TY  - EJOU
AU  - Zhao, Qin 
AU  - Wang, Yuxin 
AU  - Chen, Gang 
AU  - Zhang, Hui 
AU  - Wang, Lei 
AU  - Zhou, Mulin 
AU  - Zhang, Songfei 
AU  - Weng, Yu 

TI  - An Integrated Multi-Scale Modeling Framework for Gas Entrainment Prediction in Coalbed Methane Production Systems
T2  - Fluid Dynamics \& Materials Processing

PY  - 2026
VL  - 22
IS  - 6
SN  - 1555-2578

AB  - This study presents an integrated multi-scale framework for predicting gas entrainment and flow behavior in coalbed methane production systems under gas-liquid two-phase flow conditions. The approach combines three-dimensional computational fluid dynamics simulations, reduced-order modeling, and machine-learning-based prediction to achieve both high physical fidelity and computational efficiency. Such an improved strategy stems from a specific need. As coalbed methane extraction increasingly encounters complex multiphase flow conditions, accurate characterization of gas entrainment has become essential for improving production stability and optimizing downstream gathering and separation systems. In practice, the flow entering rod pumps frequently deviates from the conventional assumption of a purely aqueous phase, leading to strongly coupled gas-liquid interactions that are difficult to capture using traditional modeling approaches. At the same time, the computational cost associated with full three-dimensional simulations limits their applicability to real-time field operations. The present approach is introduced to map the three-dimensional flow dynamics onto a one-dimensional numerical manifold while retaining the dominant physical characteristics of the system. Building upon this reduced representation, a Stacking ensemble learning framework is developed using Support Vector Regression, Back-Propagation Neural Networks, and Random Forests as base learners, combined through an Adaptive Neuro-Fuzzy Inference System meta-learner for rapid field-scale prediction. The results show that the suction effect generated by the rod pump is the dominant mechanism governing gas entrainment. Notably, the proposed prediction model achieves high predictive accuracy, with a root mean square error of 1.75 L/min, a mean absolute error of 0.82 L/min, and a coefficient of determination of 0.98. Furthermore, analysis of the surface pipeline network indicates that terminal flow rates are characterized by low-frequency pulsations that promote stable gas-liquid interface formation within downstream separators.
KW  - Coalbed methane; gas containing produced water; reduced order analysis; large model; transport and separation

DO  - 10.32604/fdmp.2026.083174