Optimizing enhanced coalbed methane recovery in thick lignite formations: An integrated technical and economic evaluation

• A novel integrated framework combining numerical simulation, economic analysis, neural networks, and particle swarm optimization is developed for ECBM recovery optimization in thick lignite reservoirs. • Economic evaluation demonstrates that CO 2 -ECBM remains unprofitable in Erlian Basin lignite formations under current market conditions, despite potential carbon sequestration benefits. • Reservoir permeability is identified as the most critical geological parameter, with optimal well spacing showing strong permeability dependence and CO 2 injection ratio being sensitive to carbon credit economics. • The proposed hybrid neural network-particle swarm optimization approach provides an efficient method for injection scheme optimization in complex coalbed methane systems. • The study underscores the necessity of carbon market incentives while offering practical guidelines for ECBM project design in low-rank coal reservoirs to balance economic and environmental objectives. The Erlian Basin, characterized by extensive lignite deposits with considerable thickness, presents significant potential for coalbed methane (CBM) extraction. However, CBM exploration in lignite has received limited attention due to its typically low gas content, and the economic evolution of thick lignite reservoirs remains understudied. This study employs an integrated approach combining numerical simulation with economic evaluation to systematically assess the technical and economic feasibility of Enhanced Coalbed Methane (ECBM) recovery in thick lignite formations within the Erlian Basin of Inner Mongolia. We evaluate the economic feasibility of ECBM under various gas injection scenarios and optimize key operational parameters through a coupled neural network model and particle swarm optimization algorithm. The key findings are as follows: (1) Even with carbon sequestration benefits incorporated, CO 2 -ECBM projects in the Erlian Basin’s thick lignite formations remain economically unviable under current market conditions. (2) Reservoir permeability is the dominant geological parameter governing ECBM feasibility in thick lignite reservoirs. (3) The hybrid neural network–optimization algorithm demonstrates high computational efficiency in optimizing ECBM injection schemes. Optimal well spacing exhibits a strong positive correlation with reservoir permeability. The ideal CO₂ injection ratio is primarily determined by the economic balance between carbon credit pricing and CO₂ capture costs.