A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells

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.