To enhance the accuracy of the comprehensive evaluation of reservoir quality in shale oil fractured horizontal wells, the Pearson correlation analysis method was employed to study the correlations between geological parameters and their relationship with production. Through principal component analysis, the original factors were linearly combined into principal components with clear and specific physical meanings, aiming to eliminate correlations among factors. Furthermore, Gaussian membership functions were applied to delineate fuzzy levels, and the entropy weight method was used to determine the weights of principal components, establishing a fuzzy comprehensive evaluation model for reservoir quality. Without using principal component analysis, the correlation coefficient between production and evaluation results for the 40 wells in the Cangdong shale oil field was only 0.7609. However, after applying principal component analysis, the correlation coefficient increased to 0.9132. Field application demonstrated that the average prediction accuracy for the cumulative oil production per kilometer of fractured length over 12 months for the 10 applied wells was 91.8%. The proposed comprehensive evaluation method for reservoir quality can guide the assessment of reservoir quality in shale oil horizontal wells.

The conductivity of propped fractures following hydraulic fracturing is crucial in determining the success of the fracturing process. Understanding the primary factors affecting fracture conductivity and uncovering their impact patterns are essential for guiding the selection of fracturing engineering parameters. We conducted experiments to test fracture conductivity and analyzed the effects of proppant particle size, closure pressure, and fracture surface properties on conductivity. Using the orthogonal experimental method, we clarified the primary and secondary relationships of the influencing factors on conductivity. The results indicate that proppant particle size, formation closure pressure, and fracture surface properties significantly affect fracture conductivity, with the order of influence being closure pressure > fracture surface properties > proppant particle size. Using large-particle-size proppants effectively increases interparticle porosity and enhances fracture conductivity. However, large-particle-size proppants reduce the number of contact points between particles, increasing the pressure on individual particles and making them more prone to crushing, which decreases fracture conductivity. Proppants become compacted under closure pressure, leading to a reduction in fracture conductivity. Proppant particles can embed into the fracture surface under closure pressure, further impacting fracture conductivity. Compared to non-laminated fracture surfaces, proppant particles are more likely to embed into laminated fracture surfaces under closure pressure, resulting in a greater embedding depth and reduced conductivity.

The placement of proppant within fractures is critical to the effectiveness of hydraulic fracturing. To elucidate the migration and placement patterns of proppant within multi-branched fractures during hydraulic fracturing, we conducted simulation experiments under both single-fracture and multi-branched-fracture conditions, varying injection rates and proppant sizes. The results of the research indicate that increasing the injection rate effectively increases the magnitude of vortex formation at the leading edge of sandbars and the drag forces acting on the proppant particles, resulting in increased particle migration distances. However, effective proppant packing near the wellbore entrance is not achieved at higher injection rates, leaving the fractures susceptible to closure under in situ stress, thereby reducing overall fracture conductivity. In addition, increasing the proppant size results in higher settling velocities and weakens the vortex’s ability to entrain the proppant particles. This results in shorter proppant placement distances, and the proppant cannot effectively reach the distant branched fractures. In addition, the diversionary effect of the branched fractures gradually reduces the flow rate in the distant branches, resulting in poorer proppant placement efficiency. Based on these findings, we recommend an approach that initially increases injection rates while reducing proppant size to ensure proppant placement in distant wellbore fractures and branched fracture networks. Subsequently, larger proppants can be used to effectively fill fractures close to the wellbore.

A numerical model was established by using the 3D lattice method to investigate the synchronous propagation mechanism of multiple clusters of hydraulic fractures in interlayered tight sandstone reservoirs in the Songliao Basin in China. The multi-fracture synchronous propagation model under different geological factors and fracturing engineering factors was simulated. The results show that the vertical stress difference, interlayer Young’s modulus, and lithologic interface strength are positively correlated with the longitudinal propagation ability of multiple hydraulic fractures. The three clusters of hydraulic fractures can have adequate longitudinal extension capacity and transverse propagation range with 15 m cluster spacing and a 12 m³/min pumping rate. The viscosity of the fracturing fluid is positively correlated with the ability of hydraulic fracture to penetrate the interlayer longitudinally but negatively correlated with the transverse propagation length. It is recommended that high viscosity fracturing fluid is used in the early stage of multi-clustered fracturing in interlayered tight sandstone reservoirs to promote hydraulic fractures to penetrate more interlayers and communicate more pay layers in the longitudinal direction, and low viscosity fracturing fluid in the later stage to make multiple clusters of fractures propagate to the far end where possible and obtain a more ideal SRV.

Unconventional reservoirs are characterized by intricate fluid-phase behaviors, and physics-based shale oil well productivity prediction models often exhibit substantial deviations due to oversimplified theoretical frameworks and challenges in parameter acquisition. Under these circumstances, data-driven approaches leveraging actual production datasets have emerged as viable alternatives for productivity forecasting. Nevertheless, conventional data-driven architectures suffer from structural simplicity, limited capacity for processing low-dimensional feature spaces, and exclusive applicability to intra-sequence learning paradigms (e.g., production-to-production sequence mapping). This fundamentally conflicts with the underlying principles of mechanistic modeling, which emphasize pressure-to-production sequence transformations. To address these limitations, we propose a hybrid deep learning architecture integrating convolutional neural networks with bidirectional gated recurrent units (CNN-BiGRU). The model incorporates dedicated input pathways: fully connected layers for feature embedding and convolutional operations for high-dimensional feature extraction. By implementing a sequence-to-sequence (seq2seq) architecture with encoder–decoder mechanisms, our framework enables cross-domain sequence learning, effectively bridging pressure dynamics with production profiles. The CNN-BiGRU model was implemented on the TensorFlow framework, with rigorous validation of model robustness and systematic evaluation of feature importance. Hyperparameter optimization via grid searching yielded optimal configurations, while field applications demonstrated operational feasibility. Comparative analysis revealed a mean relative error (MRE) of 16.11% between predicted and observed production values, substantiating the model’s predictive competence. This methodology establishes a novel paradigm for machine learning-driven productivity prediction in unconventional reservoir engineering.