Dynamic and static integrated classification model of gas well based on XGBoost algorithm—an example from block S of Sulige tight sandstone gas field - Zhang, Shuangshuang; Yu, Xiangdong; Gao, Xiuli; Li, Donglin; Huang, Shijun (Frontiers in Earth Science, 2025)
Classification of gas wells is an important part of optimizing development strategies and increasing the recovery. The original classification standard of gas wells in the Sulige gas field has weak regularity of each parameter, large overlapping range of classification results, serious discrepancy between the dynamic and static, and low efficiency of manual classification. Aiming at this problem, this paper establishes a set of dynamic and static integrated classification model of tight sandstone gas wells in Sulige based on XGBoost algorithm. After comparison and verification, it is proved to be accurate and reliable. The model can be substituted into the static and dynamic characteristic parameters at the same time to complete the importance ranking of classification features and model training, and realize the dynamic and static integration classification of Sulige gas well. The model is applied to 553 gas wells in S block, and it is concluded that the main factors affecting the classification of gas wells are initial daily production, effective thickness of a gas layer, formation permeability, original formation pressure, and porosity. The main factors affecting the classification of class I and class II wells are initial daily production and permeability, and the main factors affecting the classification of class III wells are initial daily production and the effective thickness of the gas layer. This method improves the effectiveness of gas well classification, reduces subjectivity, and the classification results are in line with the actual situation of the field, which has guiding significance for the classification management of gas wells and the formulation of development countermeasures.