Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm

Yukun Dong, Yu Zhang, Fubin Liu and Zhengjun Zhu
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Yukun Dong: College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
Yu Zhang: College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
Fubin Liu: College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
Zhengjun Zhu: Research Institute of Exploration and Development, Tarim Oilfield Company, PetroChina, Korla 841000, China

Energies, 2022, vol. 15, issue 8, 1-18

Abstract: The optimization of injection–production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

Keywords: particle swarm optimization algorithm; injection-production optimization; curve adaptation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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