This paper outlines a method for applying stochastic representations of key macro-economic parameters and risks in evaluating the portfolio value of resource plays. Stochastic pricing, costs forecasts, and regulatory uncertainties are often neglected or im-properly applied when evaluating what are typically considered to be ‘low risk’ resource opportunities. Portfolio allocation and project development timing may be critically dependent upon these macro-economic variables, particularly given the long production life and operationally intensive nature of many resource opportunities. These techniques will allow corporate planners and E&P executives to better leverage their opportunity inventory and ensure resource play development plans are in alignment with stochastic pricing or other macro-economic forecasts.

1. Stochastic analysis of resource plays
Maximizing portfolio value and mitigating risks
SPE 134811
SPE ANNUAL TECHNICAL CONFERENCE AND EXHIBITION
Florence, Italy 20 – 22 September 2010
S T R A T E G Y. D E C I S I O N S. S U C C E S S.

2. Stochastic Analysis of Resource Plays
Maximizing Portfolio Value and Mitigating Risks
Summary
•
Unique characteristics of resource play analysis
–
–
–
•
•
Options
Dependency relationships
Uncertainties
Methodology: Simple stochastic portfolio analysis
Case study: Application of analytical approach
–
–
Single resource play (example)
E&P portfolio
 Importance of decision context
 Insights / portfolio value potential

3. Stochastic Analysis of Resource Plays
Analysis characteristics
Problem:
How to address all of the potential options, project
interdependencies, and uncertainties in evaluating a particular resource play?

4. Stochastic Analysis of Resource Plays
Analysis characteristics
Problem:
How to address all of the potential options, project
interdependencies, and uncertainties in evaluating a particular resource play?
Options (Decisions to be made)
Early leasing
or
Targeted leasing
or
Test then lease
Leasing Options
Large Pilot
or
Medium Pilot
or
Small Pilot
Pilot Options
Accelerated
or
Steady State
or
Minimal (Lease hold)
Development Pace

5. Stochastic Analysis of Resource Plays
Analysis characteristics
Problem:
How to address all of the potential options, project
interdependencies, and uncertainties in evaluating a particular resource play?
Dependencies
If Targeted leasing then
Medium or Small pilot
Early leasing
Large Pilot
Accelerated
Targeted leasing
Medium Pilot
Steady State
Test then lease
Small Pilot
Minimal (Lease hold)
If Small pilot then
Minimal pace
Leasing Options
Pilot Options
Development Pace

6. Stochastic Analysis of Resource Plays
Analysis characteristics
Problem:
How to address all of the potential options, project
interdependencies, and uncertainties in evaluating a particular resource play?
Uncertainties
Uncertainties (Surface/Subsurface): Initial rates, Resources, Pricing, Costs, Regulatory,…
Early leasing
Large Pilot
Accelerated
Targeted leasing
Medium Pilot
Steady State
Test then lease
Small Pilot
Minimal (Lease hold)
Leasing Options
Pilot Options
Development Pace

7. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Problem:
How to simplify a Complex system of analysis?
Company Performance
Project ‘D’
Project ‘A’
Project ‘B’
Project ‘C’
Simple stochastic portfolio analysis
Options: Balance portfolio performance trade-offs
Dependencies: Manage the dependencies between options
Uncertainties: Capture the range of potential outcomes

8. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Everything should be made as simple as possible,
but not simpler.”
A. Einstein

9. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Integrated scenarios: Stand-alone representations of the opportunity,
under a common set of assumptions (internally consistent)
Initial Production Rate
Option ‘A’
Option ‘A’
Operating Costs
Product pricing

10. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Integrated scenarios: Stand-alone representations of the opportunity,
under a common set of assumptions (internally consistent)
Single representation: Potentially trivial or misleading
Option ‘A’
100%
Option ‘A’

11. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Integrated scenarios: Stand-alone representations of the opportunity,
under a common set of assumptions (internally consistent)
10%
Best Case
Multiple representations: Increased PRECISION does
not necessarily mean greater ACCURACY
20%
Option ‘A’
40%
Option ‘A’
20%
10%
Worst Case

