The desorption production patterns of deep coalbed methane（CBM） vertical cluster wells

as well as the transition timing between free gas and desorbed gas

remain unclear. The dominant factors causing productivity differences are still uncertain

which hinders productivity improvement. To further evaluate the primary factors controlling productivity

a new method for assessing productivity-influencing factors was developed

based on the production dynamic parameters of 36 vertical cluster wells and using neural networks to predict bottom-hole flowing pressure. This method centered on the initial meter gas production index and integrated multiple machine-learning algorithms. The results showed that: 1） The Beggs Bill model and Gray model exhibited poor applicability for predicting the bottom-hole flowing pressure of deep CBM wells

while the single-phase gas model demonstrated reduced overall error as water production declined. Predictions using the neural network method were more accurate

with a relative error of less than 10% compared to measured values. 2） Using Kendall's tau-b correlation analysis

the discrete dominant factor was identified as the microstructural position

primarily located in uplifted positive structural zones

with the secondary factor being fracture development

categorized mainly as “well-developed” or “developed.” 3） By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors

the continuous dominant factors influencing productivity were ranked in descending order as: ash content

average construction discharge rate

total sand volume pumped

flowback rate at gas breakthrough

net pay thickness

acoustic travel time

gamma ray log value

average construction pressure

percentage of 100-mesh sand

and average gas measurement value. Engineering factors were found to have a significant impact on gas well productivity and cannot be overlooked. This method leverages the advantages of multiple machine-learning algorithms

demonstrating strong operability and improving the accuracy of CBM dynamic predictions. It aids in optimizing fracturing design parameters and provides a scientific basis for enhancing post-fracturing productivity in CBM wells.