Modeling and optimization for oil well production scheduling

Jin Lang; Jiao Zhao

doi:10.1016/j.cjche.2016.04.050

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Modeling and optimization for oil well production scheduling

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Modeling and optimization for oil well production scheduling
- Chinese Journal of Chemical Engineering Vol. 24, Issue 10, Pages: 1423-1430(2016)
- Affiliations：
  
  1. The Institute of Industrial Engineering and Logistics Optimization, Northeastern University,Shenyang,China,110819
  2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University,Shenyang,China,110819
  3. School of Automobile, Chang'an University,Xi'an,China,710064
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.04.050
  CLC：
- Published：2016
- Accepted：
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Jin Lang, Jiao Zhao. Modeling and optimization for oil well production scheduling[J]. , 2016, 24(10): 1423-1430.
DOI：

Jin Lang, Jiao Zhao. Modeling and optimization for oil well production scheduling[J]. , 2016, 24(10): 1423-1430. DOI： 10.1016/j.cjche.2016.04.050.

摘要

Abstract

In this paper

an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied. The oil well production scheduling was to determine the turn on/off status and oil flow rates of thewells in a given oil reservoir

subject to a number of constraints such as minimumup/down time limits and well grouping. The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost. Due to the NP-hardness of the problem

an improved particle swarm optimization (PSO) algorithm with a new velocity updating formula was developed to solve the problemapproximately. Computational experiments on randomly generated instanceswere carried out to evaluate the performance of the model and the algorithm's effectiveness. Compared with the commercial solver CPLEX

the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.

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