Mechanism-guided data-driven model for optimized completion design

Effective completion design in hydraulic fracturing (HF) is crucial for optimizing production in unconventional reservoirs. Traditional geometric designs often fail to account for geological and engineering heterogeneity, leading to suboptimal stimulation. This study introduces a mechanism-guided data-driven model for optimized completion design that covers the entire process from sweet spot evaluation to stage and cluster optimization. For geological sweet spot evaluation, a mechanism-guided weighted K-medoids clustering model was developed by assigning weights to petrophysical parameters based on their correlation with production profiles. Engineering sweet spots were characterized using bottomhole mechanical specific energy (MSE_b) and minimum horizontal in-situ stress (S_hmin). The completion design optimization employed dynamic programming and a hybrid multi-objective optimization approach (NSGA-Ⅱ), integrating geological and engineering sweet spots with operational constraints. The study showed a positive correlation between high-quality geological sweet spots and production (average correlation coefficient of 0.34), and a negative correlation between fluid allocation and engineering sweet spots (correlation coefficient of −0.46). Field application in the Jimsar Sag, Xinjiang, demonstrated that the proposed model significantly outperforms traditional geometric designs. Test wells showed an average 186% increase in cumulative production per 100 m over three months compared to conventional wells. The key findings of this work provide a novel technical pathway for optimized completion design of unconventional reservoirs with significant engineering applicability.