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Soft Computing

, Volume 22, Issue 3, pp 823–837 | Cite as

Combining a continuous location model and Heuristic techniques to determine oilfield warehouse locations under future oil well location uncertainty

  • Haixiang Guo
  • Wenwen Pan
  • Xiao Liu
  • Yijing Li
  • Bo Zeng
Methodologies and Application

Abstract

A rational decision regarding warehouse location can save logistics costs and improve oilfield operating efficiency. In existing research on oilfield warehouse location problems, it is usually assumed that the oil well locations are known. However, in real oilfield, operations, as well locations, are affected by underground reservoir conditions and the long-term plans of the oilfield company; future well locations that might be serviced by a warehouse are highly uncertain. In addition, previous warehouse location research has tended to solve location problems using a discrete or continuous location model without considering delivery problems. With these deficits in mind, this paper applies a Monte Carlo simulation to simulate future well locations, then selects several suitable candidates using a continuous location model and finally uses discrete location optimization to determine the optimal solution while also considering the distribution interruption problem. Finally, an oil warehouse location problem in the south of the Ordos Basin in China is given as an example of the process. Using relevant data such as number of wells, well locations and materials quantities required, Zhengning is identified as the optimal location for the storage warehouse construction. The simulation indicated that RMB 55,000 would be saved every year, proving the strength of the model to save logistics costs. In an environment in which well locations are uncertain, the combination of a continuous location model and a discrete location model can significantly enhance warehouse location logistics decisions in the oil and gas industries.

Keywords

Oilfield Warehouse location Monte Carlo simulation Centroid method Uncertain well locations 

Notes

Acknowledgments

This research has been supported by National Natural Science Foundation of China under Grant Nos. 71103163, 71573237; New Century Excellent Talents in University of China under Grant No. NCET-13-1012; Research Foundation of Humanities and Social Sciences of Ministry of Education of China No. 15YJA630019; Special Funding for Basic Scientific Research of Chinese Central University under Grant Nos. CUG120111, CUG110411, G2012002A, CUG140604, CUG160605; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan) under Grant No. H2015004B.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Haixiang Guo
    • 1
    • 2
    • 3
  • Wenwen Pan
    • 1
  • Xiao Liu
    • 1
  • Yijing Li
    • 1
  • Bo Zeng
    • 4
  1. 1.College of Economics and ManagementChina University of GeosciencesWuhanChina
  2. 2.Research Center for Digital Business ManagementChina University of GeosciencesWuhanChina
  3. 3.Mineral Resource Strategy and Policy Research Center of China University of GeosciencesWuhanChina
  4. 4.Department of Industrial EngineeringUniversity of PittsburghPittsburghUSA

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