12. Stochastic Analysis of Resource Plays
Simple stochastic portfolio analysis
Integrated scenarios: Stand-alone representations of the opportunity,
under a common set of assumptions (internally consistent)
50%
Best Case
Identify major uncertainties: Enough data to bracket
potential performance…
Option ‘A’
Would more data improve the decision?
50%
Worst Case
Option ‘A’

13. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Basic Methodology
1. Clearly define model frame
• Business issues
• Performance metrics
• Opportunities or options (decision units)
• Dependencies
• Uncertainties (correlated and uncorrelated)
2. Evaluate asset performance under different scenarios

14. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Basic Methodology
1. Clearly define model frame
• Business issues
• Performance metrics
• Opportunities or options (decision units)
• Dependencies
• Uncertainties (correlated and uncorrelated)
2. Evaluate asset performance under different scenarios
• Stochastic pricing assumption
• ‘High’ price environment
Insights…Decisions
• ‘Low’ price environment
• As part of a corporate portfolio

15. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Business Issues
•
What is the most effective leasing strategy? Large up-front or targeted
to specific areas once proven?

16. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Business Issues
•
•
What is the most effective leasing strategy? Large up-front or targeted
to specific areas once proven?
What type of pilot program should be initiated? Larger to ensure high
confidence of pilot success or smaller to reduce potential loss?

17. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Business Issues
•
•
•
What is the most effective leasing strategy? Large up-front or targeted
to specific areas once proven?
What type of pilot program should be initiated? Larger to ensure high
confidence of pilot success or smaller to reduce potential loss?
What is the optimal development pace:
• For the play on a stand alone basis?
• When the play is part of a total E&P portfolio?

18. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Performance Metrics
Balance of operational and financial measures
Growth and efficiency evaluated
Capex ($MM)
Resources (Bcfe)
700
600
Production (MMcfe/d)
1,200
1,000
800
700
600
500
800
400
500
600
300
400
300
400
200
200
100
2020
2018
2016
2014
2012
2010
2020
2018
2016
2014
2012
0
2010
2020
2018
2016
0
2014
0
2012
200
2010
100
20 year time horizon
Operating income ($MM)
Free Cash Flow ($MM)
ROACE (%)
250
300
200
200
15.0%
150
100
10.0%
100
0
50
-100
0
-200
5.0%
Expected Value
2020
2018
0.0%
2016
2020
2018
2016
2014
2012
2010
20.0%
2014
-400
25.0%
2012
-300
30.0%
2010
2020
400
2018
500
300
2016
350
2014
600
2012
400
2010
•
•

19. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Decision units
Describe each option outcome in terms of the metrics (time series data)
Pilot – Marcellus - Large
Pilot – Marcellus - Med
Pilot program
Pilot – Marcellus - Small
Input Metrics
Project
Pilot - Marcellus - Small
Outcome
HIGH - Success
Weight
0.08
NPV
Metric
Resource Adds (Bcfe)
ANNUAL OIL (MMBO)
ANNUAL GAS (BCF)
REVENUE ($MM)
4.52 Capex ($MM)
Opex-Total ($MM)
DD&A-TOTAL ($MM)
Production Taxes ($MM)
Tier2 Count
1
15.00
22.50
18.00
2
2.74
14.37
4.11
4.11
0.82
-
3
3.06
16.04
4.58
4.58
0.92
-
4
1.95
10.22
2.92
2.92
0.58
-

20. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Dependencies
Define the specific relationships between options (decisions)
Acreage – Marcellus 1
Leasing
or
•Mutually exclusive
•Must select one or the other to initiate Pilot program
Acreage – Marcellus 2
Pilot – Marcellus - Large
Pilot program
Pilot – Marcellus - Med
•Mutually exclusive
•Successful pilots allow selection of Tier 2 Marcellus wells
Pilot – Marcellus - Small
Tier 1 Marcellus
Drill wells
Tier 2 Marcellus
Tier 3 Marcellus
•Must drill Tier 1 wells before Tier 2 (unless pilot success)
•Must drill Tier 2 wells before Tier 3

21. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Uncertainties
Options
Pilot – Marcellus - Large
Pilot program
Uncorrelated risks
Success: Allows access to Tier 2 wells
Pilot – Marcellus - Med
Pilot – Marcellus - Small
Fail: requires drilling of Tier 1 wells
Pilot success probability increases (small to large)
Tier 1 Marcellus
Drill wells
Best Case: Higher IP, 5 BCFE
Tier 2 Marcellus
Tier 3 Marcellus
Worst Case: Lower IP, 3 BCFE
Improving economics (Tier 1 to Tier 2 to Tier 3)
Each with uncertain well performance

22. Stochastic Analysis of Resource Plays
Case study: Evaluation methodology
Uncertainties
Describe correlated risks
Same Pricing assumption selected for ALL cases during
each Monte Carlo simulation trial (Correlated)
Well Performance is independent across the cases assumes statistical play (uncorrelated)

23. Stochastic Analysis of Resource Plays
Case study: Analysis
Summary of Analysis
Scenarios
1. Marcellus program only – stochastic pricing: Maximize NPV
2. Marcellus program only – High price: Maximize NPV
3. Marcellus program only – Low price: Maximize NPV
4. Corporate portfolio – stochastic pricing: Maximize NPV, Maintain positive
free cash flow (self funding program)
a. Use Marcellus program as per stand-alone optimization (1. above)
b. Allow Marcellus program to be optimized at a corporate level

24. Stochastic Analysis of Resource Plays
Case study: Analysis
1. Marcellus program only: Stochastic Pricing
Objective: Maximize NPV =$540MM
Constraints: none
Targets: None
Capex ($MM)
Resources (Bcfe)
700
600
1,000
800
700
600
500
800
400
500
600
300
Self funding as early as 2014 and as
late as 2017
400
300
400
200
200
100
Operating income ($MM)
600
800
500
600
0.9
0
2020
2020
-600
0.0%
-10.0%
2018
0.1
2016
-400
10.0%
2014
0.2
20.0%
2012
2020
2018
2016
2014
0.3
P10
Expected Value
P90
2010
-200
0
2020
100
2018
0.4
2016
0.5
2014
30.0%
2012
40.0%
0.6
2010
0.7
0
200
2018
50.0%
0.8
200
300
2016
60.0%
400
400
Potential need for $400MM in funding
(P90 free cash flow outcome)
ROACE (%)
1
1,000
2012
2014
Free Cash Flow ($MM)
700
2010
2012
2010
2020
2018
2016
2014
2012
0
2010
2020
2018
2016
2014
0
2012
0
2010
100
200
-100
Narrow operational performance
indicators (uncorrelated)
Production (MMcfe/d)
1,200

25. Stochastic Analysis of Resource Plays
Case study: Analysis
1. Marcellus program only: Stochastic Pricing
Objective: Maximize NPV =$540MM
Constraints: none
Targets: None
Project
BASE - North
BASE - South
BASE - Canada
Acreage - Marcellus 1
Acreage - Marcellus 2
Pilot - Marcellus - Small
Pilot - Marcellus - Med
Pilot - Marcellus - Large
Facility Invest - Marcellus
T1 Marcellus
T1 Haynesville
T1 Pettet
T1 Cotton Valley
T1 Eagleford Oil
T2 Marcellus
T2 Haynesville
T2 Pettet
T2 Cotton Valley
T2 Eagleford Oil
T3 Marcellus
T3 Haynesville
T3 Pettet
T3 Cotton Valley
T3 Eagleford Oil
2010
1.00
1.00
.30
13.50
25.00
-
2011
-
2012
-
2013
-
1.00
1.00
1.00
.21
25.00
-
2014
-
2015
-
2016
-
.25
1.25
1.00
1.00
1.00
25.00 25.00
100.00 100.00 100.00 100.00
-
Acreage – Marcellus 2 selected
Delayed leasing program does not limit program
Pilot – Marcellus – Small selected
The higher pilot cost (Med or Large) is not offset by the loss in
value associated with drilling the Tier 1 wells

