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07-10-2016 | Methodologies and Application | Issue 3/2018

Soft Computing 3/2018

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

Journal:
Soft Computing > Issue 3/2018
Authors:
Haixiang Guo, Wenwen Pan, Xiao Liu, Yijing Li, Bo Zeng
Important notes
Communicated by V. Loia.

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.

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