Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs, but its effectiveness i
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs, but its effectiveness is under the joint action of multiple factors of complexity. Traditional analysis methods have limitations in dealing with these complex and interrelated factors, and it is difficult to fully reveal the actual contribution of each factor to the production. Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide data-driven insights into the key factors driving production. In this study, a data-driven PCA-RF-VIM (Principal Component Analysis-Random Forest-Variable Importance Measures) approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production. Four types of parameters, including log parameters, geological and reservoir physical parameters, hydraulic fracturing design parameters, and reservoir stimulation parameters, were inputted into the PCA-RF-VIM model. The model was trained using 6-fold cross-validation and grid search, and the relative importance ranking of each factor was finally obtained. In order to verify the validity of the PCA-RF-VIM model, a consolidation model that uses three other independent data-driven methods (Pearson correlation coefficient, RF feature significance analysis method, and XGboost feature significance analysis method) are applied to compare with the PCA-RF-VIM model. A comparison the two models shows that they contain almost the same parameters in the top ten, with only minor differences in one parameter. In combination with the reservoir characteristics, the reasonableness of the PCA-RF-VIM model is verified, and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area. Ultimately, the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production, as the combined importance of these top ten parameters is 91.95 % on driving natural gas production. Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