26. Stochastic Analysis of Resource Plays
Case study: Analysis
Summary of Analysis
Scenarios
1. Marcellus program only – stochastic pricing: Maximize NPV
2. Marcellus program only – High price: Maximize NPV
3. Marcellus program only – Low price: Maximize NPV
4. Corporate portfolio – stochastic pricing: Maximize NPV, Maintain positive
free cash flow (self funding program)
a. Use Marcellus program as per stand-alone optimization (1. above)
b. Allow Marcellus program to be optimized at a corporate level

27. Stochastic Analysis of Resource Plays
Case study: Analysis
2. Marcellus program only: High Price
Objective: Maximize NPV =$1,239MM
Constraints: none
Targets: None
Project
BASE - North
BASE - South
BASE - Canada
Acreage - Marcellus 1
Acreage - Marcellus 2
Pilot - Marcellus - Small
Pilot - Marcellus - Med
Pilot - Marcellus - Large
Facility Invest - Marcellus
T1 Marcellus
T1 Haynesville
T1 Pettet
T1 Cotton Valley
T1 Eagleford Oil
T2 Marcellus
T2 Haynesville
T2 Pettet
T2 Cotton Valley
T2 Eagleford Oil
T3 Marcellus
T3 Haynesville
T3 Pettet
T3 Cotton Valley
T3 Eagleford Oil
2010
1.00
1.00
.39
18.00
25.00
-
2011
-
2012
-
2013
-
1.00
1.00
1.00
.12
25.00
-
2014
-
2015
-
2016
-
.25
1.25
1.00
1.00
1.00
25.00 25.00
100.00 100.00 100.00 100.00
-
Acreage – Marcellus 2 selected
Delayed leasing program does not limit program
Pilot – Marcellus – Large selected
Under the High Price assumption ALL of the wells in the
program are economic (including the Tier 1 wells).
The Large pilot increases the total project, thus maximizing
NPV.

28. Stochastic Analysis of Resource Plays
Case study: Analysis
Summary of Analysis
Scenarios
1. Marcellus program only – stochastic pricing: Maximize NPV
2. Marcellus program only – High price: Maximize NPV
3. Marcellus program only – Low price: Maximize NPV
4. Corporate portfolio – stochastic pricing: Maximize NPV, Maintain positive
free cash flow (self funding program)
a. Use Marcellus program as per stand-alone optimization (1. above)
b. Allow Marcellus program to be optimized at a corporate level

29. Stochastic Analysis of Resource Plays
Case study: Analysis
3. Marcellus program only: Low Price
Objective: Maximize NPV = ($43MM)
Constraints: none
Targets: None
Project
BASE - North
BASE - South
BASE - Canada
Acreage - Marcellus 1
Acreage - Marcellus 2
Pilot - Marcellus - Small
Pilot - Marcellus - Med
Pilot - Marcellus - Large
Facility Invest - Marcellus
T1 Marcellus
T1 Haynesville
T1 Pettet
T1 Cotton Valley
T1 Eagleford Oil
T2 Marcellus
T2 Haynesville
T2 Pettet
T2 Cotton Valley
T2 Eagleford Oil
T3 Marcellus
T3 Haynesville
T3 Pettet
T3 Cotton Valley
T3 Eagleford Oil
2010
1.00
1.00
.04
-
-
-
2011
-
2012
-
2013
-
1.00
1.00
1.00
-
-
-
2014
-
2015
-
2016
-
-
-
-
Acreage – Marcellus 2 selected
Minimal activity to maintain position
Pilot – Marcellus – Small selected
This minimizes exposure and maintains the option of further
development (assuming future price strengthening).

