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Optimizing Chevron’s Refineries

Author

Listed:
  • Ted Kutz

    (Technical Solutions, Chevron Downstream, Richmond, California 94801)

  • Mark Davis

    (Value Chain Optimization, Chevron Downstream, Houston, Texas 77002)

  • Robert Creek

    (Process Engineering, Chevron Energy Technology Company, Richmond, California 94801)

  • Nick Kenaston

    (Process Engineering, Chevron Energy Technology Company, Richmond, California 94801)

  • Craig Stenstrom

    (Supply and Trading IT, Chevron Gas and Midstream, Richmond, California 94801)

  • Margery Connor

    (Chevron Information Technology Company, San Ramon, California 94583)

Abstract

Chevron has developed a software modeling tool that its seven company-owned refineries use to select the most profitable raw materials, evaluate product options, optimize refinery processes, and promote efficient capital investments. The tool is a linear program with distributive recursion mathematics, which Chevron uses in operations and strategic planning. Over the past 30-plus years, the company has continually improved this application of operations research, and its complementary and supporting systems and business processes, and they are now deeply embedded into the fabric of Chevron’s downstream business of reliably and efficiently supplying products to our customers. The value that these efforts bring to Chevron now approaches $1 billion annually. We estimate that the cumulative value to Chevron over the past three decades is approximately $10 billion.

Suggested Citation

  • Ted Kutz & Mark Davis & Robert Creek & Nick Kenaston & Craig Stenstrom & Margery Connor, 2014. "Optimizing Chevron’s Refineries," Interfaces, INFORMS, vol. 44(1), pages 39-54, February.
    • Handle: RePEc:inm:orinte:v:44:y:2014:i:1:p:39-54
    DOI: 10.1287/inte.2013.0727
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    References listed on IDEAS

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    1. Billy Rigby & Leon S. Lasdon & Allan D. Waren, 1995. "The Evolution of Texaco’s Blending Systems: From OMEGA to StarBlend," Interfaces, INFORMS, vol. 25(5), pages 64-83, October.
    2. Jianzhong Zhang & Nae-Heon Kim & L. Lasdon, 1985. "An Improved Successive Linear Programming Algorithm," Management Science, INFORMS, vol. 31(10), pages 1312-1331, October.
    3. W. W. Garvin & H. W. Crandall & J. B. John & R. A. Spellman, 1957. "Applications of Linear Programming in the Oil Industry," Management Science, INFORMS, vol. 3(4), pages 407-430, July.
    4. Darwin Klingman & Nancy Phillips & David Steiger & Warren Young, 1987. "The Successful Deployment of Management Science Throughout Citgo Petroleum Corporation," Interfaces, INFORMS, vol. 17(1), pages 4-25, February.
    5. Thomas E. Baker & Leon S. Lasdon, 1985. "Successive Linear Programming at Exxon," Management Science, INFORMS, vol. 31(3), pages 264-274, March.
    6. Calvin W. DeWitt & Leon S. Lasdon & Allan D. Waren & Donald A. Brenner & Simon A. Melhem, 1989. "OMEGA: An Improved Gasoline Blending System for Texaco," Interfaces, INFORMS, vol. 19(1), pages 85-101, February.
    7. C. E. Bodington & T. E. Baker, 1990. "A History of Mathematical Programming in the Petroleum Industry," Interfaces, INFORMS, vol. 20(4), pages 117-127, August.
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