John Jarvis, Claudia Johnson & Liana Vetter

1. John Jarvis, Claudia Johnson & Liana Vetter May 6, 2004

2. Description of Problem • Quest’s current gas marketing • Oneok is sole purchaser • 85% guaranteed monthly • The remainder sold daily • Pipeline serves as middleman • Goal 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

3. Variables Considered • Two different sale points • 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 (2002-2004) • Limits to set • Maximum days and amount in debt • Bounds on percentage to guarantee

4. Limits to Set • 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

5. Market 2002-2004

6. R&H Production 2004

7. Non-Stochastic Model • In 5-day test case, user provides data: • Model returns output: • Revenue: $1,002

8. Stochastic Model • Benefits of stochastic modeling • Incorporates uncertainty using probabilities of different scenarios • Calculates expected revenue based on market forecasts • Approximates actual production from forecast given • Example case • User provides data: • Model returns output: • Expected revenue: $1,039

9. Stochastic Model with 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 • Example case • User provided data: • Model returns output: • Expected revenue: $1,037

10. Sensitivity Analysis • Optimal monthly guarantee varies little when expected production data changes • Model is more sensitive to changes in market data

11. 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 • Dependant on the market which is unpredictable • Stochastic variables are based on limited data

12. 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 less additional profit than stochastic model but provides more consistency between months

13. Interface • Questions asked by interface • Probability the market will rise • Sale point • Month to forecast, days in month • Expected initial NYMEX price • Forecasted daily production • Expected beginning debt • Results of interface • Creates data file for AMPL • Data file can be run with regret model to resolve each month