30. Stochastic Analysis of Resource Plays
Case study: Analysis
Summary of Analysis
Scenarios
1. Marcellus program only – stochastic pricing: Maximize NPV
2. Marcellus program only – High price: Maximize NPV
3. Marcellus program only – Low price: Maximize NPV
4. Corporate portfolio – stochastic pricing: Maximize NPV, Maintain
positive free cash flow (self funding program)
a. Use Marcellus program as per stand-alone optimization (1. above)
b. Allow Marcellus program to be optimized at a corporate level

31. Stochastic Analysis of Resource Plays
Case study: Analysis
4a. Corporate portfolio: Includes Marcellus (stand-alone optimization)
Objective: Maximize NPV = $2.4 Billion
Constraints: Free Cash Flow Positive (2011+)
Targets: None
Capex ($MM)
Resources (Bcfe)
500
Free Cash Flow ($MM)
0.6
600
0.5
500
400
0.4
0
2020
2018
2016
0.3
2014
-500
2012
2020
2018
2016
2014
2012
-200
2010
0
2010
200
0.2
0.1
-1,000
0
2020
2018
2016
2014
P10
Expected Value
P90
2020
0.7
2018
0.8
1,000
800
2016
1,500
1,000
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
2014
0.9
1,200
Portfolio value of $2.4 Billion
ROACE (%)
1
2,000
2010
Operating income ($MM)
1,400
2012
0
2012
2020
2018
2016
2014
2012
2010
0
2010
200
1,000
2020
400
Objective met (on an expected value
basis), but significant downside risk
in 2011, 2013, 2015, and 2016
1,500
2018
600
2016
800
2,000
2014
1000
2012
1200
2,500
2010
1400
Probability of remaining cash flow
positive varies from 50 % - 70%
Production (MMcfe/d)
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0

32. Stochastic Analysis of Resource Plays
Case study: Analysis
4a. Corporate portfolio: Includes Marcellus (stand-alone optimization)
Objective: Maximize NPV = $2.4 Billion
Constraints: Free Cash Flow Positive (2011+)
Targets: None
Project
BASE - North
BASE - South
BASE - Canada
Acreage - Marcellus 1
Acreage - Marcellus 2
Pilot - Marcellus - Small
Pilot - Marcellus - Med
Pilot - Marcellus - Large
Facility Invest - Marcellus
T1 Marcellus
T1 Haynesville
T1 Pettet
T1 Cotton Valley
T1 Eagleford Oil
T2 Marcellus
T2 Haynesville
T2 Pettet
T2 Cotton Valley
T2 Eagleford Oil
T3 Marcellus
T3 Haynesville
T3 Pettet
T3 Cotton Valley
T3 Eagleford Oil
Marcellus Program
Marcellus Program 2
2010
1.00
1.00
1.00
-
-
-
1.00
2011
-
2012
-
2013
-
-
-
2014
-
2015
-
2016
-
As in Case 1. Above (Stand-alone Marcellus optimization)
Acreage – Marcellus 2 selected
Pilot – Marcellus – Small selected
-
Early investment in Cotton Valley and Haynesville as cash
flows become sufficient
18.00
10.65
-
.00
18.00
25.00
25.00
-
14.35
-
25.00
25.00
-
25.00
25.00 25.00
43.78 99.42
100.00
-

33. Stochastic Analysis of Resource Plays
Case study: Analysis
4b. Corporate portfolio: Includes Marcellus (Corporate optimization)
Objective: Maximize NPV = $2.6 Billion
Constraints: Free Cash Flow Positive (2011+)
Targets: None
Capex ($MM)
Resources (Bcfe)
Free Cash Flow ($MM)
0.6
600
0.5
500
400
0.4
0
2020
2018
2016
0.3
2014
-500
2012
2020
2018
2016
2014
2012
-200
2010
0
2010
200
0.2
0.1
-1,000
0
2020
2018
2016
2014
P10
Expected Value
P90
2020
0.7
2018
0.8
1,000
800
2016
1,500
1,000
100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
2014
0.9
1,200
2012
ROACE (%)
1
2,000
2010
Operating income ($MM)
1,400
2012
2020
2018
2016
2014
2012
2010
0
Portfolio value of $2.6 Billion or
$200MM higher than Marcellus
stand-alone optimization
2010
200
2020
400
2018
600
2016
800
2014
1000
2012
1200
2,000
1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
2010
1400
Probability of remaining cash flow
positive increased to 70% each year
Production (MMcfe/d)
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0

