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

Presentation Overview Quest Resource Corporation Model Development Model Implementation Results

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

Pipeline Schematic Pipeline Well head/site Delivery/Sale Point

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

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.

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

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

Model Development

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 (2002-2004)

Market Prices 2002-2004

R&H Sale Point Production 2004

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

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).

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

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

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

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)

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

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

Model Implementation and Results

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

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

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. 73118

Questions

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