A Hybrid PNN–XGBoost Framework for Gas–Water Flow Pattern Prediction and 3D Visualization in Near-Horizontal Wells

The distribution of gas–water two-phase flow in near-horizontal wells is influenced by factors such as wellbore inclination and phase flow rates. To explore these effects, a laboratory loop simulating downhole conditions was used to conduct experiments under varying inclinations and flow parameters. Flow patterns were classified based on visual observations and existing theory, and scatter plots were used to analyze flow regime boundaries. Three classification models were developed and compared. The proposed PNN–XGBoost framework integrates explicit second-order feature crossing with XGBoost-based importance selection prior to probabilistic neural network classification. Among the evaluated models, the PNN–XGBoost approach achieved the highest predictive performance. The model was further validated using 3D wellbore holdup imaging, confirming its robustness in flow pattern identification and its applicability to practical well logging interpretation.