###### Abstract  
As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PPS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight. Post-fracture production prediction; production segmentation; well logging; pipelines; GridSearchCV; machine learning +
Footnote †: journal: Medical  
hydraulic fractures in multilayered formations, offers insights into fracture propagation and its subsequent impact on reservoir performance [3]. However, with the introduction of hydraulic fracturing and other advanced extraction techniques, predicting post-fracture oil production capacity becomes an intricate endeavor fraught with multifaceted challenges [4]. Accurate post-fracture production prediction is paramount in enhancing well operations, mitigating operational hiatuses, and ensuring a higher return on petroleum investments. The integration of machine learning with advanced numerical techniques, like the 3D lattice method, has emerged as a promising solution for understanding fracture dynamics in various shale fabric facies, as evidenced by a case study from the Bohai Bay Basin, China [5]. Moreover, the advent of physics-informed methods for evaluating feasibility emphasizes the need to comprehend the intrinsic properties of shale reservoirs for efficient production [6]. When combined with sustainable extraction techniques, accurate production prediction can lead to more effective oil development, minimizing environmental degradation [7]. This approach provides a pathway toward a more sustainable and environmentally friendly energy economy. Nonetheless, predicting oil production capacity is an intricate endeavor with multifaceted challenges [8]. There are numerous methods for production prediction on the oil field site. One prevalent strategy involves forecasting oil production metrics for a specific well by extrapolating the anticipated oil yield. This extrapolation leans heavily on historical production metrics and pertinent reservoir data [8; 9; 10].