Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "1 John Jarvis, Claudia Johnson & Liana Vetter October 26, 2004."— Presentation transcript:

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

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

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

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

5 5 Place in the Market Quest Purchaser In-house UseExternal Sales Pipeline Transportation

6 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 7 Once discovered, the gas is extracted from the Earth and run through condensers that increase the pressure of the gas so that its flow through the pipelines is swift to one of 10 “sale points,” or gas meters, whereby the ownership is transferred to the gas Purchaser

8 8 Quest Resource Corporation Agreements are made between Quest and the Purchaser on amounts of gas to be delivered from Quest via pipeline That monthly agreement distinguishes a daily amount guaranteed by Quest to the Purchaser (contractual gas) from gas sold at the daily market price (swing gas) MS/OR methods are used to optimize the solution – the amount Quest guarantees -- and minimize the risk to Quest when guaranteeing gas to the Purchaser

9 9 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

10 10 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

11 11 Model Development

12 12 Sale Points Evaluated 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 ( )

13 13 Market Prices

14 14 R&H Sale Point Production 2004

15 15 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

16 16 Model Formulation Z i = Production; May vary due to equipment failure, geological variations, etc. X =Forecasted production amount. Y =Contractual % amount; decision variable. P i =Market Price; Affected by many outside factors (see NYMEX).

17 17 Market Price P i = Daily price assumption P i = P 0 (adjustment i) P 0 = initial market price (NYMEX) i For up-market scenario For down-market scenario

18 18 Model Formulation Over the course of a month, with each day = i a.Z i = actual units of gas (MCF) produced b.If Z i =Y, then, deliver all gas on contract If Z i Y, then, Quest repays debt to pipeline first, then sells remainder at daily market price Z i = production; P i = market price; Y = contractual %; X = forecasted amt

19 19 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

20 20 Market Scenarios Up Market Scenario –Optimal solution has Y up = minimum % –Put least amount possible on contract, rest on swing volume –Regret up = (Revenue up ) - (Revenue) Down Market Scenario –Optimal solution for Y dn = maximum % –Put maximum possible on contract, rest on swing volume –Regret dn = (Revenue dn ) - (Revenue)

21 21 Regret Objective UpRegret = Revenue up (Y up ) – Revenue up (Y) DnRegret = Revenue dn (Y dn ) – Revenue dn (Y) Min prob(up) * UpRegret + prob(dn) * DnRegret Z i = production; P i = market price; Y = contractual %; X = forecasted amt

22 22 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

23 23 Stochastic Model with Regret Example case –User provided data: –Model returns output: –Expected revenue: $1,037

24 24 Sensitivity Analysis Optimal monthly guarantee varies little when expected production data changes Model is more sensitive to changes in market data

25 25 Model Implementation and Results

26 26 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

27 27 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

28 28 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

29 29 Questions

30 30 Normalized Objective Function Min prob(up) (UpRegret / Revenue up (Y up )) + prob(dn) (DnRegret / Revenue dn (Y dn ))


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

Similar presentations


Ads by Google