1 / 24

Logistic Curves, Extraction Costs and the Effective Size of Oil Resources

Logistic Curves, Extraction Costs and the Effective Size of Oil Resources. Robert Brecha , Ph. D. Potsdam Institute for Climate Impact Research, Potsdam , Germany Permanent address: Physics Dept. and Renewable and Clean Energy Program, Univ. of Dayton, Dayton, OH, USA brecha@udayton.edu.

erna
Télécharger la présentation

Logistic Curves, Extraction Costs and the Effective Size of Oil Resources

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Logistic Curves, Extraction Costs and the Effective Size of Oil Resources Robert Brecha, Ph. D. Potsdam Institute for Climate Impact Research, Potsdam, Germany Permanent address: Physics Dept. and Renewable and Clean Energy Program, Univ. of Dayton, Dayton, OH, USA brecha@udayton.edu IAEE Stockholm June 21, 2011

  2. Outline • Logistic curves • Conventional • Nonconventional • Extraction cost estimates • Time scales • Ramp-up • Delays • Deterministic model • Optimization model • Summary and conclusions

  3. Logistic Function Q(t) ≡ cumulative production b ≡ initial rate of increase Q∞ ≡ ultimate recoverable resource tp≡ peak production date

  4. Continental US Production Cumulative production Predicted in 2010 Predicted in 1990 Predicted in 1980 Predicted in 1970 Predicted in 1960 U.S. 48 - Cumulative Production (Gb) Year

  5. Variants of Logistic Fits Yearly production data

  6. Variants of Logistic Fits Linearization

  7. World Oil

  8. Logistic Curve Fits – World Conventional Oil Production (Gb/year) Year

  9. IEA Resources and Costs 120 100 80 60 40 20 0 CTL Oil Shales GTL Deep and Arctic Production cost (2008USD) EOR Tar sands; Extra Heavy Other Conventional MENA Produced 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Resource (Gb)

  10. Deterministic Logistic Curves • Use ROW, OPEC1, OPEC2 logistic fits as starting point • Assume IEA resources for Arctic + Deep, EOR, Oil Sands, Shale Oil as future or nascent resources • Match past history, as well as growth rate for Oil Sands • Assume growth rates of other resources between 6% and 10% per year (following historical experience) • Extraction costs according to IEA, linearly interpolated across grades • Generate set of logistic curves, as well as marginal extraction cost and average cost across grades at each time point

  11. Deterministic Logistic Curves

  12. Marginal, Average & Actual Costs

  13. Marginal Cost Schematic 120 100 80 60 40 20 0 Oil Shales CTL GTL Deep and Arctic Production cost (2008USD) Tar sands; Extra Heavy EOR Cost curve, X-to-L Other Conventional Cost curve, nonconventional MENA Produced Cost curve, conventional 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Resource (Gb)

  14. Marginal Cost Schematic 120 100 80 60 40 20 0 New cost curve Oil Shales CTL GTL Deep and Arctic Production cost (2008USD) Tar sands; Extra Heavy EOR Cost curve, X-to-L Other Conventional Cost curve, nonconventional MENA Produced Cost curve, conventional 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Resource (Gb)

  15. Marginal Cost Schematic 120 100 80 60 40 20 0 New cost curve Oil Shales CTL GTL Deep and Arctic Production cost (2008USD) Tar sands; Extra Heavy EOR Other Conventional MENA Produced 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Resource (Gb)

  16. Cost-Price Correlation

  17. Optimization Routine • Use ROW, OPEC1, OPEC2 logistic fit output as starting point • Assume IEA resources for Arctic + Deep, EOR, Oil Sands, Shale Oil as future or nascent resource • Assume IEA costs across grades • Limit maximum growth rates to 10% (historical data) • Minimize the total production cost over the whole production history, subject to fulfilling historically observed production/capacity • Case 1 – Foresight assumed (based on IEA projections to 2030) • Case 2 – No foresight (based only on data to date) • Generate set of logistic curves, as well as marginal extraction cost and average cost across grades at each time point

  18. IEA Projection Foresight

  19. Marginal and Average Costs

  20. Lack of Foresight

  21. What Do We Observe?

  22. EIA – Performance Profiles of Major Energy Producers 2009

  23. Cost Indices

  24. Summary • Two models, based on logistic curve method • Extended to assume IEA estimates of large conventional and nonconventional resource base • Use historically observed growth rates of extraction for new resources • Include extraction cost information (IEA estimates) • Conclusion (from both models) is that • Resources may be present, but rate of extraction creates bottleneck • Cost (and therefore price) jumps expected due to need for “pulling forward” less attractive resources • Not caused by market imperfections, but also due to extraction pattern within grades (i.e. logistic peak) • Dynamics of flat or decreasing production for one to two decades calls into question whether the further increase seen in these simple models will ever materialize • Therefore, “Effective Peak Oil”, with large potential resources left in the ground

More Related