Sustainable supply chain of distributed multi-product gas fields based on skid-mounted equipment to dynamically respond to upstream and market fluctuations.

Efficient operation of the gas field supply chain is an important guarantee for oil and gas energy security, and it needs to dynamically adapt to upstream and market fluctuations. This paper proposes an innovative design and operation optimization mixed integer nonlinear programming (MILP) method for distributed supply chain based on skid equipment. Unlike the traditional methods, the proposed MILP method can simultaneously obtain upstream production planning, midstream modular equipment and processing capacity allocation, and downstream transportation allocation schemes for various natural gas products such as liquefied natural gas (LNG), compressed natural gas (CNG) and pipeline natural gas (PNG) of each time periods by making the maximum net present value (NPV) of the full development cycle as the target. In order to prove the superiority and usability of the proposed method, four operation scheduling modes are compared through two comprehensive case analysis. Additionally, the effects of gas well productivity, market demand and product price are investigated through sensitivity analysis. The results show that comparing with the traditional method, the proposed method can effectively improve the loading rate of the processing equipment, increase the overall revenue of gas field development, integrate the operation in the upstream, midstream and downstream of the supply chain, dynamically adapt to the gas production and marketing fluctuations. This study provides a creative way to obtain better profit, reduce energy utilization and promote cleaner production for sustainable supplies management in gas industry. • A sustainable supply chain of distributed multi-product gas field based on skid equipment. • Production planning of upstream gas wells and allocation of downstream multiple products are considered. • Multi-product, modular facilities and multiple transportation mode are integrated. • The practicality of the optimization method is testified by two case studies. • Sensitivity analysis is employed to investigate the impact of uncertainty parameters. [ABSTRACT FROM AUTHOR]