###### Abstract
Another strategy is based on the production potentialities of adjacent wells [11; 12], representing the extant geological and geophysical data on the target reservoir. This approach evaluates the economic viability of implementing new drillings and finding the sweet spots within the reservoir's geological and engineering landscape [13; 14]. Machine learning has recently gained popularity in production prediction [11; 12]. The estimated ultimate recovery (EUR) prediction model for shale gas wells is based on the multiple linear regression method. The key factors controlling productivity are analyzed using the Pearson correlation coefficient and maximum mutual information coefficient analysis method [15]. In contrast, the EUR prediction model for fractured horizontal wells in tight oil reservoirs employs a deep neural network (DNN) and demonstrates significantly higher prediction accuracy than the traditional multiple linear regression model [16]. Other studies have also shown the effectiveness of supervised learning models such as Random Forest, Gradient-Boosting Machine, and Support-Vector Machine in predicting shale gas reservoir productivity, with notable improvements when integrating the variable importance method [17]. Additionally, tree-boosting methods and time-segmented feature extraction have been effective for predicting oil production decline rates, demonstrating ML's ability to manage complex datasets [18]. The use of long short-term memory networks has also outperformed traditional methods in water saturation prediction, enhancing reservoir characterization accuracy [19]. Moreover, deep neural networks have been applied for predicting cumulative gas production, indicating a shift towards data-driven methodologies [20]. Furthermore, deep learning models have been utilized for oil and gas production forecasting, significantly reducing time and resource expenditure while maintaining high accuracy [21]. Lastly, a machine learning-based approach has been developed for reservoir identification and production prediction, underscoring the efficiency of ML in classifying reservoir types and forecasting production rates, especially with ensemble methods [22]. Reservoir simulation is a widely used technique that employs physics-based models to predict oil production under different scenarios. However, the simulation requires extensive geological and engineering data on the reservoir, which may not always be available. A deep understanding of the geology and engineering aspects of the complex reservoir is also challenging for people without professional knowledge [23]. Moreover, the assumptions made in the simulation model can impact the predictive accuracy. Reservoirs with low permeability and porosity are distinct due to their constrained pore spaces and fluid flow capabilities [24; 25; 26; 27]. At present, scholars have used advanced CT imaging technology and machine learning algorithms to study fluid flow in complex, non-uniform, porous, low permeability media [28; 29; 30]. This nature inherently complicates numerical computations for production rates [31]. The reduced permeability demands a more detailed grid division, amplifying computational intricacy and extending calculation durations [32]. Such environments can also generate numerical dispersion, causing potential mismatches between simulated outcomes and actual field data [33]. Additionally, fluid flow in these settings might not always adhere to [PERSON]'s law, requiring more nuanced modeling approaches [32]. The existing mainstream hydraulic fracturing reservoir simulation heavily relies on two-dimensional or pseudo-three-dimensional models [34; 35; 36]. These models frequently neglect the reservoir's vertical heterogeneity and omit numerous details during the simplification of physical formulae, negatively affecting the accuracy of capacity forecasting. In contrast, this study's innovation lies in fully leveraging the geometric characteristics of vertical wells and the longitudinal collection attributes of well-logging data.