Long-term dynamic allocation and maintenance planning of modular equipment to enhance gas field production flexibility.

Modular equipment is becoming popular in unconventional gas field development due to cost savings and flexibility in capacity adjustment by dynamic allocation. It is necessary to make long-term investment and maintenance planning to give full play to the advantages of modular equipment. This paper fills the gap of considering individual scheduling and the impact of utilization time on the value of the equipment in the unconventional gas field development by presenting a comprehensive method that considers the various costs of modular equipment and can facilitate tracking the usage of the equipment. A mixed integer linear programming model is developed to get the solution for the modular equipment, including the processing capacity selection, the installation and use plans, mobile scheduling, and depreciation sale plan, to enhance gas field production flexibility and achieve the best net present value. The changes of production and market demand, and the impact of utilization time on equipment depreciation are taken into account comprehensively. A real case study is used to prove the practicality and advantages of the proposed model. The results show the equipment utilization rate has been increased from 60% to 75%, achieving 10% higher economic benefits when using modular equipment compared with the traditional method in gas field production. In addition, the sensitivity analysis is implemented to investigate the influence of uncertain parameters such as output and market demand, equipment service life, and type of equipment on the equipment dynamic allocation and the economic performance. This study provides a powerful decision-making tool for gas field development to reduce costs, increase efficiency and promote clean practical production. • An equipment allocation model with long-term investment and maintenance planning is developed. • The impact of service life on modular equipment performance and value is considered. • Results yield 10% higher net present value compared to traditional method. • Sensitivity analysis is employed to investigate the impact of uncertainty parameters. [ABSTRACT FROM AUTHOR]