1. John Jarvis, Claudia Johnson & Liana Vetter
October 26, 2004

2. Presentation Overview
Quest Resource Corporation
Model Development
Model Implementation Results

3. Quest Resource Corporation
An oil and gas company whose core business is developing, producing and transporting natural gas

4. Pipeline Schematic
Pipeline
Well head/site
Delivery/Sale Point

5. Pipeline Transportation
Place in the Market
Quest
Pipeline Transportation
Purchaser
In-house Use
External Sales

6. Quest’s Financial Setting
Revenues of about $11.7 million
Access to a $150+ million debt facility for future opportunities
Over 900 miles of active pipeline transporting gas to sale points, with further construction underway.
380 wells planned to be drilled in 2005 in addition to 900+ miles of pipeline construction.

7. Current Approach Quest’s current gas marketing strategy:
Sales of gas production
85% (anticipated total produced gas) guaranteed monthly by Quest
The remainder sold daily (swing volume) via market price
Pipeline serves as middleman
Total produced gas = % gas sold on contract % gas sold daily

8. Goals of the Project
Analyze the market trends and forecasting accuracy of Quest
Determine what percentage is optimal to guarantee on contract
Create optimization model Quest can use monthly

9. Model Development

10. Sale Points Evaluated Two different sale points Historical data
R&H: large and unstable
Housel: small and unstable
Historical data
Forecasted daily production by sale point (2004)
Actual daily production by sale point (2004)
Daily NYMEX prices ( )

11. Market Prices

12. R&H Sale Point Production 2004

13. Problem Constraints Maximum days and amount in debt
Set limit of 2 days in debt based on 2004 data
Set limit of 10% of production in debt
Conservative limits to minimize risk in case of unexpected changes in production
Bounds on percentage to guarantee
Set upper limit as 95%, highest Quest has used
Set lower limit as 30% to protect against sharp decrease in production

14. Model Formulation
Zi = Production; May vary due to equipment failure, geological variations, etc.
X = Forecasted production amount.
Y = Contractual % amount; decision variable.
Pi = Market Price; Affected by many outside factors (see NYMEX).

15. Market Price Pi= Daily price assumption Pi= P0 (adjustment i)
P0= initial market price (NYMEX)
1.09
For up-market scenario
Adjustments 1 i
For down-market scenario
0.95

16. Model Formulation Over the course of a month, with each day = i
Zi= actual units of gas (MCF) produced
If Zi =Y, then, deliver all gas on contract
If Zi <Y, then, Quest must borrow difference from pipeline
Else Zi >Y, then, Quest repays debt to pipeline first, then sells remainder at daily market price
Zi = production; Pi = market price; Y = contractual %; X = forecasted amt

17. Description of Regret
Regret – difference between optimal revenue and actual revenue
Benefits of regret
Solution does well in rising and falling market
Less sensitive to predicted probabilities

18. Market Scenarios Up Market Scenario Down Market Scenario
Optimal solution has Yup = minimum %
Put least amount possible on contract, rest on swing volume
Regretup = (Revenueup) - (Revenue)
Down Market Scenario
Optimal solution for Ydn = maximum %
Put maximum possible on contract, rest on swing volume
Regretdn = (Revenuedn) - (Revenue)

19. Regret Objective UpRegret = Revenueup(Yup) – Revenueup(Y)
DnRegret = Revenuedn(Ydn) – Revenuedn(Y)
Min prob(up) * UpRegret + prob(dn) * DnRegret
Zi = production; Pi = market price; Y = contractual %; X = forecasted amt

20. Computer Implementation
User inputs:
Probability the market will rise
Sale point
Month to forecast, days in month
Expected initial NYMEX price
Forecasted daily production
Expected beginning debt
Program output:
Data file for AMPL
Can be run with regret model to resolve each month

21. Model Implementation and Results

22. Analysis and Recommendation
50-55% should be guaranteed monthly if no market predictions added from Quest
Consequences of guaranteeing 50-55%
$18,000 additional revenue from January – March 2004 for R&H
$2,400 additional revenue from January – March 2004 for Housel
Regret model yields more profit than current Quest marketing and provides more consistency between months

23. Problems and Limitations
Problems encountered
Limited historical data
Multiple daily gas prices (strip price used)
Large variability of the gas market
Difference in production records from meter inconsistency
Limitations of the solution
Dependent on the market, which is unpredictable
Stochastic variables are based on limited data

24. Letter from Quest
Thank you for allowing your students to assist us on this project. The process we went through was in itself beneficial. They have provided us information and analysis that we found to be helpful and even somewhat unexpected. The program they have given us should provide a firmer basis for our decision making for gas marketing. It should get better as time passes and we are better able to provide historical information for it. It was an educational experience for all parties concerned. Thank you for sharing them with us.
Richard Marlin Quest Cherokee, LLC 5901 N. Western, Suite 200 Oklahoma City, Ok

25. Questions

26. Normalized Objective Function
Min prob(up) (UpRegret / Revenueup(Yup)) prob(dn) (DnRegret / Revenuedn(Ydn))