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A General MINLP Model for the Multiway Valve Channel-Limited Location-Allocation Problem

Author

Listed:
  • Boyu Zhu
  • Jun Zhou
  • Guangchuan Liang
  • Xuan Zhou
  • Liulin Zhou

Abstract

As an important aspect of oil and gas field construction, oil and gas field surface engineering often affects the efficiency and safety of oil and gas production, and it requires a large investment. In the past, much work has been done on layout optimization for gathering pipeline networks. However, few studies have considered multiway valves in the optimization of pipeline networks. Compared to traditional metering processes, a process using multiway valves can reduce construction and operation costs and enable automation of the well-selection operation. In this paper, an MINLP model is established in which the number of multiway valves and their numbers of channels are considered special constraints and the number of multiway valves and the associations between wells and multiway valves are treated as optimization variables. A specific heuristic algorithm for solving this problem is also proposed in this paper. We consider the coordinates of real-world wells and of randomly generated well locations as different examples to analyze the performance of this algorithm. These examples demonstrate that the algorithm can initially adjust the network through single-step iteration, while double-step iteration is efficient when all channels of a multiway valve have been associated with pipelines, and multistep iteration can help the objective function escape from local optima. Finally, numerical analysis results prove that the proposed algorithm can be used to efficiently solve the problem of interest and exhibits stable convergence.

Suggested Citation

  • Boyu Zhu & Jun Zhou & Guangchuan Liang & Xuan Zhou & Liulin Zhou, 2019. "A General MINLP Model for the Multiway Valve Channel-Limited Location-Allocation Problem," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-20, September.
  • Handle: RePEc:hin:jnlmpe:7576093
    DOI: 10.1155/2019/7576093
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