Fracture Pressure Prediction for Tight Conglomerate Reservoirs with Analysis of Acid Pretreatment Influence

作者：Yue Wang, Qinghua Cheng, Jianchao Li, Yunwei Kang, Hui Liu, Qian Wei, Dali Guo, Zixi Guo · 发表于：Processes · 年份：2026 · DOI：10.3390/pr14081192 · 被引用次数：1 · 研究领域：Hydraulic Fracturing and Reservoir Analysis、Rock Mechanics and Modeling、Hydrocarbon exploration and reservoir analysis

Tight conglomerate reservoirs are characterized by strong heterogeneity, significant in-situ stress differences, and unbalanced fracturing stimulation, which make fracture pressure prediction challenging and severely restrict the effectiveness of reservoir stimulation and ultimate recovery. Although acid pretreatment is an effective means to reduce fracture pressure, its quantitative relationship with fracture pressure remains unclear. There is an urgent need to establish a systematic method that integrates reservoir heterogeneity characterization, data augmentation, and intelligent prediction. Aiming at the tight conglomerate reservoir in the MH Block, this study proposes an intelligent fracture pressure prediction and acid pretreatment optimization method that integrates Self-Organizing Maps (SOMs), Generative Adversarial Networks (GANs), and Transformer models. First, SOM is used to perform unsupervised clustering of logging parameters to identify different geological feature categories and achieve fine-scale characterization of reservoir heterogeneity. Second, to address the issue of limited samples within each cluster, GAN is employed for high-quality data augmentation to expand the training sample set. Finally, a fracture pressure prediction model is constructed based on the Transformer architecture, and the influence of acid treatment parameters on fracture pressure is quantitatively analyzed using the SHAP method and laboratory experiments. The results show that the p...