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Oil Case

In: Business and Management

Submitted By mcdarth
Words 793
Pages 4
Drilling for Oil example – Using @Risk

Information:
We are trying to determine the amount of oil that we expect to recover from an oil field. The unknowns are: the size of the field (in acres), the thickness of the oil-sand layer, and the primary recovery rate (in barrels per acre per foot of thickness). Based on geological information, the following probability distributions have been estimated
–Estimate of Productive Area Acres Probability 8,000 - 9,000 0.05 9,000 - 10,000 0.10 10,000 - 11,000 0.15 11,000 - 12,000 0.35 12,000 - 13,000 0.25 13,000 - 14,000 0.10
–Estimate of Pay Thickness Smallest Value: 15 ft.. Most Likely Value: 50 ft.. Largest Value: 120 ft..

–Estimate of Primary Recovery Uniform Distribution: Minimum Value: 20 bbl./acre-ft.. Maximum Value: 90 bbl./acre-ft..

Using @Risk

Open @Risk (it is available under Start->Programs->Palisade Decision Tools->@Risk 4.5 for Excel). Excel should come up with 2 additional toolbars. For this problem we will need the following distributions:
Productive area: discrete distribution, described by a histogram, represented by RISKHISTOGRM(8000, 14000, {0.05, 0.10, 0.15, 0.35, 0.25, 0.10})
Pay thickness: triangular distribution, with a minimum value of 15ft, maximum value of 120ft, and most likely value of 50ft, represented in @Risk by
RISKTRIANG(15, 50, 120)
Primary Recovery Rate: uniform distribution, with any value being equally likely in the interval 20-90 bbl./acre-ft, represented by
RISKUNIFORM(20,90).

@Risk offers the above functions, together with the ability of performing repeated calculations for different realizations for the random variables. We will first build a spreadsheet model, to calculate the...

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