## 6 Conclusions  
The post-fracturing production characteristics offer valuable insights for selecting the appropriate fracturing stage. Our analysis thoroughly examined the correlation between oil and gas reservoir productivity and logging parameters after hydraulic fracturing. A novel data volume expansion method was introduced, which involves subdividing the gas production of each well based on depth and leveraging the correlation model between petrophysical parameters and gas production to accurately match the gas production data with the accuracy of the well-logging data, establishing a one-to-one correspondence between depth and gas production. Then, a comprehensive parameter optimization was executed across twelve pipelines, employing diverse data preprocessing techniques, feature extraction methods, and machine learning models. This was achieved through the GridSearchCV hyperparameter tuning method, yielding the best-performing model for each pipeline. In conjunction with the optimized pipeline, these logging parameters were then harnessed to forecast post-fracturing production. Subsequently, we performed a comparative analysis of the productivity forecasts for various wells across different pipelines, created Taylor plots, computed the model evaluation index for all pipelines, and assessed the Pearson correlation coefficient for all wells within each pipeline. Lastly, we discussed the training process of the pipeline and the resulting predictions.  
1. After extensive hyperparameter tuning, it became evident that logging parameters can indeed correlate with productivity, and the prediction trends are generally on point. As such, this approach holds promise as a surrogate model for numerical reservoir simulations.
2. The gas production from each well was segmented by depth on the initial day. Utilizing the correlation model between petrophysical parameters and gas production, the accuracy of the well-logging data was effectively matched with the gas production data, achieving integration of depth and gas production with precision. This approach not only enables the refinement and prediction of productivity in different layers of oil and gas reservoirs, but also offers practical data support for reservoir management and hydraulic fracturing operations, holding significant practical application value/
3. In the context of oil and gas extraction from low-permeability and low-porosity formations, machine learning models offer an intuitive representation of the productivity distribution in relation to well depth. This is pivotal in guiding the selection of the most suitable fracturing stage during field operations, and this approach provides reservoir engineers with an efficient means of reservoir performance oversight.  
Conceptualization, Y.S.; Methodology, Y.S.; Software, J.W. and Y.C.; Validation, J.L.; Formal analysis, Z.W.; Investigation, T.W. and S.C.; Resources, H.L.; Writing--original draft, L.H.; Writing--review & editing, Z.Z. and L.H.; Visualization, L.H.; Supervision, A.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was approved by China Oilfield Services Limited.  
Informed Consent StatementInformed consent was obtained from all subjects involved in the study.  
The datasets presented in this article are not readily available due to confidentiality agreements with the involved corporation. Requests to access the datasets should be directed to China Oilfield Services Limited, Tianjin 300459, China.  
The authors declare no conflict of interest.