34. Stochastic Analysis of Resource Plays
Case study: Analysis
4b. Corporate portfolio: Includes Marcellus (Corporate optimization)
Objective: Maximize NPV = $2.6 Billion
Constraints: Free Cash Flow Positive (2011+)
Targets: None
Project
BASE - North
BASE - South
BASE - Canada
Acreage - Marcellus 1
Acreage - Marcellus 2
Pilot - Marcellus - Small
Pilot - Marcellus - Med
Pilot - Marcellus - Large
Facility Invest - Marcellus
T1 Marcellus
T1 Haynesville
T1 Pettet
T1 Cotton Valley
T1 Eagleford Oil
T2 Marcellus
T2 Haynesville
T2 Pettet
T2 Cotton Valley
T2 Eagleford Oil
T3 Marcellus
T3 Haynesville
T3 Pettet
T3 Cotton Valley
T3 Eagleford Oil
2010
1.00
1.00
1.00
1.00
1.00
.50
18.00
25.00
-
2011
-
2012
-
2013
-
-
-
-
18.00
18.00
25.00
6.76
25.00
-
2014
-
2015
-
2016
-
1.49
2.01
1.00
25.00 24.00
1.00
25.00 18.85 25.00 24.38
25.00 25.00 25.00
100.00 100.00 100.00 100.00
59.17 100.00
100.00 100.00 100.00
-
When Marcellus optimized with corporate portfolio:
Acreage – Marcellus 1 selected
Early acceleration of Marcellus opens up greater potential in
the Haynesville and Cotton Valley in the mid-term. Lack of
cash flow constraint in 2010 forces acreage investment into
first year.
Pilot – Marcellus – Large selected
The Large pilot allows accelerated Marcellus development
The stand-alone optimization of the Marcellus did not consider
the other near-term project potential and the need to balance
free cash flow.

35. Stochastic Analysis of Resource Plays
Case study: Summary
Leasing program decisions
•
Marcellus program only (under ALL pricing scenarios)
– Acreage-2 leasing case (delayed leasing) is selected
– At a project level, value reduced by accelerating the leasing program
•
With Marcellus as part of total E&P portfolio of options:
– Additional value by accelerating the Marcellus leasing program
– Marcellus program value reduced, but overall portfolio value increases
– Front loads larger Marcellus program – cash flow for activity in other areas
Decision context is critical in evaluation of options

36. Stochastic Analysis of Resource Plays
Case study: Summary
Pilot program decisions
•
Marcellus program only
– Pilot selection is driven by the pricing assumptions
– Small pilot is selected under both the stochastic price and Low price
scenarios.
– Large pilot is only selected under a high price assumption
•
With Marcellus as part of total E&P portfolio of options:
– Large pilot selected
– Leverages value of the Marcellus program in the early part of the plan
Identify assumptions that may drive the decision process

37. Stochastic Analysis of Resource Plays
Conclusions
Summary of analysis: Insights, Portfolio Value
•
Decision context is critical in assessing the relative values and trade-offs
associated with a set of alternatives
•
Application of stochastic pricing analysis can yield significant insights into
specific options within a portfolio
•
Pricing assumptions play a major role in project selection and portfolio
allocation.
•
Significant portfolio value may be realized by integrating scenario
analysis, stochastic forecasting methods, and portfolio analysis as
part of a resource play decision process

38. Stochastic analysis of resource plays
Maximizing portfolio value and mitigating risks
SPE 134811