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Development and Use of a Modeling System to Aid a Major Oil Company in Allocating Bidding Capital

Author

Listed:
  • Donald L. Keefer

    (Arizona State University, Tempe, Arizona)

  • F. Beckley Smith

    (Sarasota, Florida)

  • Harry B. Back

    (Alcoa Laboratories, Alcoa Center, Pennsylvania)

Abstract
Bidding at U.S. offshore oil and gas lease sales is characterized by high stakes, enormous uncertainties, and many interrelated decisions. The Lease Bidding Strategy System combined techniques from decision analysis, statistics, and nonlinear optimization to provide information and insights to management responsible for bidding at Gulf Oil Corporation. It was used prior to every major federal lease sale from September 1980 until Gulf was acquired in 1984, during which time Gulf's bids exceeded $1.5 billion. This paper describes the evolution and use of this system, emphasizing its impact on the organization. It describes efforts that gained acceptance for this system under difficult circumstances, and illustrates the importance of adapting methodology to problem changes over time. To our knowledge, this is the first public documentation of the long-term use by a major oil company of a system for constrained multiblock optimization of its bids and partnership shares at U.S. offshore lease sales.

Suggested Citation

  • Donald L. Keefer & F. Beckley Smith & Harry B. Back, 1991. "Development and Use of a Modeling System to Aid a Major Oil Company in Allocating Bidding Capital," Operations Research, INFORMS, vol. 39(1), pages 28-41, February.
    • Handle: RePEc:inm:oropre:v:39:y:1991:i:1:p:28-41
    DOI: 10.1287/opre.39.1.28
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    Citations

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    Cited by:

    1. Sharma, Sunil, 2015. "Relevance of Resource Based View Themes for Capability Evolution," IIMA Working Papers WP2015-03-30, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Insua, Insua Rios & Rios, Jesus & Banks, David, 2009. "Adversarial Risk Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 841-854.
    3. Lance W. Saunders & J. Paul Brooks & Jason R. W. Merrick & Chad W. Autry, 2020. "Addressing Economic/Environmental Sustainability Trade‐offs in Procurement Episodes with Industrial Suppliers," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1256-1269, May.
    4. Kaiser, Mark J., 2006. "Hydrocarbon production cost functions in the Gulf of Mexico," Energy, Elsevier, vol. 31(12), pages 1726-1747.
    5. Muhammad Ejaz & Nisho Rani & Muhammad Ramzan Sheikh, 2023. "First Price Sealed-Bid Auctions with Bidders’ Heterogeneous Risk Behavior: An Adversarial Risk Analysis Approach," Decision Analysis, INFORMS, vol. 20(3), pages 231-241, September.
    6. Muhammad Ejaz & Stephen Joe & Chaitanya Joshi, 2021. "Adversarial Risk Analysis for Auctions Using Mirror Equilibrium and Bayes Nash Equilibrium," Decision Analysis, INFORMS, vol. 18(3), pages 185-202, September.
    7. Robin L. Dillon & M. Elisabeth Paté-Cornell & Seth D. Guikema, 2003. "Programmatic Risk Analysis for Critical Engineering Systems Under Tight Resource Constraints," Operations Research, INFORMS, vol. 51(3), pages 354-370, June.
    8. Sharma, Sunil, 2015. "Strategic Judgment under Pervasive Uncertainty," IIMA Working Papers WP2015-03-20, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Donald L. Keefer & Craig W. Kirkwood & James L. Corner, 2004. "Perspective on Decision Analysis Applications, 1990–2001," Decision Analysis, INFORMS, vol. 1(1), pages 4-22, March.
    10. J. Eric Bickel & James E. Smith, 2006. "Optimal Sequential Exploration: A Binary Learning Model," Decision Analysis, INFORMS, vol. 3(1), pages 16-32, March.
    11. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Decision analysis in energy and environmental modeling: An update," Energy, Elsevier, vol. 31(14), pages 2604-2622.
    12. Michael H. Rothkopf, 2007. "Decision Analysis: The Right Tool for Auctions," Decision Analysis, INFORMS, vol. 4(3), pages 167-172, September.

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