Optimization of Deep Shale Gas Stage and Cluster Design Based on While-Drilling Cuttings and Sliding Window Contribution Clustering

For deep shale gas reservoirs characterized by significant burial depth, complex fracture systems, and thin individual layers, optimized segment and cluster design is critical to enhancing hydraulic fracturing performance。 This paper proposes a novel comprehensive optimization methodology that systematically integrates real-time formation data. By calibrating downhole mechanical specific energy (MSE) using while-drilling cuttings analysis, we established a rock mechanics characterization system. Combining calibrated MSE data, fracture development characteristics, and regional engineering experience, a sliding window clustering algorithm was implemented for precise horizontal well segmentation. This approach determines differentiated segment length ranges, optimizes bridge plug seating positions, and formulates coupling layout schemes. Numerical simulations demonstrate that compared with conventional geometric fracturing designs, this method achieves over 15% improvement in stimulated reservoir volume (SRV). The while-drilling cuttings data exhibit superior real-time capability and accuracy over traditional logging data, while the sliding window clustering segmentation outperforms conventional geometric designs in efficiency and precision. This methodology provides an effective technical solution for efficient shale gas development.