Fracturing Tracer Monitoring and Machine Learning-Assisted Geology-Engineering Coupled Optimization for Deep Coalbed Methane Horizontal Wells

Hong Zhuo, Zhangying Han, Shaohua Li, Xiuling He, Demei Zhang, Haibin Song, Gang Hui

Evaluating the productivity contribution of individual fracturing stages in deep coalbed methane (CBM) horizontal wells remains a critical challenge, hindering the optimization of stimulation designs. This study systematically integrates dual-phase (aqueous and gaseous) fracturing tracer monitoring with machine learning algorithms to address this issue. Based on large-scale field applications across ten deep CBM horizontal wells in the Changqing mining area of the Ordos Basin, comprising 132 monitored stages, quantitative production profile data were interpreted. Three distinct gas production archetypes—Homogeneous, Heel-Dominated, and Heterogeneous—were identified, each governed by specific geomechanical and stratigraphic controls. Pearson correlation analysis and Random Forest feature importance ranking were employed to decouple the hierarchical influence of geological parameters (Class I coal intersection length, trajectory position, coal thickness) and engineering parameters (proppant volume, pumping rate, fluid volume). A power-law correlation between Class I coal length and initial gas productivity was quantified (R2 = 0.71). For the first time, an economically viable “differentiated fracturing scale window” tailored to coal petrophysical classes and wellbore trajectory positions was defined. Subsequently, a machine learning-assisted geology-engineering closed-loop optimization methodology was established, using tracer data as a dynamic feedback bridge to iteratively refine fracturing designs. This research provides a reliable technical approach and practical template for enhancing single-well productivity and recovery efficiency in deep unconventional gas reservoirs.