Plugging is a prominent cause of reduced production in coalbed methane (CBM) wells. In this thesis, the feasibility analysis and optimal design of the acidizing of CBM wells to remove the plugging are researched. First, X-ray diffraction analysis shows that the plugging contains acid-soluble minerals and a field case indicates that the acidizing effect is positively correlated with the content of acid-soluble minerals. Inspired by this, the author analyzes influencing factors of the content of acid-soluble minerals. Well logging parameters (DEN, AC, GR) are selected to establish a neural network model to predict the content of the acid-soluble minerals. Furthermore, the feasibility criterion of the acidizing of CBM wells is proposed. Then a forward model and a parameters inversion algorithm are proposed to diagnose the plugging. The multisolution problem of parameters inversion is solved by the Gauss-Marquardt (G-M) algorithm based on the stochastic initial value and maximum probability. Combining this method with the present numerical model, the author presents an optimal design to optimize the volume and injection rate of the acid. Meanwhile, by experimental study, the author proposes a new acid formulation. Finally, the results are applied in the field to confirm the feasibility of the acidizing. The findings show that acidizing is an effective stimulation technology for specific CBM wells and the feasibility analysis and the optimal design can improve this effect.