Abstract
The physical properties of coal reservoirs, important parameters for evaluating the production potential of coalbed methane (CBM) resources, can be assessed nondestructively and in real-time using acoustic wave technology. In this study, we collected 48 low- and middle-rank coal samples oriented in different bedding directions from seven typical coal mines, encompassing the Zhunan, Tuha, and Kuqa-Bay coalfields in Xinjiang, China. We clarified the characteristics of the physical parameters (apparent density, fracture, porosity, and permeability) and acoustic wave of coal variations through acoustic wave, porosity, and permeability experiments, revealing the response law of acoustic wave characteristics to the physical parameters of coal. The results indicated that the acoustic wave velocity and dynamic elastic modulus (E (d)) of coal samples oriented in the perpendicular bedding direction are larger than those oriented in the parallel bedding direction; however, the dynamic Poisson's ratio (μ(d)) of coal samples oriented in different bedding directions does not significantly differ. The existence of fractures significantly reduces the acoustic wave velocity and E (d) of the coal. The greater the apparent density of coal, the tighter its structure, resulting in a faster acoustic wave velocity. The larger the porosity of coal, the greater its internal voids, leading to a more pronounced attenuation of acoustic energy and a slower acoustic wave velocity. The more developed and interconnected the bedding fractures of coal bodies oriented in the parallel bedding direction, the higher their permeability, resulting in a smaller decrease in acoustic wave velocity. Conversely, the more developed the bedding fractures of coal bodies oriented in the perpendicular bedding direction, the more pronounced their attenuation of acoustic wave velocity. Finally, the regression equations for E (d) with the square of P-wave velocity (V (P) (2)) and μ(d) with the square ratio of V (P) to S-wave velocity (V (P) (2)/V (S) (2)) were established for coal. The study findings can help evaluate and predict the reservoir quality of coal seams, assess CBM, and improve the safety and efficiency of its extraction.