john jarvis, claudia johnson & liana vetter

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John Jarvis, Claudia Johnson & Liana Vetter

October 26, 2004

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Presentation Overview

• Quest Resource Corporation

• Model Development

• Model Implementation Results

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Quest Resource CorporationAn oil and gas company whose core business is developing,

producing and transporting natural gas

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Pipeline Schematic

Well head/site Pipeline Delivery/Sale Point

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Place in the Market

Quest

Purchaser

In-house Use External Sales

Pipeline Transportation

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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.

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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

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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

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Model Development

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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 (2002-2004)

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Market Prices 2002-2004

Probability Average percent changeFalling 0.44 -0.055751Rising 0.56 0.096644

Market Variability

0

0.5

1

1.5

2

2.5

-0.38 -0.13 -0.03 0.02 0.07 0.16 0.26

Percent Market Change

Fre

qu

ency Lower

Higher

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R&H Sale Point Production 2004

RH Probability Average relative errorOver 0.51 1.073852Equal 0.27 1Under 0.22 0.900982

Production Variability

0123456789

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0.57 0.83 0.90 0.95 0.98 1.01 1.04 1.07 1.10 1.14 1.17

Relative Error on Forecast

Fre

qu

ency

Lower

Same

Higher

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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

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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).

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Market Price

• Pi= Daily price assumption

• Pi= P0 (adjustment i)

• P0= initial market price (NYMEX)

1

0.95

1.09

i

For up-market scenario

For down-market scenario

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Model Formulation• Over the course of a month, with each day = i

a. Zi= actual units of gas (MCF) produced

b. 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

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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

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Market Scenarios

• Up 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)

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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

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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

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Model Implementation and Results

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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

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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

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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 MarlinQuest Cherokee, LLC5901 N. Western, Suite 200Oklahoma City, Ok. 73118

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Questions

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Normalized Objective Function

• Min prob(up) (UpRegret / Revenueup(Yup)) +

prob(dn) (DnRegret / Revenuedn(Ydn))