A two-stage improved genetic algorithm-particle swarm optimization algorithm for optimizing the pressurization scheme of coal bed methane gathering networks

Abstract

Natural gas has been widely promoted by Chinese government as a clean energy source due to its odorless and smokeless combustion process. As an important part of urban natural gas supply, coal bed methane (CBM) accounted for 3.4% of the China's total natural gas production in 2017 and had an increase of 9.2% year-on-year compared with last year. In response to government promotion and so as to maintain the steady production of CBM, some enhancing-production approaches have been taken in CBM fields, and one of the effective approaches is the installation of compressors in gathering networks. However, due to the complexity of CBM gathering networks, the pressurization scheme lacks guidance in practice and is mainly decided by experience. To guide the practical production process, this paper proposes a mixed-integer nonlinear programming (MINLP) model to optimize the pressurization scheme. The MINLP model considers the cost of facilities and electricity and maximizes the daily net profit of CBM field companies. Then, a two-stage improved genetic algorithm-particle swarm optimization algorithm is proposed to solve this problem. Finally, the model is applied to a real CBM field with 132 production wells, 10 manifolds and 1 central processing facility (CPF) in China. The result shows that the optimal scheme can increase the total production from 3.39 × 105 m3/d to 3.73 × 105 m3/d with a growth rate of 10.0%, and the daily net profit increases 2.49 × 104CNY/d (about 3.70 × 103USD/d).