Modeling and optimization for oil well production scheduling

• Published in: Chinese journal of chemical engineering Vol. 24; no. 10; pp. 1423 - 1430
• Main Authors: Lang, Jin, Zhao, Jiao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2016
• Subjects: Improved particle swarm optimization; Mixed integer nonlinear programming (MINLP); Oil well production; Scheduling; Mixed integer nonlinear programming (MINLP); Improved particle swarm optimization; Scheduling; Oil well production
• ISSN: 1004-9541; 2210-321X
• DOI: 10.1016/j.cjche.2016.04.050

In this paper, an oil well production scheduling problem for the light load oil well during petroleum field exploi- tation was studied. The oil well production scheduling was to determine the turn on/offstatus and oil flow rates of the wells in a given oil reservoir, subject to a number of constraints such as minimum up/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 problem approximately. Computational experiments on randomly generated instances were carried out to eval- uate 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.