis 4
Analysis 6
CONSISTENCY
DECISION-MAKING PROCESS
PORTFOLIO
Semi-Deterministic work approach
Sub-Surface, Production, Drilling Parameters
CAPEX / OPEX and Schedule
Economic Parameters
Decision?
Integrated and Stochastic work approach
Economic Uncertainties
Sub-Surface
Production
Drilling
MONTECARLO
SIMULATION
CAPEX, OPEX and Schedule
Portfolio effects on risk
Systematic risk
Relevant
risk
Unsystematic risk
Portfolio Cannot
risk
be
reduced by
diversification.
Can be reduced in a
portfolio of assets
through diversification.
Price, currency,
oil
inflation, material cost.
gas
Exploration risks, reserves,
recovery, production,
drilling and operations.
Unsystematic
risk
Systematic
risk
Portfolio x
Size of portfolio
Field development planning
Provide clear insight into
complex projects
Economic
indicators:
EMV,NPV,IRR, etc.
Project cash flow
Prospect(s)
Tax
Producing
Reserves
Discovery?
Nr. & type of production/
Injection wells
Drill rate
Well CAPEX
schedule
Production &
Transport Facilities
Market
considerations
Well CAPEX
& OPEX
Well/Process
Capacities
CAPEX
schedule
Tariffs
rate
Process capacity
Process & Transport
EPCI time
Production
profiles
Production
build up
OPEX
CAPEX
Inflation &
Discount
CO2 fee
Gas price
Well uptime
Revenue,
oil & gas
Process
uptime
Oil price
Oil/gas price
forecast
CAPEX & OPEX
Market prognosis
Capturing the Uncertainties
Rock & Fluid
Characteristics
Recovery
Factor
PROBABILITY
Rock Volume
Parameters
Oil and Gas Reserves / Resources
CAPEX
Production Profiles
OPEX
Revenue
Tariff
PRODUCTION
Capacity Constraints
Facilities & Wells, Schedule
RESERVES
TIME
Prod.start
Cash flow
Cash Flow
Results
Cut
off NPV
Probability
Plots
Time
Plots
Decision
Trees
P&A
Abandonment
Tornado
Plots
Fiscal
Regime
Summary
Tables
Integrated Field Development Model
New / Open / Close
Save / Save As / Exit
Drilling cost and timing
Risk factors and cost implications
Run simulation
Inspect results
Comparisons
Export to STEA
CAPEX / OPEX
Phasing
Transportation and tariffs
Logistics and insurance
Project description
Responsibilities
Change Records
Model initialisation
System set-up
Production profiles
Production constraints
Available capacity
Profile preview
Exploration risks
Generate reports
Economics input
(Oil price, gas price,
discount rate, fiscal regime)
Reserves calculations
May include different:
-Geological scenarios
-Seismic interpretations
-Several sediment.models etc.
Separate analyses of field projects, concepts and sensitivites
Analysis A
Analysis B
Analysis C
Analysis D
Analysis E
Integrated Field Development Model
Compare and rank
Optimum
path basis
for decisions
Analyses
Compare
and rank
Optimize and
update
A
H
C
B
F
CONCEPTS
D
G
E’
E
E
HIGHEST
EMV
E
BACK-UP SLIDES
Deterministic vs. probabilistic approach
How can input risk and uncertainty be quantified?
DETERMINISTIC
PARAMETER 1
PARAMETER 2
PARAMETER 3
PARAMETER 4
PARAMETER 5
’high’
’high’
’high’
’high’
’high’
’base’
’base’
’base’
’base’
’base’
PROBABILISTIC
’low’
’low’
’low’
’low’
’low’
Low case
Base case
High case
PARAMETER 1
PARAMETER 2
PARAMETER 3
PARAMETER 4
PARAMETER 5
1,0
0,9
Simulation
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,0
-1000
• Three discrete outcomes
• Base Case 
Expected for the project
• High case and low case are
extremely unlikely to occur
Distribution
Distribution
Distribution
Distribution
Distribution
-500
0
500
1000
1500
2000
2500
3000
NPV (10^6 USD)
• Full range of possible outcomes
• True expected NPV
• True P90
• True P10
• Correct comparison and ranking
of options
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
CON: The mean:
• Is possibly more complicated to
comprehend and explain
• May give "infeasible" values
– Mean number of eyes of a
dice is 3.5
– Sum of 100 dice: Makes
sense
The mean is most companies’ preferred basis for decisions !
Statistical Measures
Mode
P50
Mean
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.
Drilling campaign example
n EQUAL WELLS
DETERMINISTIC
BASE
P90
STOCHASTIC
MEAN
TIME
PROBABILITY
STOCHASTIC
MEAN
P10
DETERMINISTIC
BASE
DRILLING TIME
PER WELL
# WELLS
Deterministic base: Underestimates drilling cost
Overestimates # wells drilled per year
Overestimates production first years
Courtesy of IPRES
n
Probabilistic approach
RESERVES
GRV
N/G
DRILLING
DEV.COST
PRODUCTION
S IMULATION
Presents full
range of possible
outcomes
Ø
Sw
Rc
NEXT TARGET
Bo
Key factors
contributing to
overall uncertainty
Example Contact Uncertainties - Cases
Non-communication
Communication
2577
2577
2625
2625
2647
2647
2688
2731
2731
2800
PESSIMISTIC
OPTIMISTIC
EXPECTED CASE???
Monte Carlo - Principle
Depth
conversion
adjustment
Random
Number
Generator
Probability for
Communication
Probability of
Gas-Cap
GOC
OWC
GRV
Fault
location
adjustment
N/G
Ø
Sw
Bg
Rf
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
P
  

Reserves