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Modeling the operating costs for petroleum exploration and development projects

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  • Luo, Dongkun
  • Zhao, Xu

Abstract

Since the operating cost is among the most sensitive factors to uncertainties in economic evaluation of petroleum exploration and development projects, scientific prediction of the operating cost plays an important role in accurately evaluating the viability of projects. This paper establishes the operating cost prediction model based on production decline law and learning curves through analyzing the impact of resource depletion and technological advances on unit operating cost. This analysis quantifies the effects of both learning and depletion on operating costs, and also introduces an assessment of the economic limit of stimulation treatments, which is set by comparing the unit operating cost before and after the treatments are taken. The results show the effect of resource depletion overwhelming that of technological advances for a single oilfield, and thus the operating cost is increasing over its life cycle. The influence of each parameter on the operating cost is examined, the unit operating cost in plateau phase having the largest influence. Over time, the effect of constant decline rate of the exponential decline is gradually overtaking that of unit operating cost. This model is applied in several oilfields in Tunisia, and all the exam results meet accuracy requirements.

Suggested Citation

  • Luo, Dongkun & Zhao, Xu, 2012. "Modeling the operating costs for petroleum exploration and development projects," Energy, Elsevier, vol. 40(1), pages 189-195.
    • Handle: RePEc:eee:energy:v:40:y:2012:i:1:p:189-195
    DOI: 10.1016/j.energy.2012.02.006
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    1. Krautkraemer, Jeffrey A. & Toman, Michael, 2003. "Fundamental Economics of Depletable Energy Supply," Discussion Papers 10842, Resources for the Future.
    2. Méjean, Aurélie & Hope, Chris, 2008. "Modelling the costs of non-conventional oil: A case study of Canadian bitumen," Energy Policy, Elsevier, vol. 36(11), pages 4205-4216, November.
    3. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    4. Kaiser, Mark J., 2009. "Hydrocarbon production forecast for committed assets in the shallow water Outer Continental Shelf of the Gulf of Mexico," Energy, Elsevier, vol. 34(11), pages 1813-1825.
    5. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    6. Cutler J. Cleveland & Robert K. Kaufmann, 1991. "Forecasting Ultimate Oil Recovery and Its Rate of Production: Incorporating Economic Forces into the Models of M. King Hubbert," The Energy Journal, , vol. 12(2), pages 17-46, April.
    7. Kaiser, Mark J., 2006. "Hydrocarbon production cost functions in the Gulf of Mexico," Energy, Elsevier, vol. 31(12), pages 1726-1747.
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