A dynamic evolution model of coal permeability during enhanced coalbed methane recovery by N2 injection: experimental observations and numerical simulation

China boasts abundant coalbed methane (CBM) resources whose output is significantly influenced by the permeability of coal reservoirs. However, the permeability of coal reservoirs in China is generally low, which seriously restricts the efficient exploitation of CBM. To solve this problem, enhanced coalbed methane (ECBM) recovery by N-2 injection has been widely adopted in recent years. However, there exists little research conducted on coal permeability behavior during the displacement process. In this work, a series of physical simulation experiments were conducted on CH4 displacement by N-2 injection to investigate the dynamic evolution of coal permeability. Based on the dual-porosity medium property of coal, a dynamic evolution model of coal permeability considering the combined effects of matrix shrinkage/swelling and effective stress was proposed to reflect the ECBM recovery process. The accuracy of this theoretical model was verified by matching the numerical simulation results with the experimental data. The findings show that coal permeability increases at a gradually decelerating rate with the passage of displacement time, and finally tends to be stable. In addition, raising N-2 injection pressure can dramatically enhance CH4 recovery and shorten the displacement time, which indicates that ECBM recovery by N-2 injection is a feasible technical method for low-permeability coal reservoirs. Meanwhile, the model proposed in this study can be applied to the prediction of CBM production, and is of guiding significance for engineering applications.

Automated summary

This study suggests that ECBM recovery by N-2 injection is a feasible technical method for low-permeability coal reservoirs. The research found that coal permeability increases gradually and finally stabilizes during the N-2 injection process. The proposed dynamic evolution model of coal permeability can be applied to predict CBM production and guide engineering applications.